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Process Analytical Technology
(PAT) for Bioprocessing
LCDR Cyrus Agarabi, PharmD, PhD, MBA
Division of Biotechnology Review and Research-II
OBP/OPQ/CDER
June 8, 2016
*Disclaimer: The views expressed are presented by the authors for consideration. They do not
necessarily reflect current or future FDA policy or official views of the US Government.
Outline
Background
Bioprocessing & mAb Overview
PAT Research Examples and Opportunities
2
Office of
Program and
Regulatory
Operations
Office of Policy
for
Pharmaceutical
Quality
Office of
Biotechnology
Products
Office of New
Drug Products
Office of
Lifecycle Drug
Products
Office of
Testing and
Research
Office of
Surveillance
Office of
Process and
Facilities
Office of Pharmaceutical
Quality (OPQ)
Center for Drug Evaluation and
Research (CDER)
Food and Drug Administration
(FDA)
Why Regulatory Research?
• Outreach- Internal/External Stakeholders
• Discuss regulatory research to support FDA
guidance documents (PAT, QbD, etc)
• Reviewer Training
• Internal/external training on bioprocessing
equipment and analytics
• Support Biotech Regulatory Decisions
• Support policy development
• Support biotech review decisions 4
PAT Guidance Released September 29, 2004
Scientific principles and tools – Process Understanding
– PAT Tools
– Risk-Based Approach
– Integrated Approach
Regulatory strategy accommodating innovation
– Training
– Lab research
www.fda.gov/cder/gmp
How can this be applied to biotech?
5
The Essence of PAT Product quality is monitored and controlled during the
manufacturing process.
Process decisions are based on assessments of material
attributes.
Forward-feed of incoming material
In-process monitoring & control
Critical product attributes measured/assessed
Instantaneously (on-line, in-line, at-line) or
Before decision point (near at-line)
• With as large a window as feasible 6
7 *For illustrative purposes only- Data generated by Google Trends Based on searches for “Process Analytical Technology”
Google search interest for “Process Analytical
Technology” (2004-Current)
• Where do we go from here?
• How can PAT play an important role in new initiatives?
PAT Guidance Released
0
50
100
What is an Emerging Technology? Technology with which the FDA has limited review or inspection experience New (or not previously applied to pharmaceutical applications) to support more robust, predictable, and/or cost-effective processes or novel products Innovative or novel products, manufacturing processes, or analytical technology subject to CMC review Examples: •Continuous manufacturing of drug substances and drug products
•“On-demand” manufacturing of drug products
•Aseptic filling closed system
•3-D printed tablets
•New container and closure systems for injectable products
Emerging Technology Team (ETT)
A small cross functional team with representation from all relevant CDER review and inspection programs Encourage and support the adoption of innovative technology to modernize pharmaceutical development and manufacturing Two tracks:
• Stand alone innovative or novel product, manufacturing process, or analytical technology
• Existing or planned submission(s) with the above
• Amendment to FDA Broad Agency Announcement for the Advance
Research and Development of Regulatory Science and Innovation
highlights Continuous Manufacturing as an area of interest
• Enabling Technologies for Continuous Manufacturing – Continuous processing equipment (e.g., crystallizers, coaters, and viral clearance)
– Enhanced in-line process analytical technologies
– Integrated data management and plant-wide control systems
– Process modeling and simulation
– Advanced process control (e.g., feedback, feedforward or plant-wide control)
• Continuous Manufacturing Innovation – Synthetic processes that would benefit from flow processing; syntheses that could be affected through a
reduced number of steps or that would not be feasible by batch production
– Modular or plug and play type equipment with re-usable or flexible, interchangeable parts that allows the
development of platform technologies for drug substance, drug product or end-to-end continuous processes.
– Non-column based chromatography and alternative purification techniques (e.g. continuous precipitation)
– Continuous processes for homogeneous production of final dosage forms (e.g., strip film manufacturing
system, injection molding, and printing).
– Alternatives to inherently batch unit operations (e.g. viral and sterile filtration)
– Process control systems with improved user interface (e.g. GUI) and capability for integration with new unit
operations and ancillary equipment, with reduced need for programmer hours
BARDA-FDA Continuous Manufacturing Innovations Initiative
Bioprocessing &
Monoclonal Antibody
Overview
12
Overview of Bioprocessing of Drug
Substance Protein A clarify concentrate
Cation
Exchange
Filter
Sterilize Anion
Exchange
TFF UFDF
Drug
Substance
centrifuge
Characterization Upstream
Downstream
seed
Culture
WASTE
LN2
Production culture (100’s- 10,000’s L bioreactor)
Thaw WCB
Expansion of cells (pre-culture)
13
Bioreactors (Upstream)
14
• Stirred Tank- (Traditional) • Tank with a motor driven
impeller or agitator
• Ports for probes & sampling
• Cleaned & steamed in place
• Simple scale-up
• Most common equipment
• Disposable Systems • Plastic bags or vessels
equipped with disposable plastic liners
• No cleaning required • Easy to scale-up & cost-
effective • Can be equipped with
optical sensor patches
Attributes & Combinatorics
• 2 x 6 x 6 x 4 x (10+5) x 2 = 8460 • (8460)2≈ 75 million
pyro-E
D
D
D
G
G
K
O
O
O K
pyro-E O
D
G
G
D
O
D
O
Courtesy of S. Kozlowski
• Pyro-Glu (2)
• Deamidation (3x2)
• Methionine oxidation (3x2)
• Glycation (2x2)
• High mannose,
Fucosylation G0, G1, G1,
G2 (10)
• Sialylation (+5)
• C-term Lys (2)
15
Aseptic autosampling of parallel
bioreactors
Integrated on-line analysis
Retains samples at (4oC) for
at-line and offline analysis
Integrated Bioprocessing System
5L Parallel Bioreactors (6)
Integrated analysis: pH, DO, Temp
Controls: Agitation, Acid/Base,
Heating/Cooling,
Gasses (O2, N2, CO2, Air),
Automated process adjustments
Automated Control &
Feedback In-Line
Real-time monitoring
of glucose/lactate,
Dialysis (no loss in
reactor volume),
Self-calibrating
Automated Glucose Feeding
Cell Counter /
Bioanalyzer
Chemistry & Gas, VCD, Cell
count, Osmometer, IgG module
On-Line Analysis
2D HPLC
Analysis Amino Acids
(media), Conc.,
Size Exclusion,
Impurities,
Affinity
UPLC
Analysis Conc., Size
Exclusion,
Impurities, Affinity
Future: Mass
Spectrometry
integration
At-Line Analysis
O2/CO2 Monitoring
Humidity/pressure compensation,
Real-time measurement of metabolic
processes (OUR, CER, RQ)
Reactor
Autosampler
w/ OPC
Server
ToF-MS
Analysis Comprehensive
antibody
characterization,
Glycosylation profile
Near Infrared
Real-time
monitoring of
products
chemical and
physical
properties (i.e.
homogeneity
screening)
Biomass Monitoring
Capacitance, Real-time viable cell density
16
In House Model Cell Lines Murine suspension hybridoma
- monoclonal IgG3k
• Serum free commercially
available media
Processing experience:
• Spinners (0.15-4L)
• Bioreactors 1L, 4L, 5L, 7L
• Air Wheel (15L)
Culture Length ~120 hrs
Observed Yields- 25-175mg/L
17
From Dwek et al., Annu. Rev. Immunol.2007,25:21
Chinese Hamster Ovary
(CHO) Cell (DG-44)
- monoclonal, chimeric IgG1
IgG1 binds murine Neisseria
meningitidis outer capsule
Limited small scale experience:
Shake flasks and AMBR
Source: http://www.laboratory-journal.com/science/life-sciences-biotechnologie/antibody-therapeutics
PAT Research Examples
and Opportunities
18
PAT Case Study 1: Nutrients
• Nutrient Content in Bioreactors can affect:
Cell Viability & Yield (Titer)
Glycosylation & Glycation
• Optimized and Controlled Feed Strategies
Improve CQAs and Yield
• Platform Approach – “One-Size doesn’t always fit all”
• PAT’s Role
↑ Scope, monitoring, and control of in-process nutrients, to
pull the correct “lever” to improve CQA’s
Yuk et. Al. (2011) Biotech. & Bioeng., Vol. 108, PP2600-10 19
Standard Approaches to Media
Composition Analysis • Current Standard- Automated Analyzers
Ion Specific Electrodes (ISE)
• Ammonia, Sodium, Potassium
Amperometric Electrodes- Immobilized
Enzymes
• Glucose, Lactate, Glutamine, Glutamate
Trypan Blue and Digital Imaging • VCD, Cell Count, Cell Size
www.novabiomedical.com
20
• Pro:
Minimal sample prep and operator expertise, automated, high throughput
Capable of integration with bioreactors and auto samplers
• Con:
Limited scope of analyte information
Real-time Nutrient Analysis
• BioPAT TRACE
• Glucose/Lactate
Direct measurement
Internal calibration
Orthogonal offset
• Dialysis mode
Cycles sample through in situ membrane
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Nutrients Overview:
Feeding Strategy
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Batch Fed-Batch Fed-Batch
Feed Type -- 1.5 mmol/L Gln 1.5 mmol/L Gln +
1X NEAA
Feed Volume -- 60 mL 60 mL
Feed Timing
(Automated)
-- 48, 72 h 48, 72 h
0
0.5
1
1.5
2
2.5
3
3.5
4
0 24 48 72 96 120
g/L
Time (hrs)
Batch 5 GlucosePU1 Trace
PU1 Nova
PU2 Trace
PU2 Nova
PU4 Trace
PU4 Nova
Nutrients Overview - Trace
Gln
Batch
Gln+NEAA
Batch mode consumed the least glucose.
GLN + NEAA consumed most glucose
23
Representative Viable Cell Density
24
Specific Productivity Batch 5
0.00E+00
1.00E+04
2.00E+04
3.00E+04
4.00E+04
5.00E+04
6.00E+04
Batch Gln Gln+NEAA
Spec
ific
Pro
du
ctiv
ity
(Qp
),
pg/
cells
/day
Feeding Strategies
25
26
Glycan Analysis
Feeding Strategy Overview
• Fed-batch cultures performed better
Increased viable cell density
Increased antibody production
Potential changes in glycan patterns
• On-line glucose analysis
Trends followed off-line analysis
Increased data density
Potential tool for feeding decisions
27
PAT Case Study 2*: VCD & Process Mode
ABER Biomass Probe:
•Dielectric Spectroscopy
technology
•Tiny capacitors under the
influence of an electric field
• Build up of charge→
capacitance measured
•Cells with intact plasma
membranes
• Directly proportional to the
membrane bound volume of
these viable cells.
28
ABER_VCD = ƒ(Norm_Cap , Inoculum density)
Baseline normalized (f-f0)
*In collaboration with MIT Engineering Practice School
Perfusion Control Scheme
29
FC XC FC
Nutrient Composition
Scale
Perfusion Apparatus
30
Harvest tank Feed tank
ATF
Scale
Harvest pump
Feed pump
Antifoam pump
Biomass Probe
Media and Sampling Plan
OptiCHO Medium – Commercial chemically defined medium
– NaHCO3 buffered
– 5 g/L glucose
– Supplemented with 10 mM glutamine
– 3% Antifoam C added periodically
Twice daily manual samples – Additional sample taken pre-perfusion
31
Culture Comparison: VCD
32
1.E+05
1.E+06
1.E+07
1.E+08
0 2 4 6 8 10 12 14
Via
ble
Ce
ll D
en
sit
y, c
ells
/mL
Culture time, d
ABER Perfusion
NOVA Perfusion
ABER Batch
NOVA Batch
ABER Fed-Batch
NOVA Fed-Batch
Culture Comparison: Glucose
33
0,0
1,0
2,0
3,0
4,0
5,0
6,0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Glu
co
se
(g
/L)
Time (day)
Perfusion
Batch
Fed-Batch
Fed Batch: Daily Bolus
Feeding Initiated
Perfusion Initiated
34
0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
IgG
(u
g/m
L)
Time (day)
perfusion
batch
fed-batch
Culture Comparison: Product Yields
Total Product Yields
35
PAT Exercise Findings
• Nutrient Findings Feeding strategy was not sufficient for all
cultures
Automated feeding strategy developed from online data and feedback loops is needed
• DS Findings Probe was able to track trends and fluctuations
in cellular growth and viability
Requires development of an offset based on robust off-line data Adjustments for each cell line need to be made
36
Opportunities for PAT
• Real time decision making
Feeding: Timing, quantity, composition
Harvest: Go/No-Go
Gross contamination detection
Global culture health trajectory - Course
corrections
• Support Quality by Design (QbD)
• Support Multivariate Data Analysis (MVDA)
37
Acknowledgements
OBP:K. Brorson, S. Lute, B. Chavez, A. Williams, S.
Johnson, C.J. Hsu, J. Wang, M. Landreth, M. Brown,
D. Frucht
OPQ IO: A. Fisher
Academia: S. Yoon (UMass Lowell), K. Stein (MIT)
Funding Sources: CP, RSR, MCM
38