development of innovative bioassay principles for
TRANSCRIPT
DDEEVVEELLOOPPMMEENNTT OOFF IINNNNOOVVAATTIIVVEE BBIIOOAASSSSAAYY PPRRIINNCCIIPPLLEESS FFOORR
PPHHAARRMMAACCEEUUTTIICCAALL QQUUAALLIITTYY CCOONNTTRROOLL
--
EENNTTWWIICCKKLLUUNNGG IINNNNOOVVAATTIIVVEERR BBIIOOAASSSSAAYYPPRRIINNZZIIPPIIEENN FFÜÜRR DDIIEE
PPHHAARRMMAAZZEEUUTTIISSCCHHEE QQUUAALLIITTÄÄTTSSKKOONNTTRROOLLLLEE
Der Naturwissenschaftlichen Fakultät
der Friedrich-Alexander-Universität Erlangen-Nürnberg
zur
Erlangung des Doktorgrades Dr. rer. nat.
vorgelegt von
Cornelia Andrea Zumpe
aus Nürnberg
Als Dissertation genehmigt von der Naturwissenschaftlichen Fakultät
der Friedrich-Alexander-Universität Erlangen-Nürnberg
Tag der mündlichen Prüfung: 26. November 2010
Vorsitzender der Promotionskommission: Prof. Dr. Rainer Fink
Erstberichterstatter: Prof. Dr. Monika Pischetsrieder
Zweitberichterstatter: PD Dr. Dr. Veit J. Erpenbeck
Danksagung
Die vorliegende Arbeit entstand unter der Anleitung von Prof. Dr. Monika Pischetsrieder am Institut
für Pharmazie und Lebensmittelchemie der Friedrich-Alexander-Universität Erlangen Nürnberg.
Die Arbeit wurde überwiegend bei der Firma Merck Serono in Darmstadt durchgeführt.
Zuerst möchte ich mich bei Prof. Dr. Monika Pischetsrieder für die Bereitschaft, meine Betreuung
seitens der Universität zu übernehmen, bedanken. Ich danke ihr für ihre Unterstützung und
hilfreichen Vorschläge.
Mein besonderer Dank geht an Dr. Christiane Bachmann und Dr. Nicole Wiedemann für die
fachlich kompetente Betreuung dieser Arbeit mit zahlreichen Anregungen und interessanten
Diskussionen. Vielen Dank für die Bereitstellung des spannenden Themas sowie ihr stetiges
Engagement, das erheblich zum Gelingen dieser Arbeit beigetragen hat.
PD Dr. Dr. Veit J. Erpenbeck möchte ich für die Übernahme des Zweitguachtens, sowie seine
interessanten Anregungen danken.
Prof. Dr. Kreis und Prof. Dr. Fromm danke ich für die Bereitschaft, mich in meiner
Promotionsprüfung zu prüfen.
Vielen Dank auch an Dr. Katrin Engel für ihre hilfreichen Ratschläge und stetige
Diskussionsbereitschaft.
Allen aktuellen und ehemaligen Doktoranden von Merck Serono, insbesondere Thomas Lange,
Annette Eppler, Kathrin Ziegler und Maria Leonor Alvarenga danke ich für die gute
Arbeitsatmosphäre und den Spaß bei der Arbeit.
Bei den Mitarbeitern von ADB5, Claudia Fischer, Anke Breuer, Jasmin Oestreicher und Simone
Hartl möchte ich mich für die Einarbeitung in die Zellkultur sowie die Pflege meiner Zellen während
meiner Abwesenheit bedanken.
Herzlichen Dank auch an die Laboratorien von Dr. Ralph Lindemann und Dr. Heike Dahmen für die
Möglichkeit, ihr Odyssey Infrared Imaging System und ihr FACScan Flow Cytometer benutzen zu
dürfen. Bei Melanie Daniel und Doreen Zickler möchte ich mich für die Einweisung in die Geräte
bedanken.
Desweiteren danke ich Enrico Tucci (National Institute of Statistics, Rome, Italy) für die statistische
Auswertung meiner Daten, sowie Francesco Antonetti und Beatrice Brunkhorst (beide Merck
Serono) für das Korrekturlesen meiner Artikel.
Danken möchte ich auch der Firma Merck Serono für die Finanzierung meiner Arbeit sowie der
Bereitstellung eines Arbeitsplatzes und der erforderlichen Arbeitsmittel.
Zum Schluss möchte ich mich ganz besonders herzlich bei meinen Eltern für die finanzielle und
moralische Unterstützung meines bisherigen Lebensweges bedanken. Danke für ihre Geduld, ihr
Vertrauen und ihren Rückhalt in allen Lebenssituationen.
TTAABBLLEE OOFF CCOONNTTEENNTTSS
LIST OF ABBREVIATIONS ..................................................................................................................10
LIST OF PUBLICATIONS .....................................................................................................................14
II.. IINNTTRROODDUUCCTTIIOONN .................................................................................................................................................................................................................... 1155
1. OVERVIEW OF THE IMMUNE SYSTEM ..............................................................................................15
1.1. Innate immunity .................................................................................................................15
1.2. Adaptive immunity ............................................................................................................16
1.2.1. Lymphocytes ............................................................................................................... 16
1.2.1.1. B Lymphocytes ..................................................................................................... 16
1.2.1.2. T Lymphocytes ...................................................................................................... 17
1.2.2. APCs ........................................................................................................................... 18
1.4. Cytokines ...........................................................................................................................18
1.4.1. General information ..................................................................................................... 18
1.4.2. Classification and features of cytokines ...................................................................... 19
2. INTERLEUKIN-7, ITS SIGNAL TRANSDUCTION PATHWAYS AND APPLICATION IN IMMUNOTHERAPY .......21
2.1. IL-7 ......................................................................................................................................21
2.2. IL-7R ...................................................................................................................................22
2.3. IL-7 Signal transduction pathways ..................................................................................23
2.3.1. JAK/STAT pathway ..................................................................................................... 25
2.3.2. PI3K/AKT pathway ...................................................................................................... 26
2.3.3. Ras/Raf/ERK pathway ................................................................................................. 27
2.3.4. SFK pathway ............................................................................................................... 27
2.4. IL-7 and cell cycle .............................................................................................................28
2.5. Lymphotrophic effects of IL-7 .........................................................................................28
2.5.1. Induction of anti-apoptotic factors ............................................................................... 28
2.5.2. Suppression of pro-apoptotic proteins......................................................................... 29
2.5.3. Regulation of metabolism: glucose ............................................................................. 29
2.5.4. Regulation of metabolism: pH ..................................................................................... 30
2.6. IL-7 in immunotherapy ......................................................................................................30
3. QUALITY CONTROL OF BIOPHARMACEUTICALS ...............................................................................32
3.1. General remarks about quality control ...........................................................................32
3.2. Criterions for quality control of biopharmaceuticals ....................................................32
3.2.1. Physicochemical properties ......................................................................................... 32
3.2.2. Biological activity ......................................................................................................... 33
3.2.3. Immunochemical properties ........................................................................................ 33
3.2.4. Purity, impurities and contaminants ............................................................................ 33
3.2.5. Quantity ....................................................................................................................... 34
3.3. Biological assays ..............................................................................................................34
3.3.1. Assay formats and requirements for use in QC .......................................................... 34
3.3.1.1. Overview of assay formats ................................................................................... 34
3.3.1.2. Requirements for use in QC ................................................................................. 35
3.3.2. Analysis models for potency assays ........................................................................... 36
3.3.2.1. The parallel line model .......................................................................................... 36
3.3.2.2. Logistic models (4-and 5-parameter fit) ................................................................ 37
3.3.3. Read-out system for proliferation assays .................................................................... 38
3.3.3.1. Colorimetric detection ........................................................................................... 38
3.3.3.2. Fluorescence ........................................................................................................ 39
3.3.3.3. Luminescence ....................................................................................................... 40
3.4. Investigated biopharmaceuticals ....................................................................................40
3.4.1. Cetuximab (Erbitux®) .................................................................................................. 40
3.4.2. Tucotuzumab celmoleukin (KS-IL2) ............................................................................ 41
3.4.3. Fc-IL7 .......................................................................................................................... 41
IIII.. AAIIMM OOFF TTHHIISS SSTTUUDDYY ...................................................................................................................................................................................................... 4422
IIIIII.. MMAATTEERRIIAALLSS AANNDD MMEETTHHOODDSS ........................................................................................................................................................................ 4433
1. MATERIALS ..................................................................................................................................43
1.1. Cell lines ............................................................................................................................43
1.2. Investigated biopharmaceuticals ....................................................................................44
1.2.1. Cetuximab (Erbitux®) .................................................................................................. 44
1.2.2. Tucotuzumab celmoleukin (KS-IL2) ............................................................................ 44
1.2.3. Fc-IL7 .......................................................................................................................... 44
1.3. Antibodies ..........................................................................................................................44
1.3.1. Primary antibodies ....................................................................................................... 44
1.3.1. Secondary antibodies .................................................................................................. 45
1.4. Controls .............................................................................................................................45
1.4.1. Cell control extracts ..................................................................................................... 45
1.4.2. Isotype controls ........................................................................................................... 46
1.5. Culture medium, reagents and chemicals ......................................................................46
1.6. Composition of buffer & reagent solutions ....................................................................47
1.7. Equipment ..........................................................................................................................48
1.8. Plastic ware and other materials .....................................................................................49
1.9. Software .............................................................................................................................49
2. METHODS ....................................................................................................................................50
2.1. Cell culture .........................................................................................................................50
2.1.1. CTLL-2 cells ................................................................................................................ 50
2.1.2. DiFi cells ...................................................................................................................... 50
2.1.3. Kit 225 cells ................................................................................................................. 50
2.1.4. PB-1 cells .................................................................................................................... 50
2.1.5. 2E8 cells ...................................................................................................................... 50
2.2. Determination of the cell density ....................................................................................50
2.3. Thawing of cells ................................................................................................................51
2.4. Potency assays (colorimetric proliferation assays) ......................................................51
2.4.1. Principles ..................................................................................................................... 51
2.4.1.1. CTLL-2 potency assay .......................................................................................... 51
2.4.1.2. DiFi potency assay................................................................................................ 51
2.4.2.1. Kit 225 potency assay ........................................................................................... 51
2.4.2. Protocols ...................................................................................................................... 52
2.4.2.1. CTLL-2 potency assay .......................................................................................... 52
2.4.2.2. DiFi potency assay................................................................................................ 52
2.4.2.3. Kit 225 potency assay ........................................................................................... 53
2.4.3. Data evaluation: comparison of different analysis models .......................................... 53
2.4.4. Adaption of assay parameters to luminescence and fluorescence techniques .......... 54
2.5. STAT5 phosphorylation assay ........................................................................................55
2.5.1. Principle ....................................................................................................................... 55
2.5.2. Protocol ....................................................................................................................... 55
2.6. Western blotting analysis.................................................................................................56
2.5.1. Principle ....................................................................................................................... 56
2.5.2. Protocol ....................................................................................................................... 56
2.7. Inhibition with WP1066 .....................................................................................................57
2.7.1. Principle ....................................................................................................................... 57
2.7.2. Protocol ....................................................................................................................... 57
2.8. Flow cytometric analysis .................................................................................................57
2.8.1. Principle ....................................................................................................................... 57
2.8.2. Protocol ....................................................................................................................... 57
IIVV.. RREESSUULLTTSS AANNDD DDIISSCCUUSSSSIIOONN........................................................................................................................................................................ 5588
1. COMPARISON OF DIFFERENT ANALYSIS MODELS (PARALLEL LINE, 4-AND 5-PARAMETER FIT) FOR
POTENCY ASSAYS ............................................................................................................................58
1.1. CTLL-2 potency assay ......................................................................................................58
1.1.1. Concentration ranges .................................................................................................. 58
1.1.2. Test on the necessity of “weighting” ............................................................................ 60
1.1.3. Results ......................................................................................................................... 63
1.2. DiFi potency assay ............................................................................................................69
1.2.1. Concentration ranges .................................................................................................. 69
1.2.2. Test on the necessity of “weighting” ............................................................................ 70
1.2.3. Results ......................................................................................................................... 73
1.3. Combined discussion of CTLL-2 and DiFi potency assays results .............................78
1.4. Summary and Conclusions ..............................................................................................82
2. COMPARISON OF DIFFERENT PROLIFERATION ASSAYS (COLORIMETRIC, LUMINESCENCE AND
FLUORESCENCE READ-OUTS) ............................................................................................................84
2.1. Method development ........................................................................................................84
2.2. Results ...............................................................................................................................85
2.2.2. CTLL-2 potency assay................................................................................................. 85
2.2.3. DiFi potency assay ...................................................................................................... 91
2.2.4. Kit 225 potency assay ................................................................................................. 94
2.3. Combined discussion of CTLL-2, DiFi and Kit 225 assay results ................................99
2.4. Conclusions .....................................................................................................................101
3. CHARACTERIZATION OF THE IL-7 SIGNAL TRANSDUCTION PATHWAYS AND SELECTION OF AN
APPROPRIATE TARGET FOR DEVELOPMENT OF AN INTRACELLULAR PHOSPHORYLATION ASSAY ..........102
3.1. Results and Discussion of flow cytometric analyses .................................................103
3.2. Results and Discussion of western blotting analyses ................................................106
3.2.1. Representative targets of the JAK/STAT pathway: STAT1, STAT3, STAT5, pim-1 and
bcl-2 ..................................................................................................................................... 106
3.2.1.1. STAT1 and STAT3.............................................................................................. 106
3.2.1.2. STAT5 ................................................................................................................. 108
3.2.1.3. Inhibition of STAT5 by WP1066 .......................................................................... 109
3.2.1.4. Pim-1 ................................................................................................................... 111
3.2.1.5. Bcl-2 .................................................................................................................... 112
3.2.2. Representative target of the PI3K/AKT pathway: AKT .............................................. 114
3.2.3. Representative targets of the ERK pathway: ERK and Bim...................................... 115
3.2.3.1. ERK ..................................................................................................................... 115
3.2.3.2. Bim ...................................................................................................................... 117
3.2.4. Representative target of the SFK pathway: Lck ........................................................ 118
3.2.5. Representative target of the p38 MAPK stress pathway: p38 MAPK ....................... 120
3.3. Summary and Conclusions ............................................................................................121
4. DEVELOPMENT OF A STAT5 PHOSPHORYLATION ASSAY AS AN ALTERNATIVE TO THE CLASSICAL
PROLIFERATION ASSAYS .................................................................................................................124
4.1. Assay development and optimization ...........................................................................124
4.2. Assay qualification .........................................................................................................133
4.3. Comparison of the STAT5 phosphorylation assay to the proliferation assay .........134
4.4. Conclusions .....................................................................................................................134
VV.. OOVVEERRAALLLL CCOONNCCLLUUSSIIOONNSS AANNDD OOUUTTLLOOOOKK........................................................................................................................ 113366
LIST OF FIGURES ...........................................................................................................................138
LIST OF TABLES .............................................................................................................................141
REFERENCES .................................................................................................................................142
APPENDIX: .....................................................................................................................................156
STATISTICAL ANALYSIS OF BIOASSAY RESULTS: A COMPARISON BETWEEN DIFFERENT CALCULATION
MODELS .........................................................................................................................................156
1. Objective .............................................................................................................................157
2. Definitions ...........................................................................................................................157
3. Statistical Method ..............................................................................................................157
4. Statistical evaluation .........................................................................................................158
4.1. Results of the analyses by Model ................................................................................. 158
4.1.1. CTLL-2 potency assay ........................................................................................... 158
4.1.2. DiFi potency assay................................................................................................. 161
4.1.3. Conclusions ........................................................................................................... 163
4.2. Results of the analyses by Concentration .................................................................... 166
4.2.1. CTLL-2 potency assay ........................................................................................... 166
4.2.2. DiFi potency assay................................................................................................. 168
4.2.3. Conclusions ........................................................................................................... 171
4.3. Results of the analyses by Range ................................................................................ 171
4.3.1. CTLL-2 potency assay ........................................................................................... 171
4.3.2. DiFi potency assay................................................................................................. 174
4.3.3 Conclusions ............................................................................................................ 176
5. Analysis on failure rates....................................................................................................177
5.1 CTLL-2 potency assay................................................................................................... 178
5.2 DiFi potency assay ........................................................................................................ 179
5.3 Conclusions ................................................................................................................... 180
ABSTRACT .....................................................................................................................................181
ZUSAMMENFASSUNG ......................................................................................................................182
CURRICULUM VITAE .......................................................................................................................184
LIST OF ABBREVIATIONS
4PL Four-parameter fit
5PL Five-parameter fit
ADCC Antibody-dependent cellular cytotoxicity
ADP Adenosine di-phosphate
AIT adoptive immunotherapy
AML Acute myeloid leukemia
ANOVA ANalysis Of VAriance
AP Activator protein
APC Antigen-presenting cell
API Active pharmaceutical ingredient
ATCC American Type Culture Collection
ATP Adenosine triphosphate
BCP-ALL B cell precursor acute lymphoblastic leukemia
BCR B cell receptor
BSA Bovine serum albumin
CD Cluster of Differentiation
CDK Cyclin dependent kinase
CHK Checkpoint kinase
CKI Cyclin dependent cell cycle inhibitor
CLP Common lymphoid progenitor
CRC Colorectal cancer
C test Cochran‟s test
CTL Cytotoxic T lymphocytes
CV Coefficient of variation
DC Dendritic cells
DMSO dimethyl sulfoxide
DNA Desoxy-ribonucleic acid
DPBS Dulbecco‟s Phosphate-Buffered Saline
DSMZ Deutsche Sammlung von Mikroorganismen und Zellkulturen
(German collection of microorganisms and cell cultures)
DTT dithiothreitol
EC Ethylene carbonate
EBF Early B cell factor
EGFR Epidermal Growth Factor Receptor
ELISA Enzyme-linked immuno sorbent assay
EMD Emanuel Merck, Darmstadt
EMSA Electrophoretic Mobility Shift Assay
EpCAM Epithelial cell adhesion molecule
ERK Extracellular-signal regulated kinase
ETC Electron transport chain
FACS Fluorescence Activated Cell Sorting
FBS Fetal bovine serum
FDA Food and Drug Administration
GI Gastro-intestinal
GLUT Glucose transporter
GMP Good manufacturing practice
GSK-3 Glycogen synthase kinase-3
h hour
HBSS Hank‟s Buffered Salt Solution
HIV Human immunodeficiency virus
HLA Human leukocyte antigens
HRP Horseradish peroxidase
hu human
ICH International Conference on Harmonization
IFN Interferon
Ig Immunoglobulin
IL Interleukin
IL-7R IL-7 receptor
IR Infrared
IRS Insulin receptors
iT-Reg cells Induced T-Reg cells
JAK Janus Kinase
JNK Jun N-terminal kinases
kDa Kilo Dalton
KIRA KInase Receptor Activation
KS test Kolmogorov-Smirnov test
KW test Kruskal-Wallis test
LAK cell Lymphokine activated killer cell
L test Levene‟s test
MAPK Mitogen-activated protein kinase
MCL myeloid leukemia cell differentiation protein
ME β-mercaptoethanol
MHC Major histocompatibility complex
min minute
mRNA messenger ribonucleic acid
MTS/ PMS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyll-2-(4-
sulfophenyl)-2 H-tetrazolium, inner salt)/phenazine methosulfate)
MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazoliumbromid)
mu murine
NAD Nicotinamid-adenin-dinucleotid
NBE New Biological Entity
Neg. C. Negative control
NHE sodium/hydrogen exchanger
NK cell Natural killer cell
NSCLC Non-small cell lung carcinoma
nT-Reg Natural T-Reg cells
OD Optical density
p- Phospho-
PAGE Polyacrylamide gelelectrophoresis
PAMP Pathogen-associated molecular pattern
PE Phycoerythrin
Ph. Eur. European Pharmacopeia
PI3K Phosphatidylinositol 3-kinase
PIP Phosphatidylinositol-4,5-bisphosphate
PL model Parallel line model
Pos. C. Positive control
PPBSF Pre-pro-B cell growth-stimulating factor
PVDF Polyvinylidenefluoride
QC Quality control
Rb Riboblastoma
RE Relative error
RFU Relative fluorescence units
RLU Relative light units
rpm Rounds per minute
RT Room temperature
SCF Stem cell factor
SCLC Small cell lung carcinoma
SCID Severe combined immunodeficiency syndrome
SD Standard deviation
SDS Sodium dodecyl sulfate
SFK Src family kinase
SIV Simian immunodeficiency virus
SOCS Suppressor of cytokine signaling
SOP Standard operation procedure
SH Src homology
Shc Src homology and collagen
STAT Signal Transducer and Activator of Transcription
TCR T cell receptor
TGF-β Tumor growth factor-β
TH cells T helper cells
TMB Tetramethyl benzidine
TNF Tumor necrosis factor
T-Reg cells Regulatory T cells
USP United States Pharmacopeia
WB Western blotting
XSCID X-linked severe combined immunodeficiency
XTT (sodium(2,3-bis(2-methoxy-4-nitro-5-sulfophenyl~-2H-tetrazolium-5-
carboxanilide)
LIST OF PUBLICATIONS
Parts of this work have already been published or submitted:
A. Full research papers
Zumpe C., Wiedemann N., Metzger A.U., Pischetsrieder M., Bachmann C.L. Analysis of
potency assays: comparison between the parallel line method and logistic models (four-and
five-parameter fit). J. Pharm. Biomed. Anal., 2010, under revision.
Zumpe C., Bachmann C.L., Metzger A.U., Wiedemann N. Comparison of potency assays
using different read-out systems and their suitability for quality control. J. Immunol.
Methods, 360 (2010) 129-140.
Zumpe C., Engel K., Wiedemann N., Metzger A.U., Pischetsrieder M., Bachmann C.L.
Development of a STAT5 phosphorylation assay as a rapid bioassay to assess Interleukin-
7 potency. Curr. Pharm. Biotech, 2010, under revision.
Zumpe C., Engel K., Wiedemann N., Metzger A.U., Pischetsrieder M., Bachmann C.L.,
Interleukin (IL)-7 and IL-2 signaling in human T and murine B cell lines. Cell. Signal. 2010,
submitted.
B. Posters and oral presentations
Zumpe C., Wiedemann N., Metzger A.U., Pischetsrieder M., Bachmann C.L., Analysis of
potency assays: comparison between the parallel line method and logistic models (four-and
five-parameter fit). Presented at BEBPA's 1st Inaugural Biological Assays Conference, Sept
10-12, 2008, Berlin, Germany.
Zumpe C., Bachmann C.L., Metzger A.U., Wiedemann N., Comparison of potency assays
using different read-out systems and their suitability for quality control. Presented at
BEBPA„s 2nd
Inaugural Biological Assays Conference, Sept 30 – Oct 2 2009, Rome, Italy
and at the “DPhG Doktorandentagung”, Nov 18-21, Pichlarn, Austria.
I. INTRODUCTION
15
II.. IINNTTRROODDUUCCTTIIOONN
In the following section, first, a short overview about the immune system and its components will be
given. Special focus will be drawn on cytokines and especially on Interleukin (IL)-7. IL-7 and other
cytokines are often used in combination with parts of antibodies for anti-cancer therapies. Quality
control of those biopharmaceuticals is usually achieved by using biological assays, which will be
discussed in the second part of this introduction.
1. OVERVIEW OF THE IMMUNE SYSTEM
In order to efficiently resist against pathogens like viruses, bacteria and parasites, mammalians
developed a complex immune system. This system is composed of two major subdivisions, the
innate or non-specific immune system and the adaptive or specific immune system (Janeway and
Travers, 1994). The innate immune system provides the first line of defense against pathogens,
while the adaptive immune system acts as a second line of defense, displaying a high degree of
antigen-specificity and affording protection against re-exposure to the same pathogen (Male et al.,
2006). An interaction and well functioning of both parts is required for a successful resistance
against infections.
1.1. Innate immunity
Recognized as the first line of defense, the innate immune system consists of anatomic,
physiological, phagocytic or endocytic, as well as inflammatory barriers.
The skin and internal epithelial layers act as mechanical, anatomic barriers against invading
pathogens. Associated with these protective surfaces are chemical and biological agents, e.g.
defensins and surfactants.
The humoral or physiological barriers consist of various different factors, e.g. lysozymes, which are
cleaving bacterial cell walls, interferons, which can limit virus replication in cells, as well as
lactoferrin and transferrin, which inhibit bacterial growth by binding iron, an essential nutrient for
bacteria. The major humoral non-specific defense mechanism is the complement system, a
multicomponent triggered enzyme cascade. Once activated, complement can lead to increased
vascular permeability, recruitment of phagocytic cells, as well as lysis and opsonization of bacteria.
The phagocytic or endocytic barrier of the innate immune system consists of a variety of cells of the
innate immune system, which are recruited to the sites of infection, beyond them natural killer (NK)
cells, macrophages, neutrophils and eosinophils. These cells are the main line of defense in the
non-specific immune system and exert different activities. NK and lymphokine activated killer (LAK)
cells can nonspecifically kill virus infected and tumor cells, whereas eosinophils can kill certain
parasites by releasing proteins from granula. Neutrophils can phagocytose invading organisms and
kill them intracellularly. Macrophages contribute to intracellular as well as to extracellular killing of
microorganisms, infected and altered self target cells. Furthermore, macrophages can repair tissue
and act as antigen-presenting cells (APCs), which are required for the induction of specific immune
responses.
I. INTRODUCTION
16
The inflammatory response is a complex sequence of events that is caused by tissue damage or
invading pathogenic microorganisms and leads to vasodilatation, increased capillary permeability
and influx of phagocytic active cells from capillaries into the tissue (Male et al., 2006; Goldspy,
2000; Murphy, 2008).
In addition to the activities described above, the innate immune system is also responsible for the
initial release of cytokines and chemokines that activate the adaptive immune system. The role of
cytokines will be further discussed in section I.1.3.
1.2. Adaptive immunity
If the effector mechanisms of the innate immune system fail to eliminate the invading pathogens,
the components of the specific or adaptive immune system are activated. Adaptive immunity is
characterized by antigen specificity and is capable of recognizing and selectively eliminating
specific foreign microorganisms and molecules (Roitt, 1994). This specificity is achieved through
usage of clonally distributed antigen receptors, i.e. surface Ig on antibody-producing B lymphocytes
and T cell receptors (TCRs) on the surface of T lymphocytes.
In addition to the antigen-specificity - permitting the immune system to distinguish even slight
differences among antigens - the adaptive immunity is characterized by an enormous diversity of
recognition molecules that can be generated. This represents an immunologic memory that
enables the immune system to respond to previously encountered antigens with a higher reactivity.
Adaptive immune responses can be divided into humoral and cell-mediated responses. Humoral
responses involve the interaction of B lymphocytes/cells, T lymphocytes/cells and APCs, whereas
cell-mediated responses need the cooperation of different subclasses of T cells, macrophages and,
to a lesser extent, NK cells (Dawson et al., 1996).
1.2.1. Lymphocytes
Lymphocytes are divided into B and T lymphocytes, exerting different, specific functions in the
immune system. They express specific receptors, the T and B cell antigen receptors (BCRs), which
are responsible for the enormous diversity of T and B cells and are able to discriminate between
self and non-self.
1.2.1.1. B Lymphocytes
B lymphocyte progenitors arise from hematopoietic stem cells and differentiate to B lymphocytes in
the bone marrow. During maturation, B cells go through several stages which are characterized by
changes in the rearrangement of Ig heavy and light chain genes and intracellular as well as surface
marker expression. After maturation, B cells migrate from the bone marrow to the B cell zone in
secondary lymphoid organs, such as the spleen and lymph nodes. On their surface, B cells
express the BCR, recognizing the native form of an antigen. By interaction with an antigen, B cells
start to proliferate and differentiate into effector B cells and produce and secrete large amounts of
antigen-specific memory B cells and effector plasma cells, which generate large amounts of soluble
but otherwise identical versions of the membrane-bound Ig (Abbas et al., 1996).
I. INTRODUCTION
17
This clonal selection hypothesis (Talmage, 1986) explains why subsequent responses to the same
antigen are more effective and long-lasting as in the initial response (Dawson et al., 1996). In the
first encounter with an antigen, a primary antibody response is generated; later, a re-encounter with
the same antigen causes a more rapid secondary response, producing high levels of antibodies
with a high binding affinity for the target antigen. This process is also exploited in prophylactic
vaccination.
1.2.1.2. T Lymphocytes
T lymphocyte progenitors arise from hematopoietic stem cells in the bone marrow and migrate to
the thymus to mature. T lymphocytes express the TCR, recognizing only processed antigen
peptides bound to the major histocompatibilty complex (MHC). In contrast to B cells, T cells do not
identify native, unbound antigens. It can thus be avoided that cytotoxic T cells directly lyse own
cells.
During maturation, TCRs with too low or too high affinity for MHC molecules are deleted in a two-
step selection process in the thymus. Positive selection of T cells ensures that mature TCRs bind
to self-MHC molecules which confers MHC restriction (Zinkernagel et al., 1974). T cells with high
affinity for self-MHC molecules or self-MHC plus self-antigen undergo negative selection which
ensures self-tolerance. T cells that fail positive selection or get negatively selected during
maturation die by apoptosis. After maturation, T cells can be divided into two distinct populations
depending on their functional phenotype and expression of the Cluster of Differentiation (CD)4 or
CD8 co-receptor.
CD4+ T helper cells (TH cells) are specific for antigenic peptides presented by MHC class II
molecules and upon activation, proliferate and secrete various cytokines which in turn activate B
cells, T cells, macrophages and other cells involved in the immune response.
TH cells can be classified into TH1 and TH2 cells. The differentiation of these subsets from Th0
precursor cells is driven by cytokines, especially by IL-12/IL-18 and IL-4, respectively (Kaufmann,
2002). The TH1 subset supports inflammation by secreting IL-2, interferon (IFN) -γ and tumor
necrosis factor (TNF) -ß and is responsible for classical cell-mediated functions involving CD8+ T
cells. TH2 cells produce IL-4, IL-5 and IL-10 and thus support mainly humoral immune responses
involving B cell proliferation and differentiation into antibody-secreting plasma cells and memory
cells (Mosmann, 2002).
Other groups of TH cells include the regulatory T (T-Reg) and TH-17 cell populations. T-Regs with
the phenotype CD4+CD25
+, usually secrete IL-10 and tumor growth factor-β (TGF-β). Reductions in
T-Reg cells may contribute to autoimmunity, chronic viral infection, tumor immunity and allergy
while the role of the pro-inflammatory IL-17 secreting subgroup pertains probably to host defense
and autoimmunity (Bettelli et al., 2007; Schwartz, 2005).
Activated CD8+
cytotoxic T lymphocytes (CTLs) recognize and lyse specifically autologous virus-
infected cells or tumor cells, which present foreign antigenic peptides on MHC class I molecules
(Schöllmann et al., 2004). In contrast to CD4+ T cells, they can only eliminate intracellular
pathogens.
I. INTRODUCTION
18
1.2.2. APCs
Every cell is capable of presenting endogenous antigens on class I MHC complexes whereas only
APC display endogenous as well as exogenous antigens on class I and class II MHC molecules.
The best studied APCs are macrophages, dendritic cells (DC) and B cells, but it has been shown
that 17 other cells such as liver sinusoidal endothelial cells are able to present exogenous antigens
on class II MHC molecules as well (Limmer et al., 2000).
Complexes between antigenic peptides and MHC molecules are formed by degradation of a
protein antigen by two different antigen-processing pathways. Endogenous proteins derived from
normal cells, tumors, viruses or intracellular bacteria are degraded by the proteasome into peptides
and transported into the rough endoplasmic reticulum where they are loaded onto MHC class I
molecules. The peptide-loaded MHC class I molecules present the endogenous peptide to CD8+
CTL. Exogenous antigens are internalized by APC by receptor-mediated endocytosis in all APC
and by phagocytosis and macropinocytosis in DCs and macrophages. Exogenous peptides are
loaded on MCH II molecules and presented to CD4+ T cells (Goldspy, 2000; Murphy, 2008).
1.4. Cytokines
1.4.1. General information
Cytokines are secreted proteins with growth, differentiation, and activation functions that regulate
and determine the nature of immune responses (Commins et al., 2010). They were initially
identified as products of immune cells that act as mediators and regulators of immune processes
but may be produced by other cells than immune cells and have effects on non-immune cells, as
well. Cytokines are currently being used clinically as biological response modifiers for the
treatment of various disorders. The term cytokine is a general term used to describe a large group
of proteins. Sub-classes of cytokines are for example monokines (cytokines produced by
mononuclear phagocytic cells), lymphokines (cytokines produced by activated lymphocytes,
especially TH cells) and interleukins (cytokines that act as mediators between leukocytes) (Male,
2006).
Many cytokines are pleiotropic - that means that they are produced by many different cell types and
act on various cell types - and redundant - e.g. they exert similar mechanisms of action.
Redundancy is due to the nature of the cytokine receptors. Cytokine receptors are usually
heterodimers - and sometimes heterotrimers - that can be grouped into families with common
subunits for a given family. Examples for cytokine families are shown in table 1. The subunit
common to all members of the family is responsible for cytokine binding and signal transduction.
Thus, a receptor for one cytokine can often respond to another cytokine in the same family. An
individual lacking IL-2, for example, is not adversely affected because other cytokines with the
same subunit (IL-15, IL-7, IL-9, etc.) take over its function. Similarly, a mutation in a cytokine
receptor subunit other than the one in common often has little effect. On the other hand, a
mutation in the common subunit has profound effects. For example, a mutation in the gene for the
I. INTRODUCTION
19
IL-2Rγ subunit causes human X-linked severe combined immunodeficiency (XSCID) characterized
by complete or nearly complete T and B cell defects (Male et al., 2006).
Table 1. Survey of cytokine families. Source: Commins et al. (2010)
Family Members
Hematopoietic
Common γ chain IL-2, IL-4, IL-7, IL-9, IL-15, IL-21
Shared β chain (CD131) IL-3, IL-5
Shared IL-2β chain (CD122) IL-2, IL-15
Other hematopoietic IFN-γ, IL-7, IL-13, IL-21, IL-31, TSLP
IL-1 family IL-1α, IL-1β, IL-1rα, IL-18, IL-33
gp130-utilizing IL-6, IL-11, IL-27, IL-31, ciliary neurotrophic factor, cardiotrophin 1, leukemia inhibitory factor, oncostatin M
IL-12 IL-12, IL-23, IL-35
IL-10 superfamily IL-10, IL-19, IL-20, IL-22, IL-24,
IL-26, IL-28, IL-29
IL-17 IL-17A-F, IL-25 (IL-17E)
Interferons
Type I interferons IFN-α, IFN-β, IFN-ω
Type II interferon IFN-γ (also a hematopoietic cytokine)
Type III interferons IFN-λ1 (IL-29), IFN-λ2 (IL-28A), IFN-λ3 (IL-28B)
TNF superfamily TNF-α, TNF-β, BAFF, APRIL
Cytokines often influence the synthesis of other cytokines and vice versa. They can enhance or
suppress production of other cytokines. In addition, they can influence the action of other
cytokines. These effects can be antagonistic, additive or synergistic.
1.4.2. Classification and features of cytokines
Cytokines can be sub-divided into different categories based on their functions or their source.
They can be grouped according to those that are predominantly APC or T lymphocyte derived; that
predominantly mediate cytotoxic (antiviral and anticancer), humoral, cell-mediated (TH1 and TH17),
or allergic immunity (TH2); or that are immunosuppressive (T-Reg) (Commins et al., 2010).
However, it is important to remember that any attempt to categorize them will be subject to
limitations since they can be produced by many different cells and act on many different cells.
I. INTRODUCTION
20
In table 2, the sources, targets and primary effects of some of the most prominent cytokines are
shown.
Table 2. Features of cytokines. Source: Male et al. (2006)
Cytokine Cell Source Cell Target Primary Effects
IL-1 Monocytes Macrophages Fibroblasts Epithelial cells
T cells, B cells Endothelial cells Hypothalamus Liver
Costimulatory molecule Activation (inflammation) Fever Acute phase reactants
IL-2 T cells, NK cells
T cells B cells Monocytes
Growth Growth Activation
IL-3 T cells Bone marrow progenitors
Growth and differentiation
IL-4 T cells Naive T cells T cells B cells
Differentiation into a TH2 cell Growth Activation and growth, Isotype switching to IgE
IL-5 T cells B cells Eosinophils
Growth and activation
IL-6 T cells, Macrophages, Fibroblasts
T cells, B cells Mature B cells Liver
Costimulatory molecule Growth (in humans) Acute phase reactants
IL-8 family
Macrophages, Epithelial cells, Platelets
Neutrophils Activation and chemotaxis
IL-10 T cells (TH2) Macrophages T cells
Inhibits APC activity Inhibits cytokine production
IL-12 Macrophages, NK cells
Naive T cells Differentiation into a TH1 cell
IFN-γ T cells, NK cells Monocytes Endothelial cells Many tissue cells - especially macrophages
Activation Activation Increased class I and II MHC
TGF-β T cells, Macrophages
T cells Macrophages
Inhibits activation and growth Inhibits activation
GM-CSF T cells, Macrophages, Endothelial cells, Fibroblasts
Bone marrow progenitors
Growth and differentiation
TNF-α Macrophages, T cells
Similar to IL-1 Similar to IL-1
I. INTRODUCTION
21
2. INTERLEUKIN-7, ITS SIGNAL TRANSDUCTION PATHWAYS AND APPLICATION IN
IMMUNOTHERAPY
Interleukin-7 (IL-7), also previously termed „„lymphopoietin 1‟‟ or „„pre-B cell factor‟‟, is a pleiotropic
cytokine, which is produced by stromal cells and thymic epithelial cells in lymphoid organs (Jiang et
al., 2005). Namen et al. discovered IL-7 in 1988 as a growth factor for murine pre B cells. Aside
from being an indispensable factor for the survival of lymphocytes, it also plays a decisive role in
the development of murine and human T cells. A loss of IL-7 signaling can thus lead to a severe
combined immunodeficiency syndrome (SCID) (Puel et al., 1998).
The functional role of IL-7 has not yet been fully elucidated. However, it is clear that perturbation of
this system is of biological significance. IL-7 can be fused to proteins and due to its strengthened
effect on the immune system be used in anti-cancer therapies.
2.1. IL-7
The size of the human IL-7 gene is 72kbp and encodes for a protein of 177 amino acids with a
molecular weight of 25 kilo Dalton (kDa). The size of the murine IL-7 gene is in contrast 41kbp and
encodes a 154 amino acids protein of about 18kDa. The active form of human IL-7 is a
glycoprotein, which is predicted to form a four -helix structure with a hydrophobic core (Kroemer
et al., 1996).
The homology between the murine and human amino acids sequences is 55%. The main
difference of the human protein in comparison to the murine one is an insertion of 19 amino acids
close to the C-terminal region, which appears not to be essential for activity (Goodwin et al., 1990).
23 out of the first 25 amino acids in the signal sequence of the N-terminal region are identical for
human and mouse IL-7, which leads to a 92% homology in this region. Consequently, murine IL-7
also binds to the human IL-7 receptor, which was shown by Goodwin et al. (1990).
The actual active form of IL-7 has been supposed to be a heterodimer of IL-7 and a cofactor, the
pre-pro-B cell growth-stimulating factor (PPBSF). This 55kDa complex stimulates proliferation and
differentiation of pre-pro-B cells (McKenna et al., 1998; Lai et al., 1998).
Mature IL-7 contains six cystein residues and might thus lose its biologic activity upon reduction
with thiols, e.g. ß-mercaptoethanol (Henney, 1989). Cosenza et al. (1997) investigated the tertiary
structure of IL-7 by biophysically and genetically mapping the disulfide bonds in human (hu) IL-7
using a combination of MALDI mass spectroscopy and site-directed cysteine to serine mutational
analyses. They found three cysteine residue pairs (Cys3-Cys142, Cys48-Cys93, and Cys35-
Cys130) participating in disulfide bond formation. Amongst them, only one single disulfide bond is
sufficient to retain a biologically active conformation.
The requirement for IL-7 starts from the pro-T1 and T2 cells, over the selection of CD8 cells to the
mature T cell, which also needs IL-7 for survival and homeostatic proliferation (Khaled et al., 2002).
The IL-7 receptor (IL-7R) regulates both early murine lymphocyte differentiation and proliferation. In
contrast, it cannot stimulate the proliferation of mature B cells (Lee et al., 1989).
I. INTRODUCTION
22
However, its pleiotropic effect is not only restricted to cells of the lymphoid lineage since it also
induced an increase in Desoxy-ribonucleic acid (DNA) synthesis in acute myelogenous leukemia
cells (Digel et al., 1991). Moreover, it activates macrophages in vitro (Alderson et al., 1991) and
generates LAK cells (Alderson et al., 1990).
2.2. IL-7R
IL-7 acts by binding to the IL-7R (see figure 1),
which consists of the IL-7 receptor chain (IL-
7R , CD127) and the common cytokine c chain
( c) that is also shared by the receptors for IL-2,
IL-4, IL-9, IL-15, and IL-21 (Kovanen et al., 2004).
The sequence of the extracellular domain of IL-
7R resembles thus those of other cytokine
receptors as well as those of the prolactine and
growth hormone receptors (Goodwin et al., 1990).
IL-7R belongs to the type I cytokine receptor
family. As a consequence, also the signal
transduction pathways of IL-7 are supposed to
resemble those of other cytokines, especially IL-
2. However, in contrast to IL-2R, IL-7R does not
contain a β-chain.
IL-7R is a membrane glycoprotein with a calculated weight of 49.5kDa. The observed weight for
the expressed receptor molecule might however be higher due to glycosylation or other
posttranslational modifications. The human and murine IL-7R show a high degree of homology
(64%) (Goodwin et al., 1990). All six extracellular and four intracellular cystein residues are
conserved in both receptor molecules. Like other hematopoietic growth factors, both have an
unusual distribution of amino acid residues (Moseley et al., 1989) and contain a high number of
serine and proline residues.
A small membrane-proximal domain, termed „„Box1‟‟, can be found in the IL-7R intracellular part,
which is ubiquitous throughout the type I cytokine receptor family (Murakami et al., 1991).
Furthermore, the structure of IL-7R consists of two fibronectin-like domains, four conserved
cysteine residues and a single 25 amino acid transmembrane domain. It has a 195 amino acid
cytoplasmic tail, which is sub-divided in an acidic rich region (A), a serine rich region (S), and a
tyrosine rich region (T) containing three tyrosine residues (Y401, Y449 and Y456) that are
conserved in mouse and man (Porter et al., 2001). Amongst them, the Y449 site is exceptionally
interesting because it is the origin of two fundamental IL-7 signaling pathways, the Janus Kinase
(JAK)/Signal Transducer and Activator of Transcription (STAT) pathway and the
phosphatidylinositol 3-kinase (PI3K)/AKT pathway, described in detail in section I.2.3. (Pallard et
Figure 1. 3D structure of the IL-7 receptor.
The structure of the IL-7R consists of the IL-7R
chain and the common cytokine c chain ( c), which is also shared by other cytokine receptors. Source: http://www.jpkc.yzu.edu.cn/course/yxmyx/ chapter7/il7r.gif, 20.12.09
I. INTRODUCTION
23
al., 1999). The different regions of the cytoplasmic tail (Box1, A, S and T) can serve as docking
sites for different kinases and thus activate various signaling pathways.
IL-7R exists in a low and a high affinity form, showing different binding kinetics (Goodwin et al.,
1990). It is expressed mainly by hematopoietic cells such as B and T lymphocytes as well as by
selected myeloid cells and endothelial cells. IL-7R subsists in membrane-bound and soluble
forms, which can bind IL-7 in solution. Furthermore, the existence of other isoforms, e.g. two
immunologically distinct proteins (p75 and p90) is discussed (Armitage et al., 1992). The structure
of the IL7-R isoforms has however not yet been fully elucidated.
IL-7R expression is negatively regulated by IL-7 and other cytokines (Park et al., 2004). Having
received cytokine-mediated survival signals, both, CD4+ and CD8
+ T cells reduce IL-7R
transcription in order to avoid competition with unactivated T cells for remaining IL-7.
2.3. IL-7 Signal transduction pathways
So far, relatively little is known about the IL-7 signal transduction pathways. The major pathway(s)
may not have been identified yet, but it is presumed to resemble that of other c family cytokines
(Jiang et al., 2005), particularly IL-2. As both exert similar functions, such as stimulation of T cells,
activation of macrophages and generation of LAK cells, their signal transduction pathways might be
analoguoues. However, not all pathways of IL-2 are shared by IL-7. The protein p52shc
is for
example not activated by IL-7 (Dorsch et al., 1994).
Both IL-7R and c are essential for the biological effects of IL-7. Ligand binding leads to the
heterodimerization of the two receptor chains (Ziegler et al., 2005) and activates thus the receptor
associated tyrosine Janus kinases, JAK1 (IL-7R ) and JAK3 ( c) (Suzuki et al., 2000). Thereby,
JAK3 is firstly activated by binding to c and then phosphorylates the chain-associated JAK1 and
the chain itself. As a consequence, docking sites for signaling molecules with Src homology 2
(SH2) domains were created. The most common example for this is STAT5 and to a lesser extent
STAT1 and STAT3 (Yu et al., 1998; Leonard, 2001). Their SH2 domains dock to a phosphorylated
tyrosine residue (e.g. STAT5 binds phospho-Y449 on IL-7R ) and become themselves
phosphorylated. Phosphorylated STATs dimerize and translocate into the nucleus, which leads to
an activation of specific genes (Foxwell et al., 1995). Via association with the scaffolding protein,
Gab2, STAT5 can activate two signaling pathways that are involved in cell proliferation, the
PI3K/AKT and Ras/mitogen-activated protein kinase (MAPK) pathways (Nyga et al., 2005).
However, in contrast to IL-2 and IL-15, IL-7 seems not to activate the Ras/MAPK pathway (Crawley
et al., 1996).
The most important signaling pathways of IL-7 are illustrated in figure 2. Discontinuities in the
pathways are directly associated with a significant decrease of both pre- and mature T cells
(Nosaka et al., 1995; Yao et al., 2006). Thereby, signals starting from Box1 and Y449 seem to be
particularly important since they are essential for IL-7 mediated T cell survival and proliferation
(Jiang et al., 2004). In the following, the different steps of the signaling pathways will be further
described in detail.
I. INTRODUCTION
24
Figure 2. Illustration of the most important IL-7 signaling pathways.
Figure 2 Shows the main signal transduction pathways, which are presumed to be activated by IL-7. All of these pathways are supposed to be activated by IL-2, as well.
I. INTRODUCTION
25
2.3.1. JAK/STAT pathway
JAK3 is considered to be the first step in the signaling pathway of IL-7. It is associated with the
carboxy-terminal region of c and is thus an essential transducer of c-dependent signals (Suzuki
et al., 2000). JAK3 is predominantly expressed in hematopoietic cells and is involved in IL-2, IL-4
and IL-7 receptor signaling. JAK 3 activities protect cells from apoptosis (Eynon et al., 1999; Suzuki
et al., 2000; Wen et al., 2001).
JAK1 is activated by JAK3 after IL-7 treatment. It also seems to be crucial in IL-7 signaling (Rodig
et al., 1998); however, hardly anything is published about its transduction pathways. JAK1 is
associated with the protein tyrosine kinase Pyk2, which is activated by IL-7 incubation and supports
survival of a thymocyte cell line. Furthermore, JAK1 might play a role in IL-7-induced PI3K
activation (Migone et al.,, 1998).
By downregulating JAK1 activation, IL-7 induces negative feedback on its own signaling. IL-7
treatment induces expression of suppressor of cytokine signaling (SOCS) proteins. SOCS proteins
can suppress cytokine signaling by binding and inhibiting JAKs, by competing with STATs on the
receptor docking site, or by targeting signaling proteins for proteasomal degradation (Starr et al.,
1998). The SOCS protein family consists of eight members (SOCS1–7 and CIS) which all include
a central SH2 domain. This SH2 domain binds and inhibits all JAK family members, including
JAK1. Furthermore, JAKs are dephosphorylated by the phosphatase CD45, which plays a major
role in inhibiting the activity of src kinases (Irie-Sasaki et al., 2001).
STATs are activated by JAKs via their transcriptional activation domain at the carboxy-terminus.
Transcriptional activity can be increased by serine phosphorylation within this domain, presumably
by recruiting other transcription factors. Afterwards, the adjacent SH2 domain docks to a
phosphorylated tyrosine residue (e.g. STAT5 binds phospho-Y449 on IL-7R ) and, after being itself
phosphorylated on a specific tyrosine residue, it also mediates dimerization, interacting with other
SH2 domains. In mammalian cells, seven STAT proteins have been identified (Leonard and
O'Shea, 1998). The STATs are localized as clusters on chromosomes: STAT1 and STAT4 on
chromosome 1, STAT2 and STAT6 on chromosome 10, and STAT3, STAT5A and STAT5B on
chromosome 11 in the murine system (Copeland et al., 1995).
IL-7 is supposed to activate STATs 1, 3 and 5 (Jiang et al., 2004). However, only deficiencies in
STAT5 play a crucial role in thymocyte development. IL-7 induces phosphorylation of both isoforms
of STAT5, STAT5A and B. The two isoforms can form homo or heterodimers and show a 96%
homology in their amino acid sequence.
The docking site for STAT5 is tyrosine residue Y449, on the IL-7Rα. Its activation by IL-7 induces
most of the pathways leading to IL-7 mediated survival. STAT5 regulates the expression of several
Bcl-2 family members and caspases and exerts an anti-apoptotic activity (Debierre-Grockiego,
2004). The induction of bcl-2 messenger ribonucleic acid (mRNA) is impeded by a lacking
phosphorylation of STAT5, which occurs as a consequence of mutations of tyrosine 449 in the IL-
7R chain (Kim et al., 2003). However, other pathways like the phosphorylation of the pro-
apoptotic protein Bad are not influenced by blocked STAT5.
I. INTRODUCTION
26
There is considerable evidence that STAT5 is important in T cell development. Furthermore,
STAT5 seems to play a role in adjusting the number of B cell precursors (Sexl et al., 200) and in
regulating the TCR locus accessibility and thus T cell development (Ye et al., 2001).
Phospho-STAT5 binds to the same promoter elements as the B lineage transcription factor, early B
cell factor (EBF), which might thus lead to synergisms causing the expression of Pax5, a critical
transcription factor expressed early in B lineage cells (Corcoran et al., 1998). STAT3/STAT5 can
bind directly to the pim-1 promoter, leading to an up-regulated expression of the serine/threonine
kinase pim-1. Pim-1 itself can negatively regulate the JAK/STAT pathway by binding to SOCS
proteins (Losmann et al., 1999). Upon phosphorylation, the stability of SOCS proteins and
subsequently their inhibitory effect on STATs is increased (Chen et al., 2002). In turn, the
transcriptional activity of STAT5 can be upregulated by an IL-7-induced serine phosphorylation
(Nagy et al., 2002).
The inactivation of STAT5 inhibits the proliferation of some but not all cytokine receptors; however,
the activation of STAT5 may not induce proliferation (Isaksen et al., 2002). It remains thus to be
determined whether STAT5 is required only for survival signaling or also induces cell proliferation.
Furthermore, it remains unclear, whether other pathways are involved in T cell proliferation. In each
case, STAT5 is a key molecule downstream of IL-7R and might eventually even be the most
important signaling pathway of IL-7 (Goetz et al., 2004).
2.3.2. PI3K/AKT pathway
PI3Ks are lipid kinases being important for proliferation and survival of various cell types, e.g. B
and T cell development (Datta et al., 1999). Upon IL-7 stimulation, their p85 subunit binds to the
phosphorylated Y449 residue on the IL-7R chain via an SH domain and activates the catalytic
subunit (Venkitaraman and Cowling, 1994). Mutations of Y449 block PI3K activity and impede
proliferation of B cells (Corcoran et al., 1996).
The downstream mediators of PI3K have not been fully elucidated; however possible candidates
might be insulin receptors (IRS)-1 and –2, which are associated with JAK1 and JAK3 and become
phosphorylated after IL-7 treatment. Both PI3K and IRS-1/2 are necessary for the proliferative
effects of IL-4 and IL-9 (Xiao et al., 2002).
An important key downstream target of PI3K, which had been shown in an IL-7- dependent mouse
thymocyte cell line (Li et al., 2004) and human thymocytes (Pallard et al., 1999), is the
serine/threonine kinase AKT. This critical component of the IL-2 family signaling (Kelly et al., 2002)
and probably the major effector of PI3K signaling, requires the c chain for activation (Jiang et al.,
2004). AKT is recruited upon phosphorylation and conversion of the lipid phosphatidylinositol-4,5-
bisphosphat (PIP)2 to PIP3 by PI3K (Vanhaesebroeck and Waterfield, 1999). AKT has about 900
cellular targets (Maddika et al., 2007) and is attributed to execute two major activities: metabolism
(which will be discussed in section I.2.5.3.) and regulation of cyclin-dependent kinases (CDKs),
which are required for cell cycle entry and downregulation of cell cycle inhibitors such as p27kip1
proliferation (Barata et al., 2001). AKT phosphorylates and thus inactivates Forkhead transcription
factors (Brunet et al., 1999) e.g. Foxp3, preventing the synthesis of the death proteins Bim and
I. INTRODUCTION
27
p27kip1
, which could be the trigger of apoptosis in primary human T cells (Stahl et al., 2002).
Another activity of AKT is the phosphorylation of the pro-apoptotic protein Bad, which results in its
inactivation by binding to 14-3-3 proteins (Datta et al., 2000). Thus, the PI3K/AKT pathway might
be a possibility to regulate Bad activity and consequently support pro T cell survival. However, it
has been shown that the inactivation of Bad by IL-7 only partially depends on the PI3K/AKT
pathway (Lali et al., 2004).
Furthermore, it is not sure whether the PI3K/AKT pathway is directly required for cell proliferation,
since it was found that IL-7 did not activate the PI3K pathway and phosphorylate AKT, although
cells were proliferating in presence of IL-7 (Lali et al., 2004; Osborne et al., 2007).
2.3.3. Ras/Raf/ERK pathway
Stimulation with IL-7 might also lead to activation of the p42/44 Extracellular-signal regulated
kinase (ERK) cascade, which regulates the activity of the pro-apoptotic protein Bim and the
sodium/hydrogen exchanger (NHE), leading to a neutral pH (discussed in section I.2.5.4.).
In T lineage but not B lineage cells, the induction of ERK appears to happen via the canonical
pathway involving Shc/Grb2/SOS. Alternatively, ERK activation may be linked to IL-7R signals via
Pyk2 (Benbernou et al., 2000), which becomes associated with JAK1 upon IL-7 stimulation.
However, according to Guinamard et al. (2000), this pathway might not be essential to connect the
IL-7R to the ERK pathway.
However, the ERK pathway is not induced in most cell types upon IL-7 stimulation, at least not in
murine T cell lines (Crawley et al., 1996; Kittiparin and Khaled, 2007). In contrast, it might be
induced in pre B cells (Fleming et al., 2001).
2.3.4. SFK pathway
Src family protein kinases (SFKs) are non-receptor tyrosine kinases, which are activated by IL-7
(Seckinger and Fourgereau, 1994), although none of the members yet have been shown to be
uniquely required for IL-7. SFKs include nine members, Src, Lck, Hck, Fyn, Blk, Lyn, Fgr, Yes and
Yrk, all with similar structure, but a unique sequence that confers specific functions.
Tyrosine phosphorylation of specific residues can increase or decrease the kinase activity of SFKs
and can thus be regulated by other kinases and phosphatases, such as CD45 (Huntington et al.,
2004). The two most abundantly expressed SFKs in T cells are Lck and Fyn (Mustelin, 1994).
Lck binds CD4 and CD8 co-receptor molecules and can interact with a variety of surface receptor
molecules including IL-7R (Isakov and Biesinger, 2000). It is uniquely required for thymocyte
development at the stage of pre-TCR signaling, which begins at the pro-T4 stage. In contrast, both
isoforms of Fyn, FynB (expressed in B cells) and FynT (expressed in T cells) are not uniquely
required for IL-7 receptor signaling in T cells (Lowell and Soriano, 1996). Upon IL-7 stimulation, Lck
and Fyn, which are associated with IL-7R in T lymphocytes, undergo phosphorylation (Page et al.,
1995).
In B cells the SFKs, Fyn and Lyn, are associated with the BCR and are involved in BCR-mediated
signaling; their activities are synergistic (Yasue et al., 1997).
I. INTRODUCTION
28
In conclusion, SFKs are activated by IL-7, but no single family member is essential. Thus, there
may be a requirement for redundant function from this family (Jiang et al., 2005).
2.4. IL-7 and cell cycle
IL-7 is not only important for survival of cells, but also for their proliferation. The different effects
can be distinguished from each other in cases where survival is induced without proliferation; this
occurs at low IL-7 doses (< 1ng/mL) (Li, 2004; Khaled and Durum unpublished, reviewed in Jiang
et al., 2005). However, it is still controversial whether IL-7 really works as a proliferative factor, as
oppositional results can be found in literature (Geiselhart et al., 2001; Rathmell et al., 2001; Munitic
et al., 2004; Beq et al., 2006). These disparate findings could be explained by the fact that
transcription and expression of the IL-7R is negatively regulated by its ligand, IL-7 (Park et al.,
2004; Munitic, 2004).
Although the pathway from IL-7R to cell cycle progression is still unknown, it is presumed that IL-7
regulates the transition from the G1 to the S phase (von Freeden-Jeffry et al., 1997). Important
factors for this transition are G1 cyclins and CDK2. Their activity is negatively regulated by p27kip1
,
which is a member of the CIP/KIP family of cyclin dependent cell cycle inhibitors (CKIs). For re-
activation of CDKs, the dephosphorylating activity of Cdc25A is required (Jinno et al., 1994). In
turn, Cdc25A is negatively regulated by phosphorylation, triggering its degradation through an
ubiquitin-proteasome dependent pathway (Bernardi et al., 2000). The phosphorylation of Cdc25A is
mainly induced by two kinase families: MAPK, specifically p38 MAPK (Khaled et al., 2005), and the
checkpoint kinases (CHKs) 1 and 2 (Goloudina et al., 2003). IL-7 withdrawal induces activation of
this stress kinase p38.
CDK2 may be an important downstream mediator in the IL-7 pathway, leading to proliferation.
Unlike in other growth factors however, the primary mechanism by which IL-7 induces proliferation
is supposed to be the regulation of the inhibitory factor p27kip1
and the CDK activator Cdc25a rather
than synthesis of cyclins (Jiang et al., 2005).
2.5. Lymphotrophic effects of IL-7
IL-7 is an obligate survival factor for several subsets of progenitor and mature lymphoid cells
(reviewed in Khaled, 2002, a). Deficiencies in IL-7 can lead to apoptosis in dependent cell lines
(von Freeden-Jeffry, 1997). Furthermore, IL-7 is required for homeostatic survival of peripheral T
lymphocytes (Tan et al., 2001), memory CD8 T cells (Schluns et al., 2000) and probably also CD4
cells (offset TCR engagement) (Seddon et al., 2003).
2.5.1. Induction of anti-apoptotic factors
One major factor of cell survival induction by IL-7 is the regulation of Bcl-2 family members. Bcl-2 is
a potent anti-apoptotic protein, which improves survival of T lymphocytes (Strasser et al., 1996),
but due to its anti-proliferative activity does not lead to an increase in T cells, as would a true
homeostatic regulator. However, Bcl-2 is not responsible for all IL-7 effects, as it cannot rescue
neither T cells (Nakajima and Leonard, 1999), nor B cells (Maraskovsky et al., 1998).
I. INTRODUCTION
29
Another anti-apoptotic protein of the Bcl-2 family is Bcl-XL. It is upregulated by IL-7 and rescues
primary T cells from death through IL-2 withdrawal (Amos et al., 1998). Above this, it is probably
one mediator of the PI3K pathway.
In addition to regulating Bcl-2 or Bcl-XL, IL-7 could also induce the expression of myeloid leukemia
cell differentiation protein (MCL)-1, another anti-apoptotic protein that could be responsible for
maintenance of IL-7 dependent lymphocytes, although MCL-1 induction in an IL-7-dependent
thymocyte line has not yet been observed (Opfermann et al., 2003).
2.5.2. Suppression of pro-apoptotic proteins
Aside from inducing anti-apoptotic factors, IL-7 also actively inhibits apoptosis by suppressing the
activity of pro-death proteins, which are also members of the Bcl-2 family. They are divided into two
groups: the „„multidomain‟‟ proteins, such as Bax and Bak, and the „„BH3‟‟ only proteins, such as
Bid, Bad and Bim. Upon posttranslational activation of these death proteins, their translocation to
mitochondrial membranes is initiated where they induce damage and release cytochrome c, which
in turn triggers the caspase cascade.
The „„BH3‟‟ only protein Bid binds to Bcl-2 or Bax and promotes cell death. Another „„BH3‟‟ only
protein, Bad, is phosphorylated by AKT after induction of the PI3K pathway (Franke et al., 1997).
Bad is a component of the death pathway inhibited by IL-7 and a potential apoptotic factor in T
lymphocyte development because T cells from Bad transgenic mice are highly sensitive to
apoptotic stimuli (Mok et al., 1999).
In contrast to the other two pro-apoptotic proteins already mentioned, Bim contains a hydrophobic
C-terminus in addition to the BH3 domain. It exists in three isoforms: BimS, BimL and BimEL
(O‟Connor et al., 1998). Upon apoptotic stimuli, Bim is released from the dynein motor complex by
a mechanism including phosphorylation on T56 and S58 by Jun N-terminal kinases (JNK), leading
to an increase in the apoptotic activity (Lei and Davis, 2003). Bim can be regulated by ERK, AKT,
STATs and the forkhead transcription factor, FKHR-L1. ERK reduces the apoptotic activity of Bim
and interaction with Bax by phosphorylating BimEL on three serine sites, S55, S65, and S100
(Harada et al., 2004). However as already mentioned, IL-7 treatment does not induce the ERK
pathway in most cell types. Bim plays a critical role in the cell death induced by IL-7 withdrawal
(Pellegrini et al., 2004); the relevant binding partner for Bim has been suggested to be the pro-
survival protein MCL-1, which can normally prevent the activation of Bax and Bak (Opfermann et
al., 2003).
Other pro-apoptotic proteins, which belong to the multidomain group of the Bcl-2 family having a
transmembrane domain, and which are potentially inhibited by IL-7, are Bak and Bax.
2.5.3. Regulation of metabolism: glucose
Garland and Halestrap recognized in 1997, that cytokines can upregulate glucose import and
metabolism. Thus, lacking of a cytokine signal can lead in cells depending on these cytokines to a
progressive atrophy and downregulation of glucose transporters such as GLUT1 (Summers and
Birnbaum, 1997) with an accompanying decreased activity of glycolytic enzymes (Plas et al., 2002)
I. INTRODUCTION
30
and loss of adenosine tri-phosphate (ATP) synthesis. As a consequence, cells decrease in cell size
or/and undergo apoptosis (Rathmell et al., 2000).
The downstream substrate of PI3K, AKT, seems to be important for glucose uptake, as forced
expression of AKT promoted transcription of GLUT1 (Bentley et al., 2003). Upon IL-7 withdrawal, a
loss of glucose uptake was rapidly observed, leading to the hypothesis, that T cells are dependent
on IL-7 for maintenance of metabolic activity (Rathmell et al., 2001). Since AKT is activated by IL-7,
it is likely that part of the survival signal through IL-7 also involves maintenance of glucose
metabolism.
Besides this, another nutrient transporter with high metabolic activity, the transferring receptor
(CD71), is regulated by IL-7. Its surface expression is equally blocked by PI3K inhibition.
2.5.4. Regulation of metabolism: pH
Cytokine deprivation leads to acidification in IL-7 dependent cell lines during the last steps of
apoptosis (Khaled et al., 1999). Reasons for this might be the breakdown of the pH regulatory
machinery or the release of protons from damaged mitochondria during apoptosis (Matsujama et
al., 2000). The acidic pH activates caspases and DNA nucleases and might also contribute to cell
death (Famulski et al., 1999).
Despite a metabolic activity, which generates acids, the intracellular pH of healthy growing cells is
neutral, through the action of several pH-regulatory proteins in the plasma membrane. One
example for these pH regulators in lymphocytes is the NHE, which exchanges excess intracellular
protons for extracellular sodium ions. The exchange activity of NHE can be increased by growth
factors via MAPKs. Likely pathways are MEK1, ERK and p90RSK, which phosphorylate NHE on
serine 703 in the intracellular, C-terminal domain (Takahashi et al., 1999). As a consequence, the
exchange function of NHE is increased and the acidic intracellular pH is raised up to a neutral
value. However, ERK is not activated by IL-7 (Crawley et al., 1996), although NHE is actively
phosphorylated in response to IL-7 signaling (Khaled et al., 2001). Thus another kinase increasing
NHE activity might be activated by IL-7.
In contrast to addition, withdrawal of cytokines leads to activation of stress MAPKs, e.g. p38 MAPK,
which phosphorylates NHE, leading to an alkalinization (up to levels over pH 7.5) (Khaled et al.,
1999). Due to this alkalinization, the death protein Bax translocates into mitochondria and thereby
blocks the import of adenosine di-phosphate (ADP), leading to a hyperpolarization of the
mitochondria. Consequently, ATP hydrolysis occurrs, which damages cells through the interruption
of the electron transport chain (ETC) and the subsequent generation of reactive oxygen species.
In summary, withdrawal of IL-7 creates via loss of glucose and cytosolic alkalinization severe
metabolic stress for dependent cell lines.
2.6. IL-7 in immunotherapy
Even if much progress has been made in the last years in immunotherapies to efficiently target
most cancers and infectious diseases, most of the therapies possess some disadvantages that limit
their full efficacy and development. The current limitations of most immunotherapies include e.g.
I. INTRODUCTION
31
insufficient production of T cells, as well as production of inefficient or short lived T cells, very
sensitive to apoptosis. Other problems are the fact that boosting CD4 counts but not CD8 counts,
or vice versa. Moreover it might happen that there is production of effector T cells with short lived
activity but not of central memory T cells that are responsible for long term protection, as well as
occasional production of more TH-2 or suppressor T-Regs than the required active TH-1 cells.
(Sportès and Gress, 2007; http://www.cytheris.com/ Science/interleukin7.php, 15.12.09).
Due to its multiple immune-enhancing properties, most notably in the maintenance of T cell
homeostasis, IL-7 is a very attractive candidate for immunotherapy in a wide variety of clinical
situations. In preclinical studies in murine and simian models, the efficacy of IL-7 has already been
shown. Thus, it was observed that cytotoxic T cells, which were adoptively transferred in vivo
protected mice against syngeneic tumors (Lynch et al., 1991; Jicha et al., 1991). IL-7 is now
emerging in human phase I trials as a very promising immunotherapeutic agent. The ability to
engraft human cells, both normal and neoplastic, into immunodeficient mice has allowed for the
examination of various potential biological therapies on human tumors in an in vivo setting (Mossier
et al., 1988; Rabinowich et al., 1992; Murphy et al., 1993, a). Moreover, the administration of rhIL-7
with adoptive immunotherapy (AIT) significantly prolonged the survival of human tumor-bearing
mice (Murphy et al., 1993, b). It can be assumed that IL-7 can resolve at least partly the above
mentioned limitations of current immunotherapies. This is achieved by systematically increasing the
number of CD4 and CD8 T cells in both animal models and in clinical studies, including simian
immunodeficiency virus (SIV) infected monkeys and human immunodeficiency virus (HIV) infected
patients (Sportès and Gress, 2007; Welch et al., 1989). Moreover, IL-7 administration is very potent
at producing “new long lived T cells” (i.e. naïve T cells and recent thymic emigrants) and to
augment lymphokine activated killer activity. It is particulary interesting, that IL-7 intervenes at all
stages of T cell development and maintenance. Moreover, it has been shown, that IL-7 in humans
induced a preferential expansion of naïve T cells, resulting in a broader T cell repertoire than
before the treatment; this effect was independent of age. This suggests that IL-7 therapy could
enhance immune responses in patients with limited naïve T cell numbers as in aged patients or
after disease-induced or iatrogenic T cell depletion (Nahed and Gress, 2010).
The two main immunological settings which correspond to various life threatening pathologies and
against which IL-7 could be used as an immunotherapeutic are CD4 T cell lymphopenia and the
case of inefficient specific immune response against massive antigen load such as that associated
with chronic viral infections, e.g. Hepatitis-C and HIV (http://www.glgroup.com/News/IL-7-
Immunotherapy-for-HIV-Pre-clinical-data-looks-promising-8486.html, 07.04.2010), as well as
different types of cancer (Goldrath, 2007; Sportès and Gress, 2007; http://www.cytheris.com/
Science/interleukin7.php, 15.12.09).
The Quality of IL-7 compounds can be monitored by biological assays, which will be thoroughly
discussed in the following section.
I. INTRODUCTION
32
3. QUALITY CONTROL OF BIOPHARMACEUTICALS
An important criterion in the development process of biopharmaceuticals is the monitoring of the
quality of the product. Quality control (QC) of biopharmaceuticals is achieved by using different
bioanalytical methods, amongst them biological assays, which are an indispensable method in this
context.
3.1. General remarks about quality control
Consumer and patient safety are the prerequisites for (bio)-pharmaceutical product development,
production and marketing. The ability to provide an effective, pure, safe product is the primary
factor determining the product‟s success (Doblhoff-Dier and Bliem, 1999). The quality of the
product thus needs to be controlled according to national and international guidelines. The major
factors under which the product is characterized are its purity, stability, safety and – last but not
least – its potency (International Conference on Harmonization (ICH) Q6B).
The successful development of novel pharmaceuticals cannot be achieved without the use of data
generated using validated bioanalytical methods (Nowatzke and Woolf, 2007). These bioanalytical
methods are based on a variety of physico-chemical and biological techniques such as
chromatography, immunoassay and mass spectrometry. One of the most important methods for
the QC of biopharmaceuticals are biological assays or bioassays, which measure the biological
activity of a product and are thus necesserary for a complete structural characterization of new
biological entities (NBEs). The development of new bioassay principles is a key requirement in the
QC field of biopharmaceuticals and is focus of this work.
Bioanalytical methods must be validated prior to and during use to engender confidence in the
results generated. According to the Food and Drug Administration (FDA), the fundamental criteria
for assessing the reliability and overall performance of a bioanalytical method are: the evaluation of
drug and analyte stability, selectivity, sensitivity, accuracy, precision, linearity and reproducibility.
The extent to which a method is validated is dependent on its prospective use, the number of
samples to be assayed and the use of the data (Buick et al., 1990).
3.2. Criterions for quality control of biopharmaceuticals
According to the ICH Q6B, biopharmaceuticals are characterized regarding the following aspects.
3.2.1. Physicochemical properties
In a physicochemical characterization, the composition, physical properties, and primary structure
of the desired product are determined. In addition, information regarding higher-order structure of
the desired product may be obtained by appropriate physicochemical methodologies.
Since biopharmaceuticals are at least partially produced by living organisms, an inherent degree of
structural heterogeneity can occur. Those post-translationally modified forms may be active and
harmless. Thus, if a consistent pattern of product heterogeneity is demonstrated, an evaluation of
I. INTRODUCTION
33
the activity, efficacy and safety (including immunogenicity) of individual forms may not be
necessary; however, lot-to-lot consistency has to be assured.
3.2.2. Biological activity
Since a complete structural characterization by physicochemical methods is – other than for small
molecules - not possible for biological products, bioassays are necessary for a full characterization
of a biological product to assess its biological activity. Bioassays with wider confidence limits may
be acceptable for QC of such products, when combined with a specific quantitative measure.
These methods asses the biological activity of a product, which is defined as the specific ability or
capacity of a product to achieve a defined biological effect. It can be measured using animal-based
assays, cell-based assays, biochemical assays or ligand and receptor binding assays (further
described in section 3.3.). The results of bioassays are potencies (expressed in units of activity) of
a sample in comparison to an adequate reference standard.
Potency is the quantitative measure of biological activity based on the attribute of the product which
is linked to the relevant biological properties. Mimicking the biological activity in the clinical situation
is not always necessary. A correlation between the expected clinical response and the activity in
the bioassay should be established in pharmacodynamic or clinical studies.
Since bioassays measure the potency of a product, they are also termed “potency assays”.
3.2.3. Immunochemical properties
In case of antibodies, a characterization of the immunological properties is necessary. Affinity,
avidity and immune-reactivity (including cross-reactivity) should be determined by using binding
assays of the antibody to purified antigens and defined regions of antigens. In addition, the target
molecule bearing the relevant epitope should be biochemically defined and the epitope itself
identified, when feasible.
In some cases, additional immunochemical analyses (e.g., enzyme-linked immuno sorbent assay
(ELISA) or western blotting), utilizing antibodies which recognize different epitopes of the protein
molecule, may be required to establish its identity, homogeneity or purity, or serve to quantify it.
3.2.4. Purity, impurities and contaminants
The purity of the drug substance and drug product is assessed by a combination of analytical
procedures, since the determination of absolute, as well as relative purity, presents considerable
analytical challenges, and the results are highly method-dependent. The purity is expressed in
terms of specific activity (units of biological activity per mg of product).
As a consequence of the unique biosynthetic production process, the drug substance can include
several molecular entities or variants. These molecular entities are part of the desired product,
when they are derived from anticipated post-translational modification. When they are formed
during the manufacturing process and/or storage and have properties comparable to the desired
product, they are considered product-related substances and not impurities.
I. INTRODUCTION
34
Impurities may be either process or product-related. Process-related impurities include those that
are derived from the manufacturing process, i.e. cell substrates (e.g. host cell proteins, host cell
DNA), cell culture (e.g. inducers, antibiotics, or media components), or downstream processing.
Product-related impurities (e.g. precursors, certain degradation products) are molecular variants
arising during manufacture and/or storage, which do not have properties comparable to those of
the desired product with respect to activity, efficacy, and safety. Impurities can be of known
structure, partially characterized, or unidentified. When adequate quantities of impurities can be
generated, these materials should be characterized to the extent possible and, where possible,
their biological activities should be evaluated. The acceptance criteria for impurities should be
based on data obtained from lots used in preclinical and clinical studies and manufacturing
consistency lots.
Contaminants in a product include all adventitiously introduced materials not intended to be part of
the manufacturing process, such as chemical and biochemical materials (e.g. microbial proteases),
and/or microbial species. Contaminants should be strictly avoided and/or suitably controlled with
appropriate in-process acceptance criteria or action limits for drug substance or drug product
specifications.
3.2.5. Quantity
Another critical aspect for a biotechnological and biological product is its quantity, usually
measured as protein content. The quantity determination is usually achieved in comparison to a
reference standard, but may also be independent of it. It is determined using an appropriate assay,
in most cases a physicochemical one. It is usual that the quantity values obtained may be directly
related to those found using the biological assay. When this correlation exists, it may be
appropriate to use measurement of quantity rather than the measurement of biological activity in
manufacturing processes, such as filling.
3.3. Biological assays
Biological assays, also termed bioassays or potency assays are an indispensable method for QC
of biopharmaceutical macromolecules, such as recombinant proteins or mononuclear antibodies.
3.3.1. Assay formats and requirements for use in QC
3.3.1.1. Overview of assay formats
There are various different assay formats used for QC of biopharmaceuticals. They reach from
immunoassays, such as ELISAs, over reporter gene assays, proliferation- and phosphorylation
assays to in vivo and in vitro disease assays. All of them possess their advantages and
disadvantages. In figure 3, an overview of the different assay formats is given, showing their clinical
relevance in comparison to their variability and thus suitability for QC.
I. INTRODUCTION
35
Increasing clinical relevance
In vivo disease animal model
In vivo animal assay
Cell based assay
primary cells
cell lines
Cell based biochemical assays
kinase receptoractivation
reporter gene
Binding assays
cell based receptorbinding
immunoassays
Physicochemical assays
Increasing variability
Decreasing suitability for QC
Figure 3. Overview of assay formats. Figure 3 gives an overview of the different assay formats. Assays with a high clinical relevance also show a high variability associated with a low suitability for QC and vice versa. Those parameters have to be balanced when choosing the right assay format. Source: Bachmann (2006)
The figure shows the correlation of clinical relevance versus variability, associated with the assays‟
suitability for QC. Physicochemical assays show a low variability and are thus well-suited for QC.
On the other hand, their clinical relevance lies not at hand. In contrast, in vivo disease assays are
of high clinical relevance, but also show a very high variability, which is problematic for QC
analysis. The two parameters clinical relevance and variability have thus to be balanced when
choosing the appropriate assay format.
3.3.1.2. Requirements for use in QC
As already described in section I.3.1., the successful development of novel pharmaceuticals cannot
be achieved without the use of data generated using well-functioning and validated bioanalytical
methods (Nowatzke and Woolf, 2007). All methods must be good manufacturing practice (GMP)-
conform as well as compliant with the criteria of the ICH guidelines and the guidances of the
authorities. In addition, the bioassays must be compliant with current statistical requirements of the
European Pharmacopeia (Ph.Eur.) and the United States Pharmacopeia (USP).
Assays used for quality control, release and stability testing should be precise, robust, accurate
and should show a good repeatability. To accomplish this and to be suitable for routine use in a QC
environment, they should provide a high signal to noise ratio and a stable read-out for more than
30min. Furthermore, they should be as fast as possible; kinetic measurements are usually not used
in QC.
I. INTRODUCTION
36
3.3.2. Analysis models for potency assays
The principle for the evaluation of potency assays is the comparison of a sample against a
reference standard on the basis of the measured biological activities (Ph. Eur. 6.7, 2010;
Gottschalk and Dunn, 2005, a). Due to the high variability of biologic systems, it is essential that
the tests on the standard and on the sample are carried out at the same time and under identical
conditions. Furthermore, randomization and independence of individual treatments are important
prerequisites to be considered (Ph. Eur. 6.7, 2010). After the performance of an assay and prior to
the calculation of the potency, an analysis of variance is carried out in order to check whether the
conditions of the different analysis models are fulfilled. Tests of significance of regression, linearity
(Hypothesis tests based on separate Analysis of Variance (ANOVAs)) and parallelism (Hypothesis
F-Test) are performed. As described in the Ph. Eur. 6.7, Chapter 5.3, a confidence interval of 99%
is used for the slope and a confidence interval of 95% is used for linearity and parallelism.
For evaluation of the potency, different statistical models are proposed e.g. the slope ratio assay,
comparison of EC50 values, the parallel line method and the logistic models four- and five-
parameter fit (Ph. Eur. 6.7, 2010; USP <111>, 2001; Wilbur, 1993). A good curve model should
accomplish three conditions: it must well approximate the true curve, needs to be capable of
averaging out as much of the random variation in the data as possible and should enable to predict
concentrations at points between the standard points and not only at the fitted data points
(Gottschalk and Dunn, 2005, b). Moreover, the model must be compliant with current statistical
requirements of the Ph.Eur. and the USP.
Some of the most commonly used calculation models to date are the parallel line method and the
logistic models four- and five-parameter fit, which will be presented in detail in the following.
3.3.2.1. The parallel line model
For dilution assays, usually several dose steps (e.g.
nine dilution steps, which is one commonly used
format at Merck Serono) are utilized, resulting in an
extended dose-response curve with a sigmoid shape.
Since such a high number of dose levels is not ethical
for some assay formats, e.g. in vivo assays or less
practical or since the aims of the assays might be
achieved with fewer dose responses, the dose
response range can be restricted to the linear range
of the curve. The restriction of the curve is for
example used by the parallel line (PL) assay, which
only utilizes the linear region of the sigmoid curve for
potency determinations, because this is the region of
the curve where the dose response is the steepest,
providing the best estimate of relative potency
(Finney, 1978; Bunch et al., 1990). The asymptotes are not taken into consideration (see figure 4).
Figure 4. Graphical presentation of the parallel line model.
Example of an assay with three dilution steps, analyzed by the PL model. Source: Ph. Eur. 6.7, chapter 5.3 (2010)
I. INTRODUCTION
37
Figure 5. Graphical presentation of the four-parameter fit. Example of an assay with 9 dilution steps, analyzed by the 4PL model. Source: Ph. Eur. 6.7, chapter 5.3 (2010)
The relationship between the dose X and the response Y is given by the following equation:
In which a is the intercept and b is the slope of the line.
Often, the dose must be log transformed to result in a straight line. Then, X equals log (dose).
Statistical validity on the following hypotheses can be tested:
1. The dose-response relationship is linear for the standard and the sample preparation.
2. The dose-response curve has a significant slope.
3. The dose-response curves of standard and sample preparation are parallel.
In comparison to the traditional single-point assay, the parallel line model has the advantage (by
checking the hypotheses mentioned above) that a linear dose-response correlation can be proven
and a dose-independent relative potency is obtained. The potency is defined as the distance
between the linear slope of the sample and the slope of the reference standard. Therefore, at least
three dilutions for both the test serial and the reference are required.
3.3.2.2. Logistic models (4-and 5-parameter fit)
In contrast to the PL model, the logistic models
consider the whole dose response range, resulting in
the advantage that additional information about the
asymptotes is given. This might lead to a higher
precision of the assay since the whole curve is
considered for potency determination. Disadvantages
in comparison to the PL model are the higher number
of dose levels required and the fact that these models
are currently less precisely described in the Ph. Eur.
The four-parameter fit (4PL) calculates a curve based
on four-parameters (Volund et al., 1978; Findlay and
Dillard, 2007): the response at infinite dose
(saturation), the response at minimal dose (extinction),
the 50% effective dose (ED50) and the slope of the
line (see figure 5).
This results in the following equation, describing the 4PL model:
in which Y is the response, D is the response at infinite analyte concentration, A is the response at
zero analyte concentration, X is the analyte concentration, C is the inflection point on the calibration
curve (IC 50 ), and B is a slope factor (Findlay et al., 2007).
Y = a + bX (Eq. 1)
Y = D + (A-D)/ [1+ (X/C)B]
(Eq. 2)
I. INTRODUCTION
38
By adding a fifth parameter, G, which controls the degree of asymmetry of the curve, the four-
parameter logistic model is expanded to the five-parameter fit (5PL) (Prentice et al., 1976; Straume
et al., 1994). With the extra flexibility afforded by its asymmetry parameter, the 5PL is able to
virtually eliminate the lack-of-fit error that occurs when the 4PL is fitted to asymmetric dose-
response data.
Consequently, it is advised to use the 5 PL model for calculation of asymmetric curves. The
equation for the 5PL model is the following:
The choice for the right model is a matter of frequent discussions and depends on the assay set
up. As a general rule, during validation of the assay it has to been shown that the assay including
the evaluation model chosen meets predefined acceptance criteria for e.g. accuracy and precision.
By this, the choice of the evaluation model can be justified.
3.3.3. Read-out system for proliferation assays
The most commonly assays used in QC of biopharmaceuticals are proliferation assays.
In the past, cell proliferation was mainly measured by using radioactive compounds, e.g. 51
Cr and
[3H]thymidine (Schlager and Adams, 1983) or tetrazolium salts like 3-(4,5-Dimethylthiazol-2-yl)-2,5-
diphenyltetrazoliumbromid (MTT) (Denizot and Lang, 1986; Hansen et al, 1989; Mosmann, 1983)
and sodium(2,3-bis(2-methoxy-4-nitro-5-sulfophenyl~-2H-tetrazolium-5-carboxanilide (XTT)
(Roehm, 1991). Although [3H]
thymidine is still used because of its high sensitivity, it is increasingly
replaced by more convenient, less harmful non-radioactive alternatives (Hamid, 2004; Rotter and
Oh, 1996). Three of the most commonly used reagents in this regard are the tetrazolium salt and
MTT analogue 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl-2-(4-sulfophenyl)-2H-
tetrazolium) /phenazine methosulfate (MTS/PMS) (Goodwin et al., 1995), the fluorescent dye
Alamar Blue (Ahmed et al., 1994) and the ATP bioluminescence assay using the luciferase
reaction (Crouch et al., 1993; Kangas et al., 1984). The basic principles of these read-outs are
described in the following.
3.3.3.1. Colorimetric detection
The MTS/PMS assay is a colorimetric assay, which is based on the reduction of a tetrazolium salt
(MTS) to an intensely colored formazan by the mitochondria enzymes (dehydrogenases) of living
cells (Goodwin et al., 1995) (see figure 6). PMS acts as an intermediate electron acceptor and
accelerates the reaction. The amount of the produced formazan is directly proportional to the
number of living cells in culture and can be measured at 492nm (Malich et al., 1997).
In contrast to MTT, MTS/PMS has the advantage of being water-soluble. Thus, the solubilization
step of the crystals prior to measurement, which is required for MTT, can be eliminated. This
Y = D + (A-D)/ [1+ (X/C)B]G
(Eq. 3)
I. INTRODUCTION
39
reduces both the risk of microbial contamination of the assay and the sample processing time
(Stevens and Olsen, 1993; Rotter and Oh, 1996).
Comparing the MTS substrate to another tetrazolium salt, XTT, Goodwin et al. (1995) discovered
that the XTT/PMS, but not the MTS/PMS, reagent mixture was unstable, which favors the use of
MTS instead of XTT. As a consequence, the MTS/PMS substrate was used as an example for the
colorimetric detection in this study, since it seemed to be the most advantageous of the three
tetrazolium salts.
Figure 6. Reaction equation of the colorimetric read-out. The basic principle of this reaction is the reduction of a tetrazolium salt into an intensely colored formazan, accelerated by the electron acceptor PMS. (NAD: Nicotinamid-adenin-dinucleotid, EC: ethylene carbonate)
3.3.3.2. Fluorescence
The fluorescent dye Alamar Blue has been proposed as a substitute for [3H]
thymidine (Ahmed et al.,
1994) and an alternative for the MTT assay (Pagé et al., 1993).
The native, oxidized state of Alamar Blue, resazurin, is non-fluorescent and blue. When
internalized by living cells, oxidoreductases and the mitochondrial electron transport chain reduce
this reagent intracellulary to resofurin, a highly fluorescent red dye (Goegan et al., 1995) (see figure
7). The amount of the fluorescence detected is proportional to the number of living cells.
The main advantage of this reagent is its low toxicity and its non-destructive effect on cells, in
contrast to most of the other reagents including MTS/PMS and the ATP bioluminescence assay.
These latter reagents induce cell death and consequently allow performing endpoint assays only,
whereas the use of Alamar Blue enables to carry out kinetic measurements of the changes in cell
proliferation, viability and metabolic activity (Pagé et al., 1993; Zhi-Jun et al., 1997). Moreover,
Hamid et al. (2004) found a higher sensitivity of the Alamar Blue assay in the detection of cytotoxic
effects compared with the MTT assay.
N
NN
N
NO2
SO3Na
SO3Na
I
N
NN
HN
NO2
SO3Na
SO3Na
I
EC-H EC
NAD+ NADH
RS
Tetrazolium salt (MTS)
Formazan
I. INTRODUCTION
40
Figure 7. Reaction equation of the fluorescence read-out. The basic principle of this reaction is the reduction of the blue, non-fluorescent resazurin to the red fluorescent dye resofurin.
3.3.3.3. Luminescence
The ATP-bioluminescence assay measures the number of viable cells using the firefly luciferase-
luciferin system (Crouch et al., 1993) and can likewise be used as a read-out for proliferation
assays. Luciferase catalyzes the formation of light from ATP and luciferin in presence of Mg2+
ions
(see figure 8). The emitted light intensity is linearly related to the amount of ATP present and is
thus a direct measure of the number of viable cells (Andreotti et al., 1995; Mueller et al., 2004).
The ATP assay is supposed to be much more sensitive than the MTT assay and has slightly better
reproducibility (Petty et al., 1995; Weyermann et al., 2005). Disadvantages are the higher costs of
the luminescence reagent and the time-dependency (of some reagents) of the luminescence signal
(Weyermann et al., 2005).
Figure 8. Reaction equation of the luminescence read-out. The basic principle is the reaction from luciferin to oxyluciferin in presence of ATP and Mg
2+ions, catalyzed by
the enzyme luciferase.
3.4. Investigated biopharmaceuticals
The following biopharmaceuticals were used for the test of the different types of bioassays.
3.4.1. Cetuximab (Erbitux®)
Cetuximab is a recombinant, human/mouse chimeric monoclonal antibody that binds specifically to
the extracellular domain of the EGFR.
O
N
OOH
O
O
N
OOH
NADH/H+ NAD+, H2O
Resazurin Resofurin
N
SOH
S
N COOH
N
SOH
S
N OH
hv
Luciferase
Luciferin
ATP, Mg2+OxyluciferinAMP, CO2
I. INTRODUCTION
41
EGFR is a major target of anti-cancer therapies, because its level of expression on tumor cells is a
hint regarding the aggressiveness of the tumor. Epidermal growth factors bind specifically on the
EGFR, inducing cell signaling cascades for cell proliferation. As a consequence, the tumor burden
increases and metastasizes. The anti-EGFR antibody blocks binding of growth factors to EGFR
specifically. Thus, proliferation and metastasis of cancer cells is blocked.
The investigated anti-EGFR antibody is composed of the Fv regions of a murine anti-EGFR
antibody with human IgG1 heavy and kappa light chain constant regions. It is produced in
mammalian (murine myeloma) cell culture and has a molecular weight of approx. 152kDa.
Therapeutic indications are colorectal cancer (CRC), squamous cell carcinoma of the head and
neck, non-small cell lung carcinoma (NSCLC), pancreatic cancer and renal cell carcinoma.
3.4.2. Tucotuzumab celmoleukin (KS-IL2)
Tucotuzumab celmoleukin is a genetically engineered fusion protein (immunocytokine), comprizing
a humanized monoclonal antibody directed against the Epithelial Cell Adhesion Molecule (EpCAM)
genetically fused to a human cytokine (IL-2). It is derived from an IgG1 mouse monoclonal antibody
reactive with the EpCAM antigen. EpCAM is one of the first tumor-associated antigens and is
expressed at high level and frequency on most human carcinomas; it is a prime target for
immunotherapies. Targeting of IL-2 to EpCAM-expressing tumors of epithelial origin produces a
multi-factorial immune response through antibody-dependent cellular cytotoxicity (ADCC) and
cytokine stimulatory effects in a wide range of tumor types. Apart from this antibody-dependent
effectors‟ mechanism, also a cytokine induced mechanism is induced due to the comprized IL-2
molecules. Thus, typical IL-2 responses are stimulated, such as inducing CTL and NK cells -
capable of recognizing and killing tumor cells.
Tumors that can be targeted with this molecule are most epithelial cancers, such as ovarian, small
cell lung carcinoma (SCLC), NSCLC, prostate, gastro-intestinal (GI) and breast cancer.
3.4.3. Fc-IL7
This cytokine fusion protein consists of the Fc domain of an antibody, linked to an IL-7 molecule.
IL-7 is a cytokine being important for the development of lymphocytes and T cells, which has
already been discussed thoroughly in the Introduction section. The Fc Domain, Fcγ2h, shows little
or no interaction with FcRs and leads to a significantly longer half-life than the pure IL-7; moreover,
it is more potent. It can thus be given less frequently.
It can be used against the following targeted indications:
Following autologous hematopoietic stem cell transplantation to reduce infection and
increase T cell response and survival;
Following standard chemotherapy regimens to increase anti-tumor response and survival;
In combination with tumor vaccine or adoptive T cell transfer in either of the above two
settings.
Furthermore, its applicability can be expanded against chronic or tropical disease including HIV,
malaria, dengue fever and auto-immune diseases.
II. AIM OF THIS STUDY
42
IIII.. AAIIMM OOFF TTHHIISS SSTTUUDDYY
First aim of this study was the evaluation of optimized proliferation assays regarding different
analysis models and read-out systems newly established in the Merck Serono Bioassay
laboratory.
Concerning the analysis models, the parallel line model was compared to the logistic models, four-
and five-parameter fit. For this comparison, the different models were classified regarding
accuracy, precision and failure rate.
Regarding the read-out systems, the colorimetric read-out, using the MTS/PMS reagent, was
compared to luminescence and fluorescence techniques, using CellTiter- Glo®
and Alamar BlueTM
,
respectively. The parameters considered for this comparison were sensitivity - implying the needed
cell amount -, signal to noise ratios, incubation times, as well as accuracy and precision of the
different read-out systems.
Second aim of this study was to develop and qualify an additional, faster assay as an alternative to
the commonly used proliferation assays since those assays usually require several days of
incubation time with active pharmaceutical ingredient (API). One possibility for this purpose is the
use of phosphorylation assays, which can be completed much faster than the end-point
proliferation assays.
Development of the phosphorylation assay was done by using the example of Fc-IL7, a cytokine
fusion protein which contains IL-7. Consequently, it was the aim to screen the signaling pathways
of IL-7 by western blotting in order to identify a suitable target for development of a phosphorylation
assay. In parallel, the IL-7 and IL-2 signal transduction cascades were elucidated using western
blotting and flow cytometry since the signaling pathways of IL-7 belong to the less characterized
pathways in comparison to other cytokines.
III. MATERIALS AND METHODS
43
IIIIII.. MMAATTEERRIIAALLSS AANNDD MMEETTHHOODDSS
1. MATERIALS
1.1. Cell lines
Designation Description Cell type Culture
medium
Source
CTLL-2 Cytotoxic murine T lymphocytes,
dependent on IL-2
suspension RPMI 1640 ATCC
DiFi Human, Epidermal Growth Factor
receptor (EGFR)-positive carcinoma
cells
adherent DMEM/F12 ImClone
Kit 225 Human IL-2 dependent T
lymphocytes, proliferating also in
presence of IL-7
suspension RPMI 1640 EMD
Serono
PB-1 Murine IL-7 dependent pre-B cells suspension McCoy‟s 5A DSMZ
2E8 Murine IL-7 dependent B lymphocytes suspension IMDM +
Glutamax
ATCC
(ATCC: American Type Culture Collection; EMD: Emanuel Merck, Darmstadt; DSMZ: Deutsche Sammlung von Mikroorganismen und Zellkulturen)
In figure 9, microscopic images of the cell lines, used in this study, are shown.
PB-1 2E8 Kit 225
CTLL-2 DiFi
Figure 9. Microscopic images of the cell lines, used in this study.
Pictures were made in an amplification of 100x for PB-1, 2E8 and DiFi cells and an amplification of 200x for Kit 225 cells, by the laboratory of Dr. Christa Burger, Merck Serono, Darmstadt. The picture of the CTLL-2 cells is obtained from the ATCC at a scale bar of 100µm.
III. MATERIALS AND METHODS
44
1.2. Investigated biopharmaceuticals
In this study, different biopharmaceuticals were investigated, all from Merck Serono, Darmstadt.
1.2.1. Cetuximab (Erbitux®)
Cetuximab is a recombinant, human/mouse chimeric monoclonal antibody that binds specifically to
the extracellular domain of the EGFR. It is further described under I.3.4.1.
The quality of this product with regards to its potency can be controlled by using the DiFi potency
assay (described under III.2.4.1.2.).
1.2.2. Tucotuzumab celmoleukin (KS-IL2)
Tucotuzumab celmoleukin is an immunocytokine, comprizing a humanized monoclonal antibody
directed against the EpCAM genetically fused to a human cytokine (IL-2). It is further described
under I.3.4.2.
The quality of this product with regards to its potency can be controlled by using the CTLL-2
potency assay (described under III.2.4.1.1.).
1.2.3. Fc-IL7
This cytokine fusion protein consists of the Fc domain of an antibody, linked to an IL-7 molecule. It
is further described under I.3.4.3.
The quality of this product can be controlled by using the Kit 225 potency assay (described under
III.2.4.1.3.) or the STAT5 phosphorylation assay (described under III.2.5.).
1.3. Antibodies
1.3.1. Primary antibodies
Specificity Species/isotype Used
concentration
Application Company
AKT Rabbit IgG 1:1,000 WB CellSignalling
p-AKT (Ser473) Rabbit IgG 1:1,000 WB CellSignalling
Bcl-2 Rabbit IgG 1:1,000 WB CellSignalling
Bim Rabbit IgG 1:1,000 WB CellSignalling
p-Bim (Ser55) Rabbit IgG 1:1,000 WB CellSignalling
CD25 (human) Mouse IgG1 қ,
PE conjugated
20µg/mL FACS Becton Dickinson
CD25 (mouse) Rat IgG1 λ,
PE conjugated
20µg/mL FACS Becton Dickinson
CD122 (hu) Mouse IgG1 қ,
PE conjugated
20µg/mL FACS Becton Dickinson
CD122 (mu) Rat IgG2b қ,
PE conjugated
20µg/mL FACS Becton Dickinson
III. MATERIALS AND METHODS
45
CD127 (hu) Mouse IgG1 қ,
PE conjugated
20µg/mL FACS Becton Dickinson
CD127 (mu) Rat IgG2b қ,
PE conjugated
20µg/mL FACS Becton Dickinson
ERK Rabbit IgG 1:1,000 WB CellSignalling
p-ERK
(Thr202/ Tyr204)
Rabbit IgG 1:1,000 WB CellSignalling
Lck Rabbit IgG 1:1,000 WB CellSignalling
p-Lck (Tyr505) Rabbit IgG 1:1,000 WB CellSignalling
p38 MAPK Rabbit IgG 1:1,000 WB CellSignalling
p-p38 MAPK
(Thr180/Tyr182)
Rabbit IgG 1:1,000 WB CellSignalling
Pim-1 Mouse IgG 1:100 WB Santa Cruz
STAT1 Rabbit IgG 1:1,000 WB CellSignalling
p-STAT1 (Tyr701) Rabbit IgG 1:1,000 WB CellSignalling
STAT3 Rabbit IgG 1:1,000 WB CellSignalling
p-STAT3 (Tyr705) Rabbit IgG 1:1,000 WB CellSignalling
STAT5 Rabbit IgG 1:1,000 WB CellSignalling
p-STAT5 (Tyr694) Rabbit IgG 1:1,000 WB CellSignalling
STAT5 Rabbit IgG 1:100 ELISA CellSignalling
p-STAT5 (Tyr694) Mouse IgG 1:100 ELISA CellSignalling
(FACS: Fluorescence Activated Cell Sorting; WB: Western blotting; PE: Phycoerythrin)
1.3.1. Secondary antibodies
Specificity Species/isotype Used
concentration Application Company
Rabbit
Alexa Fluor 680
goat IgG 1:10,000 WB Invitrogen
Mouse
Alexa Fluor 680
goat IgG 1:10,000 WB Invitrogen
Rabbit HRP-linked IgG 1:1,000 ELISA CellSignalling
Rabbit HRP-linked IgG 1:2,500 ELISA Promega
(HRP: horseradish peroxidase)
1.4. Controls
1.4.1. Cell control extracts
Control Used for detection of Application Company
C6 glioma cells +
anisomycin
p-p38 MAPK WB CellSignalling
III. MATERIALS AND METHODS
46
CCRF-HSB-2 Lck, p-Lck WB Santa Cruz
Hela + IFN-α p-STAT5 WB CellSignalling
HT29 + IFN-α p-STAT1, p-STAT3 WB Inhouse
Hut 78 Bim, p-Bim WB Santa Cruz
Jurkat Bcl-2 WB Santa Cruz
Jurkat + Calycurin A p-AKT WB CellSignalling
K562 Pim-1 WB Santa Cruz
Kit 225 + FBS p-ERK WB Inhouse
Kit 225 + H2O2 p-p38 MAPK WB Inhouse
NIN/3T3 + heat shock p-p38 MAPK WB Santa Cruz
1.4.2. Isotype controls
Control Isotype Application Company
PE Mouse IgG1 қ
isotype control
Mouse (BALB/c) IgG1 қ FACS Becton Dickinson
PE Rat IgG1 қ isotype
control
Rat (LOU) IgG2b қ FACS Becton Dickinson
1.5. Culture medium, reagents and chemicals
Reagent Company
AIM-V medium Invitrogen
Alamar Blue™ Promega
Bovine serum albumin (BSA), Albumin fraction V Merck KGaA
β-mercaptoethanol (ME) Invitrogen
BME vitamins Promo Cell
Carbonate buffer Fluka Chemie
Cell Titer-Glo® Promega
Dimethyl sulfoxide (DMSO) Sigma
Dithiothreitol (DTT) Merck KGaA
DMEM/F12 medium Invitrogen
Dulbecco‟s phosphate buffered saline (DPBS)
without calcium and magnesium
PAA
Fetal bovine serum (FBS) Gibco or Invitrogen (for culture of 2E8 cells)
Glutamine Invitrogen
Glycine Merck KGaA
H2SO4 Merck KGaA
Hank‟s Buffered Salt Solution (HBSS) Sigma
HEPES Invitrogen
III. MATERIALS AND METHODS
47
Hu IL-2 Promo Kine (for culture of CTLL-2 cells) or
R&D Systems (for culture of Kit 225 cells)
Hu IL-7 R&D Systems
IMDM + Glutamax medium Invitrogen
McCoy‟s 5A medium Invitrogen
MEM essential amino acids Invitrogen
MEM non-essential amino acids Sigma
Methanol Merck KGaA
MTS/PMS cell proliferation reagent Promega
Mu IL-7 R&D Systems
NaCl Merck KGaA
Odyssey blocking buffer LI-COR
Orthovanadate Calbiochem
Roche complete mini-protease inhibitor cocktail Roche Diagnostics GmbH
RPMI 1640 medium Invitrogen
Sodium dodecyl sulfate (SDS) Merck KGaA
Sodium pyruvate Invitrogen
SeeBlue® Plus2 prestained standard (1x) Invitrogen
Tetramethyl benzidine (TMB) Moss Inc.
Tris-glycine-SDS running buffer (10x) Anamed Electrophorese GmbH
Tris-glycine-SDS sample buffer (2x) Anamed Electrophorese GmbH
Tris(hydroxymethyl)-aminoethan Merck KGaA
Triton X-100 Merck KGaA
Trypan blue solution Invitrogen
Trypsin Invitrogen
Tween 20 Merck KGaA
WP1066 Sigma
1.6. Composition of buffer & reagent solutions
FACS buffer DPBS + 5% FBS
Transfer buffer 48mM tris(hydroxymethyl)-aminoethan 39mM glycine 20% methanol 1.3mM SDS pH 9.2
Blocking buffer (WB) Odyssey blocking buffer / PBS 1:2
Blocking buffer (STAT5 assay) DPBS + 1% BSA (Albumin fraction V)
Incubation buffer (primary antibody) Odyssey blocking buffer / PBS (+ 0.2% Tween 20) 1:2
III. MATERIALS AND METHODS
48
Incubation buffer (secondary antibody) Odyssey blocking buffer / PBS (+ 0.2%
Tween 20 and 0.02% SDS) 1:2
Ponceau S solution 0.1g Ponceau S 5mL acetic acid 100% 95mL Milli Q water
Washing buffer (WB) DPBS + 0.1% Tween 20
Washing buffer (STAT5 assay) DPBS + 0.05% Tween 20
Lysis buffer 150mM NaCl 50mM HEPES 0.5% Triton X-100 2mM Orthovanadate 1x Roche complete mini-protease inhibitor cocktail in water
1.7. Equipment
Equipment Company
Balance MC210P Sartorius AG
EL808 Ultra Microplate Reader Bio-Tek
Centrifuge (MiniSpin) Eppendorf AG
Electrophoreses Blue Power 3000 Serva
Electrophoretic Transfer Kit 2117-250
NOVABLOT
LKB Bromma
FACScan™ flow cytometer Becton Dickinson
Focussing chamber Multiphor II Amersham Pharmacia Biotech
Focussing chamber XCell Sure Lock™ Invitrogen
Humidified CO2 cell culture incubator Kendro Laboratory products GmBH
Laminar flow biosafety cabinet Kojair Tech Oy
Magnetic stirrer IKA Werke GmbH & Co. KG
Megafuge 1.0 with rotor 2704 Kendro Laboratory products GmBH
Microscope DMIL Leica Microsystems GmbH
Neubauer hemocytometer and corresponding
coverslip
Marienfeld
Odyssey Infrared Imaging System LI-COR
pH meter Metrohm AG
Plate shaker Janke & Kunkel, IKA Werke GmbH & Co. KG
Power Supply Power Ease 500 Invitrogen
Synergy 2 Reader Bio-Tek
Water bath Gesellschaft für Labortechnik mbH
III. MATERIALS AND METHODS
49
1.8. Plastic ware and other materials
Materials Company
4-20% tris/glycine-gels Anamed Electrophorese GmbH
Cell culture flasks, 75 cm2 Falcon
Cell culture flasks, 25 cm2 Falcon
Cell culture plates, 96 well Greiner (suspension cells)
Cell culture plates, 96 well Falcon (adherent cells)
Chromatography paper 20x20cm Whatman
Black plates, 96 well Corning Costar
ELISA plates Nunc Brand, Thermo Fisher Scientific
FACS tubes Becton Dickinson
Falcon Blue Max Polypropylene coniconal tube
(15mL and 50mL)
Becton Dickinson
Multipipettes and corresponding sterile tips Eppendorf AG
Pipet boy accu IBS Integra Biosciences
PVDF membranes Millipore
Safe-lock tubes (0.5mL, 1.5mL, 2mL and 5mL) Eppendorf
Single (1-2500µL) and multi channel pipettes
and corresponding sterile tips
Eppendorf AG
Sterile pipettes for single use (5-50mL) Falcon
Sterile reagent reservoirs Corning Costar
White plates, 96 well Corning Costar
1.9. Software
Software Company
Cellquest Beckton Dickinson
Gen5 Bio-Tek
KC4 Bio-Tek
Odyssey 2.1 LI-COR
PLA 1.2 and 2.0 Stegmann Systems
III. MATERIALS AND METHODS
50
2. METHODS
2.1. Cell culture
2.1.1. CTLL-2 cells
CTLL-2 cells were cultured in RPMI 1640 medium containing 10% FBS and 80 U/mL IL-2 in a
density of 1x105 cells/mL (cultivation period of two days) or 3x10
4 cells/mL (cultivation period of
three days).
Flasks were incubated at 37°C, 5% CO2 and 95% relative humidity.
2.1.2. DiFi cells
DiFi cells were cultured in DMEM/F12 medium containing 10% FBS in a density of 1x106 cells/mL.
Flasks were incubated at 37°C, 7% CO2 and 95% relative humidity.
2.1.3. Kit 225 cells
Cells were cultured in RPMI 1640 medium containing 10% FBS and 45U/mL IL-2 in a density of
2.5x104 cells/mL.
Flasks were incubated at 37°C, 5% CO2 and 95% relative humidity.
2.1.4. PB-1 cells
Cells were cultured in McCoy‟s 5A medium supplemented with 15% FBS, 1mM sodium pyruvate,
2mM glutamine, 0.4% BME vitamins, 1xMEM non-essential amino acids, 0.5xMEM essential amino
acids, 50µM ME and 50ng/ml huIL-7 in a density of 1x106 cells/mL.
Flasks were incubated at 37°C, 5% CO2 and 95% relative humidity.
2.1.5. 2E8 cells
Cells were cultured in IMDM + Glutamax medium supplemented with 20% FBS, 50µM β-ME and
1ng/ml muIL-7 in a density of 5x105 cells/mL.
Flasks were incubated at 37°C, 7% CO2 and 95% relative humidity.
2.2. Determination of the cell density
The cell density of a suspension was determined by mixing approximately 50μl of the cell
suspension with an equal volume of trypan blue solution. The cells in the resulting suspension were
counted directly in a Neubauer chamber (volume: 0.1 µl/grid) using a microscope. Dead cells were
identified by cytoplasmic staining with trypan blue. A grid at the bottom of the chamber was used to
count the cells and the cell density (cells per mL) was calculated with the following formula:
Cellular density (cells/mL) = number of cells on the grid x dilution factor x104 mL
-1.
Adherent DiFi cells were trypsinized and washed with HBSS prior to determination of the cell
density.
III. MATERIALS AND METHODS
51
2.3. Thawing of cells
Cell aliquots were taken out of the liquid nitrogen tank and warmed immediately in a water bath to
37°C. Cells were transferred to a 15mL Falcon tube containing 9mL assay medium. Cells were
centrifuged and resuspended in assay medium. They were checked for their viability by trypan blue
stain. Cells were cultured in appropriate conditions and the following day, the cells were evaluated
for viability and density.
2.4. Potency assays (colorimetric proliferation assays)
2.4.1. Principles
2.4.1.1. CTLL-2 potency assay
The purpose of this test is to determine the biological activity (potency) of tucotuzumab celmoleukin
by a proliferation assay dependent on IL-2. The cells used for this bioassay (CTLL-2) are murine
CTLs; consequently, the assay is termed “CTLL-2 potency assay” in this study. CTLL-2 cells
depend on the growth factor IL-2 concerning proliferation and viability. The proliferation of the cells
is linear within a specific IL-2 concentration range.
According to the plate layout (see figure 10), both one dilution series of standard and sample is
pipetted in a 96 well plate. Afterwards the cells are seeded in the plate. Cell proliferation is
measured by means of tetrazolium salt MTS/PMS, which is reduced to the water soluble and
colored formazan by mitochondria enzymes (dehydrogenases) of living cells. After measuring the
absorption of the formazan photometrically, a dose response curve is plotted. As alternative to the
MTS/PMS reagent, luminescence and fluorescence read-outs are tested (see section IV.2.).
The activity of the sample is determined in comparison to the reference standard using the parallel-
line method, or the logistic models, four-and five-parameter fit (see section IV.1.).
2.4.1.2. DiFi potency assay
This test aims for determination of the potency of cetuximab. The chimeric, monoclonal anti-EGFR
antibody binds specifically to the EGFR and inhibits thus cell proliferation. The cells used for this
assay – DiFi cells - are human, EGFR positive carcinoma cells; consequently, the assay is termed
“DiFi potency assay” in this study.
The performance of the assay follows the same principle as described for the CTLL-2 potency
assay.
2.4.2.1. Kit 225 potency assay
The purpose of this test is to determine the potency of a Fc-IL7 by a proliferation assay dependent
on IL-7. The cells used for this bioassay (Kit 225 K6 T) are human T lymphocytes whose receptor
has a high affinity to IL-7; consequently, the assay is termed “Kit 225 potency assay” in this study.
The proliferation of the cells is linear within a specific concentration range.
III. MATERIALS AND METHODS
52
1 2 3 4 5 6 7 8 9 10 11 12
A Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium
B Medium Cells + Std 1
Cells + Std 2
Cells + Std 3
Cells + Std 4
Cells + Std 5
Cells + Std 6
Cells + Std 7
Cells + Std 8
Cells + Std 9
Cells + Medium
Medium
C Medium Cells + Std 1
Cells + Std 2
Cells + Std 3
Cells + Std 4
Cells + Std 5
Cells + Std 6
Cells + Std 7
Cells + Std 8
Cells + Std 9
Cells + Medium
Medium
D Medium Cells + Std 1
Cells + Std 2
Cells + Std 3
Cells + Std 4
Cells + Std 5
Cells + Std 6
Cells + Std 7
Cells + Std 8
Cells + Std 9
Cells + Medium
Medium
E Medium Cells +
P 1 Cells +
P 2 Cells +
P 3 Cells +
P 4 Cells +
P 5 Cells +
P 6 Cells +
P 7 Cells +
P 8 Cells +
P 9 Cells + Medium
Medium
F Medium Cells +
P 1 Cells +
P 2 Cells +
P 3 Cells +
P 4 Cells +
P 5 Cells +
P 6 Cells +
P 7 Cells +
P 8 Cells +
P 9 Cells + Medium
Medium
G Medium Cells +
P 1 Cells +
P 2 Cells +
P 3 Cells +
P 4 Cells +
P 5 Cells +
P 6 Cells +
P 7 Cells +
P 8 Cells +
P 9 Cells + Medium
Medium
H Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium Medium
The performance of the assay follows the same principle as described for the CTLL-2 potency
assay.
Figure 10. General plate layout for potency assays. Figure 10 shows the general plate layout used in this study, which is one commonly used plate layout at Merck Serono for potency assays. In case of the DiFi assay, PBS is used instead of assay medium for the outer wells.
2.4.2. Protocols
2.4.2.1. CTLL-2 potency assay
The assay was performed using a solution of tucotuzumab celmoleukin. The antibody preparations
were diluted in RPMI 1640 medium to nine decreasing dilution steps. 100 L/well of this solution
was transferred to a 96 well plate (Greiner) in triplicates and mixed with an equal volume of cell
suspension that had been prewashed twice with HBSS and resuspended in assay medium to reach
a concentration of 5x105 cells/mL. The plate was incubated for 24 2 hours (h) at 37°C, 5% CO2
and 95% relative humidity. Then, 40 L/well of a MTS/PMS solution was added. Following another
incubation time of 2.5 – 3h, the absorbance was measured at 490nm (Bio-Tek EL808 Ultra
Microplate Reader) and the potency was calculated using the PLA software (PLA 2.0, Stegmann
Systems; settings see section III.2.4.3).
2.4.2.2. DiFi potency assay
DiFi cells were washed with HBSS and trypsinized. Cells were resuspended in DMEM/F12 medium
to reach a concentration of 1x105 cells/mL. 100 L of this cell suspension was transferred to the
inner wells of a 96 well plate (Falcon) and incubated for 15 5 minutes (min) at 37°C and 7% CO2.
11 L/well of a cetuximab‟s dilution in DPBS (1%, pH 7.2, sterile) was added at nine dilution steps
to a concentration range of 5 - 55nM. Each concentration was added in triplicates.
The plate was incubated for 96 2h at 37°C, 7% CO2 and 95% relative humidity.
III. MATERIALS AND METHODS
53
Then, 40 L/well of a MTS/PMS solution was added. Following another incubation time of 75
15min, the absorbance was measured at 490nm (Bio-Tek EL808 Ultra Microplate Reader). The
potency result was calculated using the PLA software (settings see section III.2.4.3).
2.4.2.3. Kit 225 potency assay
The assay was performed using the cytokine fusion protein Fc-IL7. The IL-7 preparations were
diluted in RPMI 1640 medium to nine decreasing dilution steps in a concentration range of 0.35-
40ng/mL. 70 L/well of this solution was transferred in triplicates to the corresponding inner well of a
96 well plate (Greiner) and mixed with an equal volume of starved cell suspension (3 days
starvation in AIM-V medium) that had been prewashed twice with HBSS and resuspended in assay
medium to reach a concentration of 1.43x106 cells/mL (1x10
5 cells/well).
The plate was incubated for 96 2h at 37°C, 5% CO2 and 95% relative humidity.
Then, 40 L/well of a MTS/PMS solution (Promega) was added. Following another incubation time
of 3 – 4h, the absorbance was measured at 490nm (Bio-Tek EL808 Ultra Microplate Reader). The
potency result was calculated using the PLA software (settings see section III.2.4.3).
2.4.3. Data evaluation: comparison of different analysis models
For comparison of the different analysis models (see section IV.1.), the CTLL-2 and DiFi assays
were performed using three different concentration ranges.
The first one was optimized for the parallel line model (A: CTLL-2: 5-30ng/mL, DiFi: 2-12nM), the
second one adapted to the logistic models (B: CTLL-2: 0.75-100ng/mL, DiFi 0.25-95nM) and the
third one was in between the other two ranges and had been used in a validated potency assay
before (C: CTLL-2: 6.8-80ng/mL, DiFi: 5-55nM).
For both assay systems, six repetitions were performed for each sample concentration of 50%,
100% and 150% in comparison to the 100% standard. The percentages were based on the original
sample concentrations, e.g. 100% equals a dose range of 5 - 30ng/mL, 50% equals a dose range
of 2.5 – 15ng/mL and 150% equals a dose range of 7.5 – 45ng/mL. The experiments were
performed independently on different days. Experiments with concentration ranges (B) and (C)
were analyzed using PL, 4PL and 5PL; experiments with the closer concentration range (A) only
using PL. The selected ranges for calculation of the potencies in the PLA software were “best
range” for PL analysis (automatic detection individual range; minimal number of points: three) and
“full range” for 4 and 5PL analyses. Results were expressed as potency of the sample relative to
the reference standard.
For evaluation of the data, tests of significance of regression, linearity (Hypothesis tests based on
separate Analysis of Variance (ANOVAs)) and parallelism (Hypothesis F-Test for significant
deviations from parallelism) were used. The confidence interval was 99% for the slope and 95% for
linearity and parallelism according to the guidelines of Ph. Eur. 6.7, Chapter 5.3. No outlier tests
were performed.
Mean values, as well as standard deviations (SD [%]), coefficients of variation (CV [%]) and mean
relative errors (RE [%]; definition: mean value of the difference between the measured and the
III. MATERIALS AND METHODS
54
expected activity, divided by the expected activity) were calculated, in order to compare accuracy
and precision of the different models. For the purpose of testing a significant difference between
the models, a statistical analysis was performed by Enrico Tucci (National Institute of Statistics,
Rome, Italy; used software: Statgraphics, Warrenton, VA; see Appendix). For statistical evaluation,
the recovery was used instead of the calculated potency result since the data referred to different
concentration levels (50%, 100%, 150%). In order to check whether the data could be adequately
modeled by a normal distribution, the Kolmogorov-Smirnov (KS) test was used. If this was the
case, the Cochran‟s (C) test was applied for variance check of the SDs. Otherwise, the Levene‟s
(L) test was used. In cases where standard deviations were not significantly different, an ANOVA
test was performed on the mean values. In other cases, a Kruskal-Wallis (KW) test was applied on
the medians instead of the mean values.
The failure rate consists of the ratio of failed assays to all assays performed in percent. Failed
assays were defined as assays which failed due to linearity and similarity failure of the PLA test
based on preset acceptance criteria (99% confidence interval for the slope and 95% for linearity
and parallelism). For comparison of the failure rates, the hypothesis was made, that the failure rate
was the same for all three models and was equal to the mean value. The mean value was then
compared to the individual failure rates. A p-value < 0.05 indicated a statistically significant
difference.
Furthermore, it was investigated whether it was necessary to use “weighted” regressions.This is
the case, if the SDs of the different dilution points are not constant over the concentration range.
For application of “weights”, different transformations exist, e.g. reciprocal, log or square root
transformations. In this study, calculations according to DeSilva et al. (2003) and the USP <111>
(2001) were done, in order to find out whether the weighting procedure was necessary. According
to DeSilva et al. (2003), weighting is appropriate, if a plot of the pooled SDs versus the mean
responses reveals a linearly increasing relationship on a logarithmic scale. According to the USP,
weighting is recommended, when the plot of the sample variance is proportionally increasing to a
function of dose, e.g. dose squared. If the data follow a horizontal line, no weighting is needed.
2.4.4. Adaption of assay parameters to luminescence and fluorescence techniques
For comparison of the different read-out systems (see section IV.2.), the following parameters had
to be modified when using CellTiter- Glo® or Alamar Blue™ instead of MTS/PMS for all three
potency assays, CTLL-2, DiFi and Kit 225: concentration ranges, volume of substrates, APIs and
cells, incubation times with substrate and cell densities.
Modifications were considered successful if the signal to noise ratio could be increased.
Exact parameters developed for each assay system are shown in the results section.
For luminescence measurements, white plates, for fluorescence measurements, black plates were
used. Plates were measured in the Synergy 2 Reader (Bio-Tek) (wavelengths for fluorescence: Ex
= 530nm; Em = 600nm; 50% mirror). Potency results were determined using the PLA software.
III. MATERIALS AND METHODS
55
2.5. STAT5 phosphorylation assay
2.5.1. Principle
When IL-7 binds to the IL-7R on Kit225 cells, a signal transduction cascade is initiated, leading to
the phosphorylation of STAT5. The amount of phosphorylated STAT5 can be measured using this
ELISA based assay: If phospho-STAT5 is in the sample, it binds to the ELISA plate, coated over
night with anti-phospho-STAT5 antibody. After immobilization of the antigen (STAT5), the anti-
STAT5 detection antibody is added, forming a complex with the antigen. Between each step, the
plate is washed with a mild detergent solution to remove any particles that are not specifically
bound. The detection antibody is detected by a secondary HRP-conjugated antibody. HRP is an
enzyme, which catalyzes the conversion of the substrate TMB to a colored product, whose
absorption can be measured at 450nm.
2.5.2. Protocol
The assay was performed using two separate microtiter plates: the first one for cell stimulation and
lysis, the second one for ELISA.
The ELISA plate was coated over night with anti-phospho-STAT5 capture antibody (1:100 in 50mM
carbonate buffer, pH 9.6, 100µL/well) at 4°C and blocked the next day with 1% BSA (Albumin
fraction V) in DPBS for approx. 90min with gentle agitation at approx. 300 rounds per minute (rpm).
Kit 225 cells were starved 1 day without growth factor in a density of 5x105 cells/mL. The next day,
cells were washed twice with HBSS and resuspended in AIM-V medium to reach a concentration of
7.15x105 cells/mL (5x105 cells/well). 70 L of this cell suspension was transferred to the inner wells
of a 96 well plate (Greiner). 10 L/well of Fc-IL7 was added at nine dilution steps to a concentration
range of 0.4 – 100ng/mL. Each concentration was added in triplicates.
The plate was incubated for 10-15min at 37°C, 5% CO2 and 95% relative humidity.
20µL of 5 fold concentrated lysis buffer [750mM NaCl, 250mM HEPES, 2.5% Triton X-100, 10mM
Orthovanadate, 5x Roche complete mini-protease inhibitor cocktail in water] was added directly
(without centrifugation) to each inner well of the 96 well plate. The plate was agitated with 300rpm
on a plate shaker for 60min at RT.
Then, the cell lysates were transferred to the blocked ELISA plate (85µL/well) and incubated for
60min at room temperature (RT) under agitation. The plate was washed three times (DPBS +
0.05% Tween 20) and incubated with anti-STAT5 detection antibody (1:100 in blocking buffer,
100µL/well) over night at 4°C. The free antibody was washed away and 100µL/well HRP-
conjugated anti-rabbit antibody (1:2,500 in blocking buffer) was added. The plate was agitated
gently (300rpm) on a plate shaker for 60min at RT.
After washing, 100µL TMB was added to each well. The reaction was allowed to proceed for 20min
and then stopped with 100µL/well 1N H2SO4.
The absorbance was measured at 450nm (Bio-Tek EL808 Ultra Microplate Reader) and the
potency result was calculated using the parallel line method (PLA 2.0, Stegmann Systems).
III. MATERIALS AND METHODS
56
During assay optimization, different parameters were varied. For this, experiments were run in
triplicates to confirm the results. A high opticale density (OD) difference between 100ng/mL IL-7
and the control (0ng/mL IL-7) was used as a quality criterion in order to find the best conditions.
After assay optimization, the assay was qualified with respect to accuracy and precision. Therefore,
three repetitions were performed on three different sample concentrations of 50%, 100% and 150%
in comparison to the 100% standard (n = 9). The assays were performed independently on
different days.
REs as well as SDs and CVs were calculated, in order to evaluate accuracy and precision of the
assay.
2.6. Western blotting analysis
2.5.1. Principle
Immunoblotting or western blotting is used to identify specific antigens recognized by polyclonal or
monoclonal antibodies. In this study, Western blotting was used to identify targets which are up- or
downregulated by stimulation with IL-7 and which could consequently be used as targets for a
suitable bioassay.
Following solubilization with SDS and DTT, the proteins are separated by SDS-Polyacrylamide
gelelectrophoresis (SDS-PAGE) and the antigens are electrophoretically transferred to a
polyvinylidenfluorid (PVDF) membrane, a process that can be monitored by reversible staining with
Ponceau S. The transferred proteins are bound to the surface of the membrane, providing access
to immune-detection reagents. After non-specific binding sites are blocked by immersing the
membrane in a solution containing a protein or detergent blocking agent, the membrane is probed
with the primary antibody and washed. The antibody-antigen complexes are identified with a
secondary Alexa Fluor 680 anti-IgG antibody and detected using infrared (IR) fluorescence.
2.5.2. Protocol
Cells were starved in culture medium without growth factor for 1 day prior to incubation with IL-7 or
IL-2. The stimulation experiment was carried out with 100ng/mL IL-7 or IL-2 for various times at
37°C. After centrifugation, cells were solubilized with 100µL lysis buffer [150mM NaCl, 50mM
HEPES, 0.5% Triton X-100, 2mM Orthovanadate, 1x Roche complete mini-protease inhibitor
cocktail in water] and incubated for 1h at RT with gentle agitation. The lysates were centrifuged for
5min at 1100rpm to remove cell debris.
Cell lysates were subjected to SDS-PAGE using 4-20% tris/glycine-gels. Due to different protein
contents of the cells and to achieve a homogenous loading of the gel with equal protein amounts
for each cell line, the following cell numbers were used per lane: Kit 225: 3.5x104 cells/ lane, PB-1:
2.8x105 cells/ lane and 2E8: 2.5x10
5 cells/ lane. The separated proteins were electrotransferred to
a PVDF membrane in transfer buffer (48mM TrisBase, 39mM glycine, 20% methanol, 1.3mM SDS;
pH 9.2). Equivalent loading of the gel was checked by Ponceau S staining. The membrane was
incubated with Odyssey blocking buffer / PBS 1:2 for 1h and then incubated with different
antibodies (see section 1.3) over night at 4°C. The secondary Alexa Fluor 680 antibody was
III. MATERIALS AND METHODS
57
applied for 1h at RT in the dark. The blots were detected using IR fluorescence, Odyssey Infrared
Imaging System (LI-COR).
2.7. Inhibition with WP1066
2.7.1. Principle
(E)-3(6-bromopyridin-2-yl)-2-cyano-N-((S0-1-phenylethyl)acrylamide), WP1066, is a novel, more
potent AG490 analogue (Iwamaru, 2007). It is supposed to inhibite JAK2 and its downstream STAT
and PI3K pathways (Ferrajoli, 2007).
Incubation of a sample with WP1066 prior to stimulation with IL-2 or IL-7 was used to investigate
whether the signaling pathway was blocked by WP1066.
2.7.2. Protocol
WP1066 was dissolved in DMSO. A stock solution was made at a concentration of 10mM in DMSO
and stored at –20°C.
Prior to stimulation with IL-2 or IL-7, cells were incubated with 10µM WP1066 for 24h. Cells were
then incubated with IL-2 or IL-7 for 10min at 37°C and subjected to western blotting analysis as
described previously.
2.8. Flow cytometric analysis
2.8.1. Principle
Flow cytometry is a technique for counting and examing microscopic particles, such as cells.
Particles are hydrodynamically-focused and pass a beam of light. As a consequence, they scatter
light and fluorochromes are excited to a higher energy state. This combination of scattered and
fluorescent light is picked up by the detectors, and, by analyzing fluctuations in brightness at each
detector, it is then possible to derive various types of information about the physical and chemical
structure of each individual particle.
A specialized type of flow cytometry is FACS: Cells are incubated with fluorescence-labeled
antibodies, directed against specific surface antigens on the cells (e.g. CD127 = IL-7R) and then
sorted according to these attributes.
2.8.2. Protocol
For preparation of the flow cytometric analysis, 2 - 3x105 cells, which had been starved 24h without
growth factor, were centrifuged and washed twice with 4mL DPBS supplemented with 5% FBS.
Cells were then either labeled with isotype controls (PE mouse IgG1 қ or PE rat IgG2b қ isotype
controls) or PE-conjugated CD25 (detection of IL-2Rα), CD122 (detection of IL-2Rβ) or CD127
(detection of IL-7Rα). After 30min incubation on ice, cells were washed again and resuspended in
250µL DPBS + 5% FBS.
Cells were analyzed with a FACScan flow cytometer using Cellquest software (Becton Dickinson).
10,000 cells were analyzed from each sample. Cell debris and clumps were excluded by setting a
gate on forward scatter versus side scatter.
IV. RESULTS AND DISCUSSION
58
IIVV.. RREESSUULLTTSS AANNDD DDIISSCCUUSSSSIIOONN
1. COMPARISON OF DIFFERENT ANALYSIS MODELS (PARALLEL LINE, 4-AND 5-PARAMETER FIT)
FOR POTENCY ASSAYS
For evaluation of the potency of a biological sample in comparison to a reference standard,
different statistical models are proposed (see Introduction, section I.3.3.2.). The most commonly
used calculation models to date are the parallel line method and the logistic models four- and five-
parameter fit. The PL model only utilizes the linear range of the curve for potency determination,
whereas the logistic models consider the whole dose response range including the lower and the
upper asymptote of the curve. The difference between the 4PL and 5PL model consists of an
additional fifth asymmetry parameter for the latter model.
In this study, the three analysis models for potency assays were compared to each other regarding
their suitability for QC purposes by means of the parameters accuracy, precision and failure rate.
This was done by using two different bioassay systems, the CTLL-2 and DiFi potency assays, and
three different sample concentrations of 50%, 100% and 150% in comparison to the reference
standard. The analysis models were compared to each other within the same software. The PLA
software from Stegmann Systems (Rodgau, Germany) was selected, which is an accepted and
widely used software in the QC analysis field for potency assays.
1.1. CTLL-2 potency assay
1.1.1. Concentration ranges
The CTLL-2 potency assay measures the IL-2 activity of immunocytokines by cell proliferation of
IL-2 dependent cells.
The assay was performed as described under Materials and Methods. In order to be able to
compare the different models, first of all, adequate concentration ranges and curves for the PL
model and the logistic models were developed using the original assay (concentration range C) as
a basis. The assay was performed on three different concentration ranges: the first one was
optimized for the PL model (A: 5-30ng/mL, see figure 11A) with the aim to have as many points as
possible in the linear range of the curve, the second one adapted to the logistic models (B: 0.75-
100ng/mL, see figure 11B) with the aim to have three points each in the minimal, linear and
maximal range of the curve and the third one was in between the other two ranges and had been
used in a validated potency assay before (C: 6.8-80ng/mL, see figure 11C).
IV. RESULTS AND DISCUSSION
59
Standard Curve
0
0,2
0,4
0,6
0,8
1
1,2
1 10 100
x
y
Concentration [ng/mL]
OD
valu
es
at
490
nm
A
Standard Curve
0
0,2
0,4
0,6
0,8
1
1,2
0,1 1 10 100 1000
x
y
OD
va
lue
s a
t 4
90
nm
B
Concentration [ng/mL]
Standard Curve
0
0,2
0,4
0,6
0,8
1
1,2
1 10 100
x
y
Concentration [ng/mL]
OD
va
lues a
t 4
90
nm
C
Figure 11. Concentration ranges of the CTLL-2 potency assay. The assay was performed on three different concentration ranges convenient for the different analysis models. The resulting, representative curves are shown: a close range (A: 5-30ng/mL, see figure 11A), a wide range (B: 0.75-100ng/mL, see figure 11B) and an intermediate range (C: 6.8-80ng/mL, see figure 11C). Green points indicate the individual results, red points the mean values of the triplicates.
IV. RESULTS AND DISCUSSION
60
1.1.2. Test on the necessity of “weighting”
It is frequently discussed in literature that it might be appropriate to use “weighted” regressions if
variance homogeneity of the data is not given (USP <111>,2001; Ph. Eur., 2010; DeSilva et al.,
2003). This is the case, if the SDs of the different dilution points are not constant over the
concentration range. This characteristic is termed “heteroscedasticity” (Findlay and Dillard, 2007).
Often, SDs are higher at increasing concentrations. This leads to a domination of the high
responses over the low responses. In order to avoid this, weighted regressions are used, which
place less “weight” on responses that exhibit higher variation and guarantee that all dilution points
contribute equally. Weighting might lead to a broader assay range and more accurate and precise
results. For application of “weights”, different transformations exist, e.g. reciprocal, log or square
root transformations. The right transformation is dependent on different factors and has to be
determined individually for each concentration point prior to the weighting procedure.
In literature, different criteria for tests on the necessity of weighting can be found. According to
DeSilva et al. (2003), weighting is appropriate, if a plot of the pooled SDs versus the mean
responses reveals a linearly increasing relationship on a logarithmic scale. According to the USP,
weighting is recommended, when the plot of the sample variance is proportionally increasing to a
function of dose, e.g. dose squared. If the data follow a horizontal line, no weighting is needed.
In this study, the calculations were made according to both, DeSilva et al. and the USP and the
results from these calculations are shown in figures 12 and 13.
When the pooled SDs were plotted against the mean absorbance (according to DeSilva et al.; see
figure 12), a slight trend to increasing SDs at increasing responses could be noted in some of our
curves. However, there was no linearly related relationship between them and the slope was < 1 in
all cases. In most cases, SDs increased first and than remained constant or even decreased again.
A
CTLL-2 assay: concentration range 5 - 30ng/mL
y = 0,6635x - 1,9713
R2 = 0,8199
-2,5
-2
-1,5
-1
-0,5
0
0 0,2 0,4 0,6 0,8 1
absorbance
log
[s
tan
da
rd d
ev
iati
on
]
IV. RESULTS AND DISCUSSION
61
B
CTLL-2 assay: concentration range 0.75 - 100ng/mL
y = 0,7018x - 1,9726
R2 = 0,7456
-2,5
-2
-1,5
-1
-0,5
0
0 0,2 0,4 0,6 0,8 1
absorbance
log
[sta
nd
ard
devia
tio
n]
C
CTLL-2 assay: concentration range 0.75 - 100ng/mL
y = 0,7018x - 1,9726
R2 = 0,7456
-2,5
-2
-1,5
-1
-0,5
0
0 0,2 0,4 0,6 0,8 1
absorbance
log
[sta
nd
ard
devia
tio
n]
Figure 12. Plot of the pooled standard deviations (n = 18) vs. the measured mean responses for each concentration point. Illustrations are given for the different concentration ranges of the CTLL-2 potency assay (fig. 12A: 5-30ng/mL; fig. 12B: 0.75–100ng/mL; fig. 12C: 6.8-80ng/mL). According to DeSilva (2003), weighting is appropriate, if a plot of the pooled standard deviations vs. the mean responses reveals a linearly increasing relationship on a logarithmic scale.
When the pooled SDs were plotted against dose squared (according to the USP; see figure 13), no
increase at increasing concentrations could be noticed. The data followed approximately a straight
line.
IV. RESULTS AND DISCUSSION
62
A
CTLL-2 assay: concentration range 5 - 30ng/mL
y = 0,0003x - 1,6192
R2 = 0,3591
-2,5
-2
-1,5
-1
-0,5
0
0 200 400 600 800 1000
dose squared
log
[s
tan
da
rd d
ev
iati
on
]
B
CTLL-2 assay: concentration range 0.75 - 100ng/mL
y = 3E-05x - 1,6148
R2 = 0,1623
-2,5
-2
-1,5
-1
-0,5
0
0 2000 4000 6000 8000 10000 12000
dose squared
log
[sta
nd
ard
devia
tio
n]
C
CTLL-2 assay: concentration range 6.8 - 80ng/mL
y = 2E-05x - 1,4535
R2 = 0,1152
-2,5
-2
-1,5
-1
-0,5
0
0 1000 2000 3000 4000 5000 6000 7000
dose squared
log
[sta
nd
ard
devia
tio
n]
Figure 13. Plot of the pooled standard deviations (n = 18) vs. the dose squared for each concentration point. Illustrations are given for the different concentration ranges of the CTLL-2 potency assay (fig. 13A: 5-30ng/mL; fig. 13B: 0.75–100ng/mL; fig. 13C: 6.8-80ng/mL). According to the USP, weighting is recommended, when the plot of the sample variance is proportionally increasing to a function of dose, e.g. dose squared. If the data follow a horizontal line, no weighting is needed.
IV. RESULTS AND DISCUSSION
63
A B CA B C
It might be discussed whether weighting in case of this study would be appropriate according to the
calculations proposed by DeSilva et al.; in any case, it was not necessary according to the
calculations made according to the USP, the regulatory guideline.
As a consequence, unweighted regressions were used in this study for all concentration ranges of
the CTLL-2 potency assay.
1.1.3. Results
For the CTLL-2 potency assay, six repetitions were performed for each sample concentration of
50%, 100% and 150% (0.5, 1 and 1.5 relative potency) in comparison to the 100% standard,
independently on different days. Experiments with concentration ranges (B) and (C) were analyzed
using PL, 4PL and 5PL, experiments with the closer concentration range (A) only using PL. Mean
values, as well as SDs, CVs and REs were calculated, in order to compare accuracy and precision
of the different models. For the purpose of investigating the difference between the models, a
statistical analysis was performed by Enrico Tucci (Statgraphics, Warrenton, VA; see Appendix;
further described under Materials and Methods). Furthermore, the failure rates of the different
models were compared to each other. Failed assays were defined as assays which failed due to
linearity and similarity failure of the PLA test based on preset acceptance criteria (see sections
I.3.3.2. and II.2.4.3.).
Results of the CTLL-2 potency assay for the PL model, 4PL model and 5PL model varied between
75.3% and 147.4% recovery, 84.8% and 140.0% recovery and 84.2% and 137.6% recovery,
respectively. A mean accuracy (including all results) of 104.0% (recovery) 11.8% (mean RE) was
calculated for the PL model, whereas accuracies of 102.4% 8.5% and 102.3% 8.5% were
assessed for the 4PL and 5PL model, respectively. As an example, individual, representative
graphs are shown in figure 14.
Figure 14. Individual, representative graphs (calculated by PLA) of a CTLL-2 potency assay. Results of an assay with a target concentration of 150%, analyzed by the PL model (see figure 14A), the 4PL model (see figure 14B) and the 5PL model (see figure 14C) are shown.
IV. RESULTS AND DISCUSSION
64
Results with an expected activity of 50% varied between 98.0% and 116.0% recovery with REs
between 7.5% and 21.8% and CVs between 9.3% and 21.7%. They are shown in table 3 and figure
15.
Table 3. Results CTLL-2 potency assay: expected activity 50% (i.e. relative potency: 0.5).
Figure 15. Results CTLL-2 potency assay: expected activity 50%. Mean values of six repetitions are shown for each concentration range and analysis model. Error bars indicate the mean CVs. The red, dashed line demonstrates the expected activity of 50%.
-
PL
(5-
30ng/
mL)
PL
(0.75-
100ng/
mL)
4PL
(0.75-
100ng/mL)
5PL
(0.75-
100ng/mL)
PL
(6.8-
80ng/mL)
4PL
(6.8-
80ng/mL)
5PL
(6.8-
80ng/mL)
Result assay 1 0.597 0.466 failed failed 0.737 0.7 0.688
Result assay 2 0.733 0.431 0.47 0.487 0.417 failed failed
Result assay 3 0.514 0.575 0.57 0.579 0.619 failed failed
Result assay 4 0.500 0.468 0.46 0.462 0.559 0.557 0.561
Result assay 5 0.509 0.508 0.54 0.539 0.585 0.585 0.581
Result assay 6 0.626 0.492 0.51 failed 0.429 0.443 0.442
mean 0.579 0.490 0.510 0.517 0.558 0.571 0.568
recovery 116.0 98.0 102.0 103.4 111.5 114.3 113.6
RE [%] 16.0 7.5 7.6 8.5 21.8 20.0 19.4
CV [%] 15.7 10.0 9.3 10.2 21.7 18.5 17.8
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
80,0
PL (5-
30ng/mL)
PL (0.75-
100ng/mL)
4PL (0.75-
100ng/mL)
5PL (0.75-
100ng/mL)
PL (6.8-
80ng/mL)
4PL (6.8-
80ng/mL)
5PL (6.8-
80ng/mL)
acti
vit
y [
%]
IV. RESULTS AND DISCUSSION
65
Results with an expected activity of 100% varied between 98.5% and 106.0% recovery with REs
between 3.4% and 10.9% and CVs between 4.8% and 15.3%. They are shown in table 4 and figure
16.
Table 4. Results CTLL-2 potency assay: expected activity 100% (i.e. relative potency: 1.0).
-
PL
(5-
30ng/
mL)
PL
(0.75-
100ng/
mL)
4PL
(0.75-
100ng/mL)
5PL
(0.75-
100ng/mL)
PL
(6.8-
80ng/mL)
4PL
(6.8-
80ng/mL)
5PL
(6.8-
80ng/mL)
Result assay 1 1.167 1.010 0.98 1.01 1.048 1.028 1.029
Result assay 2 1.065 0.979 failed failed 0.808 0.861 0.86
Result assay 3 1.142 0.941 failed failed 0.997 1.008 1.003
Result assay 4 0.986 1.212 1.07 1.101 0.857 1.022 1.022
Result assay 5 1.030 0.964 0.99 0.975 1.233 1.045 1.046
Result assay 6 0.968 0.93 failed failed 0.968 1.018 1.019
mean 1.059 1.006 1.012 1.028 0.985 0.997 0.997
recovery 106.0 100.6 101.2 102.9 98.5 99.7 99.7
RE [%] 7.5 6.8 3.4 4.5 10.9 4.3 4.3
CV [%] 7.7 10.4 4.8 6.3 15.3 6.8 6.9
Figure 16. Results CTLL-2 potency assay: expected activity 100%. Mean values of six repetitions are shown for each concentration range and analysis model. Error bars indicate the mean CVs. The red, dashed line demonstrates the expected activity of 100%.
0,0
20,0
40,0
60,0
80,0
100,0
120,0
0 PL (5-
30ng/mL)
PL (0.75-
100ng/mL)
4PL (0.75-
100ng/mL)
5PL (0.75-
100ng/mL)
PL (6.8-
80ng/mL)
4PL (6.8-
80ng/mL)
5PL (6.8-
80ng/mL)
acti
vit
y [
%]
IV. RESULTS AND DISCUSSION
66
0,0
20,0
40,0
60,0
80,0
100,0
120,0
140,0
160,0
180,0
200,0
PL (5-
30ng/mL)
PL (0.75-
100ng/mL)
4PL (0.75-
100ng/mL)
5PL (0.75-
100ng/mL)
PL (6.8-
80ng/mL)
4PL (6.8-
80ng/mL)
5PL (6.8-
80ng/mL)
acti
vit
y [
%]
Results with an expected activity of 150% varied between 96.3% and 106.9% recovery with REs
between 5.3% and 19.5% and CVs between 6.5% and 22.6%. They are shown in table 5 and figure
17.
Table 5. Results CTLL-2 potency assay: expected activity 150% (i.e. relative potency: 1.5).
-
PL
(5-
30ng/mL)
PL
(0.75-
100ng/
mL)
4PL
(0.75-
100ng/
mL)
5PL
(0.75-
100ng/mL)
PL
(6.8-
80ng/mL)
4PL
(6.8-
80ng/mL)
5PL
(6.8-
80ng/mL)
Result assay 1 1.762 1.493 failed 1.495 2.075 1.774 1.777
Result assay 2 1.469 1.13 1.342 1.347 1.205 1.272 1.263
Result assay 3 1.537 1.754 1.633 failed 2.005 failed failed
Result assay 4 1.397 1.553 1.554 1.549 1.335 1.482 1.481
Result assay 5 1.43 1.59 failed failed 1.393 1.477 1.441
Result assay 6 1.351 1.433 1.38 1.388 1.607 failed failed
mean 1.491 1.492 1.477 1.445 1.603 1.501 1.491
recovery 99.4 99.5 98.4 96.3 106.9 100.1 99.4
RE [%] 7.2 9.3 5.8 5.3 19.5 9.1 9.9
CV [%] 9.9 14.0 9.5 6.5 22.6 13.8 14.3
Figure 17. Results CTLL-2 potency assay: expected activity 150%.
Mean values of six repetitions are shown for each concentration range and analysis model. Error bars indicate the mean CVs. The red, dashed line demonstrates the expected activity of 150%.
IV. RESULTS AND DISCUSSION
67
In general, results were slightly more accurate and precise at a target concentration of 100% in
comparison to the target concentrations of 50% and 150%. Nevertheless, results were – except
some individual results of 50% and 150% - all lying inside in the acceptable range of ± 30%, which
is the applied QC criterion at Merck Serono for bioassays.
Regarding the accuracies of the mean values (including all results), no major differences between
the three models were observed, even if the mean RE was a bit higher for the PL model than for
the logistic models. However, the logistic models were more precise than the PL model. For the PL
model, a mean coefficient of variance of 14.1% (n = 54 assays) was calculated in contrast to 10.4%
for the 4PL model (n = 36 assays) and 10.3% for the 5PL model (n = 36 assays). This was due to
the fact, that individual results of the logistic models were more accurate than those of the PL
model in particular for results having a large deviation from the expected activity.
According to the statistical analysis performed, the data were normally distributed (KS-test: p =
0.07). Neither for the standard deviations (C-test: p = 0.05) nor for the mean values (ANOVA: p =
0.83) significant differences were observed. This was also applicable when individual data - e.g.
results from one plate analyzed using all three models – were considered (data not shown). For the
three concentration levels 50%, 100% and 150% and for the different concentration ranges A, B
and C, the KW-test was applied, because standard deviations differed significantly (C-test: p <
0.05). Similar to the analysis of the overall results, this test did not show any significant difference
between the different models (KW-test on concentration levels: p = 0.12; KW-test on concentration
ranges: p = 0.23).
The statistical similarity of the results of the different concentration levels 50%, 100% and 150%
underlined that the different models worked appropriately over the whole range between 50% and
150% of the sample concentration in comparison to the standard. Results were not only accurate
and precise at sample concentrations near to the standard concentration, but also at
concentrations which differed more from the expected standard concentration, at the end of the
tested assay range (50% and 150%).
The statistical similarity of the results of the different concentration ranges A, B and C lead to the
assumption that the choice of the concentration range seemed not to be essentially important.
Even if the curve parameters were not derived from the mass action law or other reaction
equations, but from shape fitting only, accurate and precise results were obtained for all of the
models and all concentration ranges selected. Thus, there was no significant difference between
the results of the very close range A having almost all concentration points in the linear range of
the curve and the results of the wide range B having only three points in the concentration
dependent region of the curve.
More eye-catching than the differences in accuracy and precision was in contrast the failure rate of
the different models including all assays with failed tests of linearity or parallelism according to
preset acceptance criteria.
IV. RESULTS AND DISCUSSION
68
While all assays were valid for the PL model, failure rates of 27.8% for the 4PL model (ten out of 36
assays, two of these due to lack of linearity and eight of these due to lack of parallelism) and 30.6%
for the 5PL model (eleven out of 36 assays, one of these due to lack of linearity and ten of these
due to lack of parallelism) were noticed, resulting in a significant difference between the PL model
(p < 0.05) and the logistic models based on the failure rate mean of 16.7%.
An overall summary of the CTLL-2 assay results is shown in table 6.
Table 6. Summary of CTLL-2 assay results.
Concentration
range; dilution
factor
Analysis
model
Activity
expected
[%]
Activity
measured
(range)
[%]
Mean
activity
measured
[%]
Recovery
[%]
RE
[%]
CV
[%]
n
(valid
assays out
of six
repetitions)
5-30ng/mL; 1.25 PL
50
50.0 – 73.3 58.0 116.0 16.0 15.7 6
0.75-100ng/mL;
1.85
PL 43.1 – 57.5 49.0 98.0 7.5 10.0 6
4PL 46.0 – 57.0 51.0 102.0 7.6 9.3 5
5PL 46.2 – 57.9 51.7 103.4 8.5 10.2 4
6.8-80ng/mL; 1.36
PL 42.9 – 73.7 55.8 111.5 21.8 21.7 6
4PL 44.3 – 70.0 57.1 114.3 20.0 18.5 4
5PL 44.2 – 68.8 56.8 113.6 19.4 17.8 4
5-30ng/mL; 1.25 PL
100
96.8 – 116.7 106.0 106.0 7.5 7.7 6
0.75-100ng/mL;
1.85
PL 93.0 – 121.2 100.6 100.6 6.8 10.4 6
4PL 98.0 – 107.0 101.2 101.2 3.4 4.8 3
5PL 97.5 – 110.1 102.9 102.9 4.5 6.3 3
6.8-80ng/mL; 1.36
PL 80.8 – 123.3 98.5 98.5 10.9 15.3 6
4PL 86.1 – 104.5 99.7 99.7 4.3 6.8 6
5PL 86.0 – 104.6 99.7 99.7 4.3 6.8 6
5-30ng/mL; 1.25 PL
150
135.1 – 176.2 149.1 99.4 7.2 9.9 6
0.75-100ng/mL;
1.85
PL 113.0 – 175.4 149.2 99.5 9.3 14.0 6
4PL 134.2 – 163.3 147.8 98.4 5.8 9.5 4
5PL 134.7 – 154.9 144.5 96.3 5.3 6.5 4
6.8-80ng/mL; 1.36
PL 120.7 – 207.5 160.3 106.9 19.5 22.6 6
4PL 127.2 – 177.4 150.1 100.1 9.1 13.8 4
5PL 126.3 – 177.7 149.1 99.4 9.9 14.3 4
IV. RESULTS AND DISCUSSION
69
1.2. DiFi potency assay
1.2.1. Concentration ranges
The DiFi potency assay measures the inhibitory activity of anti-EGFR antibodies by impeded cell
proliferation of EGFR positive cells.
The assay was performed as described under Materials and Methods. Similar to the CTLL-2 assay,
the DiFi assay was equally performed on three different concentration ranges: a narrow range A (2-
12nM, see figure 18A) with the aim to place as many points as possible in the linear range of the
curve, a wide range B (0.25-95nM, see figure 18B) with the aim to have three points each in the
minimal, linear and maximal range of the curve and an intermediate range C (5-55nM, see figure
18C).
Standard Curve
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
1 10 100
x
y
Concentration [nM]
OD
valu
es
at
490
nm
A
Standard Curve
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
0,1 1 10 100 1000
x
y
OD
va
lue
s a
t 4
90
nm
B
Concentration [nM]
IV. RESULTS AND DISCUSSION
70
Standard Curve
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
1 10 100
x
y
Concentration [nM]
OD
va
lue
s a
t 4
90
nm
C
Figure 18. Concentration ranges of the DiFi potency assay. The assay was performed on three different concentration ranges convenient for the different analysis models. The resulting, representative curves are shown: a close range A (2-12nM, see figure 18A), a wide range B (0.25-95nM, see figure 18B) and an intermediate range C (5-55nM, see figure 18C). Green points indicate the individual results, red points the mean values of the triplicates.
1.2.2. Test on the necessity of “weighting”
In order to investigate whether weighting was necessary in case of the DiFi assay, we made the
same calculations according to DeSilva et al. (2003) and the USP (2001) as for the CTLL-2
potency assay. The pertaining illustrations are shown in figures 20 and 21.
According to the calculations proposed by DeSilva et al. (see figure 19), again, a slight trend to
increasing SDs at increasing responses could be noted in some of our curves, which was however
again not consistent through the whole range (since SDS increased first and than remained
constant or even decreased again).
A
DiFi assay: concentration range 2 - 12nM
y = -0,1037x - 1,3702
R2 = 0,0357
-2,5
-2
-1,5
-1
-0,5
0
0 0,2 0,4 0,6 0,8 1
absorbance
log
[sta
nd
ard
devia
tio
n]
IV. RESULTS AND DISCUSSION
71
B
DiFi assay: concentration range 0,25 - 95.5nM
y = 0,7858x - 2,0106
R2 = 0,5711
-2,5
-2
-1,5
-1
-0,5
0
0 0,2 0,4 0,6 0,8 1
absorbance
log
[sta
nd
ard
devia
tio
n]
C
DiFi assay: 5 - 55nM
y = 0,9397x - 2,0963
R2 = 0,6215
-2,5
-2
-1,5
-1
-0,5
0
0 0,2 0,4 0,6 0,8 1
absorbance
log
[sta
nd
ard
devia
tio
n]
Figure 19. Plot of the pooled standard deviations (n = 18) vs. the measured mean responses for each concentration point. Illustrations are given for the different concentration ranges of the DiFi potency assay (fig. 19A: 2-12nM; fig. 19B: 5-55nM; fig. 19C: 0.25-95nM). According to DeSilva (2003), weighting is appropriate, if a plot of the pooled standard deviations vs. the mean responses reveals a linearly increasing relationship on a logarithmic scale.
According to the calculations proposed by the USP (see figure 20), the data followed approximately
a straight line, which was in all three cases even slightly decreasing.
IV. RESULTS AND DISCUSSION
72
A
DiFi assay: concentration range 2 - 12nM
y = -0,0002x - 1,4107
R2 = 0,0153
-2,5
-2
-1,5
-1
-0,5
0
0 20 40 60 80 100 120 140
dose squared
log
[sta
nd
ard
devia
tio
n]
B
DiFi assay: concentration range 0,25 - 95.5nM
y = -7E-05x - 1,6244
R2 = 0,4267
-2,5
-2
-1,5
-1
-0,5
0
0 2000 4000 6000 8000 10000
dose squared
log
[sta
nd
ard
devia
tio
n]
C
DiFi assay: 5 - 55nM
y = -0,0002x - 1,5457
R2 = 0,6223
-2,5
-2
-1,5
-1
-0,5
0
0 500 1000 1500 2000 2500 3000 3500
dose squared
log
[sta
nd
ard
devia
tio
n]
Figure 20. Plot of the pooled standard deviations (n = 18) vs. the dose squared for each concentration point. Illustrations are given for the different concentration ranges of the DiFi potency assay (fig. 20A: 2-12nM; fig. 20B: 5-55nM; fig. 20C: 0.25-95nM). According to the USP, weighting is recommended, when the plot of the sample variance is proportionally increasing to a function of dose, e.g. dose squared. If the data follow a horizontal line, no weighting is needed.
IV. RESULTS AND DISCUSSION
73
As a consequence, unweighted regressions were used in this study for all concentration ranges of
the DiFi potency assay.
1.2.3. Results
Results of the DiFi assay for the PL model, 4PL model and 5PL model varied between 75.6% and
147.6% recovery, those of the 4PL model between 81.8% and 125.4% recovery and those of the
5PL model between 79.9% and 125.4% recovery, respectively. A mean accuracy of 97.2%
(recovery) 11.8% (mean RE) was obtained using the PL model, whereas accuracies of 99.4%
7.1% and 99.1% 7.3% were assessed for the 4PL and 5PL model, respectively. As an example,
individual, representative graphs are shown in figure 21.
Figure 21. Individual, representative graphs (calculated by PLA) of a DiFi potency assay. Results of an assay with a target concentration of 100%, analyzed by the PL model (see figure 21A), the 4PL model (see figure 21B) and the 5PL model (see figure 21C) are shown.
Results with an expected activity of 50% varied between 95.3% and 99.9% recovery with REs
between 7.8% and 18.8% and CVs between 12.3% and 25.6%. The recoveries obtained are
summarized in table 7 and figure 22.
A B CA B C
IV. RESULTS AND DISCUSSION
74
Table 7. Results DiFi potency assay: expected activity 50% (i.e. relative potency: 0.5).
Figure 22. Results DiFi potency assay: expected activity 50%. Mean values of six repetitions are shown for each concentration range and analysis model. Error bars indicate the mean CVs. The red, dashed line demonstrates the expected activity of 50%.
Results with an expected activity of 100% varied between 92.4% and 99.2% recovery with REs
between 3.8% and 12.4% and CVs between 5.3% and 13.4%. They are shown in table 8 and figure
23.
-
PL
(2-
12nM)
PL
(0.25-
95nM)
4PL
(0.25-
95nM)
5PL
(0.25-
95nM)
PL (5-
55nM)
4PL (5-
55nM)
5PL (5-
55nM)
Result assay 1 0.430 0.467 0.461 0.464 0.615 0.609 0.608
Result assay 2 0.738 0.413 0.409 0.396 0.472 0.484 0.482
Result assay 3 0.436 0.522 0.52 0.522 0.380 0.466 0.464
Result assay 4 0.420 0.626 0.627 0.627 0.528 0.509 0.505
Result assay 5 0.442 0.488 failed failed 0.378 0.426 0.437
Result assay 6 0.447 0.411 0.44 0.44 0.487 0.502 0.497
mean 0.486 0.488 0.491 0.490 0.477 0.500 0.499
recovery 97.1 97.6 98.3 98.0 95.3 99.9 99.8
RE [%] 18.8 12.3 13.5 14.0 14.2 8.1 7.8
CV [%] 25.6 16.5 17.5 18.2 18.9 12.3 20.2
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
80,0
PL (2-12nM) PL (0.25-
95nM)
4PL (0.25-
95nM)
5PL (0.25-
95nM)
PL (5-55nM) 4PL (5-
55nM)
5PL (5-
55nM)
ac
tiv
ity
[%
]
IV. RESULTS AND DISCUSSION
75
Table 8. Results DiFi potency assay: expected activity 100% (i.e. relative potency: 1.0).
- PL (2-
12nM)
PL
(0.25-
95nM)
4PL
(0.25-
95nM)
5PL
(0.25-
95nM)
PL (5-
55nM)
4PL (5-
55nM)
5PL (5-
55nM)
Result assay 1 0.963 0.936 0.972 0.946 1.033 1.04 1.042
Result assay 2 1.064 1.159 1.041 1.03 1.035 1.03 1.038
Result assay 3 0.941 0.942 failed failed 0.875 0.888 0.886
Result assay 4 0.925 0.839 0.943 0.909 1.000 failed failed
Result assay 5 0.877 0.897 0.904 0.9 0.988 1.003 1.000
Result assay 6 0.773 0.804 0.946 0.94 0.973 0.993 0.993
mean 0.924 0.930 0.962 0.945 0.984 0.991 0.992
recovery 92.4 93.0 96.1 94.5 98.4 99.1 99.2
RE [%] 9.8 12.4 5.5 6.7 3.9 3.8 4.0
CV [%] 10.4 13.4 5.3 5.4 9.3 6.1 6.4
Figure 23. Results DiFi potency assay: expected activity 100%.
Mean values of six repetitions are shown for each concentration range and analysis model. Error bars indicate the mean CVs. The red, dashed line demonstrates the expected activity of 100%.
Results with an expected activity of 150% varied between 92.4% and 99.2% recovery with REs
between 4.3% and 14.7% and CVs between 5.8% and 18.5%. They are shown in table 9 and figure
24.
0,0
20,0
40,0
60,0
80,0
100,0
120,0
PL (2-
12nM)
PL (0.25-
95nM)
4PL (0.25-
95nM)
5PL (0.25-
95nM)
PL (5-
55nM)
4PL (5-
55nM)
5PL (5-
55nM)
acti
vit
y [
%]
IV. RESULTS AND DISCUSSION
76
Table 9. Results DiFi potency assay: expected activity 150% (i.e. relative potency: 1.5).
- PL (2-
12nM)
PL
(0.25-
95nM)
4PL
(0.25-
95nM)
5PL
(0.25-
95nM)
PL (5-
55nM)
4PL (5-
55nM)
5PL (5-
55nM)
Result assay 1 1.468 1.32 1.557 failed 1.418 1.443 1.435
Result assay 2 1.424 1.834 1.681 1.727 1.484 1.438 1.442
Result assay 3 1.581 1.356 failed failed 1.291 1.365 1.402
Result assay 4 1.211 1.625 1.56 1.56 1.270 failed failed
Result assay 5 1.448 1.924 1.48 1.494 2.016 1.728 1.704
Result assay 6 1.66 1.382 1.502 1.515 1.420 1.461 1.483
mean 1.465 1.573 1.556 1.574 1.483 1.487 1.493
recovery 97.7 104.9 103.7 104.9 98.9 99.1 99.5
RE [%] 7.7 14.7 4.3 5.1 12.6 6.9 5.9
CV [%] 10.5 16.6 5.8 6.7 18.5 9.4 8.1
Figure 24. Results DiFi potency assay: expected activity 150%. Mean values of six repetitions are shown for each concentration range and analysis model. Error bars indicate the mean CVs. The red, dashed line demonstrates the expected activity of 150%.
For the DiFi assay, in general, the same tendencies as for the CTLL-2 potency assay were
observed. The mean RE and thus the accuracy of the assay was slightly better for the logistic
models, but not significantly different. The precision of the PL model was with a coefficient of
variation of 15.1% (n = 54 assays) again lower as those of the 4PL and 5PL model with 9.4% (n =
36 assays). As before no significant difference could be noted between the mean values: according
0,0
20,0
40,0
60,0
80,0
100,0
120,0
140,0
160,0
180,0
200,0
PL (2-
12nM)
PL (0.25-
95nM)
4PL
(0.25-
95nM)
5PL
(0.25-
95nM)
PL (5-
55nM)
4PL (5-
55nM)
5PL (5-
55nM)
acti
vit
y [
%]
IV. RESULTS AND DISCUSSION
77
to the performed ANOVA test, a p-value of 0.68 was calculated. Since data were not normally
distributed in this case (KS-test: p = 0.02), the Levene‟s test was performed for variance check of
the standard deviations (L-test: p = 0.13).
Also neither for the individual values (data not shown), nor for the different concentration levels
(KS-test: p = 0.17, C-test: p < 0.05, KW-test: p = 0.14), nor for the different concentration ranges
(KS-test: p = 0.009, L-test: p = 0.41, ANOVA test: p = 0.63) a significant difference was observed.
As a consequence, for both assays neither the selected concentration level (50%, 100% or 150%),
nor the chosen concentration range (e.g. closer or wider range) had a statistically significant impact
on the result of the assay.
For the DiFi assay, a higher failure rate of the 4PL and 5PL model in comparison to the PL model
was observed. This finding was comparable to the results of the CTLL-2 assay. All performed
assays for the PL model were valid, whereas the logistic assays showed a higher failure rate
(definition see section II.2.4.3.), which was again significantly different from that of the PL model.
The failure rate of the 5PL model was with 16.7% (six out of 36 assays, three of these due to lack
of parallelism and three of these due to lack of linearity and parallelism) again slightly more
elevated as for the 4PL model with 13.9% (five out of 36 assays, two of these due to lack of
linearity, one of these due to lack of parallelism and two of these due to lack of both). This
difference between the logistic models was however not statistically significant.
An overall summary of the DiFi assay results is shown in table 10.
IV. RESULTS AND DISCUSSION
78
Table 10. Summary of DiFi assay results.
Concentration
range; dilution
factor
Analysis
model
Activity
expected
[%]
Activity
measured
(range)
[%]
Mean
activity
measured
[%]
Recovery
[%]
RE
[%]
CV
[%]
n
(valid
assays out
of six
repetitions)
2-12nM; 1.25 PL
50
42.0 – 73.8 48.6 97.1 18.8 25.6 6
0.25-95nM; 2.1
PL 41.1 – 63.2 48.8 97.6 12.3 16.5 6
4PL 40.9 – 62.7 49.1 98.3 13.5 17.5 5
5PL 39.6 – 62.7 49.0 98.0 14.0 18.2 5
5-55nM; 1.35
PL 37.8 – 61.5 47.7 95.3 14.2 18.9 6
4PL 42.6 – 60.9 49.9 99.9 8.1 12.3 6
5PL 43.7 – 60.8 49.9 99.8 7.8 11.8 6
2-12nM; 1.25 PL
100
77.3 – 106.4 92.4 92.4 9.8 10.4 6
0.25-95nM; 2.1
PL 80.4 – 115.9 93.0 93.0 12.4 13.5 6
4PL 90.4 – 104.1 96.1 96.1 5.5 5.3 5
5PL 90.0 – 103.0 94.5 94.5 6.7 5.4 5
5-55nM; 1.35
PL 87.5 – 103.5 98.4 98.4 3.9 9.3 6
4PL 88.8 – 104.0 99.1 99.1 3.8 6.1 5
5PL 88.6 – 104.2 99.2 99.2 4.0 6.4 5
2-12nM; 1.25 PL
150
121.1 – 166.0 146.5 97.7 7.7 10.5 6
0.25-95nM; 2.1
PL 132.0 – 192.4 157.4 104.9 14.7 16.6 6
4PL 148.0 – 168.1 155.6 103.7 4.3 5.0 5
5PL 149.1 – 172.7 157.4 104.9 5.1 6.7 4
5-55nM; 1.35
PL 127.0 – 201.6 148.3 98.9 12.6 18.5 6
4PL 136.5 – 172.8 148.7 99.1 6.9 9.4 5
5PL 140.2 – 170.4 149.3 99.5 5.9 8.1 5
1.3. Combined discussion of CTLL-2 and DiFi potency assays results
Already in the 1970s, the question of the right analysis model for bioassays was discussed. Thus
different mathematical methods for calculation of radioimmunoassay results were compared
(Sandel and Vogt, 1978; Rodbard et al., 1978). Rodgers (1984) summarized most of the methods
known to this time and also raised the question for the best model. To date no clear
recommendation for the right choice of analysis model can be given, since it depends on the
experimental setup and each of the methods possesses its advantages and disadvantages.
IV. RESULTS AND DISCUSSION
79
However, it was already shown that results varied by using different calculation models (Jeffcoate
and Das, 1977; Pegg and Miner, 1982).
In this study, no significant difference was observed between the PL, 4PL and 5PL models
regarding accuracy and precision. Nevertheless, small differences between the models could be
noticed. For both assay types, the precision of the logistic models was with a CV of approximately
10% higher than that of the PL model with a CV of approximately 15%. This was the same for the
accuracy with a mean RE of 7 - 8% for the logistic models versus 12% for the PL model. Similar
results have already been observed by Plikaytis (1991), who compared different models (log-log
model, two forms of the logit-log model and 4PL) for quantification of Neisseria meningitides Group
A Polysaccharide Antibody Levels by ELISA. They noted even larger differences between the
different models as in our case. The calculated precisions were 21.6% (n = 7) for the log-log model,
which was the least precise of the four methods compared, in contrast to 3.0% (n = 7) for the 4PL
model, being the most precise of the four methods.
A reason for the higher accuracy and precision of the logistic models might be the better assurance
of similarity between sample and reference, because the entire curve is considered for potency
calculation and not only the linear part as for the PL model (Findlay and Dillard, 2007).
Consequently, more data points are used. Moreover, during linearization of the curve, an
underlying bias might be inserted which could lead to the lacking precision of the PL model (Findlay
and Dillard, 2007; Rodgers, 1984). This bias is dependent upon concentration: It is greatest at the
ends of the assay range, which explains the larger deviations from the target concentrations at
concentration levels of 50% and 150%, in comparison to the concentration level of 100%.
For both assay types, the failure rate of the logistic models was more elevated and significantly
different from that of the PL model. Thereby, the failure rate of the 5PL model was even higher then
that of the 4PL model. This observation is supported by Findlay and Dillard (2007), who advice to
use the 5PL model only in cases with a clear asymmetry of the curve since in other cases the
additional fifth parameter might destabilize the fitting algorithm.
This would lead to the conclusion that the 4PL model fitted the curve better than the 5PL model,
e.g. the differences between the observed data and the fitted regression were smaller for the 4PL
model.
In order to further explore which model fitted the CTLL-2 and DiFi data better, the mean RE [(back
calculated – nominal concentration)/nominal concentration*100] was calculated for each individual
concentration, which gives a measure of the goodness of fit for each model (Findlay and Dillard,
2007). For backcalculation of the concentration points, the equations of the 4PL and 5PL model
were used, described in the Introduction section I.3.3.2.2.
The pertaining illustrations of the mean REs over the different concentration points are shown in
figure 25.
IV. RESULTS AND DISCUSSION
80
A
CTLL-2 assay: concentration range 0.75 - 100ng/mL
-25
-20
-15
-10
-5
0
5
10
15
20
25
10054,0529,215,88,554,62,51,350,75
dose
%R
E [
resid
uals
/measu
red
resp
on
se*1
00]
4PL
5PL
B
CTLL-2 assay: concentration range 6.8 - 80ng/mL
-25
-20
-15
-10
-5
0
5
10
15
20
25
8058,843,315,823,417,212,59,36,8
dose
%R
E [
resid
uals
/measu
red
resp
on
se*1
00]
4PL
5PL
C
DiFi assay: concentration range 0.25 - 95.5nM
-25
-20
-15
-10
-5
0
5
10
15
20
25
0,25 54,05 1,1 15,8 4,9 10,2 21,4 45 95,5
dose
%R
E [
resid
uals
/measu
red
resp
on
se*1
00]
4PL
5PL
IV. RESULTS AND DISCUSSION
81
D
DiFi assay: concentration range 5 - 55nM
-25
-20
-15
-10
-5
0
5
10
15
20
25
5 54,05 9,1 15,8 16,6 22,4 30,3 40,9 55,2
dose
%R
E [
resid
uals
/measu
red
resp
on
se*1
00]
4PL
5PL
Figure 25. % REs [residuals/ measured response*100] of the 4PL and 5PL models for each concentration point. Residuals are the difference between the backcalculated and nominal concentration. The smaller the residuals and the % REs are, the better is the curve fit of the model. Illustrations are given for the different concentration ranges of the CTLL-2 (fig. 27A: 0.75–100ng/mL; fig. 27B: 6.8-80ng/mL) and DiFi (fig. 27C: 5-55nM; fig. 27D: 0.25-95nM) potency assays.
Visual inspection of figure 25 and calculation of the mean RE over all concentration points showed
that the two models were approximately equivalent for the concentration range 0.75–100ng/mL of
the CTLL-2 potency assay (see figure 25A) and the concentration range 5–55nM of the DiFi
potency assay (see figure 25D). The mean REs for the 4PL and 5PL models were 6.5% and 6.4%
(CTLL-2 assay) as well as 9.8% and 10.5% (DiFi assay), respectively. For the concentration range
0.25–95nM of the DiFi potency assay, the 4PL model fitted the curve better with a mean RE of
8.9% in comparison to 14.3% of the 5PL model (see figure 25C). In contrast, in case of the
concentration range 6.8-80ng/mL of the CTLL-2 potency assay, the fit of the 5PL model was with
7.3% RE better than those of the 4PL model with 14.4% RE (see figure 25B). This was in good
agreement with the shapes of the curves since by visual inspection of the curves, this latter curve
seemed to be asymmetric since the lower plateau of the curve was missing (see figure 13C). In
opposition to this, the other three curves (see figures 13 and 20) seemed to be symmetric.
Interestingly, the results of the calculated potencies were not influenced by the fits of the models.
Even if one of the models fitted the curve better, hardly any differences were noticed in the
calculated potencies.
The higher failure rate of the logistic models in comparison to the PL model supports the discussion
about the appropriate test of similarity, which is currently on-going between the USP and the Ph.
Eur. The Ph. Eur. recommends using the difference test (F-test) - an ANOVA test - whereas the
USP advices to use an equivalence test. The problem of the ANOVA technique, which was also
used in this case, is that it is hyper-sensitive, even to negligible deviations from parallelism. This is
especially the case for models with a very high precision. In those cases, a slight difference
between the sample and the reference standard will already be denoted as statistically different by
IV. RESULTS AND DISCUSSION
82
the ANOVA technique. The ability to detect lack of satisfactory “goodness-of-fit” for a curve
improves since the precision of the assay improves (Dudley et al., 1985). As a consequence, the
more precise the assay is, the higher the possibility is to fail the test of parallelism. This could
explain the higher failure rate of the logistic models in comparison to the PL model, observed
during this study. Likewise, the higher the number of replicates and the higher the number of dose
levels is, the higher the possibility is to detect lack of fit, and hence may introduce the need to
utilize more complex models. Conversely, when experimental errors are large, and replicates are
few, simple models are likely to be “adequate” (Dudley et al., 1985).
In order to solve the problem of the high failure rate of the logistic models, an alternative
equivalence test was proposed to the ANOVA test (Story et al., 1986). This test determines a
critical value for parallelism mean by multiplying the appropriate critical F value for parallelism by
the value for error mean square, providing a limiting value for the fiducial limits. This approach
should enable to improve bioassay technique by accepting precise assays but not doubtful assay
results. The equivalence test is discussed as alternative to the ANOVA test, but is also not free of
criticisms (Draper and Smith, 1998; Bates and Watts, 1988).
The problem of the right test of parallelism has also already been discussed by Plikaytis et al.
(1994), who developed seven guidelines applicable to most immunoassays, which should serve as
an alternative to the standard ANOVA technique. Other proposals are the weighted-sum-of-
squares regression (Gottschalk and Dunn, 2005, a) and the testing of the null hypothesis for a
specified difference between the slopes of two bioassay curves (Callahan and Saijadi, 2003;
Plikaytis and Carlone, 2005).
It still needs to be determined which of the tests mentioned above is the most advantageous. In
any case, by replacing the ANOVA test by one of them, the failure rate of the logistic models might
be reduced.
Another factor which might influence the decision for a specific analysis model is the number of
dilution steps on a plate. In this study, nine dilution steps were used for all three models, PL, 4PL
and 5PL; there was consequently no difference in the workload of the different assays. However,
whilst all of the nine steps are definitely required for the logistic models, a reduction of the dilution
steps is possible for the PL model since it usually only uses three to four steps in the linear range
of the curve. In an additional study, the number of dilutuion steps was reduced to three and four
steps, respectively. Reduction to three dilution steps was not possible due to a too high number of
failed assays; however satisfactory results were obtained by using four dilutions steps (data not
shown). As a conclusion of this reduction from nine to four dilution steps, three samples could be
analyzed on one plate only, reducing the workload considerably. This fact favours the use of the PL
model.
1.4. Summary and Conclusions
This study is a systematic and thorough comparison addressing the question of potency assay
evaluation by different analysis models using actual data of two different bioassays.
IV. RESULTS AND DISCUSSION
83
In summary, this study showed that no significant differences regarding accuracy and precision
between the three different models for potency assay calculation were observed, neither for the
CTLL-2 assay (ANOVA test: p = 0.83), nor for the DiFi assay (ANOVA test: p = 0.68).
All important parameters of both assays are summarized in table 11.
Table 11. Summary and comparison between the different models.
Criteria Assay type PL 4PL 5PL
Total amount of
analyzed assays
CTLL-2 54 36 36
DiFi 54 36 36
Accuracy
(Recovery ± RE)
[%]
CTLL-2 104.0 11.8 102.4 8.5 102.3 8.5
DiFi 97.2 11.8 99.4 7.1 99.1 7.3
Precision [%] CTLL-2 14.1 10.4 10.3
DiFi 15.1 9.4 9.4
Failure rate [%]
(n)
CTLL-2 0
(0)
27.8
(10)
30.6
(11)
DiFi 0
(0)
13.9
(5)
16.7
(6)
The logistic models show a slightly better accuracy and precision in comparison to the PL model, in
exchange, they have a higher failure rate, which differs significantly from that of the PL model.
These parameters have to be balanced during the model selection process in method
development. Moreover, it has to be considered on the one hand, that the PL model is more
precisely described in the Ph. Eur., chapter 5.3 and that applying this model can reduce the
number of concentration levels and workload. On the other hand, the better assurance of similarity
between sample and reference, leading to a higher accuracy and precision of the logistic models
needs to be accounted for. Also the possibility of the application of an alternative similarity test
(instead of the F-test) should be taken into consideration, because this might reduce the failure rate
of the logistic models.
Above this, it has always to be kept in mind that the appropriate model depends on the performed
type of assay set up.
Thus, the preferred model has to be chosen individually. Since there were no significant differences
regarding accuracy and precision and since the failure rate of the logistic models was much higher
than for the PL model, I decided to use further on the PL model for the following studies described
in this thesis.
IV. RESULTS AND DISCUSSION
84
2. COMPARISON OF DIFFERENT PROLIFERATION ASSAYS (COLORIMETRIC, LUMINESCENCE AND
FLUORESCENCE READ-OUTS)
Some of the most commonly used read-out systems for potency assays to date are based on
colorimetric, luminescence or fluorescence reactions. At the Bioassay laboratory at Merck Serono,
the colorimetric system, utilizing the tetrazolium salt and MTT analogue MTS/PMS, was originally
used for detection of all kinds of proliferation assays. MTS is a tetrazolium salt, which is reduced to
an intensely colored formazan by the mitochondria enzymes of living cells (Goodwin et al., 1995).
PMS acts as an intermediate electron acceptor and accelerates the reaction. The amount of the
produced formazan is direct proportional to the number of living cells in culture and can be
measured at 492nm (Malich et al., 1997).
In this study, it was investigated, whether alternative luminescence or fluorescence reagents were
more advantageous in comparison to the colorimetric system and which system was the most
favourable for QC purposes.
As an example for a luminescence read-out, CellTiter- Glo® (Promega) was used. This
bioluminescent assay uses an enzyme, luciferase, which catalyzes the formation of light in the
presence of ATP and luciferin. The emitted light intensity correlates with the amount of ATP
present and consequently provides a direct measure of the number of viable cells (Crouch et al,
1993).
The substrate used for fluorescence measurements was Alamar BlueTM
(Invitrogen). The native,
oxidized form of the Alamar Blue reagent is taken up readily by the cells and is reduced
intracellularly by oxidoreductases and the mitochondrial electron transport chain, with a
corresponding shift in its absorbance and fluorescence (Ahmed et al., 1994; Goegan et al., 1995).
Consequently, the initial dark blue color of the oxidized state changes to a highly fluorescent red
dye, and becomes a direct estimate of the intracellular rate of reduction, and thus a direct estimate
of the viable cell number.
The three reagents were compared to each other regarding their suitability for QC of
biopharmaceuticals. Each of the three read-outs was tested with each of the three cell lines CTLL-
2, DiFi and Kit 225, allowing a thorough and systematic analysis of the advantages and
disadvantages of the proliferation assays.
2.1. Method development
The change from the coloriometric read-out to luminescence or fluorescence detection made, as a
first step, modification of several parameters in comparison to the originally used colorimetric assay
necessary.
Thus, the following parameters were investigated and modified, starting from the original
colorimetric assay (described under Materials and Methods):
Concentration ranges
Incubation times (with substrate)
IV. RESULTS AND DISCUSSION
85
Volume of APIs and cells
Cell densities
Modifications were considered successful if the signal to noise ration could be increased (see
figure 26).
Curve
<Plate Layout Settings>
Lu
m
0,01 0,1 1 10 100
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
Concentration [ng/mL]
Re
lati
ve li
ghtu
nit
s(R
LU)
Figure 26. Illustration of the signal to noise ratio of a representative dose response curve. As an example, a concentration curve of a CTLL-2 potency assay, detected by luminescence, is shown. The signal to noise ratio is indicated as the ratio of the highest concentration to the control, both marked in red. Blue points indicate the individual results, orange points the mean values of the triplicates.
The resulting adapted parameters are described in the results section for each individual assay
(CTLL-2, DiFi and Kit 225).
Furthermore, it was investigated, whether it was possible to reduce the incubation time without
substrate, since incubation times were very long, especially for the DiFi and the Kit 225 assay,
having both an incubation time of 96h. However, this was not possible neither for luminescence,
nor for fluorescence read-outs, since signal to noise ratios were not stable and not high enough
after shorter incubation times.
Other parameters to be varied during the method development were the right amounts of substrate
for each read-out system and each individual assay and the right sensitivity of the luminescence
reader (not described individually).
2.2. Results
2.2.2. CTLL-2 potency assay
For the CTLL-2 assay, the concentration range of the colorimetric assay was adapted from 6.8 –
80ng/mL to 0.15 – 50ng/mL for the luminescence technique and 0.25 – 100ng/mL for the
fluorescence technique. This was necessary since the originally used concentration range did not
fit well to the luminescence and fluorescence detection. Performing the assay according to the
IV. RESULTS AND DISCUSSION
86
concentration range used for colorimetric detection (6.8 – 80ng/mL) according to the valid standard
operation procedure (SOP) the following curve was obtained, using the MTS/PMS reagent (see
figure 27):
0
0,2
0,4
0,6
0,8
1
1,2
1 10 100
y
x
Standard Curve
Concentration [ng/mL]
OD
val
ue
sat
49
0nm
Figure 27. Concentration curve of a CTLL-2 potency assay, detected by colorimetric read-out.
An exemplary, resulting concentration curve is shown by using a concentration range of 6.8 – 80ng/mL. Green points indicate the individual results, red points the mean values of the triplicates.
This curve was not appropriate for QC purposes since the lower asymptote was not covered. Thus,
the original colorimetric potency assay was optimized to a concentration range of 1.5 – 50ng/mL
(see figure 28).
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1 10 100
y
x
Standard Curve
Concentration [ng/mL]
OD
val
ue
sat
49
0nm
Figure 28. Optimized curve of a CTLL-2 potency assay, detected by colorimetric read-out.
An exemplary, resulting concentration curve is shown by optimizing the assay to a concentration range of 1.5 – 50ng/mL. Green points indicate the individual results, red points the mean values of the triplicates.
IV. RESULTS AND DISCUSSION
87
By using the original concentration range of 6.8 – 80ng/mL, similar curves to the one shown in
Figure 29 were obtained for luminescence and fluorescence techniques:
Curve
<Plate Layout Settings>
Lu
m
1 10 100
2800000
3000000
3200000
3400000
3600000
3800000
4000000
4200000
4400000
4600000
4800000
5000000
Concentration [ng/mL]
Re
lati
ve li
ghtu
nit
s(R
LU)
Figure 29. Concentration curve of a CTLL-2 potency assay, detected by luminescence read-out. An exemplary, resulting concentration curve is shown by using a concentration range of 6.8 – 80ng/mL. Blue points indicate the individual results, orange points the mean values of the triplicates.
Since this concentration range did not fit well, it had to be adapted to 0.15 – 50ng/mL for the
luminescence technique and 0.25 – 100ng/mL for the fluorescence technique (see figures 30 and
31).
Curve
<Plate Layout Settings>
Lu
m
0,01 0,1 1 10 100
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
Concentration [ng/mL]
Re
lati
ve li
ghtu
nit
s(R
LU)
Figure 30. Optimized curve of a CTLL-2 potency assay, detected by luminescence read-out. An exemplary, resulting concentration curve is shown by using a concentration range of 0.15 – 50ng/mL. Blue points indicate the individual results, orange points the mean values of the triplicates.
IV. RESULTS AND DISCUSSION
88
Curve
<Plate Layout Settings>
53
0/2
5,6
00
/40
0,01 0,1 1 10 100 1000
0
100000
200000
300000
400000
500000
600000
700000
Concentration [ng/mL]
Re
lati
ve fl
uor
esce
nce
un
its
(RFU
)
Figure 31. Optimized curve of a CTLL-2 potency assay, detected by fluorescence read-out. An exemplary, resulting concentration curve is shown by using a concentration range of 0.25 – 100ng/mL. Blue points indicate the individual results, orange points the mean values of the triplicates.
Thus, the luminescence read-out detected approx. ten times lower immunocytokine concentrations
than the colorimetric read-out.
Another parameter to be modified was the used cell amount, e.g. the cell volume and the cell
density. Using luminescence and fluorescence detection, the cell density could be reduced from
5*105 cells/mL to 2.5*10
5 cells/mL and the used cell number, as well as the amount of API, be
reduced from 100µL/well to 50µL/well leading to a four times lower consumption of cells for the
Alamar Blue and ATP assays in contrast to the MTS assay. This reduction was not possible for the
colorimetric read-out since it lead to significant decreases of the OD differences and consequently
to a decrease of the signal to noise ratios.
It can be concluded that both, the Alamar Blue and the ATP bioluminescence assay showed a
considerably higher sensitivity in comparison to the MTS assay. This is in line with previous results
published by Hamid et al. (2004), Petty et al. (1995), Slater (2001) and Weyermann et al. (2005).
Regarding the incubation time with substrate, the luminescence read-out was the fastest of the
three read-outs since the plate could already be measured 5 - 10min after substrate addition. In
contrast, the incubation time with substrate was 2.5 – 3h for the colorimetric system and 24 4h for
fluorescence. The longer incubation time is the most obvious disadvantage of the Alamar Blue
assay in comparison to the ATP assay since it takes a whole day to complete the assay.
An important criterion for the robustness of an assay is the signal to noise ratio. The higher this
ratio, the more robust and precise is the assay because of the steeper slope of the curve. For the
CTLL-2 potency assay, mean ratios of 2.4 for the colorimetric read-out, 7.3 for the fluorescence
read-out and 13.7 for the luminescence read-out were measured, respectively. The highest signal
to noise ratio is thus another advantage of the ATP assay.
The only detriment of the ATP assay is the higher costs for the CellTiter- Glo® reagent.
IV. RESULTS AND DISCUSSION
89
In order to evaluate accuracy and precision of the read-outs, three repetitions were performed for
all three read-out systems on three different sample concentrations of 50%, 100% and 150% in
comparison to the 100% standard (n = 9). The repetitions were performed independently on
different days; assays for the different read-out techniques were performed on the same day.
Results are summarized in tables 12 to 14.
Table 12. Results qualification CTLL-2 assay: expected activity 50% (i.e. relative potency: 0.5).
Read-out Colorimetric Luminescence Fluorescence
Result day 1 0.607 0.548 0.588
Result day 2 0.404 0.503 0.602
Result day 3 0.597 0.568 0.425
Mean 0.536 0.540 0.538
Recovery [%] 107.2 107.9 107.7
RE [%] 20.0 7.9 17.7
CV [%] 21.4 6.2 18.3
Table 13. Results qualification CTLL-2 assay: expected activity 100% (i.e. relative potency: 1.0).
Read-out Colorimetric Luminescence Fluorescence
Result day 1 1.141 0.985 1.043
Result day 2 0.956 1.053 0.966
Result day 3 1.115 1.050 1.059
Mean 1.071 1.029 1.023
Recovery [%] 107.1 102.9 102.3
RE [%] 10.4 3.9 4.5
CV [%] 9.4 3.7 4.9
Table 14. Results qualification CTLL-2 assay: expected activity 150% (i.e. relative potency: 1.5).
Read-out Colorimetric Luminescence Fluorescence
Result day 1 1.783 1.488 1.406
Result day 2 1.411 1.377 1.490
Result day 3 1.499 1.408 1.520
Mean 1.564 1.424 1.472
Recovery [%] 104.3 95.0 98.1
RE [%] 8.3 5.0 2.8
CV [%] 12.4 4.0 4.0
IV. RESULTS AND DISCUSSION
90
All results were within the acceptable range of ± 30%, which is usually applied for bioassays at
Merck Seono. Consequently, all read-out systems were qualified.
Regarding the summarized results comprising all three concentration levels of 50%, 100% and
150%, recoveries varied between 80.8% and 119.4% for the colorimetric read-out, 91.8% and
113.6% for luminescence and 85.0% and 120.4% for fluorescence. Mean REs (mean deviations
from the expected activity) of 12.9%, 5.6% and 8.3% were calculated for the colorimetric,
luminescence and fluorescence system, respectively. No major differences between the different
read-out systems regarding accuracy were observed.
The mean CV (13.6%) was slightly higher for the MTS assay in comparison to the Alamar Blue
assay (6.9%) and the ATP assay (4.3%). This observation is consistent with the higher signal to
noise ratios of the latter assays, leading to a higher precision of the assay.
An overall summary of the most important parameters of the CTLL-2 potency assay and a
comparison between the different read-outs is shown in table 15.
Table 15. Summary of CTLL-2 assay parameters and comparison between the different read-out systems.
Read-out Colorimetric Luminescence Fluorescence
Concentration range 1.5 – 50ng/mL 0.15 – 50ng/mL 0.25 – 100ng/mL
Signal to noise ratio, mean
(range)
2.4
(1.8 - 3.2)
13.7
(6 - 22)
7.3
(5 - 9)
Volume of API and cells 100µL/well 50µL/well 50µL/well
Cell density 5*105 cells/mL 2.5*10
5 cells/mL 2.5*10
5 cells/mL
Incubation time (with
substrate)
2.5 – 3h 5 - 10min 24 ± 4h
Recovery (range) 80.8% - 119.4% 91.8% - 113.6% 85.0% - 120.4%
Accuracy
(mean RE [%]; n = 9) 12.9% 5.6% 8.3%
Precision
(Mean CV [%]; n = 9)
13.6% 4.3% 6.9%
Costs/plate (substrate
only)
Luminescence > Fluorescence > Colorimetric
IV. RESULTS AND DISCUSSION
91
2.2.3. DiFi potency assay
The DiFi assay was originally performed on a concentration range of 5-55nM (see figure 32).
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
1 10 100
y
x
Standard Curve
Concentration [nM]
OD
val
ue
sat
49
0nm
Figure 32. Concentration curve of a DiFi potency assay, detected by colorimetric read-out. An exemplary, resulting concentration curve is shown by using a concentration range of 5- 55nM. Green points indicate the individual results, red points the mean values of the triplicates.
Again, this concentration range was not adequate for luminescence and fluorescence
measurements (figure 33).
Curve
<Plate Layout Settings>
Lu
m
0 10 20 30 40 50 60
300000
400000
500000
600000
700000
800000
900000
1000000
1100000
Concentration [nM]
Re
lati
ve li
ghtu
nit
s(R
LU)
Figure 33. Concentration curve of a DiFi potency assay, detected by luminescence read-out. An exemplary, resulting concentration curve is shown by using a concentration range of 5- 55nM. Blue points indicate the individual results, orange points the mean values of the triplicates.
As a consequence, the concentration range was adapted to 0.5 – 16.7nM, for both, luminescence
and fluorescence (see figure 34).
IV. RESULTS AND DISCUSSION
92
Curve
<Plate Layout Settings>
53
0/2
5,6
00
/40
0,01 0,1 1 10 100
50000
100000
150000
200000
250000
300000
350000
400000
Concentration [nM]
Re
lati
ve li
ghtu
nit
s(R
LU)
Figure 34. Concentration curve of a DiFi potency assay, detected by luminescence read-out.
An exemplary, resulting concentration curve is shown by using a concentration range of 0.5- 16.7nM. Blue points indicate the individual results, orange points the mean values of the triplicates.
Thus, again, the higher sensitivity of the Alamar Blue and ATP assays in comparison to the MTS
assay could be demonstrated. Similar to the CTLL-2 assay, approx. ten times lower antibody
concentrations could be detected: 0.5nM in contrast to 5nM. For the luminescence system, the cell
density used originally for the MTS read-out could be reduced from 5*105 cells/mL to 2.5*10
5
cells/mL and for the fluorescence system it could even be reduced to 1*105 cells/mL.
The incubation time with substrate was again the shortest for the luminescence read-out (5 -
10min), followed by the colorimetric system (75 15min) and the fluorescence read-out (4 0.5h).
In comparison to the CTLL-2 assay, however, the difference between the ATP and Alamar Blue
assay was not that significant since the difference by using the fluorescence technique consisted of
4h only as opposed to a whole day.
In contrast to the CTLL-2 assay only slight differences were observed between the different read-
outs regarding the signal to noise ratios. The calculated mean ratios of 2.2 for MTS, 3.0 for ATP
and 1.8 for Alamar Blue were all relatively low.
In line with the CTLL-2 assay, three repetitions of the assay on different days were performed on
the different concentration levels of 50%, 100% and 150% for all three read-outs (n = 9). Results
are summarized in tables 16 to 18.
IV. RESULTS AND DISCUSSION
93
Table 16. Results qualification DiFi assay: expected activity 50% (i.e. relative potency: 0.5).
Read-out Colorimetric Luminescence Fluorescence
Result day 1 0.373 0.412 0.459
Result day 2 0.538 0.562 0.464
Result day 3 0.571 0.505 0.461
Mean 0.494 0.493 0.461
Recovery [%] 98.8 98.6 92.3
RE [%] 15.7 10.3 7.7
CV [%] 21.5 15.4 0.5
Table 17. Results qualification DiFi assay: expected activity 100% (i.e. relative potency: 1.0).
Read-out Colorimetric Luminescence Fluorescence
Result day 1 0.972 1.031 0.980
Result day 2 0.965 0.936 1.012
Result day 3 0.996 1.013 1.022
Mean 0.978 0.993 1.005
Recovery [%] 97.8 99.3 100.5
RE [%] 2.2 3.6 1.8
CV [%] 1.7 5.1 2.2
Table 18. Results qualification DiFi assay: expected activity 150% (i.e. relative potency: 1.5).
Read-out Colorimetric Luminescence Fluorescence
Result day 1 1.399 1.542 1.417
Result day 2 1.402 1.515 1.462
Result day 3 1.531 1.441 1.570
Mean 1.444 1.499 1.483
Recovery [%] 96.3 100.0 98.9
RE [%] 5.1 2.6 4.2
CV [%] 5.2 3.5 5.3
Recoveries including all results of the different concentration levels of 50%, 100% and 150% varied
between 74.6% and 114.2% for the colorimetric read-out, 82.4% and 112.4% for luminescence and
91.8% and 104.7% for fluorescence. Despite the lower signal to noise ratios in comparison to the
CTLL-2 assay, results of the DiFi assay were more accurate and precise. Thus, mean REs (mean
deviations from the expected activity) of 7.7%, 5.5% and 4.6% and mean CVs of 6.6%, 6.0% and
3.4% were calculated for the colorimetric, luminescence and fluorescence system, respectively.
Consequently, no major differences between the three read-outs were observed for the DiFi assay
regarding accuracy and precision.
IV. RESULTS AND DISCUSSION
94
An overall summary of the most important parameters of the DiFi potency assay and a comparison
between the different read-outs is shown in table 19.
Table 19. Summary of DiFi assay parameters and comparison between the different read-out systems.
2.2.4. Kit 225 potency assay
In general, for the Kit 225 potency assay, the same trends as for the CTLL-2 and DiFi potency
assays could be observed.
The originally used concentration range of 5.8 – 25ng/mL for the Kit 225 assay was not suitable,
neither for the MTS read-out (see figure 35), nor for fluorescence and luminescence (see figure
37).
Read-out Colorimetric Luminescence Fluorescence
Concentration range 5 – 55nM 0.5 – 16.7nM 0.5 – 16.7nM
Signal to noise ratio 2.2
(1.8 – 2.6)
3.0
(2.6 – 3.6)
1.8
(1.4 – 2.1)
Cell density 5*105 cells/mL 2.5*10
5 cells/mL 1*10
5 cells/mL
Incubation time (with
substrate) 75 15min 5 - 10min 4 0.5h
Recovery (range) 74.6% - 114.2% 82.4% - 112.4% 91.8% - 104.7%
Accuracy
(mean RE; n = 9) 7.7% 5.5% 4.6%
Precision
(Mean CV; n = 9) 6.6% 6.0% 3.4%
Costs/plate (substrate
only) Luminescence > Fluorescence > Colorimetric
IV. RESULTS AND DISCUSSION
95
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1 10 100
y
x
Standard Curve
Concentration [ng/mL]
OD
val
ue
sat
49
0nm
Figure 35. Concentration curve of a Kit 225 potency assay, detected by colorimetric read-out. An exemplary, resulting concentration curve is shown by using a concentration range of 5.8 - 25ng/mL. Green points indicate the individual results, red points the mean values of the triplicates.
Thus, the concentration range for the colorimetric read-out was adapted to 0.35 – 40ng/mL (see
figure 36).
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
0,1 1 10 100
y
x
Standard Curve
Concentration [ng/mL]
OD
val
ue
sat
49
0nm
Figure 36. Optimized curve of a Kit 225 potency assay, detected by colorimetric read-out. An exemplary, resulting concentration curve is shown by using a concentration range of 0.35 - 40ng/mL. Green points indicate the individual results, red points the mean values of the triplicates.
For luminescence and fluorescence the curve shown in figure 37, was obtained by using the
original concentration range of 5.8 – 25ng/mL.
IV. RESULTS AND DISCUSSION
96
Curve
<Plate Layout Settings>
Lu
m
1 10 100
1200000
1300000
1400000
1500000
1600000
1700000
1800000
1900000
2000000
2100000
2200000
2300000
Concentration [ng/mL]
Re
lati
ve li
ghtu
nit
s(R
LU)
Figure 37. Concentration curve of a Kit 225 potency assay, detected by luminescence read-out. An exemplary, resulting concentration curve is shown by using a concentration range of 5.8 - 25ng/mL. Blue points indicate the individual results, orange points the mean values of the triplicates.
Since this range was again not adequate, it was adapted to 0.025 – 40ng/mL, for both,
luminescence and fluorescence, resulting in the curve shown in figure 38.
Curve
<Plate Layout Settings>
53
0/2
5,6
00
/40
0,001 0,01 0,1 1 10 100
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
Concentration [ng/mL]
Re
lati
ve li
ghtu
nit
s(R
LU)
Figure 38. Optimized curve of a Kit 225 potency assay, detected by luminescence read-out.
An exemplary, resulting concentration curve is shown by using a concentration range of 0.025 - 40ng/mL. Blue points indicate the individual results, orange points the mean values of the triplicates.
It can be concluded, that the higher sensitivity of the luminescence and fluorescence read-outs
could again be highlighted since they detected drug concentrations of 0.025ng/mL in contrast to
0.35ng/mL detected by the colorimetric system.
IV. RESULTS AND DISCUSSION
97
The cell density for luminescence and fluorescence could even be reduced to a tenth of the original
density of 100.000 cells/well. Furthermore, the cell suspension volume could be reduced from
70µL/well to 50µL/well. This led to significant reductions of costs and resources for cell culture
when using the Alamar Blue or ATP assays instead of the MTS assay.
Luminescence with an incubation time with substrate of 5 - 10min was again the fastest method. In
contrast, the colorimetric and fluorescence systems needed incubation times of 3 – 4h and 6 1h,
respectively.
The mean signal to noise ratios were 2.5 for MTS, 4.9 for ATP and 3.9 for Alamar Blue.
Consequently, higher ratios were observed for fluorescence and especially for luminescence than
for the colorimetric system, like for the CTLL-2 assay, even if differences were not that significant in
this case.
Subsequently, the Kit 225 assay was equally qualified with respect to accuracy and precision.
Results of the different concentration levels of 50%, 100% and 150% are summarized in tables 20
to 22.
Table 20. Results qualification Kit 225 assay: expected activity 50% (i.e. relative potency: 0.5).
Read-out Colorimetric Luminescence Fluorescence
Result day 1 0.711 0.412 0.514
Result day 2 0.449 0.487 0.405
Result day 3 0.443 0.462 0.561
Mean 0.534 0.454 0.493
Recovery [%] 106.9 90.7 98.7
RE [%] 21.3 9.3 11.3
CV [%] 28.6 8.4 16.2
Table 21. Results qualification Kit 225 assay: expected activity 100% (i.e. relative potency: 1.0).
Read-out Colorimetric Luminescence Fluorescence
Result day 1 0.979 0.951 1.013
Result day 2 1.008 1.024 0.971
Result day 3 1.117 0.985 0.971
Mean 1.035 0.987 0.985
Recovery [%] 103.5 98.7 98.5
RE [%] 4.9 3.0 3.0
CV [%] 7.0 3.7 2.5
IV. RESULTS AND DISCUSSION
98
Table 22. Results qualification Kit 225 assay: expected activity 150% (i.e. relative potency: 1.5).
Read-out Colorimetric Luminescence Fluorescence
Result day 1 1.467 1.585 1.634
Result day 2 1.373 1.546 1.476
Result day 3 1.522 1.358 1.432
Mean 1.454 1.496 1.514
Recovery [%] 96.9 99.8 100.9
RE [%] 4.0 6.1 5.0
CV [%] 5.2 8.1 7.0
During assay qualification, recoveries including all results of all three concentration levels varied
between 88.6% and 124.2% for the colorimetric, 82.4% and 105.7% for the luminescence and
81.0% and 108.9% for the fluorescence system.
Regarding accuracy, no major differences were observed for the different read-outs since mean
REs of 10.1%, 6.1% and 6.4% were calculated for the MTS, ATP and Alamar Blue assays,
respectively.
Luminescence and fluorescence techniques had mean CVs of 6.5% and 7.0% and therefore
seemed a bit more precise than the colorimetric system showing 10.0%. The higher precision is in
good agreement with the higher signal to noise ratios and has already been observed for the CTLL-
2 potency assay.
An overall summary of the most important parameters of the Kit 225 potency assay and a
comparison between the different read-outs is shown in table 23.
IV. RESULTS AND DISCUSSION
99
Table 23. Summary of Kit 225 assay parameters and comparison between the different read-out systems.
2.3. Combined discussion of CTLL-2, DiFi and Kit 225 assay results
As already described in the Introduction section I.3.3., assays used for QC, release and stability
testing should be precise, robust, accurate and should show a good repeatability. To accomplish
this and to be suitable for routine use in a QC environment, they should provide a high signal to
noise ratio and a stable read-out for more than 30min. Furthermore, they should be as fast as
possible; kinetic measurements are usually not used for QC.
In this regard, the three different proliferation assays used in this study were compared to each
other, considering the above mentioned parameters.
Regarding the results of all three potency assays, no major differences regarding accuracy and
precision between the different read-outs were observed. The similarity of the results of different
substrates has already been observed by Mueller et al. (2004) who compared MTT, ATP and
calcein assays and found a high correlation between them. In this study, all three read-outs
provided accurate and reproducible results. Consequently, they are all suitable for use in QC.
Workload and hands-on time were the same for all three assays. Nevertheless, the different read-
out techniques possess some advantages and disadvantages.
From the results of this study, it is clear that the luminescence and fluorescence techniques are
more sensitive than the colorimetric read-out. They could detect approx. ten times lower drug
concentrations and the assay could be performed using considerably lower cell numbers. The
higher sensitivity of the ATP assay in comparison to a colorimetric system has already been
observed by Petty et al. (1995). By comparing the sensitivity of MTT- and ATP-based assays using
Read-out Colorimetric Luminescence Fluorescence
Concentration range 0.35 – 40ng/mL 0.025 – 40ng/mL 0.025 – 40ng/mL
Signal to noise ratio, mean
(range)
2.5
(2.2 – 2.7)
4.9
(3.5 – 6.3)
3.9
(3.2 – 4.8)
Cell density 100.000 cells/well 10.000 cells/well 10.000 cells/well
Volume of API and cells 70 L/well 50 L/well 50 L/well
Incubation time (with
substrate) 3 – 4h 5 - 10min 6 1h
Recovery (range) 88.6% - 124.2% 82.4% - 105.7% 81.0% - 108.9%
Accuracy
(mean RE [%]; n = 9) 10.1% 6.1% 6.4%
Precision
(Mean CV [%]; n = 9) 10.0% 6.5% 7.0%
Costs/plate (substrate
only) Luminescence > Fluorescence > Colorimetric
IV. RESULTS AND DISCUSSION
100
Daudi and CCRF-CEM cell lines, they found a detection limit of the luminescence method of
approx. 1.500 cells/well in contrast to 25.000 cells/well for the colorimetric system. Comparable
observations that confirm the higher sensitivity of the ATP assay were made by Eirheim et al.
(2004), Ulukaya et al. (2008), Couch and Slater (2001) and Weyermann et al. (2005). The higher
sensitivity of Alamar Blue in comparison to the MTT reagent has already been shown as well, e.g.
by Hamid et al. (2004), who compared the two reagents for high throughput screening using 117
different drugs.
Apart from the higher sensitivity of the luminescence and fluorescence techniques in comparison to
the colorimetric system, the ATP and Alamar Blue assays possess another advantage: Their signal
to noise ratios are higher. Differences between the ratios depend on the different cell lines. Nearly
the same ratios for all three read-outs were observed using the DiFi potency assay, in contrast,
approx. 3 times higher ratios were calculated for fluorescence in comparison to the colorimetric
system and approx. 5 - 6 times higher ratios were obtained for luminescence using the CTLL-2
potency assay.
The results of this study suggest that the signal to noise ratios tend to be highest for luminescence
and the lowest for the colorimetric system. In cases of a higher signal to noise ratio, this seemed to
result in a higher precision of the assay. Thus, mean CVs of 13.6% and 10.0% were obtained using
the MTS reagent for the CTLL-2 and Kit 225 assays, respectively, whereas the Alamar Blue assay
provided CVs of 6.9% and 7.0%, respectively and the ATP assay CVs of 4.3% and 6.5%,
respectively.
So far, the luminescence and fluorescence read-outs seem to be equivalent and slightly superior to
the colorimetric system for use in QC. Squatrito et al. (1995) compared Alamar Blue and ATP
bioluminescence assays for cytotoxicity screening assays and only found a poor correlation
between them with a clear recommendation for the ATP assay. In contrast, this study showed that
both read-outs are suited excellently for potency assays in QC. There were hardly any differences
in the calculated potencies and both assays are extremely sensitive. Underestimation of cytotoxic
effects using the Alamar Blue assay (Squatrito et al., 1995) is not of relevance in case of potency
assays since the sample is directly compared to a reference standard on the same 96 well plate.
Nevertheless, the fluorescence technique is less convenient in comparison to the luminescence
read-out due to its longer incubation time with substrate. The incubation times varied significantly
between the different potency assays. This is due to the fact that each cell line has unique
metabolic properties and has to be characterized individually to determine the appropriate
incubation time (Nakayama et al., 1997). The disadvantage of the longer incubation time varied in
our study between a few hours (4 - 6h) in case of the DiFi and Kit 225 assays to a whole day (24h)
in case of the CTLL-2 potency assay.
Reasons for the longer incubation times of Alamar Blue are its low toxicity and non-destructive
effect on cells. Especially for performing kinetic measurements of the changes in cell proliferation
IV. RESULTS AND DISCUSSION
101
and viability this is of great advantage (Pagé et al., 1993; Zhi-Jun et al., 1997). In contrast to most
other reagents including the MTS and ATP read-outs, Alamar Blue does not require lysis of cells.
Thus, kinetic measurements as well as further immunological studies of the cells are possible.
However, this is of less importance in QC. Here, the longer incubation time is a disadvantage in
comparison to the colorimetric and especially the luminescence technique that require shorter
incubation times.
Finally, the only disadvantage of the luminescence technique observed in this study has to be
mentioned. The costs for the CellTiter- Glo® reagent for one plate are higher as those for the MTS
reagent and also as those for the Alamar Blue reagent. This effect could however at least partially
be reduced due to the lower number of cells required. As less cells need to be cultivated, the costs
for culture medium, growth factors etc. could be decreased.
2.4. Conclusions
Taken together, this study showed that all three read-out systems, the MTS/PMS assay, the
Alamar Blue assay and the ATP bioluminescence assay, are suited well for use in QC since they
produce reliable and reproducible results. Workload and hands-on time were the same for all three
assays.
From the results of this study however, it can be concluded that the luminescence technique seems
to be the most advantageous of the three read-outs for potency assays in QC. It is extremely
sensitive, therefore leading to cell savings and provides the highest signal to noise ratios, leading
to very precise results. Moreover, it is the fastest of the three methods for all assays investigated,
since the plate can already be measured 5 - 10min after addition of substrate.
It can thus be concluded that it is recommendable to replace the colorimetric read-out by the
luminescence technique.
IV. RESULTS AND DISCUSSION
102
3. CHARACTERIZATION OF THE IL-7 SIGNAL TRANSDUCTION PATHWAYS AND SELECTION OF AN
APPROPRIATE TARGET FOR DEVELOPMENT OF AN INTRACELLULAR PHOSPHORYLATION ASSAY
In pharmaceutical QC, the potency of biopharmaceuticals is commonly assessed by using
proliferation assays. However, other assay formats like reporter gene or phosphorylation assays
are equally possible. The aim of this study was to develop an alternative intracellular
phosphorylation assay for Fc-IL7 since the originally used proliferation assay suffered from a very
long incubation time and a poor robustness.
Therefore, first of all, the IL-7 signal transduction pathways had to be screened for a suitable target
for development of a phosphorylation assay. This study was further expanded to a comparison
between different human T and murine B cell lines, as well as to a comparison between IL-2 and
IL-7 signaling. The cell lines used for this comparison were the following:
Kit 225 cells are human IL-2 dependent T lymphocytes, derived from a patient with T cell chronic
lymphocytic leukemia with OKT3+, -T4
+, - T8
– phenotype (Hori et al., 1987). Mehrotra et al. (1995)
showed that Kit 225 cells were equally proliferating in presence of IL-7.
Due to the fact that Kit 225 cells are stimulated by both, IL-2 and IL-7, they are suited well for a
comparison between those two γc cytokines.
Furthermore, two different B cell lines were used, at a different stage of maturation:
First, the pre-B cell line, PB-1, derived from bone marrow cells from CBA/C57BL mice and cloned
in the presence of IL-7 (Mire-Sluis et al., 2000); and second, the lymphocyte clone 2E8, which was
isolated from murine long-term bone marrow cultures and used for the initial selection of a series of
stromal-cell clones (Pietrangeli et al., 1988). To my knowledge, no IL-2 dependency of those two
cell lines has been published to date.
In order to elucidate the IL-7 signaling cascade, representative targets of some of the most
important pathways, presumed to be activated by IL-7, were selected. In comparison to other
cytokines, relatively little is known about the IL-7 pathways. Some pathways which are presumed to
be activated by IL-7, and consequently were investigated in this study are the JAK/STAT, the
PI3K/AKT, the ERK, the SFK and the p38MAPK pathways. They are described in detail in the
Introduction section I.2.3.
The pathways were studied in three different cell lines (Kit 225, PB-1 and 2E8 cells) using western
blotting and the receptor densities of the different cells were measured using flow cytometric
analysis. Moreover, IL-2 and IL-7 signaling pathways were compared in Kit 225 cells. Prior to
western blotting and flow cytometric analyses, cells were starved for one day without growth factor
and were then incubated with IL-2 or IL-7 for various times at 37°C as described under Materials
and Methods. Experiments were performed at least in duplicates starting from activation of the
cells. One representative example is shown. The correct bands in the western blotting analyses
were identified with the help of a positive control and the denoted molecular weight of the
respective protein.
IV. RESULTS AND DISCUSSION
103
3.1. Results and Discussion of flow cytometric analyses
In order to compare the IL-2R and IL-7R densities of the different cell lines, flow cytometric
analyses were performed on the three cell lines. IL-7R was present on all three cell lines (see
figure 39). However, expression was relatively low on Kit 225 cells, where the increase in
fluorescence intensity was smaller than 1 log. The highest IL-7R expression was noticed for 2E8
cells.
Figure 39. Flow cytometric analysis of IL-7Rα densities on A) Kit 225, B) PB-1, and C) 2E8 cells.
In figure 39D, all histograms of the different cell lines were overlaid in order to compare the receptor densities of the different cell lines to each other. Cells were either labeled with isotype controls (red histogram) or CD127 antibodies (green histogram), both PE conjugated. IL-7Rα was present on all cell lines; the density was the lowest on Kit 225 cells and the highest on 2E8 cells. The shown histograms are representatives of two independent experiments.
IV. RESULTS AND DISCUSSION
104
As expected, IL-2R density was much higher than IL-7R density on Kit 225 cells. Surprisingly,
this was the same for PB-1 and 2E8 cells, even if the increase in fluorescence intensity was
smaller than for Kit 225 cells (see figure 40).
Figure 40. Flow cytometric analysis of IL-2Rα densities on A) Kit 225, B) PB-1, and C) 2E8 cells. In figure 40D, all histograms of the different cell lines were overlaid in order to compare the receptor densities of the different cell lines to each other. Cells were either labeled with isotype controls (red histogram) or CD127 antibodies (green histogram), both PE conjugated. IL-2Rα was present on all cell lines; the density was the lowest on PB-1 cells and the highest on Kit 225 cells. The shown histograms are representatives of two independent experiments.
CD25 and thus IL-2R expression has already been described for PB-1 cells (Mire-Sluis, 2000);
nevertheless no IL-2 dependency has been reported neither for them, nor for the 2E8 cells.
Consequently, in addition, the IL-2R densities were analyzed on the different cell lines. IL-2R is
the part of the receptor, which is presumed to be responsible for signal transduction (Minami et al.,
1993). It could be clearly shown that IL-2R is not expressed on PB-1 and 2E8 cells, but on Kit 225
cells (see figure 41).
IV. RESULTS AND DISCUSSION
105
Figure 41. Flow cytometric analysis of IL-2Rβ densities on A) Kit 225, B) PB-1, and C) 2E8 cells.
In figure 43D, all histograms of the different cell lines were overlaid in order to compare the receptor densities of the different cell lines to each other. Cells were either labeled with isotype controls (red histogram) or CD127 antibodies (green histogram), both PE conjugated. IL-2Rβ was present only on Kit 225 cells; not on PB-1 and 2E8 cells. The shown histograms are representatives of two independent experiments.
From the results of the flow cytometric analyses, IL-7 induced signal transduction pathways can be
expected in all three cell lines. IL-2 induced pathways should be activated in Kit 225 cells only;
even to a stronger extent than IL-7 induced pathways due to the higher density of IL-2Rα in
comparison to IL-7Rα. In general, no IL-2 mediated pathways would be expected in PB-1 and 2E8
cells, even if they express the IL-2Rα chain. However, they do not express the -chain of the IL-2R.
A whole IL-2R, consisting of α-, β-, and γ-chain is necessary for signal transduction. Moreover, the
β-chain is the part of the receptor, which is presumed to be responsible for signal transduction.
This is in line with literature results since no IL-2 dependency has been published for these two cell
lines. It might be possible, that those differences in the expression pattern of the different receptors
are due to the difference between murine (PB-1 and 2E8) and human (Kit 225) cell lines. This is
possible; however, it has been described in literature that the receptors are highly homologous, e.g.
the human and murine IL-7R shows a homology of 64% (Goodwin et al., 1990). All six extracellular
and four intracellular cystein residues are conserved in both receptor molecules. Consequently the
possibility is rather low that the differences in the receptor expression are species dependent.
IV. RESULTS AND DISCUSSION
106
3.2. Results and Discussion of western blotting analyses
3.2.1. Representative targets of the JAK/STAT pathway: STAT1, STAT3, STAT5, pim-1 and bcl-2
The JAK/STAT pathway is the first pathway, which is induced upon receptor activation. After JAK1
and JAK3 are activated, STATs bind to the receptor and become tyrosine phosphorylated. STAT1,
STAT3 and STAT5 were selected as examples of these pathways, in order to see, whether these
molecules were activated upon IL-7 and/or IL-2 stimulation.
3.2.1.1. STAT1 and STAT3
In this study, no phosphorylation of STAT1 (Tyr701) and STAT3 (Tyr705) were found upon IL-7
stimulation in all three cell lines, although unphosphorylated STAT molecules were present in the
cells. In contrast, both molecules were activated by IL-2 in Kit 225 cells, even if signals were very
weak (see figures 42 and 43).
STAT1 p-STAT1
kDa
250
98
64
50
36
16kDa
250
98
64
50
36
16
Figure 42. Western blotting analysis of STAT1 and phospho (p)-STAT1 in Kit 225, PB-1 and 2E8 cells. Cells were either incubated 10min at 37°C with 100ng/mL IL-7 or IL-2 or used untreated, followed by immunoblotting with anti-STAT1 and anti-phospho STAT1 antibodies as described under Materials and Methods. The figure shows that STAT1 phosphorylation (MW ≈ 85kDa) is only induced by IL-2 in Kit 225 cells. Lysates from HT29 cells incubated with interferon α served as a positive control; untreated Kit 225 cells as a negative control. The blots were developed using IR fluorescence and are one selected representative of three independent experiments.
IV. RESULTS AND DISCUSSION
107
STAT3 p-STAT3
kDa
250
98
64
50
36
16
kDa
180
26
4937
115
82
Figure 43. Western blotting analysis of STAT3 and phospho-STAT3 in Kit 225, PB-1 and 2E8 cells. Cells were either incubated 10min at 37°C with 100ng/mL IL-7 or IL-2 or used untreated, followed by immunoblotting with anti-STAT3 and anti-phospho STAT3 antibodies as described under Materials and Methods. The figure shows that STAT3 phosphorylation (MW ≈ 80kDa) is only induced by IL-2 in Kit 225 cells. Lysates from HT29 cells incubated with interferon α served as a positive control; untreated Kit 225 cells as a negative control.The blots were developed using IR fluorescence and are one selected representative of three independent experiments.
IL-2 mediated STAT1 and STAT3 activation in Kit 225 cells have already been shown by
Delespine-Carmagnat et al. (2000). As expected, no IL-2 mediated STAT1 and STAT3 activation
has been observed in PB-1 and 2E8 cells, emphasizing the fact that the -chain of the IL-2
receptor is responsible for signal transduction.
It is unclear, why STAT1 and 3 were not phosphorylated upon IL-7 stimulation in all three cell lines
since this has already been shown for T as well as B cells. IL-7 induced STAT1 and 3 activation
has for example been demonstrated in B cell precursor acute lymphoblastic leukemia (BCP-ALL)
cells (van der Plas et al., 1996), CD4+ naïve T cells (Li et al., 2009) and HBP T lymphoblasts
(Rosenthal et al., 1997). However, to my knowledge, STAT1 and STAT3 activation has been
observed only in primary cells, not in cell lines, so far. Moreover, it has to be mentioned that the IL-
7 induced STAT3 phosphorylation was much weaker than the IL-2 induced phosphorylation in T
lymphoblasts (Rosenthal et al., 1997). In this study, only a very weak signal was obtained for
STAT3 phosphorylation in Kit 225 cells upon IL-2 stimulation, already suggesting that no or only
very slight signals could be obtained upon IL-7 stimulation, due to the lower IL-7 receptor densities.
Culture conditions in this study were similar to those of Rosenthal et al. (1997), who also starved
the cells from IL-2 containing medium for 16 – 48h prior to restimulation with IL-2 or IL-7, making
the two studies comparable.
In conclusion, STAT1 and STAT3 activation seem not to occur upon IL-7 stimulation in all three cell
lines investigated and only play a minor role in Kit 225 cells treated with IL-2.
IV. RESULTS AND DISCUSSION
108
3.2.1.2. STAT5
In contrast to STAT1 and 3, it could be demonstrated that STAT5 was phosphorylated at Tyr694 by
IL-7 in all three cell lines after 10min incubation at 37°C (see figure 44).
STAT5 p-STAT5
kDa
98
64
50
36
16
kDa
16
50
36
250
98
64
Figure 44. Western blotting analysis of STAT5 and phospho-STAT5 in Kit 225, PB-1 and 2E8 cells.
Cells were either incubated 10min at 37°C with 100ng/mL IL-7 or IL-2 or used untreated, followed by immunoblotting with anti-STAT5 and anti-phospho STAT5 antibodies as described under Materials and Methods. The figure shows that STAT5 phosphorylation (MW ≈ 90kDa) is induced by IL-7 in all three cell lines and by IL-2 in Kit 225 cells only. In these cells, IL-2 induced phosphorylation is much stronger than IL-7 induced phosphorylation. Lysates from Hela cells incubated with interferon α served as a positive control (pos. C.); untreated Kit 225 cells as a negative control (neg. C.).The blots were developed using IR fluorescence and are one selected representative of at least five independent experiments.
The results are in line with previous observations describing IL-7 induced STAT5 phosphorylation
in several different cells, e.g. in CD34+ thymocytes (Pallard et al., 1999), primary human T and NK
cells (Yu et al., 1998), CT6 cells (Foxwell et al., 1995), HEK293T, CHO and COS-7 cells (Toda et
al., 2008), as well as in PB-1 cells (Hirokawa et al., 2003). To my knowledge, this has not yet been
published for IL-7 induced STAT5 phosphorylation in 2E8 and Kit 225 cells. Interestingly, the two
distinct bands of the different isoforms, STAT5A and STAT5B could clearly be identified in Kit 225
cells, whereas only one single band could be detected in PB-1 and 2E8 cells. This might lead to the
conclusion that only one isoform is activated in PB-1 and 2E8 cells, which is most probably
STAT5A (due to the higher molecular weight of this band; see Delespine-Carmagnat et al., 2000).
This isoform is activated in a stronger way in Kit 225 cells, too.
Like IL-7, IL-2 equally induced STAT5 activation in Kit 225 cells, showing even a much stronger
signal than IL-7. This is in good agreement with the stronger STAT3 phosphorylation by IL-2 in T
lymphoblasts in comparison to IL-7, shown by Rosenthal et al. (1997), already discussed, and the
higher IL-2R densities in comparison to IL-7R, observed in this study.
In general, IL-2 induced STAT5 phosphorylation was much stronger than IL-2 induced STAT1 and
STAT3 activation. The same phenomen has already been shown in Kit 225 cells by Delespine-
Carmagnat et al. (2000) using western blotting and electrophoretic mobility shift assay (EMSA).
IV. RESULTS AND DISCUSSION
109
The culture conditions for the Kit 225 cells were comparable; however Carmagnat et al. (2000)
utilized antibiotic supplemented medium and starved the stells for 48h instead of 24h. IL-7
mediated effects in Kit 225 cells have not been investigated by them.
From the results discussed above, it can be concluded, that STAT5 is probably more important
than the other STATs in IL-2 and IL-7 signaling.
In order to further explore this pathway and to compare the different cell lines, additional kinetic
measurements were performed (see figure 45). Therefore, incubation times of 10min, 30min, 1h,
3h, 6h, 18h and 24h were used with IL-2 and IL-7, respectively.
Kit 225
+ IL-2
PB-1
+ IL-7
2E8
+ IL-7
Kit 225
+ IL-7
C
10
min
30
min 1h 3h 6h 18h 24h
kDa
90
90
90
90
Figure 45. Kinetic measurements of STAT5 phosphorylation.
Cells were incubated with IL-7 and IL-2 for the indicated time points between 10min and 24h. Differences in kinetics were observed between the different cell lines. The blots were developed using IR fluorescence and are one selected representative of two independent experiments.
In Kit 225 cells, a relatively stable signal for STAT5 was obtained between 10min and 6h with a
maximum after 30min, which decreased after longer incubation times (18h and 24h), for both, IL-2
and IL-7. In contrast, in 2E8 and PB-1 cells, only a weak signal (especially for PB-1) was noticed
after 10min incubation time, which then increased and remained approximately constant up to at
least 24h (experiment was completed after 24h – longer incubation times were not tested). This
difference might be explained by the different manners in which IL-7 reacts in the different cell
types. Apparently, STAT5 phosphorylation is faster induced in T than in B cells in this study;
however the signal is more stable in B cells.
3.2.1.3. Inhibition of STAT5 by WP1066
In order to clarify whether STAT5-phosphorylation could be inhibited, and thus to verify that STAT5
was really activated upon IL-7 stimulation, the JAK2/STAT3 inhibitor WP1066 was used. WP1066
IV. RESULTS AND DISCUSSION
110
is a novel, more potent AG490 analogue (Iwamaru et al., 2007). It has been observed that WP1066
inhibited JAK2 and its downstream STAT and PI3K pathways and induced caspase-dependent
apoptosis of acute myeloid leukemia (AML) cells (Ferrajoli et al., 2007). Moreover, it activated Bax,
suppressed the expression of c-myc, bcl-xL and mcl-1, and induced apoptosis (Iwamaru et al.,
2007).
Since some STATs share the same regulatory pathway, WP1066 is supposed not only to inhibit
STAT3, but also STAT5 phosphorylation. This has already been observed in OCIM2 and K562
cells (Ferrajoli et al., 2007), as well as in U87-MG cells (Iwamaru et al., 2007); in contrast STAT5
phosphorylation was not inhibited by WP1066 in U373-MG cells.
In order to test whether STAT5 phosphorylation could be inhibited in our cell lines by WP1066, the
cells were incubated with 10µM WP1066 for 24h prior to stimulation with IL-2 or IL-7. Cells were
then subjected to western blot analysis and incubated with anti-phospho-STAT5 antibody as
described under Materials and Methods.
In this study, STAT5 phosphorylation was inhibited by WP1066 in Kit 225 and 2E8 cells, but not in
PB-1 cells upon the given conditions (see figure 46). Thus, it can be concluded, that WP1066
reacts differently in the different cell lines, which is in line with the observation made by Iwamaru et
al. (2007) who found differences between U87-MG and U373-MG cells.
kDa
98
64
50
36
16
Inhibition of p-STAT5 with WP1066
Figure 46. Inhibition of STAT5 phosphorylation by WP1066. Prior to stimulation with IL-2 or IL-7, cells were incubated with 10µM WP1066 for 24h, as described under Material and Methods. WP1066 inhibits STAT5 phosphorylation in Kit 225 cells and 2E8 cells; but not in PB-1 cells. The blots were developed using IR fluorescence and are one selected representative of two independent experiments.
The selected conditions, 10µM WP1066 and 24h, should ensure an inhibitory effect according to
other publications, already mentioned. Iwamaru et al. (2007) observed an inhibitory effect of
WP1066 between 5h and 24h using 10µM WP1066, whereas Ferrajoli et al. (2007) already noticed
his effectt after 2h using 2µM WP1066. Nevertheless, longer incubation times with higher
concentrations of WP1066 were tested in PB-1 cells, in order to check whether an inhibitory effect
could be seen. Therefore, extreme conditions using 100µM for 24h, 10µM for 48h and 100µM for
IV. RESULTS AND DISCUSSION
111
48h were used. However, viability of PB-1 cells was strongly decreased upon these conditions
allowing no proper analysis since the protein content decreased too much in comparison to the
untreated sample.
It can be concluded, that WP1066 reacted differently in the three cell lines according to standard
conditions reported in the literature. This might be explained by the differences between the three
cell lines as well as by a lacking specificity of WP1066.
3.2.1.4. Pim-1
It has been reported that STAT5 can bind directly to the pim-1 promoter at the ISFR/GAS-
sequence, leading to an up-regulated expression of the serine/threonine kinase pim-1. Pim-1 itself
can negatively regulate the JAK/STAT pathway by binding to suppressor of SOCS proteins, a
group of negative regulators of STAT activity (Losmann et al., 1999).
IL-7 induced pim-1 up-regulation has already been detected using gene array analysis in D1 cells
after 2h incubation with IL-7 (Kim et al., 2003). Furthermore, increased mRNA expression of pim-1
upon IL-7 stimulation has been observed in TSH119 cells (Sato et al., 2001) and pro-B cells (Goetz
et al., 2004).
It should thus be investigated, whether an up-regulated expression of pim-1 and an inhibitory effect
of STAT5 phosphorylation could be found in the cell lines used.
After 10min incubation time, no pim-1 kinase was expressed in all three cell lines (see figure 47).
Pim-1
kDa
64
50
36
16
98
250
Figure 47. Western blotting analysis of pim-1 in Kit 225, PB-1 and 2E8 cells. Cells were either incubated 10min at 37°C with 100ng/mL IL-7 or IL-2 or used untreated, followed by immunoblotting with anti-pim-1 antibody as described under Materials and Methods. The figure shows that pim-1 (MW ≈ 33kDa) is not expressed in all three cell lines after 10min incubation with IL-2 or IL-7. Lysates from K562 cells served as a pos. C.; untreated Kit 225 cells as a neg. C. The blots were developed using IR fluorescence and are one selected representative of three independent experiments.
However, pim-1 was up-regulated upon longer incubation times with IL-2 and Il-7 (see figure 48).
IV. RESULTS AND DISCUSSION
112
Kit 225
+ IL-2
PB-1
+ IL-7
2E8
+ IL-7
Kit 225
+ IL-7
C
10
min
30
min 1h 3h 6h 18h 24h
kDa
33
33
33
33
Figure 48. Kinetic measurements of pim-1 up-regulation.
Cells were incubated with 100ng/mL IL-7 and IL-2 for the indicated time points between 10min and 24h. Pim-1 was up-regulated in all three cell lines; however differences in kinetics were observed between the different cell lines. The blots were developed using IR fluorescence and are one selected representative of two independent experiments.
In Kit 225 cells, pim-1 was detected after 3h and 6h incubation time, after longer incubation times
(18h and 24h) only a very weak signal remained. This was the same for IL-2 and IL-7 stimulated
cells; however, signals were again much weaker upon IL-7 stimulation. In 2E8 and PB-1 cells, the
pim-1 kinase could already be detected after 30min and 10min incubation with IL-7, respectively.
(In PB-1 cells, a slight pim-1 expression could already be noticed in the untreated control in the
kinetic experiment.) The signal increased up to 3h and 1h, respectively, and then remained
constant for both cell lines up to 24h. Consequently, again, differences between B and T cells were
observed.
However, in both cases, no inhibitory effect on STAT5 phosphorylation could be detected. In case
of the Kit 225 cells, both STAT5 and pim-1 decreased upon longer incubation times (18h, 24h); in
contrast, the signals remained constant in 2E8 and PB-1 cells.
In general, IL-7 induced phosphorylation of STAT5 and up-regulation of pim-1 was always stronger
in PB-1 and in 2E8 cells than in Kit 225 cells, which was in good agreement with the results of the
flow cytometric analyses, showing lower IL-7R densities of the Kit 225 cells in comparison to the
other two cell lines.
3.2.1.5. Bcl-2
After phosphorylation, STAT5 induces a downstream cascade, e.g. it dimerizes and translocates to
the nucleus where it exerts its effect on transcription of regulated target genes. One possible
IV. RESULTS AND DISCUSSION
113
downstream target of STAT5 are members of the bcl-2 family (Debierre-Grockiego, 2004). Bcl-2 is
a potent anti-apoptotic protein, which improves survival of T lymphocytes (Strasser et al., 1996).
Since STAT5 phosphorylation was observed in all three cell lines, it could be supposed that IL-7
mediated bcl-2 up-regulation could be detected in the cell lines investigated. This has for example
already been reported for D1 cells (Kim et al., 2003), immature thymocytes (von Freeden-Jeffry et
al., 1997) and human T lymphocytes (Amos et al., 1998).
However, no bcl-2 expression was found in Kit 225 cells. In contrast, bcl-2 was expressed in 2E8
and PB-1 cells (see figure 49). Consequently, differences in the expression pattern could be seen
between T and B cells. Indeed, there was no difference between treated and untreated PB-1 and
2E8 cells. No up-regulation of bcl-2 could be detected within 6h incubation time with IL-7 (data not
shown). Thus, according to this data, the bcl-2 pathway was not specifically induced by IL-7 in all
three cell lines investigated.
Bcl-2
kDa
64
50
36
16
98
250
Figure 49. Western blotting analysis of bcl-2 in Kit 225, PB-1 and 2E8 cells. Cells were either incubated 10min at 37°C with 100ng/mL IL-7 or IL-2 or used untreated, followed by immunoblotting with anti-bcl-2 antibody as described under Materials and Methods. The figure shows that bcl-2 (MW ≈ 28kDa) was expressed in PB-1 and 2E8 cells only; and not in Kit 225 cells. Lysates from Jurkat, cells served as a pos. C., untreated Kit 225 cells as a neg. C. The blots were developed using IR fluorescence and are one selected representative of three independent experiments.
A possible reason, why no up-regulation of bcl-2 was found, might be the fact that the starvation
time without IL-7 of 24h was too long since it has been described that bcl-2 levels rapidly declined
already after 2-3h (Kim et al., 2003). Moreover, IL-7 does not necessarily enhance bcl-2
expression, but only protects cells from a decline in bcl-2, as it has already been observed in T
cells (Kim et al., 1998; Hernández-Caselles et al., 1995) and 103 cells (Banerjee and Rothman,
1998). Thus, in Kit 225 cells, supposably no up-regulation could be detected since no bcl-2 was
expressed or has already completely disappeared due to the too long incubation time. In PB-1 and
IV. RESULTS AND DISCUSSION
114
2E8 cells probably only a preserving but no stimulating effect of IL-7 could be observed. In line with
this, no significant changes in bcl-2 expression in neither 697neo nor 697D5A4 cells were observed
upon IL-7 stimulation (Lavin et al., 2004).
Moreover, it has been reported that PI3K activation was mandatory for IL-7–mediated bcl-2 up-
regulation (Barata et al., 2004), and no IL-7 induced AKT (and consequently presumably no PI3K
activation) was observed in the cell lines investigated, which will be discussed in the following.
3.2.2. Representative target of the PI3K/AKT pathway: AKT
Another kinase, which is supposed to be activated upon IL-7 stimulation, is PI3K. Their p85 subunit
binds to the phosphorylated Y449 residue on the IL-7R chain via an SH domain and activates
subsequently the catalytic subunit. The major effector of PI3K signaling is the serine/threonine
kinase AKT, which was selected as a representative target of this pathway.
In this study, no IL-7 mediated AKT activation was detected in all three cell lines investigated. In
contrast, AKT was phosphorylated at Ser473 upon IL-2 stimulation in Kit 225 cells (see figure 50).
This has already been shown for Kit 225 cells by Avota et al. (2001), who analyzed IL-2R signaling
in primary human T cells and Kit 225 cells. As expected, no IL-2 induced AKT activation was found
in PB-1 and 2E8 cells due to the lacking -chain of the IL-2R.
AKT p-AKT
kDa
250
98
64
50
36
kDa
98
64
50
36
1616
--
Figure 50. Western blotting analysis of AKT and phosphor-AKT in Kit 225, PB-1 and 2E8 cells.
Cells were either incubated 10min at 37°C with 100ng/mL IL-7 or IL-2 or used untreated, followed by immunoblotting with anti-AKT and anti-phospho-AKT antibodyies as described under Materials and Methods. The figure shows that AKT phosphorylation (MW ≈ 60kDa) is only induced by IL-2 in Kit 225 cells. Lysates from Jurkat cells incubated with calyculin A and LY294002 served as a pos. C. and neg. C., respectively.The blots were developed using IR fluorescence and are one selected representative of three independent experiments.
Again, it remains unclear, why no IL-7 mediated AKT activation was found in the cell lines tested
since this has already been reported for D1 cells (Li et al., 2004) and T-ALL cells (Barata et al.,
2004). On the other side, it has been reported that IL-7 did not activate the PI3K pathway and did
IV. RESULTS AND DISCUSSION
115
not phosphorylate AKT, although cells were proliferating in presence of IL-7 (Lali et al., 2004).
Moreover, Osborne et al. (2007) observed that AKT phosphorylation was only induced by IL-2, not
by IL-7 in T cells, confirming the results of this study.
Lali et al. (2004) reported that IL-2 induced two waves of PI3K activity in CT6 and F7 cells: one
rapid wave occurring within minutes and a second later wave, occurring within hours (3h – 6h),
which contributed to T cell growth. They presumed that IL-7 only induced the second, later wave of
PI3K/AKT activation. Consequently, some kinetic measurements with longer incubation times with
IL-2 and IL-7 were performed, in order to see whether this second wave was induced in our cell
lines. However, no AKT activation was found within 6h incubation time with IL-7 in all three cell
lines (see figure 51). Besides, no second wave of IL-2 mediated AKT activation was found in Kit
225 cells. Instead, the signal was stable between 10min and 30min and then decreased upon
longer incubation times.
Kit 225 stimulated with IL-2 and IL-7 2E8 and PB-1 stimulated with IL-7
kDa
250
986450
36
16
kDa
98
64
50
36
16
250
Figure 51. Kinetic measurements of AKT phosphorylation. Cells were incubated with IL-7 and IL-2 for the indicated time points between 10min and 6h. No IL-7 induced AKT phosphorylation was observed for all time points in all three cell lines; the signal for IL-2 mediated AKT phosphorylation in Kit 225 cells was stable between 10min and 30min and then decreased upon longer incubation times. The blots were developed using IR fluorescence and are one selected representative of two independent experiments.
3.2.3. Representative targets of the ERK pathway: ERK and Bim
Another pathway, which is induced by some of the c family cytokines, is the p42/44 ERK cascade,
which regulates the activity of the pro-apoptotic protein Bim and the NHE, regulating the pH of the
cell.
3.2.3.1. ERK
Like STAT1, STAT3 and AKT, ERK phosphorylation (Thr202/ Tyr204) was only induced by IL-2 in
Kit 225 cells. This has already been shown in Kit 225 cellls by Blanchard et al. (2000) and Arnaud
et al. (2004). In PB-1 and 2E8 cells, again, no IL-2 induced ERK activation was found, due to the
IV. RESULTS AND DISCUSSION
116
lacking -chain of the IL-2R. No IL-7 induced phosphorylation was found in all three cell lines
investigated (see figure 52).
ERK p-ERK
kDa
250
98
64
50
36
kDa
250
98
64
50
36
16
16
Figure 52. Western blotting analysis of ERK and phospho-ERK in Kit 225, PB-1 and 2E8 cells. Cells were either incubated 10min at 37°C with 100ng/mL IL-7 or IL-2 or used untreated, followed by immunoblotting with anti-ERK and anti-phospho-ERK antibodyies as described under Materials and Methods. The figure shows that ERK phosphorylation (MW ≈ 42/44kDa) is only induced by IL-2 in Kit 225 cells. Lysates from Kit 225 cells incubated with FBS served as a pos. C.; untreated Kit 225 cells as a neg. C. The blots were developed using IR fluorescence and are one selected representative of three independent experiments.
Furthermore, kinetic measurements revealed no IL-7 induced phosphorylation in all three cell lines
investigated between 10min and 6h incubation time with IL-7. The signal for IL-2 induced ERK
phosphorylation was stable between 10min and 1h and then decreased upon longer incubation
times (see figure 53).
2E8 and PB-1 stimulated with IL-7
kDa
98
64
50
36
16
kDa
Kit 225 stimulated with IL-2 and IL-7
98
64
50
36
16
Figure 53. Kinetic measurements of ERK phosphorylation. Cells were incubated with IL-7 and IL-2 for the indicated time points between 10min and 6h. No IL-7 induced ERK phosphorylation was observed for all time points in all three cell lines; the signal for IL-2 mediated ERK phosphorylation in Kit 225 cells was stable between 10min and 1h and then decreased upon longer incubation
IV. RESULTS AND DISCUSSION
117
times.The blots were developed using IR fluorescence and are one selected representative of two independent experiments.
In contrast to some of the other signal transduction pathways investigated so far, which were
supposed to be activated by IL-7, but were not induced in the cell lines investigated in this study,
results of this study are in good agreement with current literature regarding the ERK pathway.
Like for AKT, Osborne et al. (2007) observed that ERK phosphorylation was only induced by IL-2,
not by IL-7 in T cells, confirming the results of this study. According to Crawley et al. (1996), the
ERK pathway was not induced in most cell types upon IL-7 stimulation, at least not in murine T cell
lines (reviewed in Kittiparin and Khaled, 2007). In contrast to normal T cells, it has however been
shown to be activated in T-ALL cells (Barata et al., 2004) and in pre-B cells (Fleming et al., 2001).
The reason, why the ERK pathway is not activated by IL-7 in most cell lines, might be explained by
the lacking -chain of IL-7. The acidic region of the IL-2R is the docking site for Src homology and
collagen (Shc), which is the adaptor molecule for the ERK pathway. Since no ß-chain is present on
the IL-7R, Shc can bind and the signal transduction cascade can not be initiated. Similar
observations have already been made by Hsu et al. (2008), who found Shc activation by IL-2 only,
and not by IL-7 in common lymphoid progenitors (CLPs).
3.2.3.2. Bim
Bim is a pro-apoptotic member of the bcl-2 family, existing in three isoforms: BimS, BimL and BimEL
(O‟Connor et al., 1998). Bim has a role in the death of antigen-activated T cells (Snow et al., 2008)
and deficiency of Bim rescued the T cell depleted phenotype of IL-7 receptor deficient mice
(Pellegrini et al., 2004). Upon apoptotic stimuli, Bim is released from the dynein motor complex by
a mechanism including phosphorylation on T56 and S58 by JNK, leading to an increase in the
apoptotic activity (Lei et al., 2003). It is potentially regulated by IL-7 (Li et al., 2009) and can be
phosphorylated and thus inactivated by STAT5, AKT and especially ERK (Harada et al., 2004). Bim
might thus be a target of IL-7 signaling (Jiang et al., 2005). It has been observed, that IL-7
decreased the amount of Bim in T cells derived from tumor-bearing mice, which was associated
with a decrease in the apoptosis rate of these cells (Andersson et al., 2009).
According to the results of this study, however, Bim was not expressed in Kit 225 cells. In PB-1 and
2E8 cells, it was expressed but no regulatory effect of IL-7 could be observed between 10min and
6h incubation time with IL-7 (see figure 56; kinetics not shown). Thus, Bim regulation was not
specifically mediated by IL-7 in all three cell lines. This is in line with observations made by Oliver
et al. (2004), who reported that Bim was expressed in all stages of B cell maturation, but its level
was unaffected by IL.7. Comparable observations were made by Li et al. (2010), who noticed that,
even if deletion of Bim partially protected an IL-7-dependent T cell line and peripheral T cells from
IL-7 deprivation, IL-7 withdrawal did not increase Bim mRNA or protein expression but did only
induce a change in BimEL isoelectric point and reactivity with an anti-phosphoserine antibody. Li et
al. (2010) concluded that maintenance of peripheral T cells by IL-7 was partly mediated through
inhibiting Bim activity at the posttranslational level.
IV. RESULTS AND DISCUSSION
118
The apoptotic activity of Bim and the interaction with Bax, another pro-apoptotic protein, can be
regulated by phosphorylating BimEL on three serine sites, S55, S65, and S100 (Harada et al.,
2004). It should consequently be explored, whether Bim was phosphorylated in the cell lines
investigated, by using an anti-phospho-Ser55-antibody. However, no phosphorylation of Bim was
found between 10min and 6h incubation time with IL-7 in all three cell lines (see figure 54; kinetics
not shown). This was the same for IL-2 induced phophorylation, even if both, ERK and AKT –
presumed to phosphorylate Bim - were activated upon IL-2 stimulation in Kit 225 cells.
Bim p-Bim
kDa
250
98
64
50
36
16
kDa
250
98
64
50
36
16
Figure 54. Western blotting analysis of Bim and phospho-Bim in Kit 225, PB-1 and 2E8 cells. Cells were either incubated 10min at 37°C with 100ng/mL IL-7 or IL-2 or used untreated, followed by immunoblotting with anti-Bim and anti-phospho-Bim antibodies as described under Materials and Methods. The figure shows that Bim (MW ≈ 25kDa) was expressed in PB-1 and 2E8 cells only; and not in Kit 225 cells. Lysates from HuT78 cells served as a pos. C.; untreated Kit 225 cells as a neg. C. The blots were developed using IR fluorescence and are one selected representative of three independent experiments.
3.2.4. Representative target of the SFK pathway: Lck
SFKs are non-receptor tyrosine kinases, which are supposed to be activated by IL-7 (Seckinger et
al., 1994), although none of the members yet have been shown to be uniquely required for IL-7.
SFKs include nine members, beyond them Lck, which we selected as representative target of this
family since it is one of the two most abundantly expressed SFKs in T cells (Mustelin et al., 1994).
It can be co-immunoprecipitated with the β-chain of the IL-2R indicating its association with the
receptor complex. Upon IL-7 stimulation, Lck undergoes phosphorylation (Page et al., 1995).
In this study, no Lck expression was found in Kit 225 cells. This confirms the observations of Mills
et al. (1992), who showed that Kit 225 cells are lacking Lck expression, but can nevertheless
activate JAK3. In contrast, Lck and phospho-Lck (Tyr505) were expressed in PB-1 and 2E8 cells
(see figure 55).
IV. RESULTS AND DISCUSSION
119
Lck p-Lck
kDa
250
98
64
50
36
16kDa
250
98
64
50
36
16
Figure 55. Western blotting analysis of Lck and phospho-Lck in Kit 225, PB-1 and 2E8 cells. Cells were either incubated 10min at 37°C with 100ng/mL IL-7 or IL-2 or used untreated, followed by immunoblotting with anti-Bim and anti-phospho-Bim antibodies as described under Materials and Methods. The figure shows that Lck and p-Lck (MW ≈ 56kDa) were expressed in PB-1 and 2E8 cells only; and not in Kit 225 cells. Lysates from CCRF-HSB-2 cells served as a pos. C.; untreated Kit 225 cells as a neg. C. The blots were developed using IR fluorescence and are one selected representative of three independent experiments.
For 2E8 cells, Lck expression has already been observed by Ishihara et al. (1991). In addition to
bcl-2 and Bim, Lck is the third target, which was not expressed in Kit 225 cells, but only in the two
B cell lines, PB-1 and 2E8. Therein, clear differences in the expression pattern of T and B cells
could be identified in this study. However, these observations are in opposition to those of
Venkitaraman and Cowling (1992) who reported that phospho-Lck was predominantly T-
lymphocyte-restricted, and was only expressed at low levels in a few B lymphocyte lines. Another
possibility would be that the difference in the expression pattern is due to the difference between
human (Kit 225) and murine (PB-1 and 2E8) cell lines. However, as already reported, the human
and murine IL-7R show a high degree of homology (64%) (Goodwin et al., 1990). Moreover, all of
the three positive controls for bcl-2 (Jurkat), Bim (HuT 78) and Lck (CCRF-HSB-2) are of human
origin. Thus, those targets are in general expressed in human cell lines and failure of the antibodies
etc. can be excluded.
As for bcl-2 and Bim however, again, no difference between treated and untreated PB-1 and 2E8
cells was found. No effect of IL-7 could be seen between 10min and 6h incubation time (data not
shown). The reason for this remains unclear since IL-7 induced Lck-phosphorylation has already
been observed in T cells (Sharfe and Roifman, 1997; Page et al., 1995). Moreover, IL-7 mediated
up-regulation of Lck has been noticed in D1 cells (Kim et al., 2003). On the other hand, no increase
in phospho-Lck activity was observed in Nalm-6 cells (Seckinger et al., 1994). In this cell line, the
only member of the Src family to be stimulated by IL-7 was Fyn.
It can be concluded, that in this study, Lck was not specifically activated upon IL-7 stimulation in all
three cell lines investigated. However, it is possible that other members of the Src family might be
initiated upon IL-7 stimulation in these cell lines, which might be a target for further explorations.
IV. RESULTS AND DISCUSSION
120
3.2.5. Representative target of the p38 MAPK stress pathway: p38 MAPK
Withdrawal of IL-7 has been shown to induce a stress pathway activating p38 MAPK (Rajnavolgyi
et al., 2002). Activation of p38 MAPK leads to impeded cell proliferation and an alkalinization of the
pH in the cell (Khaled et al., 1999).
In this study, the cell lines were deprived for 24h of growth factor before re-stimulation with IL-2 or
IL-7. After these 24h, a possible phophorylation of p38 MAPK (Thr180/Tyr182) was observed in
2E8 cells. The band of the phosphorylated protein is in agreement with the molecular weight of p38
MAPK (43kDa); however, it is not in concordance with the possible band of the unphosphorylated
protein. Thus, it is unclear, whether p38 MAPK was phosphorylated or not in 2E8 cells. In contrast,
a phosphorylation of p38 MAPK can clearly be excluded in PB-1 and Kit 225 cells (see figure 56).
p38 MAPK p-p38 MAPK
kDa
98
64
50
36
kDa
250
9864
5036
16 16
Figure 56. Western blotting analysis of p38 MAPK and phospho-p38 MAPK in Kit 225, PB-1 and 2E8 cells. Cells were either incubated 10min at 37°C with 100ng/mL IL-7 or IL-2 or used untreated, followed by immunoblotting with anti-p38 MAPK and anti-phospho-p38 MAPK antibodyies as described under Materials and Methods. The figure shows that p38 MAPK phosphorylation (MW ≈ 43kDa) is only induced in 2E8 cells after 24h starvation time without growth factor. Unfortunately, no adequate pos. C. was found untreated Kit 225 cells served as a neg. C.); however the detected signals are in good agreement with the molecular weight of p38 MAPK. The blots were developed using IR fluorescence and are one selected representative of three independent experiments.
Probably, the starvation time was not long enough or, in contrast, even too long, in order to activate
p38 MAPK in those cell lines. Thus, again, possible differences between the different cell lines
were detected, which have already been observed for other targets. Differences between 2E8 and
Kit 225 cells might be due to the differences between B and T cells, whereas differences between
2E8 and PB-1 cells might be explained by the different maturation stages since PB-1 cells are of an
earlier stage of B cell maturation than 2E8 cells. Furthermore, 2E8 cells are IgM positive, whereas
PB-1 cells are not. The resulting differences in their biological responses have already been
IV. RESULTS AND DISCUSSION
121
reported by Mire-Sluis et al. (2000). Moreover, the IL-7Rα density was higher on 2E8 cells in
comparison to the other cell lines, resulting probably in a stronger reaction of those cells.
Another indicator that p38 MAPK was really phosphorylated in 2E8 cells after 24h starvation time
was the fact that the signal decreased after re-addition of IL-7. Thus, the signal mainly disappeared
after 3h incubation time with IL-7 (see figure 57). It can be concluded, that p38 MAPK is induced
upon IL-7 withdrawal and specifically reduced upon IL-7 stimulation in this cell line.
Kit 225 stimulated with IL-2 and IL-7 2E8 and PB-1 stimulated with IL-7
kDa
250
98
6450
36
16
kDa
98
64
50
36
16
250
Figure 57. Kinetic measurements of p38 MAPK phosphorylation. Cells were incubated with IL-7 and IL-2 for the indicated time points between 10min and 6h. In 2E8 cells, the figure shows an IL-7 mediated down-regulation of phospho-p38 upon longer incubation times. The signal mainly disappeared after 3h incubation time. In the other cell lines no effect could be seen. The blots were developed using IR fluorescence and are one selected representative of two independent experiments.
Another cell line, in which p38 MAPK was induced upon IL-7 withdrawal was the D1 cell line
(Rajnavologyi et al., 2002). In these cells, phosphorylation of p38 MAPK was already induced after
4 - 6h (Khaled et al., 2001) and 2h (Rajnavologyi et al., 2002) of IL-7 withdrawal, respectively, and
resulted in expression of jun and fos family members and enhanced activator protein (AP)-1
activity, contributing to cell death.
Thus, the starvation time of 24h should in any case be long enough to activate p38 MAPK. It is
however possible, that this starvation time was even too long. In Kit 225 and PB-1 cells, it might
probably be of interest to study different starvation times without growth factor, in order to see
whether p38 MAPK could be induced upon longer or shorter time points.
3.3. Summary and Conclusions
The aim of this study was to select a target, which is suitable for development of a phosphorylation
assay and to shed light on the IL-7 signal transduction pathways due to the scarce literature reports
about this particular cytokine. Therefore, representative targets of some of the most important
pathways, presumed to be activated by IL-7, were selected and analyzed by using three different
cell lines.
Four investigated targets, STAT1, STAT3, AKT and ERK were activated upon IL-2 stimulation only,
not upon IL-7 stimulation, even if those two cytokines were presumed to initiate similar pathways,
IV. RESULTS AND DISCUSSION
122
since both contain the common c chain and react in a comparable manner. However, there are
nevertheless differences between those two cytokines, already reported by others (e.g. Rosenthal
et al., 1997; Osborne et al., 2007) and confirmed in this study. This might probably inter alia be due
to the lacking -chain of the IL-7R.
Different expression pattern could be observed between the different cell lines since bcl-2, Lck and
Bim were expressed in the two B cell lines PB-1 and 2E8 only, and not in the T cell line Kit 225.
However none of these targets was specifically regulated by IL-7. The reason for this remains
unclear since some of the up-stream molecules such as STAT5 were activated by IL-7 and IL-2
stimulation. Furthermore, 2E8 is the only cell line, in which p38 MAPK was induced after 24h
starvation time and regulated by IL-7.
In this study, the only pathway identified to be induced in all three cell lines by IL-7, is the STAT5
pathway and the associated up-regulation of pim-1. STAT5 seems thus to be the most promising
target for development of a phosphorylation assay, especially due to the fast phosphorylation
reaction. A gene-expression assay using the up-regulation of pim-1 would of course equally be
possible but would take much more time (several hours in contrast to mapprox. 10min incubation
time with IL-7 for the STAT5 phosphorylation assay). In the STAT5/pim-1 pathway, only slight
differences in kinetics could be observed between the different cell lines. In Kit 225 cells, IL-7
induced phosphorylation was much weaker than IL-2 induced phosphorylation, which was in good
agreement with the IL-2R and IL-7R densities observed in the flow cytometric analyses. STAT5
phosphorylation could be inhibited by WP1066 in Kit 225 and 2E8 cells. In contrast, it was not
inhibited in PB-1 cells, showing another difference between the three cell lines.
Table 24 presents an overview, which of the analyzed targets of the IL-7 signaling pathways were
expressed in and activated (up-/down-regulated or phosphorylated) in the different cell lines
investigated.
Table 24. Summary of western blotting results.
Target Expressed in Activated in
Kit 225 PB-1 2E8 Kit 225
by IL-2
Kit 225
by IL-7
PB-1 by
IL-7
2E8 by
IL-7
STAT1 √ √ √ √ - - -
STAT3 √ √ √ √ - - -
STAT5 √ √ √ √ √ √ √
Pim-1 - - - √ √ √ √
Bcl-2 - √ √ - - - -
AKT √ √ √ √ - - -
Lck - √ √ - - - -
ERK √ √ √ √ - - -
Bim - √ √ - - - -
p38 MAPK √ √ √ - - - √
IV. RESULTS AND DISCUSSION
123
It can be concluded, that this study serves as a systematic and thorough study of the most
important signaling pathways of IL-7 in the three cell lines that were investigated. Interesting
differences were observed between different cell lines and between the two c cytokines IL-2 and
IL-7. However, it remains to be clarified why some of the pathways, presumed to be activated by
IL-7, were not induced by IL-7 stimulation. STAT5 has been emphasized to be probably the most
important signaling pathway of IL-7 before (Goetz et al., 2004), and this study confirms this
hypothesis. STAT5 was consequently selected for development of an intracellular phosphorylation
assay, described in the following section.
IV. RESULTS AND DISCUSSION
124
4. DEVELOPMENT OF A STAT5 PHOSPHORYLATION ASSAY AS AN ALTERNATIVE TO THE
CLASSICAL PROLIFERATION ASSAYS
Usually, the parameters used for determination of the biological activity are survival or proliferation
of the cell. However, the disadvantage of these classical “endpoint-assays” is that they require very
long incubation times, up to several days, since they measure the downstream events of a cellular
response. Additionally to the long incubation time, this might lead to a higher variability of the assay
(Sadick et al., 1999).
The colorimetric proliferation assay, which was developed previously for QC of Fc-IL7 at Merck
Serono, takes eight days until finalization. Moreover, it suffers from a poor robustness, since the
dynamic range (OD difference between the highest and the lowest value of the dose-response
curve) is not wide enough. As a consequence, many assays fail the set acceptance criteria
because of too low OD differences. Therefore, there is the need for a faster and more robust assay
format.
Using western blotting, STAT5 was identified as a target of IL-7 signaling in Kit 225 cells (see
section IV.3.) and a STAT5 phosphorylation assay was developed for this target. Detection of EPO-
induced phosphorylation of STAT5 in HEL/EpoR cells has for example already been used by the
SureFire™ assay (Perkin Elmer, Waltham, MA), utilizing the AlphaScreen® (Perkin Elmer)
technology or by a microsphere-based flow cytometry phospho-STAT5 assay using xMAP®
(Luminex® Corp., Austin, TX) technology in high-throughput screening (HTS) applications (Binder
et al., 2008).
The assay, which was developed in this study, is an ELISA based assay, using two different
microtiter-plates, one for cell stimulation and lysis, the other one for the ELISA. The procedure is
mainly based on the “Kinase receptor activation” (KIRA) assay, measuring ligand-induced receptor
tyrosine kinase activation in terms of receptor phosphorylation. This assay was developed by
Sadick et al. (1996, 1997, and 1999) as an alternative to endpoint-assays. It assesses the initial
events of the cellular response, which happen directly after the ligand binds to the receptor. The
KIRA assay is a highly reproducible and fast assay, showing a high correlation to the classical
proliferation assays (Sadick et al., 1999). In contrast to the KIRA assay, the STAT5
phosphorylation assay measures the intracellular signal transduction pathways, which are induced
directly after receptor activation. Moreover, it should also be suitable for use of suspension cells
since the original KIRA assay was developed for adherent cells only. The aim of this study was it
thus to develop a reliable, accurate and fast alternative to the classical proliferation assay for IL-7.
4.1. Assay development and optimization
The final assay was performed as described under Materials and Methods. A schematic diagram is
shown in figure 58.
During assay optimization, different parameters were varied. The experiments were run in
triplicates to confirm the results. A high OD difference between 100ng/mL IL-7 and the control
(0ng/mL IL-7) was used as a quality criterion in order to identify the best conditions.
IV. RESULTS AND DISCUSSION
125
Coating of ELISA plate over
night with capture antibody
(100µL/well, 4°C)
Washing
90min
Blocking with 1% BSA
Washing
1
2
+
Stimulation of starved cells with IL-7
(10 - 15min, 37°C)
Addition of lysis buffer
60min
3
4
Transfer of cell lysate to ELISA
plate (85µL/well)
over night (4°C)
Addition of detection
antibody (100µL/well)
Washing
Incubation5
7
6
60min
Washing
Addition of secondary HRP-
conjugated antibody
(100µL/well)
60min
Addition of TMB
(100µL/well)
Stop reaction with
H2SO4 (100µL/well)
Washing
20min
Measurement of absorption
at 450nm
8
9
10
anti-p-STAT5 capture antibody
Cell lysate containing p-STAT5
anti-STAT5 detection antibody
secondary HRP-conjugated
anti-rabbit antibody
ELISA plate 96 well cell culture plate
Cell lysis
Kit 225 cells IL-7
Figure 58. Schematic diagram of the STAT5 phosphorylation assay. The assay was performed using two separate microtiter plates: the first one for cell stimulation and lysis, the second one for ELISA.
IV. RESULTS AND DISCUSSION
126
During assay optimization, different compositions of the blocking buffer were tested using 0.5%,
1% and 2% BSA. However, the composition did not influence the results induced by 100 ng/mL IL-
7 since mean OD differences of 0.86 (0.5% BSA), 0.93 (1% BSA) and 0.90 (2% BSA) were
observed. Variation of the blocking time (30min steps between 1h and 3h) showed a maximum of
the mean OD difference after 90min incubation time by the 100ng/mL IL-7 positive control (see
figure 59). Thus, the blocking time was fixed to 90min using 1% BSA in DPBS.
Variation of blocking times
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
1,8
1h 1.5h 2h 2.5h 3h
blocking time
OD
valu
es
100ng/mL IL-7
Control
Figure 59. Variation of blocking times. The plate was incubated with blocking buffer (1% BSA in DPBS) for several times between 1h and 3h. The highest OD difference was reached after 1.5h. The shown data are mean values derived from three independent experiments; error bars indicate the mean standard deviations.
Moreover, different cell densities and starvation conditions for Kit 225 cells were tested.
Cell densities of 5x104 cells/well, 1x10
5 cells/well, 2x10
5 cells/well, 5x10
5 cells/well and 1x10
6
cells/well were stimulated with 100ng/mL IL-7 (see figure 60). Mean OD differences increased
strongly from 0.4 (5x104 cells/well) to 1.7 (5x10
5 cells/well) and then remained constant when
higher cell densities were used. The cell density was consequently fixed to 5x105 cells/well
resulting in a higher consumption of cells in comparison to the proliferation assay as the latter
assay needed only 1x105 cells/well.
IV. RESULTS AND DISCUSSION
127
Variation of cell densities
0
0,5
1
1,5
2
2,5
3
5x104cells/well1x105cells/well2x105cells/well5x105cells/well1x106cells/well
cell density
OD
valu
es
100ng/mL IL-7
control
Figure 60. Variation of cell densities. The assay was performed using cell densities of 5x10
4, 1x10
5, 2x10
5, 5x10
5 and 1x10
6 cells/well. Mean OD
differences increased strongly from 0.4 (5x104
cells/well) to 1.7 (5x105 cells/well) and then remained constant
when using higher cell densities. The shown data are mean values derived from three independent experiments; error bars indicate the mean standard deviations.
Cells were starved three to four days in AIM-V Medium or one day only in RPMI 1640 medium
without growth factor. The RPMI medium was supplemented with 10% FBS or without FBS (see
figure 61).
Variation of starvation conditions
0,00
0,20
0,40
0,60
0,80
1,00
1,20
1,40
1 day in RPMI1640
with FBS (5x105
c/well)
1 day in RPMI1640
without FBS (5x105
c/well)
4 days in AIM-V (5x105
c/well)
starvation conditions
OD
valu
es
100ng/mL IL-7
control
Figure 61. Variation of starvation conditions Proir to the assay, cells were starved four days in AIM-V Medium or one day only in RPMI 1640 medium without growth factor. The RPMI medium was supplemented with 10% FBS or without FBS. The best starvation condition was one day in RPMI supplemented with 10% FBS. The shown data are mean values derived from three independent experiments; error bars indicate the mean standard deviations.
IV. RESULTS AND DISCUSSION
128
Results after stimulation with 100ng/mL IL-7 indicated that the best starvation condition was one
day in RPMI supplemented with 10% FBS. A mean OD difference of 0.8 could be reached; in
contrast cells starved for both three days in AIM-V or for one day in RPMI 1640 without FBS
resulted in mean OD differences of only 0.5.
One possible reason for the lower OD difference of cells starved in RPMI without FBS in
comparison to the cells cultivated in medium supplemented with FBS might be the lower viability of
the cells which was only about 65 - 70% in contrast to at least 90% of the cells cultivated with FBS.
As a consequence, Kit 225 cells were starved 1 day without growth factor in RPMI 1640 + 10%
FBS in a density of 5x105 cells/mL.
Modifications were also necessary in order to generate crude lysates. Kit 225 cells are suspension
cells. In contrast to an assay using adherent cells, cells need to be collected by centrifugation first
and lysis of the cells has to take place in solution whereas for adherent cells, medium can be
removed by aspiraton and the lysis buffer can be added directly to the cell layer. Accordingly, some
modifications of the assay procedure were necessary such as direct addition of 20µL of 5 fold
concentrated lysis buffer (without centrifugation) to each well of the 96 well plate.
Furthermore, lysis time, incubation time with cell lysate and incubation time with detection antibody
were optimized.
Variation of the lysis time using incubation times of 15min, 30min, 60min and 90min, showed a
clear maximum in the OD difference after 60min stimulation with 100ng/mL IL-7 (see figure 62).
The OD difference increased up to 60min and decreased after longer incubation times. Thus, the
lysis time was fixed to 60min.
Variation of lysis times
0
0,2
0,4
0,6
0,8
1
1,2
1,4
15min 30min 60min 90min
incubation time
OD
valu
es
100ng/mL IL-7
Control
Figure 62. Variation of lysis times. The plate was incubated with 5 fold concentrated lysis buffer for 15min, 30min, 60min and 90min. The highest OD difference was reached after 60min. The shown data are mean values derived from three independent experiments; error bars indicate the mean standard deviations.
IV. RESULTS AND DISCUSSION
129
The incubation time with cell lysate - from cells that had been stimulated with 100 ng/mL IL-7 or
used unstimulated - was varied using incubation times of 1h, 2h and 3h (see figure 63). Since the
highest OD difference could be reached after 1h incubation time (0.98 in contrast to 0.75 after 2h
and 0.65 after 3h), the incubation time was shortened from 2h to 1h.
Variation of incubation times with cell lysate
0,00
0,20
0,40
0,60
0,80
1,00
1,20
1,40
1,60
1,80
1h 2h 3h
incubation time
OD
valu
es
100ng/mL IL-7
Control
Figure 63. Variation of incubation times with cell lysate. The plate was incubated with cell lysate for 1h, 2h and 3h. The highest OD difference was reached after 1h. The shown data are mean values derived from three independent experiments; error bars indicate the mean standard deviations.
The plate was incubated with anti-STAT5 detection antibody over night at 4°C. Shorter incubation
times with detection antibody were equally tested in order to see whether it was possible to
complete the assay on the same day. This was feasible; however, it implicated lower OD
differences. After 2h and 5h incubation time at RT, OD differences of 0.5 and 0.6 were measured,
respectively; in contrast after incubation over night at 4°C, a mean OD difference of 0.9 could be
observed (see figure 64). It is thus more advantageous to incubate the plate with detection
antibody over night.
IV. RESULTS AND DISCUSSION
130
Variation of incubation times with detection antibody
0
0,2
0,4
0,6
0,8
1
1,2
1,4
2h at RT 5h at RT over night (ca. 16-18h)
at 4°C
incubation time
OD
valu
es
100ng/mL IL-7
Control
Figure 64. Variation of incubation times with anti-STAT5 detection antibody. The plate was incubated with anti-STAT5 detection antibody for 2h (RT), 5h (RT) or over night (4°C). The highest OD difference was reached upon incubation over night. The shown data are mean values derived from three independent experiments; error bars indicate the mean standard deviations.
Another important step was the optimization of the secondary HRP conjugated antibody. The
choice of the secondary antibody had a major influence on the assay results: Thus OD differences
between 0.12 and 0.62 were obtained dependent on the source of the secondary HRP conjugated
antibody (see figure 65).
Variation of secondary HRP conjugated antibodies
0,00
0,20
0,40
0,60
0,80
1,00
1,20
sec. Ab from Kit (CellSignaling, 1:
1000)
sec. Ab (Promega, 1: 2500)
secondary antibody source
OD
valu
es
100ng/mL IL-7
control
Figure 65. Variation of secondary HRP conjugated antibodies. The assay was performed using a secondary antibody from two different sources. By replacing the antibody, an approx. 4-5 fold increase in the OD difference could be noticed. The shown data are mean values derived from three independent experiments; error bars indicate the mean standard deviations.
IV. RESULTS AND DISCUSSION
131
Higher concentration of the chosen secondary antibody (1:1,000 instead of 1:2,500) did not lead to
a further increase of the OD difference since both, OD values of the sample and the control rose in
a similar way.
The incubation time with secondary HRP conjugated antibody was also varied (see figure 66). OD
differences increased from 30min (0.6) to 45min (0.8) and reached a plateau after 60min (1.1)
(75min: 1.0). Thus, the incubation time was set to 60min.
Variation of incubation times with secondary antibody
0
0,5
1
1,5
2
2,5
30min 45min 60min 75min
incubation time
OD
valu
es
100ng/mL IL-7
control
Figure 66. Variation of incubation times with secondary HRP conjugated antibody.
The plate was incubated with secondary HRP conjugated antibody for 30min, 45min, 60min and 75min. The highest OD difference was reached after 60min. The shown data are mean values derived from three independent experiments; error bars indicate the mean standard deviations.
Variation of the incubation time with TMB showed mean OD differences of 0.6 (5min), 0.8 (10min),
0.9 (15min), 1.0 (20min) and 1.0 (25min) (see figure 67). As a consequence, slightly longer
incubation times with TMB of about 20min seem to be more advantageous.
IV. RESULTS AND DISCUSSION
132
Variation of incubation times with TMB
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
1,8
5min 10min 15min 20min 25min
incubation time
OD
valu
es
100ng/mL IL-7
control
Figure 67. Variation of incubation times with TMB.
The plate was incubated with TMB for various times between 5min and 25min. The highest OD difference was reached after 20min. The shown data are mean values derived from three independent experiments; error bars indicate the mean standard deviations.
At the end of assay optimization, the appropriate concentration range of Fc-IL7 was determined.
The concentration range was adapted to 0.4 – 100ng/mL; a representative curve is shown in figure
68.
0
0,5
1
1,5
2
2,5
0,1 1 10 100 1000
y
x
Standard Curve
Concentration [ng/mL]
OD
valu
es
at490nm
Figure 68. Concentration curve of the STAT5 phosphorylation assay.
The concentration range of the STAT5 phosphorylation assay was evaluated to be 0.4 – 100ng/mL cytokine fusion protein containing IL-7. The resulting, representative curve is shown in figure 68. Green points indicate the individual results, red points the mean values of the triplicates.
IV. RESULTS AND DISCUSSION
133
4.2. Assay qualification
After assay development and optimization, the assay was qualified with respect to accuracy and
precision. For this purpose, three repetitions were performed on three different sample
concentrations of 50%, 100% and 150% in comparison to the 100% 100 ng/mL IL-7 standard (n =
9). The assays were performed independently on different days.
Qualification results showed a good recovery with a mean value of 101.9% and a low mean RE of
7.1%. The precision of the assay was with a mean CV of 8.9% high. An overall summary of the
qualification results is shown in table 25.
Table 25. Results of assay qualification.
Sample
concentration
Individual
potency
results
Mean Recovery
[%] RE [%] SD [%] CV [%]
50%
(rel.potency: 0.5)
(n = 3)
0.586
0.442
0.482
0.503 100.7 10.8 7.4 14.8
100%
(rel.potency: 1.0)
(n = 3)
0.998
1.066
1.028
1.031 103.1 3.2 3.4 3.3
150%
(rel. potency: 1,5)
(n = 3)
1.591
1.618
1.374
1.528 101.8 7.4 13.4 8.8
Overall summary
(n = 9) - 1.019 101.9 7.1 8.1 8.9
Results at the low and the high concentration level (50% and 150%) showed a bit larger deviations
from the expected concentration. Thus an RE of 10.8% and a CV of 14.8% were assessed for the
50% concentration level. For the 150% concentration level, an RE of 7.4% and a CV of 8.8% were
calculated. In contrast, results of the 100% concentration level were more accurate and precise,
showing a mean RE of 3.2% and a mean CV of 3.4%.
Nevertheless, REs and CVs for all concentration levels were all in the acceptable range of ± 30%,
which is usually applied for bioassays.
It can be concluded, that the STAT5 phosphorylation assay is a very accurate and precise assay,
being suitable for use in quality control. It can serve as alternative to the classical proliferation
assay.
IV. RESULTS AND DISCUSSION
134
4.3. Comparison of the STAT5 phosphorylation assay to the proliferation assay
By comparing the STAT5 phosphorylation assay to the originally used colorimetric proliferation
assay, it was observed, that more cells were needed for the phosphorylation assay: 5x105
cells/well versus 1x105 cells/well for the proliferation assay. This is due to the lower sensitivity of
the phosphorylation assay in comparison to the end-point assay. Similar observations have already
been made for the KIRA assay. Sadick et al. (1999) estimated this assay to be about ten times less
sensitive than end-point assays. The higher sensitivity of the end-point assay was most likely
caused by the signal amplification provided by the kinase cascade. The lower sensitivity of the
STAT5 phosphorylation assay in comparison to the proliferation assay might be explained by the
same fact since the STAT5 phosphorylation is one of the first steps in the kinase cascade.
The higher number of cells needed for the phosphorylation assay leads to higher costs for the cell
culture. Moreover, the costs of the reagents needed for the phosphorylation assay, especially for
the antibodies, are much higher than for the colorimetric proliferation assay.
The advantage of the phosphorylation assay is that it is completed much faster than the
proliferation assay. The proliferation assay needs three days starvation of the cells in AIM-V
medium, one day to perform the assay plus four days incubation time with API. It takes thus eight
days to complete the assay. In contrast, for the phosphorylation assay, cells are seeded and
starved and the ELISA plate is coated on the first day, and the assay is performed on the second
day. It can either be finalized on the same day – implying however, lower OD differences, which
might lead to a lower precision and robustness of the assay – or be completed on the third day. It
can be concluded that – even if a reduction of the starvation time from three days to one day would
be equally possible for the proliferation assay – the assay procedure time is at least reduced from
six to three days by using the phosphorylation instead of the proliferation assay.
Another benefit of the phosphorylation assay is its higher robustness. The proliferation assay
showed sometimes very low OD differences below 0.5. As a consequence, many assays failed the
acceptance criteria, because the OD differences were too low. This is not the case for the
phosphorylation assay showing usually OD differences above 0.7, even up to 1.5. The problem of
the low robustness can thus be resolved by replacing the proliferation by the phosphorylation
assay.
4.4. Conclusions
As an alternative to classical proliferation assays, a rapid phosphorylation assay was developed,
measuring IL-7 induced STAT5 phosphorylation in Kit 225 cells. The assay is very accurate and
precise with a mean RE of 7.1% and a mean CV of 8.9%. Consequently, this assay serves as a
good and reliable alternative to classical end-point assays and is well-suited for use in QC of
biopharmaceuticals.
Despite of the higher costs of this assay - needed for cell culture and reagents - it has the
advantage to be much faster and more robust than the proliferation assay. The total time of the
assay could be reduced at least from six to three days. The OD differences obtained for the
IV. RESULTS AND DISCUSSION
135
negative and positive control increased from values below 0.5 for the proliferation assay to values
above 0.7, and even up to 1.5, for the STAT5 phosphorylation assay. In addition, the signal to
noise ratios rose from values around 1.5 – 1.8 for the proliferation assay to values about 2.3 – 3.3
for the STAT5 phosphorylation assay. Signal to noise ratios above 2.0 are recommended for
bioassays.
The assay has been developed for a cytokine fusion protein containing IL-7 but could also be used
for other ligands inducing the JAK/STAT pathway and thus STAT5 phosphorylation, leading to a
more universal application of the assay. Moreover, it should be possible to further expand the
applicability of the assay by replacing STAT5 by another intracellular target, e.g. of the PI3K/AKT
or the Ras/Raf/ERK pathway. As a consequence, the assay format could be used for many
different classes of biopharmaceuticals.
V. OVERALL CONCLUSIONS AND OUTLOOK
136
VV.. OOVVEERRAALLLL CCOONNCCLLUUSSIIOONNSS AANNDD OOUUTTLLOOOOKK
Biopharmaceuticals such as antibodies or cytokine fusion proteins are of growing interest in anti-
cancer therapy. In order to ensure consumer and patient safety, the quality of these products
needs to be controlled according to national and international guidelines. The major factors under
which the product is characterized are its purity, stability, safety and potency. The method of choice
for QC of biological products with regards to their potency is the use of biological assays, making
them to a very important instrument in pharmaceutical industry without which the development of
novel pharmaceuticals would not be possible.
The aim of this study was to optimize existing methods for biological assays and to implement
alternative assay formats. The methods were characterized according their sensitivity, accuracy,
precision, linearity and reproducibility.
First of all, analysis methods and read-out systems for proliferation assays, usually applied for QC
of biopharmaceuticals, were compared. The aim was it to find the best analysis model and the
most appropriate read-out system beyond the methods investigated.
Regarding the comparison of the analysis models, the parallel line model, which was used for QC
purposes at Merck Serono before, was opposed to the logistic models, four- and five parameter fit.
The results of this comparison revealed a slightly better accuracy and precision of the logistic
models in comparison to the PL model; however, differences were not significant. On the other
side, the logistic models showed a significantly higher failure rate, including all assays with failed
tests of linearity or parallelism according to preset acceptance criteria. As a consequence, no clear
recommendation for the appropriate analysis model can be given. All of the three models are suited
well for analysis of potency assays. The choice of the model has to be made individually and
depends on the performed type of assay set up. It can not be concluded, which of the models will
be the most commonly applied model in future. Besides, it has to be remembered that there are
other models like the slope ratio assay or comparison of EC50 values, which might equally be used
instead of the models tested in this study, even if the PL, 4PL and 5PL models are the mainly used
calculation models to date.
Regarding the comparison of read-out systems, it was tested in this study, whether luminescence
or fluorescence systems could be of advantage in comparison to the colorimetric system, originally
in use for detection of proliferation assays at Merck Serono. According to the results of this study,
both, the luminescence and fluorescence system, that is CellTiter- Glo®
and Alamar BlueTM
, were
much more sensitive in comparison to the colorimetric MTS/PMS assay. The signal to noise ratios,
which are a surrogate parameter for the robustness of an assay, were the lowest for the
colorimetric system and the highest for the luminescence technique. In comparison to the Alamar
Blue assay, which needs several hours to be completed, the ATP assay has the advantage to be
much faster since the plate can already be measured 5 - 10min after addition of substrate. These
factors make the luminescence read-out the most promising of the three methods investigated,
even if all three methods are suited well for use in QC, since they produce reliable and reproducible
results. The detriment of the little higher costs of the CellTiter- Glo®
reagent in comparison to the
V. OVERALL CONCLUSIONS AND OUTLOOK
137
MTS/PMS reagent should easily be balanced by the higher sensitivity, robustness and rapidness of
this assay. As a consequence, it would be of advantage to use the luminescence read-out for
development of future assays and to probably even replace the MTS/PMS reagent by CellTiter-
Glo® for assays already in place.
As an alternative to the commonly used proliferation assays, an intracellular phosphorylation assay
was developed for Fc-IL7, since the originally used proliferation assay suffered from a very long
incubation time and a poor robustness. In order to find a target for development of the
phosphorylation assay, the IL-7 signaling pathways were screened by western blotting. A
thourough study was done since relatively little was known about this cytokine, making it to an
interesting target for further explorations. Consequently, the IL-7 signaling pathways were
compared in three different cell lines (PB-1, 2E8 and Kit 225). In addition, the IL-7 pathways were
opposed to the IL-2 pathways. The results of the study revealed interesting differences between
the different cell lines, especially in the expression pattern of several proteins. Regarding the
comparison of the two γc cytokines, some pathways were initiated by IL-2 only, and not by IL-7.
The only pathway identified in this study to be induced in all three cell lines by IL-7, was the STAT5
pathway and the associated induction of pim-1. STAT5 has been emphasized to be probably the
most important signaling molecule of IL-7 before (Goetz et al., 2004), and this study confirms this
hypothesis. Nevertheless, other signaling pathways need to be involved in IL-7 signaling, especially
because the activation of STAT5 is supposed not to induce proliferation of cells (Isaksen et al.,
2002). But which other pathways are these? At least in this study and in the three cell lines
investigated, no other pathway could be identified. The IL-7 signaling pathways are thus not
completely elucidated and are worth to be studied further.
According to the western blotting analyses, STAT5 was identified as the most appropriate target for
development of a phosphorylation assay. The developed STAT5 phosphorylation assay was very
accurate and precise. It can serve as a good and reliable alternative to classical end-point assays
and is well-suited for use in QC of biopharmaceuticals. The main advantage of this assay are its
rapidness - reducing the total time of the assay from six to three days – and the higher robustness
in comparison to the proliferation assay. The applicability of the assay might be expanded by
replacing STAT5 by other intracellular targets making the assay format suitable for QC of many
different classes of biopharmaceuticals. The development of such assays might be of interest for
new projects. However, the costs of the phosphorylation assay are much higher in comparison to
the proliferation assay, making it difficult to implement those assays for routine use. Nevertheless,
the STAT5 phosphorylation assay is an interesting alternative assay format. In addition to the
phosphorylation assays, other innovative assay formats like reporter gene assays might be
interesting to be developed and to be compared to the assays already in place in order to
implement new technologies and to benefit of the possible advantages.
138
LIST OF FIGURES
Figure 1. 3D structure of the IL-7 receptor. ................................................................................. 22
Figure 2. Illustration of the most important IL-7 signaling pathways. ...................................... 24
Figure 3. Overview of assay formats. ........................................................................................... 35
Figure 4. Graphical presentation of the parallel line model. ...................................................... 36
Figure 5. Graphical presentation of the four-parameter fit. ....................................................... 37
Figure 6. Reaction equation of the colorimetric read-out. ......................................................... 39
Figure 7. Reaction equation of the fluorescence read-out. ....................................................... 40
Figure 8. Reaction equation of the luminescence read-out. ...................................................... 40
Figure 9. Microscopic images of the cell lines, used in this study. .......................................... 43
Figure 10. General plate layout for potency assays. .................................................................. 52
Figure 11. Concentration ranges of the CTLL-2 potency assay. ............................................... 59
Figure 12. Plot of the pooled standard deviations (n = 18) vs. the measured mean responses
for each concentration point. ........................................................................................................ 61
Figure 13. Plot of the pooled standard deviations (n = 18) vs. the dose squared for each
concentration point. ....................................................................................................................... 62
Figure 14. Individual, representative graphs (calculated by PLA) of a CTLL-2 potency assay.
.......................................................................................................................................................... 63
Figure 15. Results CTLL-2 potency assay: expected activity 50%. .......................................... 64
Figure 16. Results CTLL-2 potency assay: expected activity 100%. ........................................ 65
Figure 17. Results CTLL-2 potency assay: expected activity 150%. ........................................ 66
Figure 18. Concentration ranges of the DiFi potency assay. .................................................... 70
Figure 19. Plot of the pooled standard deviations (n = 18) vs. the measured mean responses
for each concentration point. ........................................................................................................ 71
Figure 20. Plot of the pooled standard deviations (n = 18) vs. the dose squared for each
concentration point. ....................................................................................................................... 72
Figure 21. Individual, representative graphs (calculated by PLA) of a DiFi potency assay. .. 73
Figure 22. Results DiFi potency assay: expected activity 50%. ................................................ 74
Figure 23. Results DiFi potency assay: expected activity 100%. .............................................. 75
Figure 24. Results DiFi potency assay: expected activity 150%. .............................................. 76
Figure 25. % REs [residuals/ measured response*100] of the 4PL and 5PL models for each
concentration point. ....................................................................................................................... 81
Figure 26. Illustration of the signal to noise ratio of a representative dose response curve. 85
Figure 27. Concentration curve of a CTLL-2 potency assay, detected by colorimetric read-
out. ................................................................................................................................................... 86
Figure 28. Optimized curve of a CTLL-2 potency assay, detected by colorimetric read-out. 86
Figure 29. Concentration curve of a CTLL-2 potency assay, detected by luminescence read-
out. ................................................................................................................................................... 87
Figure 30. Optimized curve of a CTLL-2 potency assay, detected by luminescence read-out.
.......................................................................................................................................................... 87
139
Figure 31. Optimized curve of a CTLL-2 potency assay, detected by fluorescence read-out.
.......................................................................................................................................................... 88
Figure 32. Concentration curve of a DiFi potency assay, detected by colorimetric read-out.91
Figure 33. Concentration curve of a DiFi potency assay, detected by luminescence read-out.
.......................................................................................................................................................... 91
Figure 34. Concentration curve of a DiFi potency assay, detected by luminescence read-out.
.......................................................................................................................................................... 92
Figure 35. Concentration curve of a Kit 225 potency assay, detected by colorimetric read-
out. ................................................................................................................................................... 95
Figure 36. Optimized curve of a Kit 225 potency assay, detected by colorimetric read-out. . 95
Figure 37. Concentration curve of a Kit 225 potency assay, detected by luminescence read-
out. ................................................................................................................................................... 96
Figure 38. Optimized curve of a Kit 225 potency assay, detected by luminescence read-out.
.......................................................................................................................................................... 96
Figure 39. Flow cytometric analysis of IL-7Rα densities on A) Kit 225, B) PB-1, and C) 2E8
cells. ............................................................................................................................................... 103
Figure 40. Flow cytometric analysis of IL-2Rα densities on A) Kit 225, B) PB-1, and C) 2E8
cells. ............................................................................................................................................... 104
Figure 41. Flow cytometric analysis of IL-2Rβ densities on A) Kit 225, B) PB-1, and C) 2E8
cells. ............................................................................................................................................... 105
Figure 42. Western blotting analysis of STAT1 and phospho (p)-STAT1 in Kit 225, PB-1 and
2E8 cells. ....................................................................................................................................... 106
Figure 43. Western blotting analysis of STAT3 and phospho-STAT3 in Kit 225, PB-1 and 2E8
cells. ............................................................................................................................................... 107
Figure 44. Western blotting analysis of STAT5 and phospho-STAT5 in Kit 225, PB-1 and 2E8
cells. ............................................................................................................................................... 108
Figure 45. Kinetic measurements of STAT5 phosphorylation. ................................................ 109
Figure 46. Inhibition of STAT5 phosphorylation by WP1066. .................................................. 110
Figure 47. Western blotting analysis of pim-1 in Kit 225, PB-1 and 2E8 cells. ...................... 111
Figure 48. Kinetic measurements of pim-1 up-regulation. ....................................................... 112
Figure 49. Western blotting analysis of bcl-2 in Kit 225, PB-1 and 2E8 cells. ....................... 113
Figure 50. Western blotting analysis of AKT and phosphor-AKT in Kit 225, PB-1 and 2E8
cells. ............................................................................................................................................... 114
Figure 51. Kinetic measurements of AKT phosphorylation. .................................................... 115
Figure 52. Western blotting analysis of ERK and phospho-ERK in Kit 225, PB-1 and 2E8
cells. ............................................................................................................................................... 116
Figure 53. Kinetic measurements of ERK phosphorylation. ................................................... 116
Figure 54. Western blotting analysis of Bim and phospho-Bim in Kit 225, PB-1 and 2E8 cells.
........................................................................................................................................................ 118
140
Figure 55. Western blotting analysis of Lck and phospho-Lck in Kit 225, PB-1 and 2E8 cells.
........................................................................................................................................................ 119
Figure 56. Western blotting analysis of p38 MAPK and phospho-p38 MAPK in Kit 225, PB-1
and 2E8 cells. ................................................................................................................................ 120
Figure 57. Kinetic measurements of p38 MAPK phosphorylation. ......................................... 121
Figure 58. Schematic diagram of the STAT5 phosphorylation assay..................................... 125
Figure 59. Variation of blocking times. ...................................................................................... 126
Figure 60. Variation of cell densities. ......................................................................................... 127
Figure 61. Variation of starvation conditions ............................................................................ 127
Figure 62. Variation of lysis times. ............................................................................................. 128
Figure 63. Variation of incubation times with cell lysate. ........................................................ 129
Figure 64. Variation of incubation times with anti-STAT5 detection antibody. ..................... 130
Figure 65. Variation of secondary HRP conjugated antibodies. ............................................. 130
Figure 66. Variation of incubation times with secondary HRP conjugated antibody. .......... 131
Figure 67. Variation of incubation times with TMB. .................................................................. 132
Figure 68. Concentration curve of the STAT5 phosphorylation assay. ................................. 132
141
LIST OF TABLES
Table 1. Survey of cytokine families. ........................................................................................... 19
Table 2. Features of cytokines. ..................................................................................................... 20
Table 3. Results CTLL-2 potency assay: expected activity 50% (i.e. relative potency: 0.5). .. 64
Table 4. Results CTLL-2 potency assay: expected activity 100% (i.e. relative potency: 1.0). 65
Table 5. Results CTLL-2 potency assay: expected activity 150% (i.e. relative potency: 1.5). 66
Table 6. Summary of CTLL-2 assay results. ................................................................................ 68
Table 7. Results DiFi potency assay: expected activity 50% (i.e. relative potency: 0.5). ....... 74
Table 8. Results DiFi potency assay: expected activity 100% (i.e. relative potency: 1.0). ..... 75
Table 9. Results DiFi potency assay: expected activity 150% (i.e. relative potency: 1.5). ..... 76
Table 10. Summary of DiFi assay results. ................................................................................... 78
Table 11. Summary and comparison between the different models......................................... 83
Table 12. Results qualification CTLL-2 assay: expected activity 50% (i.e. relative potency:
0.5). .................................................................................................................................................. 89
Table 13. Results qualification CTLL-2 assay: expected activity 100% (i.e. relative potency:
1.0). .................................................................................................................................................. 89
Table 14. Results qualification CTLL-2 assay: expected activity 150% (i.e. relative potency:
1.5). .................................................................................................................................................. 89
Table 15. Summary of CTLL-2 assay parameters and comparison between the different read-
out systems. .................................................................................................................................... 90
Table 16. Results qualification DiFi assay: expected activity 50% (i.e. relative potency: 0.5).
.......................................................................................................................................................... 93
Table 17. Results qualification DiFi assay: expected activity 100% (i.e. relative potency: 1.0).
.......................................................................................................................................................... 93
Table 18. Results qualification DiFi assay: expected activity 150% (i.e. relative potency: 1.5).
.......................................................................................................................................................... 93
Table 19. Summary of DiFi assay parameters and comparison between the different read-out
systems. .......................................................................................................................................... 94
Table 20. Results qualification Kit 225 assay: expected activity 50% (i.e. relative potency:
0.5). .................................................................................................................................................. 97
Table 21. Results qualification Kit 225 assay: expected activity 100% (i.e. relative potency:
1.0). .................................................................................................................................................. 97
Table 22. Results qualification Kit 225 assay: expected activity 150% (i.e. relative potency:
1.5). .................................................................................................................................................. 98
Table 23. Summary of Kit 225 assay parameters and comparison between the different read-
out systems. .................................................................................................................................... 99
Table 24. Summary of western blotting results. ....................................................................... 122
Table 25. Results of assay qualification. ................................................................................... 133
142
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156
APPENDIX:
STATISTICAL ANALYSIS OF BIOASSAY RESULTS: A COMPARISON BETWEEN DIFFERENT
CALCULATION MODELS
Author: Enrico Tucci
Researcher
National Institute of Statistics
Viale Liegi, 13 - 00198 Rome, Italy
Email: [email protected]
Tel.: +39 06 8522 7342
Cell.: +39 328 7518313
Statistical consultant
M&D - Management and Development srl
Via Andrea Del Castagno, 60 - 00142 Roma, Italy
Email: [email protected]
Tel +39 06 5911461
Fax: +39 06 5910274
157
1. Objective
The aim of this report is to analyze the impact on the performance of the bioassay results obtained
in the department Analytical Development Biotech Products at Merck Serono in Darmstadt at:
different calculation Models
different Concentration levels
different Ranges.
Furthermore, this document verifies whether the difference obtained in the failure rates using
different calculation models is significant or not.
2. Definitions
Dependent variable: the observed variable in an experiment or study whose changes are
determined by the presence or degree of one or more independent variables.
Independent variable: the manipulated variable in an experiment or study whose presence or
degree determines the change in the dependent variable.
Stratification variable: the factor that can be used to separate data into subgroups in order to
analyse them separately and have separate results.
Results of Analyses: the dependent variable whose determinations are the bioassay results.
Concentration: the independent variable whose determinations are “0.5”, “1.0” and “1.5”.
Recovery: the dependent variable obtained by the ratio between the Results of Analyses and
the Concentration level.
Results of AnalysesRecovery =
Concentration Equation 1
Assay: the stratification variable whose determinations are “1” and “2”
Model: the independent variable whose determinations are “PL”, “4PL” and “5PL”.
Range: the independent variable whose determinations are “5-30ng/mL”, “0.75-100ng/mL”,
“6.8-80ng/mL” for the CTLL-2 potency assay and “2-12nM”, “0.25-95nM”, “5-55nM” for The DiFi
potency assay.
Plate: the stratification variable whose determinations are “1”, “2”, “3”, “4”, “5” and “6”.
3. Statistical Method
Since data refer to different concentration levels (0.5, 1.0 and 1.5), the statistical evaluation will be
performed using the recovery instead of the simple bioassay result.
In order to analyze the differences between the bioassay results at different levels of the
independent variables (calculation model, concentration level and range), an ANalysis Of VAriance
(ANOVA) has been performed. The aim of the ANOVA is, in fact, to test whether the averages of
the bioassay results obtained at different determinations of the independent variable are
statistically different or not. In other words, the One-Way ANOVA procedure is designed to
construct a statistical model describing the impact of a single categorical factor (for example,
158
Model) on a dependent variable (Recovery). Tests are run to determine whether or not there are
significant differences between the means.
In the Anova:
Residuals are assumed to be normally distributed
Variance is assumed approximately constant for all factor levels.
Moderate departures from normality of the residuals are of little concern. It is useful to check the
residuals, though, because they are an opportunity to learn more about the data. In order to check
if the residuals can be adequately modelled by a normal distribution the Kolmogorov-Smirnov Test
has been used.
Variance Check is used in the One-Way ANOVA procedures to test the hypothesis that the
standard deviations within each group are equal. If the observations can be modelled by a normal
distribution, the Cochran‟s Test is more reliable. Otherwise the Levene‟s Test will be used.
Cochran‟s Test: compares the maximum within-sample variance to the average within-sample
variance. The test is appropriate only if all group sizes are equal.
Levene‟s Test: performs a one-way analysis of variance on the absolute differences between each
observation and its corresponding group mean.
A P-value<0.05 indicates a significant difference between the standard deviations.
4. Statistical evaluation
4.1. Results of the analyses by Model
4.1.1. CTLL-2 potency assay
The aim of this section is to analyze if the results obtained with different Model (PL 4PL, and 5PL)
are significant different or in other words, to determine whether or not the Model (PL, 4PL and 5PL)
has an impact on the Recovery for the CTLL-2 potency assay.
The ANOVA is used to analyze the relationships between different Models (PL 4PL, and 5PL) and
the Recovery obtained.
The Kolmogorov-Smirnov Test is a goodness of fit tests for residual. It determines whether residual
can be adequately modelled by a normal distribution.
Kolmogorov-Smirnov Test
Normal
DPLUS 0.12526
DMINUS 0.0563622
DN 0.12526
P-Value 0.0741446
Since the smallest P-value amongst the tests performed is greater than 0.05, we can not reject the
idea that Residual comes from a normal distribution with 95% confidence.
159
Histogram for residuals
-0,33 -0,13 0,07 0,27 0,47
Residual
0
10
20
30
40fr
eq
ue
ncy
Distribution
Normal
The statistic displayed in this table tests the null hypothesis that the standard deviations of
Recovery within each of the 3 levels of Model (PL 4PL, and 5PL) is the same. Of particular interest
is the P-value. Since the P-value is greater than 0.05, there is not a statistically significant
difference amongst the standard deviations at the 95.0% confidence level.
Variance Check
Test P-Value
Cochran's C 0.476798 0.0538804
The ANOVA table decomposes the variance of Recovery into two components: a between-group
component and a within-group component. The F-ratio, which in this case equals 0.18815, is a
ratio of the between-group estimate to the within-group estimate. Since the P-value of the F-test is
greater than 0.05, there is not a statistically significant difference between the mean Recovery from
one level of Model to another at the 95.0% confidence level.
ANOVA Table for Recovery by Model
Source Sum of
Squares
Df Mean Square F-Ratio P-Value
Between groups 0.00747792 2 0.00373896 0.19 0.8288
Within groups 2.02697 102 0.0198722
Total (Corr.) 2.03445 104
160
4PL 5PL PL
Means and 95,0 Percent Confidence Intervals (internal s)
Model
0,97
0,99
1,01
1,03
1,05
1,07
1,09
Re
co
ve
ry
This table shows the mean Recovery for each level of Model. It also shows the standard error of
each mean, which is a measure of its sampling variability. The standard error is formed by dividing
the standard deviation at each level by the square root of the number of observations at that level.
The table also displays an interval around each mean. The intervals currently displayed are 95.0%
confidence intervals for each mean separately. 95.0% of such intervals will contain the true means.
Table of Means for Recovery by Model with 95.0 percent confidence intervals
Stnd. error
Model Count Mean (individual) Lower limit Upper limit
4PL 26 1.02412 0.023186 0.976363 1.07187
5PL 25 1.02284 0.02355 0.974235 1.07144
PL 54 1.04035 0.0216765 0.996874 1.08383
Total 105 1.03216
4PL 5PL PL
Residual Plot for Recovery
-0,44
-0,24
-0,04
0,16
0,36
0,56
res
idu
al
Model
161
The same calculations have also been made for each individual plate (plate 1-6) in order to see
whether significant differences between the different models could be observed when regarding
one individual assay result. These calculations are not shown in detail here, but are summarized in
the summary table for the CTLL-2 potency assay.
4.1.2. DiFi potency assay
The aim of this section is to analyze if the results obtained with Model (PL, 4PL and 5PL) are
significant different or in other words, to determine whether or not the Model (PL, 4PL and 5PL) has
an impact on the Recovery for the DiFi potency assay.
An ANalysis Of VAriance (Anova) is used to analyze the relationships between different Models
(PL, 4PL and 5PL) and the Recovery obtained. It compares the three models in order to see if any
of the model means is statistically different from the others in the DiFi potency assay.
The Kolmogorov-Smirnov Test is a goodness of fit tests for residual. It determines whether residual
can be adequately modelled by a normal distribution.
Kolmogorov-Smirnov Test
Normal
DPLUS 0.135552
DMINUS 0.0583647
DN 0.135552
P-Value 0.0292179
Since the smallest P-value amongst the tests performed is less than 0.05, we can reject the idea
that Residual comes from a normal distribution with 95% confidence.
Histogram for Residual
-0,26 -0,06 0,14 0,34 0,54
Residual
0
10
20
30
40
50
fre
qu
en
cy
Distribution
Normal
The statistic displayed in this table tests the null hypothesis that the standard deviations of
Recovery within each of the 3 levels of Model (PL, 4PL and 5PL) is the same. Of particular interest
162
is the P-value. Since the P-value is greater than 0.05, there is not a statistically significant
difference amongst the standard deviations at the 95.0% confidence level.
Variance Check
Test P-Value
Levene's 2.09923 0.127347
The ANOVA table decomposes the variance of Recovery into two components: a between-group
component and a within-group component. The F-ratio, which in this case equals 0.378565, is a
ratio of the between-group estimate to the within-group estimate. Since the P-value of the F-test is
greater than 0.05, there is not a statistically significant difference between the mean Recovery from
one level of Model to another at the 95.0% confidence level.
ANOVA Table for Recovery by Model
Source Sum of
Squares
Df Mean Square F-
Ratio
P-Value
Between groups 0.0118103 2 0.00590517 0.38 0.6857
Within groups 1.74707 112 0.0155988
Total (Corr.) 1.75888 114
4PL 5PL PL
Means and 95,0 Percent Confidence Intervals (internal s)
Model
0,93
0,95
0,97
0,99
1,01
1,03
Re
co
ve
ry
This table shows the mean Recovery for each level of Model. It also shows the standard error of
each mean, which is a measure of its sampling variability. The standard error is formed by dividing
the standard deviation at each level by the square root of the number of observations at that level.
The table also displays an interval around each mean. The intervals currently displayed are 95.0%
confidence intervals for each mean separately. 95.0% of such intervals will contain the true means.
163
Table of Means for Recovery by Model with 95.0 percent confidence intervals
Stnd. error
Model Count Mean (individual) Lower limit Upper limit
4PL 31 0.993871 0.0173448 0.958448 1.02929
5PL 30 0.991433 0.0182236 0.954162 1.0287
PL 54 0.972444 0.020291 0.931746 1.01314
Total 115 0.983174
4PL 5PL PL
Residual Plot for Recovery
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
res
idu
al
Model
Comparable to the CTLL-2 potency assay, the same calculations have also been made for each
individual plate (plate 1-6) in order to see whether significant differences between the different
models could be observed when regarding one individual assay result. These calculations are not
shown in detail here, but are summarized in the summary table for the DiFi potency assay.
4.1.3. Conclusions
The ANOVA always shows that there is not a significant statistically difference between the means
from one level of Model to another at the 95.0% confidence level, except for the results of the Plate
2 (CTLL-2 potency assay).
As for the Plate 2 (CTLL-2 potency assay), the Variance Check shows that there is a statistically
significant difference amongst the standard deviations at the 95.0% confidence level. This violates
one of the important assumptions underlying the analysis of variance and invalidates the statistical
tests. Therefore the ANOVA can not be performed. In this case, the presence of 1 outside point
suggests using a more robust test which compares medians instead of means (Kruskal-Wallis
Test) and the result is that there is not a statistically significant difference amongst the medians at
the 95.0% confidence level.
164
Summary Table for Model (CTLL-2 potency assay)
Comments
YES NO
Assay 1
Test for normality KS Test P-Value=0.07. The observation can be adequately
modeled by a normal distribution
Variance Check Cochran's P-Value=0.05.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.83. There is not a statistically
significant difference between the means
Assay 1 and Plate 1
Test for normality KS Test P-Value=0.44. The observation can be adequately
modeled by a normal distribution
Variance Check Cochran's P-Value=1.0.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.94. There is not a statistically
significant difference between the means
Assay 1 and Plate 2
Test for normality KS Test P-Value=0.56. The observation can be adequately
modeled by a normal distribution
Variance CheckCochran's and Bartlett's P-Value < 0.05.There is a
statistically significant difference amongst the standard
deviations
Test on Medians : Kruskal-
Wallis Test Kruskal-Wallis Test P-Value=1.0. There is not a statistically
significant difference amongst the medians
Assay 1 and Plate 3
Test for normality KS Test P-Value=0.75. The observation can be adequately
modeled by a normal distribution
Variance Check Cochran's P-Value=0.63.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.88. There is not a statistically
significant difference between the means
Assay 1 and Plate 4
Test for normality KS Test P-Value=0.95. The observation can be adequately
modeled by a normal distribution
Variance Check Cochran's P-Value=0.24.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.73. There is not a statistically
significant difference between the means
Assay 1 and Plate 5
Test for normality KS Test P-Value=0.80. The observation can be adequately
modeled by a normal distribution
Variance Check Cochran's P-Value=0.70.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.97. There is not a statistically
significant difference between the means
Assay 1 and Plate 6
Test for normality KS Test P-Value=0.54. The observation can be adequately
modeled by a normal distribution
Variance Check Cochran's P-Value=0.25.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.76. There is not a statistically
significant difference between the means
P-value > 0.05
Results of the analyses by Model
165
Summary Table for Model (DiFi potency assay)
Comments
YES NO
Assay 2
Test for normality KS Test P-Value=0.02. The observation can't be adequately
modeled by a normal distribution
Variance Check Levene's P-Value=0.13.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.68. There is not a statistically
significant difference between the means
Assay 2 and Plate 1
Test for normality KS Test P-Value=0.35. The observation can be adequately
modeled by a normal distribution
Variance Check Cochran's P-Value=1.0.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.62. There is not a statistically
significant difference between the means
Assay 2 and Plate 2
Test for normality KS Test P-Value=0.91. The observation can be adequately
modeled by a normal distribution
Variance Check Cochran's P-Value=0.15.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.47. There is not a statistically
significant difference between the means
Assay 1 and Plate 3
Test for normality KS Test P-Value=0.51. The observation can be adequately
modeled by a normal distribution
Variance Check Cochran's P-Value=0.58.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.78. There is not a statistically
significant difference between the means
Assay 2 and Plate 4
Test for normality KS Test P-Value=0.60. The observation can be adequately
modeled by a normal distribution
Variance Check Cochran's P-Value=1.0.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.40. There is not a statistically
significant difference between the means
Assay 2 and Plate 5
Test for normality KS Test P-Value=0.28. The observation can be adequately
modeled by a normal distribution
Variance Check Cochran's P-Value=0.16.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.97. There is not a statistically
significant difference between the means
Assay 2 and Plate 6
Test for normality KS Test P-Value=0.65. The observation can be adequately
modeled by a normal distribution
Variance Check Bartlett's P-Value=0.09.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.31. There is not a statistically
significant difference between the meansKS=Kolmogorov-Smirnov
P-value > 0.05
Results of the analyses by Model
166
4.2. Results of the analyses by Concentration
4.2.1. CTLL-2 potency assay
The aim of this section is to analyze if the results obtained with Concentration (0.5, 1.0 and 1.5) are
significant different or in other words, to determine whether or not the Concentration (0.5, 1.0 and
1.5) has an impact on the Recovery for the CTLL-2 potency assay.
An ANalysis Of VAriance (Anova) is used to analyze the relationships between different
Concentration (0.5, 1.0 and 1.5) and the Recovery obtained. It compares the three Concentrations
in order to see if any of the concentration means is statistically different from the others in the
CTLL-2 potency assay.
The Kolmogorov-Smirnov Test is a goodness of fit tests for residual. It determines whether
Residual can be adequately modelled by a normal distribution.
Kolmogorov-Smirnov Test
Normal
DPLUS 0.107995
DMINUS 0.0422386
DN 0.107995
P-Value 0.17278
Since the smallest P-value amongst the tests performed is greater than 0.05, we can not reject the
idea that Residual comes from a normal distribution with 95% confidence.
Histogram for Residual
-0,29 -0,09 0,11 0,31 0,51
Residual
0
10
20
30
40
fre
qu
en
cy
Distribution
Normal
The statistic displayed in this table tests the null hypothesis that the standard deviations of
Recovery within each of the 3 levels of Concentration is the same. Of particular interest is the P-
value. Since the P-value is less than 0.05, there is a statistically significant difference amongst the
standard deviations at the 95.0% confidence level. This violates one of the important assumptions
underlying the analysis of variance and will invalidate the statistical tests.
167
Variance Check
Test P-Value
Cochran's C 0.518319 0.010969
Bartlett's 1.14048 0.00133671
This table shows the mean Recovery for each level of Concentration. It also shows the standard
error of each mean, which is a measure of its sampling variability. The standard error is formed by
dividing the standard deviation at each level by the square root of the number of observations at
that level. The table also displays an interval around each mean. The intervals currently displayed
are 95.0% confidence intervals for each mean separately. 95.0% of such intervals will contain the
true means.
Table of Means for Recovery by Concentration with 95.0 percent confidence intervals
Stnd. error
Concentration Count Mean (individual) Lower limit Upper limit
0.5 35 1.08217 0.0288667 1.02351 1.14084
1 36 1.01075 0.0149833 0.980332 1.04117
1.5 34 1.00335 0.0236528 0.955231 1.05147
Total 105 1.03216
0,5
1
1,5
Box-and-Whisker Plot
0,75 0,95 1,15 1,35 1,55
Recovery
Co
nce
ntr
ati
on
This graph shows 3 box-and-whisker plots, one for each level of Concentration. The rectangular
part of the plot extends from the lower quartile to the upper quartile covering the center half of each
sample. The center lines within each box show the location of the sample medians. The plus signs
indicate the location of the sample means. The whiskers extend from the box to the minimum and
maximum values in each sample except for any outside or far outside points, which will be plotted
separately. Outside points are points which lie more than 1.5 times the interquartile range above or
below the box and are shown as small squares. Far outside points are points which lie more than
168
3.0 times the interquartile range above or below the box and are shown as small squares with plus
signs through them. In this case, there are 8 outside points. This might suggest the presence of
outliers.
Therefore, it is strongly recommended when the result of a test is so sensitive to outliers the use of
the less sensitive test. In this case a test on the medians has been chosen (Kruskal-Wallis Test)
which compares medians instead of means.
Kruskal-Wallis Test for Recovery by Concentration
Concentration Sample Size Average Rank
0.5 35 61.1429
1 36 51.5417
1.5 34 46.1618
Test statistic = 4.2991 P-Value = 0.116537
The Kruskal-Wallis test tests the null hypothesis that the medians of Recovery within each of the 3
levels of Concentration are the same. The data from all the levels is first combined and ranked from
smallest to largest. The average rank is then computed for the data at each level. Since the P-
value is greater than 0.05, there is not a statistically significant difference amongst the medians at
the 95.0% confidence level.
4.2.2. DiFi potency assay
The aim of this section is to analyze if the results obtained with Concentration (0.5. 1.0 and 1.5) are
significant different or in other words to determine whether or not the Concentration (0.5. 1.0 and
1.5) has an impact on the Recovery for the DiFi potency assay.
An ANalysis Of VAriance (Anova) is used to analyze the relationships between different
Concentration (0.5. 1.0 and 1.5) and the Recovery obtained. It compares the three Concentrations
in order to see if any of the concentration means is statistically different from the others in the DiFi
potency assay.
The Kolmogorov-Smirnov Test is a goodness of fit tests for residual. It determines whether
Residual can be adequately modelled by a normal distribution.
Kolmogorov-Smirnov Test
Normal
DPLUS 0.102855
DMINUS 0.0514361
DN 0.102855
P-Value 0.175569
Since the smallest P-value amongst the tests performed is greater than 0.05, we can not reject the
idea that Residual comes from a normal distribution with 95% confidence.
169
Histogram for Residual
-0,26 -0,06 0,14 0,34 0,54
Residual
0
10
20
30
40
50
fre
qu
en
cy
Distribution
Normal
The statistic displayed in this table tests the null hypothesis that the standard deviations of
Recovery within each of the 3 levels of Concentration is the same. Of particular interest is the P-
value. Since the P-value is less than 0.05, there is a statistically significant difference amongst the
standard deviations at the 95.0% confidence level. This violates one of the important assumptions
underlying the analysis of variance and will invalidate the statistical tests.
Variance Check
Test P-Value
Cochran's C 0.567348 0.00059957
Bartlett's 1.18186 0.0000964052
This table shows the mean Recovery for each level of Concentration. It also shows the standard
error of each mean, which is a measure of its sampling variability. The standard error is formed by
dividing the standard deviation at each level by the square root of the number of observations at
that level. The table also displays an interval around each mean. The intervals currently displayed
are 95.0% confidence intervals for each mean separately. 95.0% of such intervals will contain the
true means.
Table of Means for Recovery by Concentration with 95.0 percent confidence intervals
Stnd. error
Concentration Count Mean (individual) Lower limit Upper limit
0.5 40 0.97975 0.0253103 0.928555 1.03095
1 38 0.959684 0.0125334 0.934289 0.985079
1.5 37 1.011 0.019152 0.972158 1.04984
Total 115 0.983174
170
0,5
1
1,5
Box-and-Whisker Plot
0,75 0,95 1,15 1,35 1,55
Recovery
Co
nce
ntr
ati
on
This graph shows 3 box-and-whisker plots, one for each level of Concentration. The rectangular
part of the plot extends from the lower quartile to the upper quartile covering the center half of each
sample. The center lines within each box show the location of the sample medians. The plus signs
indicate the location of the sample means. The whiskers extend from the box to the minimum and
maximum values in each sample except for any outside or far outside points, which will be plotted
separately. Outside points are points which lie more than 1.5 times the interquartile range above or
below the box and are shown as small squares. Far outside points are points which lie more than
3.0 times the interquartile range above or below the box and are shown as small squares with plus
signs through them. In this case, there are 4 outside points.
Therefore, it is strongly recommended when the result of a test is so sensitive to outliers the use of
the less sensitive test. In this case a test on the medians has been chosen (Kruskal-Wallis Test)
which compares medians instead of means.
Kruskal-Wallis Test for Recovery by Concentration
Concentration Sample Size Average Rank
0.5 40 53.0375
1 38 54.6184
1.5 37 66.8378
Test statistic = 3.87707 P-Value = 0.143915
The Kruskal-Wallis test tests the null hypothesis that the medians of Recovery within each of the 3
levels of Concentration are the same. The data from all the levels is first combined and ranked from
smallest to largest. The average rank is then computed for the data at each level. Since the P-
value is greater than 0.05, there is not a statistically significant difference amongst the medians at
the 95.0% confidence level.
171
4.2.3. Conclusions
The Variance Check shows that there is a statistically significant difference amongst the standard
deviations at the 95.0% confidence level both for the CTLL-2 and DiFi potency assays. This
violates one of the important assumptions underlying the ANalysis Of VAriance and invalidates the
statistical tests. Therefore the ANOVA can not be performed. In this case, the presence of outside
points suggests to use a more robust test which compares medians instead of means (Kruskal-
Wallis Test) and the result is that there is not a statistically significant difference amongst the
medians at the 95.0% confidence level.
Summary Table for Concentration
Comments
YES NO
Assay 1
Test for normality KS Test P-Value=0.17. The observation can be adequately
modeled by a normal distribution
Variance CheckCochran's and Bartlett's P-Value < 0.05.There is a
statistically significant difference amongst the standard
deviations
Test on Medians : Kruskal-
Wallis Test Kruskal-Wallis Test P-Value=0.12. There is not a
statistically significant difference amongst the medians
Assay 2
Test for normality KS Test P-Value=0.17. The observation can be adequately
modeled by a normal distribution
Variance CheckCochran's and Bartlett's P-Value < 0.05.There is a
statistically significant difference amongst the standard
deviations
Test on Medians : Kruskal-
Wallis Test Kruskal-Wallis Test P-Value=0.14. There is not a
statistically significant difference amongst the mediansKS=Kolmogorov-Smirnov
P-value > 0.05
Results of the analyses by Concentration
4.3. Results of the analyses by Range
4.3.1. CTLL-2 potency assay
The aim of this section is to analyze if the results obtained with Range (5-30ng/mL, 0.75-100ng/mL
and 6.8-80ng/mL) are significant different or in other words to determine whether or not the Range
(5-30ng/mL, 0.75-100ng/mL and 6.8-80ng/mL) has an impact on the Recovery for the CTLL-2
potency assay.
An ANalysis Of VAriance (Anova) is used to analyze the relationships between different Range (5-
30ng/mL, 0.75-100ng/mL and 6.8-80ng/mL) and the Recovery obtained. It compares the three
Ranges in order to see if any of the Range means is statistically different from the others in the
CTLL-2 potency assay.
The Kolmogorov-Smirnov Test is a goodness of fit tests for residual. It determines whether
Residual can be adequately modelled by a normal distribution.
172
Kolmogorov-Smirnov Test
Normal
DPLUS 0.120484
DMINUS 0.0580533
DN 0.120484
P-Value 0.0948716
Since the smallest P-value amongst the tests performed is greater than 0.05. We can not reject the
idea that Residual comes from a normal distribution with 95% confidence.
Histogram for Residual
-0,29 -0,09 0,11 0,31 0,51
Residual
0
10
20
30
40
50
fre
qu
en
cy
Distribution
Normal
The statistics displayed in these tables test the null hypothesis that the standard deviation of
Recovery within each of the 3 levels of Range is the same. Of particular interest are the P-values.
Since the greater P-value is less than 0.05, there is a statistically significant difference amongst the
standard deviations at the 95.0% confidence level. This violates one of the important assumptions
underlying the analysis of variance and will invalidate the statistical tests.
Variance Check
Test P-Value
Cochran's C 0.506552 0.017787
Bartlett's 1.15402 0.00075371
This table shows the mean Recovery for each level of Range. It also shows the standard error of
each mean, which is a measure of its sampling variability. The standard error is formed by dividing
the standard deviation at each level by the square root of the number of observations at that level.
The table also displays an interval around each mean. The intervals currently displayed are 95.0%
confidence intervals for each mean separately. 95.0% of such intervals will contain the true means.
173
Table of Means for Recovery by Range with 95.0 percent confidence intervals
Stnd. error
Range Count Mean (individual) Lower limit Upper limit
(0.75-100ng/mL) 41 1.00066 0.014348 0.97166 1.02966
(5-30ng/mL) 18 1.07111 0.0329012 1.0017 1.14053
(6.8-80ng/mL) 46 1.045 0.0249638 0.99472 1.09528
Total 105 1.03216
(0.75-100ng/mL)
(5-30ng/mL)
(6.8-80ng/mL)
Box-and-Whisker Plot
0,75 0,95 1,15 1,35 1,55
Recovery
Ra
ng
e
This graph shows 3 box-and-whisker plots, one for each level of Range. The rectangular part of
the plot extends from the lower quartile to the upper quartile, covering the center half of each
sample. The center lines within each box show the location of the sample medians. The plus signs
indicate the location of the sample means. The whiskers extend from the box to the minimum and
maximum values in each sample, except for any outside or far outside points, which will be plotted
separately. Outside points are points which lie more than 1.5 times the interquartile range above or
below the box and are shown as small squares. Far outside points are points which lie more than
3.0 times the interquartile range above or below the box and are shown as small squares with plus
signs through them. In this case, there is 1 outside point.
Therefore, it is strongly recommended when the result of a test is so sensitive to one observation
the use of the less sensitive test. In this case, a test on the medians has been chosen (Kruskal-
Wallis Test) which compares medians instead of means.
Kruskal-Wallis Test for Recovery by Range
Range Sample Size Average Rank
(0.75-100ng/mL) 41 47.5
(5-30ng/mL) 18 61.7222
(6.8-80ng/mL) 46 54.4891
Test statistic = 2.92379 P-Value = 0.231797
174
The Kruskal-Wallis tests the null hypothesis that the medians of Recovery within each of the 3
levels of Range are the same. The data from all the levels is first combined and ranked from
smallest to largest. The average rank is then computed for the data at each level. Since the P-
value is greater than 0.05, there is not a statistically significant difference amongst the medians at
the 95.0% confidence level.
4.3.2. DiFi potency assay
The aim of this section is to analyze if the results obtained with Range (5-30ng/mL, 0.75-100ng/mL
and 6.8-80ng/mL) are significant different or in other word, to determine whether or not the Range
(5-30ng/mL, 0.75-100ng/mL and 6.8-80ng/mL) has an impact on the Recovery for the DiFi potency
assay.
An ANalysis Of VAriance (Anova) is used to analyze the relationships between different Range (5-
30ng/mL, 0.75-100ng/mL and 6.8-80ng/mL) and the Recovery obtained. It compares the three
Ranges in order to see if any of the Range means is statistically different from the others in The
DiFi potency assay.
The Kolmogorov-Smirnov Test is a goodness of fit tests for residual. It determines whether
Residual can be adequately modelled by a normal distribution.
Kolmogorov-Smirnov Test
Normal
DPLUS 0.15246
DMINUS 0.058621
DN 0.15246
P-Value 0.0095328
Since the smallest P-value amongst the tests performed is less than 0.05, we can reject the idea
that Residual comes from a normal distribution with 95% confidence.
Histogram for Residual
-0,3 -0,1 0,1 0,3 0,5 0,7 0,9
Residual
0
10
20
30
40
50
fre
qu
en
cy
Distribution
Normal
175
The statistic displayed in this table tests the null hypothesis that the standard deviations of
Recovery within each of the 3 levels of Range is the same. Of particular interest is the P-value.
Since the P-value is greater than 0.05, there is not a statistically significant difference amongst the
standard deviations at the 95.0% confidence level.
Variance Check
Test P-Value
Levene's 0.89453 0.4117
The ANOVA table decomposes the variance of Recovery into two components: a between-group
component and a within-group component. The F-ratio, which in this case equals 0.461729, is a
ratio of the between-group estimate to the within-group estimate. Since the P-value of the F-test is
greater than 0.05, there is not a statistically significant difference between the mean Recovery from
one level of Range to another at the 95.0% confidence level.
ANOVA Table for Recovery by Range
Source Sum of Squares Df Mean Square F-Ratio P-Value
Between groups 0.0143836 2 0.00719182 0.46 0.6314
Within groups 1.74449 112 0.0155758
Total (Corr.) 1.75888 114
(0.25-95nM) (2-12nM) (5-55nM)
Means and 95,0 Percent Confidence Intervals (internal s)
Range
0,87
0,9
0,93
0,96
0,99
1,02
1,05
Re
co
ve
ry
This table shows the mean Recovery for each level of Range. It also shows the standard error of
each mean, which is a measure of its sampling variability. The standard error is formed by dividing
the standard deviation at each level by the square root of the number of observations at that level.
The table also displays an interval around each mean. The intervals currently displayed are 95.0%
confidence intervals for each mean separately. 95.0% of such intervals will contain the true means.
176
Table of Means for Recovery by Range with 95.0 percent confidence intervals
Stnd. error
Range Count Mean (individual) Lower limit Upper limit
(0.25-95nM) 47 0.98834 0.0184397 0.951223 1.02546
(2-12nM) 18 0.957222 0.0369164 0.879335 1.03511
(5-55nM) 50 0.98766 0.015549 0.956413 1.01891
Total 115 0.983174
(0.25-95nM) (2-12nM) (5-55nM)
Residual Plot for Recovery
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
res
idu
al
Range
4.3.3 Conclusions
The Variance Check shows that there is a statistically significant difference amongst the standard
deviations at the 95.0% confidence level for the CTLL-2 potency assay. This violates one of the
important assumptions underlying the ANOVA and invalidates the statistical tests. Therefore the
ANOVA can not be performed. In this case, the presence of outside points suggests using a more
robust test which compares medians instead of means (Kruskal-Wallis Test) and the result is that
there is not a statistically significant difference amongst the medians at the 95.0% confidence level.
As for the DiFi potency assay, the ANOVA shows that there is not a significant statistically
difference between the means from one level of Range to another at the 95.0% confidence level.
177
Summary Table for Range
Comments
YES NO
Assay 1
Test for normality KS Test P-Value=0.09. The observation can be adequately
modeled by a normal distribution
Variance CheckCochran's and Bartlett's P-Value < 0.05.There is a
statistically significant difference amongst the standard
deviations
Test on Medians : Kruskal-
Wallis Test Kruskal-Wallis Test P-Value=0.23. There is not a
statistically significant difference amongst the medians
Assay 2
Test for normality KS Test P-Value=0.009. The observation can't be
adequately modeled by a normal distribution
Variance Check Levene's P-Value=0.41.There is not a statistically
significant difference amongst the standard deviations
Test on Means: ANOVA ANOVA P-Value=0.63. There is not a statistically
significant difference between the meansKS=Kolmogorov-Smirnov
P-value > 0.05
Results of the analyses by Range
5. Analysis on failure rates
In order to verify whether there is a significant difference in the failure rates (FR) between the
different calculation models or not, the results have been summarized in the following table.
PL 4PL 5PL Total
Assay1
Number of failures 0 10 11 21
Number of observations 54 36 36 126
Failure rate [%] 0 27,78 30,56 16,67
Assay2
Number of failures 0 5 6 11
Number of observations 54 36 36 126
Failure rate 0 13,89 16,67 8,73
Total
Number of failures 0 15 16 31
Number of observations 108 72 72 252
Failure rate 0 20,83 22,22 12,30
It is clear from the simple observation of the failure rates that there is a significant difference
between PL and the other models (4PL and 5PL) in both assays. This difference is greater if only
the results of the CTLL-2 potency assay (Assay 1) are taken into account.
In any case, statistical tests have been performed separately for the CTLL-2 potency assay (Assay
1) and the DiFi potency assay (Assay 2).
The Hypothesis is that the failure rate is the same for the three models and that it is equal to the
mean value (16.67% for the CTLL-2 potency assay and 8.73% for the DiFi potency assay).
178
5.1 CTLL-2 potency assay
Hypothesis Tests: PL Failure rate = 0.16667
Sample proportion = 0.0
Sample size = 54
Approximate 95.0% confidence interval for p: [0.0; 0.0660315]
The two hypotheses to be tested are:
Null Hypothesis: PL Failure rate = 0.16667
Alternative: not equal
P-Value = 0.000105962
Reject the null hypothesis for alpha = 0.05.
In this sample of 54 observations, the sample proportion equals 0.0. Since the P-value for the test
is less than 0.05, the null hypothesis is rejected at the 95.0% confidence level. The confidence
interval shows that the values of theta supported by the data fall between 0.0 and 0.0660315.
Hypothesis Tests 4PL Failure rate = 0.16667
Sample proportion = 0.27778
Sample size = 36
Approximate 95.0% confidence interval for p: [0.142004; 0.451864]
The two hypotheses to be tested are:
Null Hypothesis: 4PL Failure rate = 0.16667
Alternative: not equal
P-Value = 0.130431
Do not reject the null hypothesis for alpha = 0.05.
In this sample of 36 observations, the sample proportion equals 0.27778. Since the P-value for the
test is greater than or equal to 0.05, the null hypothesis cannot be rejected at the 95.0% confidence
level. The confidence interval shows that the values of theta supported by the data fall between
0.142004 and 0.451864.
Hypothesis Tests 5PL Failure rate = 0.16667
Sample proportion = 0.30556
Sample size = 36
Approximate 95.0% confidence interval for p: [0.163477; 0.481075]
The two hypotheses to be tested are:
Null Hypothesis: 5PL Failure rate = 0.16667
Alternative: not equal
P-Value = 0.0569794
Do not reject the null hypothesis for alpha = 0.05.
179
In this sample of 36 observations, the sample proportion equals 0.30556. Since the P-value for the
test is greater than or equal to 0.05, the null hypothesis cannot be rejected at the 95.0% confidence
level. The confidence interval shows that the values of theta supported by the data fall between
0.163477 and 0.481075.
5.2 DiFi potency assay
Hypothesis Tests: PL Failure rate = 0.0873
Sample proportion = 0.0
Sample size = 54
Approximate 95.0% confidence interval for p: [0.0; 0.0660315]
The two hypotheses to be tested are:
Null Hypothesis: PL Failure rate = 0.0873
Alternative: not equal
P-Value = 0.0144127
Reject the null hypothesis for alpha = 0.05.
In this sample of 54 observations, the sample proportion equals 0.0. Since the P-value for the test
is less than 0.05, the null hypothesis is rejected at the 95.0% confidence level. The confidence
interval shows that the values of theta supported by the data fall between 0.0 and 0.0660315.
Hypothesis Tests: 4PL Failure rate = 0.0873
Sample proportion = 0.13889
Sample size = 36
Approximate 95.0% confidence interval for p: [0.0466783; 0.294976]
The two hypotheses to be tested are:
Null Hypothesis: 4PL Failure rate = 0.0873
Alternative: not equal
P-Value = 0.404229
Do not reject the null hypothesis for alpha = 0.05.
In this sample of 36 observations, the sample proportion equals 0.13889. Since the P-value for the
test is greater than or equal to 0.05, the null hypothesis cannot be rejected at the 95.0% confidence
level. The confidence interval shows that the values of theta supported by the data fall between
0.0466783 and 0.294976.
Hypothesis Tests: 5PL Failure rate = 0.0873
Sample proportion = 0.16667
Sample size = 36
Approximate 95.0% confidence interval for p: [0.0637223; 0.32812]
180
The two hypotheses to be tested are:
Null Hypothesis: 5PL Failure rate = 0.0873
Alternative: not equal
P-Value = 0.179011
Do not reject the null hypothesis for alpha = 0.05.
In this sample of 36 observations, the sample proportion equals 0.16667. Since the P-value for the
test is greater than or equal to 0.05, the null hypothesis cannot be rejected at the 95.0% confidence
level. The confidence interval shows that the values of theta supported by the data fall between
0.0637223 and 0.32812.
5.3 Conclusions
The statistical tests confirm that there is a statistically significant difference between the failure rate
of PL and the failure rates of the others (4PL and 5PL).
181
ABSTRACT
Keywords: Bioassay, quality control, analysis models, read-out systems, STAT5 phosphorylation
assay, Interleukin-7
The quality of biopharmaceuticals is usually monitored by using biological assays. The aim of this
study was to optimize existing methods for biological assays and to implement alternative assay
formats. First, analysis models and read-out systems for proliferation assays, which are frequently
applied for quality control purposes, were compared.
Regarding the comparison of analysis models, the parallel line model was compared to the logistic
models four- and five-parameter fit. The results of this comparison revealed a slightly better
accuracy and precision of the logistic models in comparison to the parallel line model; however
differences were not statistically significant different. On the other side, the logistic models showed
a significantly higher failure rate.
Regarding the comparison of read-out systems, it was tested in this study, whether luminescence
or fluorescence systems could be of advantage in comparison to the colorimetric system, originally
in use for detection of the response of proliferation assays at Merck Serono. From the results of
this study, it can be concluded that the luminescence technique seems to be the most
advantageous of the three read-outs for potency assays. It is extremely sensitive, therefore leading
to cell savings and provides the highest signal to noise ratios, leading to very precise results.
Moreover, it is the fastest of the three methods for all assays investigated (CTLL-2, DiFi and Kit
225 potency assay), since the plate can already be measured 5 - 10min after addition of substrate.
As an alternative to the commonly used proliferation assays, an intracellular phosphorylation assay
was developed for a cytokine fusion protein including Interleukin (IL)-7, since the originally used
proliferation assay suffered from a very long incubation time and a poor robustness. Using western
blotting, STAT5 was identified as a target for development of this assay. This study was further
expanded to a comparison of IL-7 signaling in different cell lines (Kit 225, PB-1 and 2E8) and to a
comparison between the signaling pathways of the two γc cytokines IL-7 and IL-2. The study
showed that some of the investigated targets were induced by IL-2 stimulation only, and not by IL-7
stimulation. Regarding the comparison between the different cell lines, interesting differences in the
expression pattern and kinetics of activation by IL-7 could be observed. However, in this study, the
only pathway identified to be activated in all three cell lines by IL-7, was the STAT5 pathway and
the associated induction of pim-1, making STAT5 a suitable target for development of the
phosphorylation assay and the IL-7 signal transduction cascade an interesting target for further
explorations.
The developed STAT5 phosphorylation assay showed very accurate and precise results. The main
advantage of this assay are its rapidness - reducing the total time of the assay from six to three
days – and the higher robustness in comparison to the proliferation assay. It can serve as a fast
and reliable alternative to classical end-point assays and is well-suited for use in quality control of
biopharmaceuticals.
182
ZUSAMMENFASSUNG
Stichworte: Bioassay, Qualitätskontrolle, Auswertemodelle, Read-out Systeme, STAT5
Phosphorylierungsassay, Interleukin-7
Die Qualität von Biopharmazeutika wird üblicherweise durch Bioassays kontrolliert. Das Ziel dieser
Arbeit war, bestehende Methoden für Bioassays zu optimieren und alternative Assayformate zu
implementieren. Zunächst wurden Auswertemethoden und Read-out Systeme für
Proliferationsassays, die häufig für Qualitätskontrollzwecke eingesetzt werden, miteinander
verglichen.
Hinsichtlich des Vergleichs von Auswertemodellen wurde das Parallel Line Model mit den beiden
logistischen Modellen, Vier- und Fünf-Parameter Fit verglichen. Die Ergebnisse dieser Studie
zeigten eine etwas höhere Genauigkeit und Präzision der logistischen Modelle im Vergleich zum
Parallel Line Model; die Unterschiede waren jedoch statistisch nicht signifikant. Andererseits waren
die Fehlerraten der logistischen Modelle signifikant höher.
Für den Vergleich verschiedener Read-out Systeme wurde in dieser Studie getestet, ob
Lumineszenz oder Fluoreszenz Systeme vorteilhaft gegenüber dem kolorimetrischen System sind,
das bei Merck Serono ursprünglich für die Detektion der Antwort von Proliferationsassays
verwendet wurde. Aus den Ergebnissen dieser Studie kann geschlussfolgert werden, dass die
Lumineszenztechnik die vielversprechendste der drei Read-out Methoden für Bioassays ist. Sie ist
extrem sensitiv und mindert somit den Verbrauch an Zellen, liefert die höchsten Signal-Rausch-
Verhältnisse und führt zu sehr präzisen Resultaten. Außerdem ist sie die schnellste der drei
Methoden für alle untersuchten Assays (CTLL-2, DiFi und Kit 225 Assay), da die Platte bereits 5 -
10min nach Substratzugabe gemessen werden kann.
Als Alternative zu den allgemein verwendeten Proliferationsassays wurde ein intrazellulärer
Phosphorylierungsassay für ein Zytokinfusionsprotein mit Interleukin (IL)-7 Anteil entwickelt, da die
Inkubationszeit des ursprünglichen Proliferationsassays sehr lang war und die Robustheit des
Assays nicht angemessen war. Mittels Westernblotanalyse wurde STAT5 als Target für diesen
Assay identifiziert. Diese Studie wurde ausgeweitet zu einem Vergleich der IL-7
Signaltransduktionswege in verschiedenen Zelllinien (Kit 225, PB-1 und 2E8) und zu einem
Vergleich der Signaltransduktionswege der beiden γc Zytokine, IL-7 und IL-2. Die Ergebnisse der
Studie zeigten, dass einige der untersuchten Targets nur durch IL-2 Stimulation und nicht durch IL-
7 Stimulation induziert wurden. In den verschiedenen Zelllinien konnten interessante Unterschiede
der Expressionsmuster und Aktivierungskinetiken durch IL-7 beobachtet werden. Jedoch war der
einzige, identifizierbare Signaltransduktionsweg, der in dieser Studie durch IL-7 aktiviert wurde, der
STAT5 Signalweg und die damit verbundene Induktion von Pim-1. Dies machte STAT5 zu einem
geeigneten Target für die Entwicklung eines Phosphorylierungsassays und die IL-7
Signaltransduktionskaskade zu einem interessanten Ziel für weitere Untersuchungen.
183
Der entwickelte STAT5 Phosphorylierungsassay zeigte sehr genaue und präzise Ergebnisse. Die
wesentlichen Vorteile dieses Assays sind seine Schnelligkeit – da die Gesamtdauer des Assays
von sechs auf drei Tage reduziert werden konnte – und die höhere Robustheit im Vergleich zum
Proliferationsassay. Er ist somit eine schnelle und zuverlässige Alternative im Vergleich zu den
klassischen Proliferationsassays und ist gut geeignet für die Qualitätskontrolle von
Biopharmazeutika.
184
CURRICULUM VITAE Persönliche Daten
Name: Cornelia Andrea Zumpe
Geburtsdatum/-Ort: 06.02.1983 in Nürnberg
Familienstand: ledig
Staatsangehörigkeit: deutsch
Anschrift: Martin-Gnad-Str. 45, 90427 Nürnberg
Schulbildung
1989 – 1993 Grundschule Großgründlach
1993 – 2002 Wilhelm-Löhe-Schule Nürnberg
Jun 2002 Allgemeine Hochschulreife
Hochschulbildung
Okt 2002 – Sep 2005 Bachelorstudium „Molecular Science“ (B.Sc.)
Universität Erlangen-Nürnberg
Thema der Bachelorarbeit: „Einfluss des Protein-
/Hilfsstoffverhältnisses auf die Stabilität von Katalase bei der
Sprühtrocknung“
Okt 2005 – Mai 2007 Masterstudium „Molecular Life Science“ (M.Sc.)
Universität Erlangen-Nürnberg
Nov 2006 – Apr 2007 Masterarbeit am „Institut Polytechnique LaSalle Beauvais“
Thema: „Analysis of protein adducts formed during thermal
treatment of infant formulas by GC-MS/MS“
Wissenschaftliche Tätigkeit
Aug 2007 - dato Wissenschaftliche Tätigkeit bei der Firma Merck Serono, in
Kooperation mit der FAU Erlangen-Nürnberg (Institut für
Lebensmittelchemie)
185
Eidesstaatliche Erklärung gem. § 4.2 und 4.6 der Promotionsordnung der Naturwissenschaftlichen
Fakultät der Friedrich-Alexander-Universität Erlangen-Nürnberg.
Ich erkläre hiermit, dass ich die vorliegende Arbeit ohne fremde Hilfe angefertigt und nur die im
Literaturverzeichnis angeführten Quellen und Hilfsmittel verwendet habe.
Desweiteren erkläre ich hiermit, dass ich an keiner anderen Stelle ein Prüfungsverfahren
beantragt, bzw. die Dissertation in dieser oder einer anderen Form bereits anderweitig als
Prüfungsarbeit verwendet oder einer anderen Fakultät als Dissertation vorgelegt habe.
Ort, Datum Unterschrift