risked-based development and cmc question-based review: asking the right questions for process...

Risked-Based Development and CMC Question-Based Review:

Asking the Right Questions for Process Understanding, Control and Filing

Kenneth R. Morris, Ph.D.Department of Industrial and Physical Pharmacy

Purdue University

OPS-SABJuly 20th, 2004

Current vs. QBD Desired State Companies may or may not have

info but it’s not always in the filing

Reviewers must go through cycle of info requests and questions

Companies may or may not have clear scientific rationales for choices but are not always sharing it.

Reviewers must often “piece together” data and observations to discover the rationale for a spec, method, formula, process, etc.

Reviewers are analyzing the data they often must tease out of the company

Companies include needed Companies include needed data with filing and could data with filing and could share it prior to the filingshare it prior to the filing

Companies include the Companies include the data analysis to produce data analysis to produce meaningful summaries and meaningful summaries and scientific rationalesscientific rationales

Reviewers assess the Reviewers assess the rationales and summarized rationales and summarized data presentations as data presentations as satisfactory or notsatisfactory or not

Risk Based Development: a simple concept

1. Use sound scientific principles in the design of the DF and Process

2. Identify the critical attributes (CAs) for the raw materials

3. Identify the process critical control points for the processes (PCCPs)

4. Employ the proper analyses and PAT concepts for process understanding and control

5. Tie it all together with the appropriate informatics to feed the information forward and backwards for QbD and continuous improvement and innovation = reduced risk

Associated regulatory question rationale?

Risked Based Development - RBD

RBD is all about “feeding forward” (after Ali Afnan)

1. Exploring the characteristics of the RMs, and possible variability in RM and processing that are expected to impact on required DF performance

2. Deciding on a DF based on #1 (+ business case) and selection of possible processes

3. Deciding what data are necessary to access the probable success of #2 (1st,principles, lit, DOE)

4. Collect and analyze the data (here comes PAT) Gap analysis - refining models as development

proceeds

5. Continuous improvement

Example: Solid Oral Dosage Forms

Does it work?

Can we make it?

How realistic is RBD? As all good pharm. Scientist/Engineer know:

• A formula without a process is (e.g.) a pile of powder

Even during API characterization, developing a formula implies an expected DF and process or range of choices (e.g., you don’t use compaction aids for lyophiles)

So API characteristics are among the 1st information you need to feed forward

So what’s different about the new GMP? Models, data, and informatics – the process!

Accessing solubility impact at preformulation:Yalkowsky’s Modified Absorption Parameter (QSAR & Combinatorial Science, 22, 247-257 2003)

Relationship to human intestinal fraction absorbed, FA, to the absorption parameter, , of the ‘rule of unity’

Lumen

ow

O

K

]Klog)MP(..[

ow

ow

Dose,max

K

250105010

41

Variability is the Enemy

ProcessRM Input Product

Adapted from Rick Cooley, Eli Lilly, and Jon Clark CDER-FDA

variable

FIXED!!???

You CANNOT have a constant output from a fixed process and variable input - KRM

Adjustable!

Variability

Example: CMC-API Selection Rationale/Process for DF Development How do you know what questions to ask?

What’s the 1st API question you’d want the answer to if designing a DF or in evaluating the appropriateness of the selected API attributes?

The 2nd?, etc…The development scientist and the

regulator are asking many of the same questions.

API and Excipient Selection Rationale

time

1st p

rinci

ples

DF

SOLID LIQUID SEMISOLID INHALED

OTHER CAPSULE TABLET

WET GRAN DRY GRAN DC

PS SHAPE MECH

CYCLE ON DATARISK ASSESSMENT

API

SOLID/CA? LIQUID/CA? SEMISOLID/CA? BIOLOGICAL/CA?

ID CAs

API

SOLID/CA? LIQUID/CA? SEMISOLID/CA? BIOLOGICAL/CA?

CRYSTAL AMORPH

P’MORPH HYDRATE OTHER

CLASSIFI-CATION

STABILITYMICRO-

MERITICS

DECISION TREE, e.g. Q6AOr New

OPT

ID PCCPsRevise

Amount of data needed-DOE?

Data Treatment for Fit?

Response Factor(s)?

Possible PCCPs based on the RMs?

Possible PCCPs from Process Model?

What is the model for the process?

What Processes are viable?

What Process is consistent with the RM CAs based DF?

Process Design/Selection Rationale

From RM CA

selection

An Example: Q6A polymorph decision tree

This is great. If you understand the solid state and no polymorphs are formed, you’re done!

If there are forms, they must be understood, e.g.:

What are the relative stabilities of low energy forms? These are the “right” questions for scientist and regulators

An Example con’t: Q6A polymorph decision tree

We’re OK at first but when considering the product the logical 1st question should be:

Based on what is known about the material AND the process, what if any changes in form would be EXPECTED?

If the answer is none based on the scientific understanding, then a confirmatory test during development should suffice

Otherwise, the next question should be:

Is the observed change the Expected one?

What was the rationale for selecting the processing step responsible for the change?

Then we’re back to the tree


Here it might be reasonable to be asked:

Does the performance testing relate to the performance of interest?

If the answer is yes based on the scientific understanding, then we’re back on track

A next question might be: based on the understanding of the form’s behavior what would the expect trend in transformation be?

Does the observed change correspond to an understood and expected transformation?

If not, the system is not as well understood as thought!


One Example: Mechanical Properties as a CA, the Hiestand Indices

-The Bonding Index for the survival of strength after decompression:

BI = tensile strength/hardness = σT/H (>0.005)

- The Brittle Fracture Index measures the ability of a material to relieve stress by plastic deformation around a defect:

BFI = tensile strength of a compact with a defect/without = 0.5[(σT/σTo)-1] (<0.20)

-The Strain Index measures the relative strain during decompression after plastic deformation:

SI = Hardness/Reduced Modulus of Elasticity = H/E’Hiestand, E., Rationale for and the Measurement of Tableting Indices, in Pharmaceutical Powder Compaction Technology, G. Alderborn and C. Nystrom, Editors. 1996, Marcel Dekker, Inc.: New York

Rowe, R.C. and R.J. Roberts, Mechanical Properties, in Pharmaceutical Powder Compaction Technology, G. Alderborn and C. Nystrom, Editors. 1996, Marcel Dekker, Inc.: New York.

Phenacetin - fracture on Phenacetin - fracture on decompression the importance of BIdecompression the importance of BI

BFI = 0.4 (Moderate)

BI = 0.005 (Low)

SI = 0.013 (Low)

BI = tensile strength/hardness = σT/H (>0.5x10-2)

Courtesy of Greg Amidon, Pfizer, Previously Presented at AAPS 2002

or U.of Mich. Solids course

Bonding Index of:Bonding Index of:Excipients DrugsExcipients Drugs

CaS

O4*H

2OM

annitolH

yd. LactoseS

ucroseS

D Lactose

MC

C P

H101

MC

C P

H102

MC

C P

H103

Bon

ding

Ind

ex

0.005

0.015

0.025

0.035

0.000

0.010

0.020

0.030

0.040

Acetam

in.A

spirinFlurbip.P

NU

-AP

NU

-BP

NU

-CP

NU

-DP

NU

-E

Bon

ding

Inde

x

0.005

0.015

0.025

0.000

0.010

0.020

BI = tensile strength/hardness = σT/H (>0.5x10-2)

Courtesy of Greg Amidon, Pfizer, Previously Presented at AAPS 2002 or U.of Mich. Solids course

Erythromycin - fracture on ejection Erythromycin - fracture on ejection the importance of the BFIthe importance of the BFI

BFI = 0.7 (High)

BI = 0.03 (High)

SI = 0.04 (High)

BFI = 0.5[(σT/ σTo)-1] (<0.20)



Brittle Brittle Fracture Fracture Index of Index of

Excipients at Excipients at a solid a solid

fraction of 0.9fraction of 0.9

Sorbitol

MC

C, m

edium

MC

C, m

ed, RM

MC

C, coarse

Povidone

Ca S

O4 di-H

2O

Croscarm

ellose

Mannitol

Sucrose

Hydrous Lactose

SD

Lactose

Corn S

tarch

Brit

tle F

ract

ure

Inde

x

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

BFI = tensile strength of a compact with/without a defect = 0.5[(σT/ σTo)-1]

(<0.20)



Effect of the Addition of a Non-brittle Material to a Brittle Drug (Methenamine, Flurbiprofen, Drug X (Pfizer))

Adding only 30% of a non-brittle excipient makes the mixture much less brittle.

% Drug Mixed with Excipient0 20 40 60 80 100

Brit

tle F

ract

ure

Inde

x

0.0

0.2

0.4

0.6

0.8

1.0

Courtesy of Greg Amidon, Pfizer, Previously Presented at AAPS 2002 or U.of Mich.

Empirical Modeling of a Binary MixtureEmpirical Modeling of a Binary Mixture

log(Hmix) = log(HC2/HC1)*(%C2/100) + log(HC1)

Hmix

C1 C2Component

Courtesy of Greg Amidon, Pfizer, Previously Presented at AAPS 2002 or U.of Mich.

Leuenberger and others have 1st principle models to extend the concepts (Powder Technology 111 2000 145–153)

Risk Based Development-CMC questions






1. Were the principles appropriately applied?

2. How were the CAs identified and the formula designed?

ID of PCCPs

PCCP variables –Model dev/refine

What variables should be important?

Sensor development and refinement

What variables are measurable?

Production - Scale up/down/same

Product performance

PCCP variables –Model dev/refine

What variables should be important?

Sensor development and refinement

What variables are measurable?

Production - Scale up/down/same

Product performance

PCCPs and Scale up with Monitoring

The basic approach is captured as two simple process understanding (i.e. PAT) premises:

1. PCCPs are preserved throughout scale-up► the magnitude of the responses may not scale

directly, but the variables being monitored reflect the “state” of the process

2. Monitoring material properties makes scaling less equipment dependent (as opposed to only monitoring equipment properties)

► equipment differences (scale and type) may have an effect, however, differences in the material should reflect significant changes in the PCCPs

Equipment:Chilsonator IR220 (Fitzpatrick)CDI-NIR; Texture Analyzer

Roll speed: 4 - 12 rpm

VFS Speed: 200 rpm

HFS Speed: 30 rpm

Roll Pressure: 6560

3 point beam bendingE = F l3/ 4 h3b

0.0

0.1

0.2

0.3

0.4

0.5

0.6

1000 1200 1400 1600 1800 2000 2200

Wavelength (nm)

NIR

Ab

sora

nce

4

6

8

10

12

Roll Speed (rpm)

MCC compacts

VFS Speed: HFS Speed:

Roll Gap: Roll Pressure:

194 - 197 rpm 29 - 30 rpm 0.031 - 0.038" 6551 lb/in

Average NIR Spectrum (n = 13)Gupta, et.al., in press, J.Pharm.Sci.

Dry Granulation by Roller Compaction

y = 21.54e-0.4493x

R2 = 0.9884

0

2

4

6

8

10

12

14

16

18

20

4 5 6 7 8 9 10 11 12

Roll Speed (RPM)

Fo

rce

at

bre

ak (

N)

Avicel® PH-200 compacts


Roll Gap: Roll Pressure:

194 - 197 rpm 29 - 30 rpm 0.031 - 0.038" 6551 lb/in

y = 0.3672x + 0.1754

R2 = 0.9899

0.15

0.20

0.25

0.30

0.35

0.40

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Force at break/Thickness/Width (N/mm2)

Slo

pe

of

NIR

Sp

ec

tru

m

4 5 6

7 8 9

10 11 12

Roll Speed (RPM)

Avicel® PH-200 compacts


Roll Pressure:

200 rpm30 rpm 6560 lb/in

The strength is a linear function of the density which is monitored by NIR

Semi Empirically

F=(SNIR-0.17)/0.37

Gupta, et.al, in press, J.Pharm.Sci.

Avicel® PH-200 Milled Compacts

0

200

400

600

800

1000

3 4 5 6 7 8 9 10 11 12 13

Roll Speed (rpm)

Pa

rtic

le S

ize

(m

)

Day1

Day2

Increaing Roll Speed

d90

d50

d10

Avicel® PH-200 Milled Compacts

0

200

400

600

800

1000

1200

2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

1 / Slope NIR Spectrum

Par

ticl

e S

ize

(m

)Day1

Day2

Increaing Roll Speed

d90

d50

d10

The particle sizes of the milled material is also manifest in the slope of the NIR signal (as predicted)

Dry Granulation by Roller Compaction


Real-Time Setup

Similar trends (as seen before) were observed for Thickness, Width, Force at break and Young’s Modulus


0.20

0.24

0.28

0.32

0.36

0.40

0 4 8 12 16 20 24 28 32Time (minutes)

Slo

pe

of

NIR

Sp

ec

tru

m

On-Line Data

Off-Line Data

630

200

10 50

250

840

200

630

150

420

100

1230

200

1030

200

830

200

RollHFSVFS

Speeds

On-line vs. Off-line Slope Data and Post Milling PSGupta, et.al, in press, J.Pharm.Sci.

0.24

0.26

0.28

0.30

0.32

0.00 0.01 0.02 0.03 0.04 0.05

Force/Thickness/Width (N/mm2)

Slo

pe

of

NIR

Sp

ec

tru

m

50/ 8 /25 50/10/25 60/ 8 /25 60/ 6 /35 60/ 8 /35 60/10/35

Red Symbols: No Vacuum .

Blue Symbols: 0.31 Bars Vacuum

FeederSpeed / Roll Speed / Roll Pressure

Scale Up: 10% Tolmetin Compacts


Alexanderwerk’s WP 120 x 40Formulations:100% Avicel® PH200 (MCC), 10% Tolmetin, 30% DiTab®, 60% MCC

8 Compactor settings studied prepared with and without vacuum






1. Were the principles appropriately applied?

2. How were the CAs identified and the formula designed?

3. Ditto for PCCPs

4. What were the bases for the analyses selection?

5. What are the supporting data for all of the above?

6. Product Development History

Risk Based Development-CMC questions

Summary: PAT, GMPs, RBD, PCCP

Asking the right questions at the right time Feeding forward and back between disciplines Designing the product and process against

meaningful metrics (performance, stability etc..)

• MUST start in R&D

• Development of meaningful specs

• Real time monitoring Process understanding for quality and control

• Known functionality (i.e., models) against which data are used to control to the mark

What do you get at each stage? Early development – CMC go/no go decisions with

a higher level of certainty, i.e., reduced risk Late phase development – clear formulation and

process design rationales

• Control strategies based on understanding to reduce the risk

• Facilitation of clear regulatory queries and logical responses

Tech transfer – more realistic processes to transfer (Gerry Migliaccio’s “leg up”)

• Fewer “surprises” (analogous to forward pass)

• Easier approval process and inspections

Acknowledgments

Dr. Gregory Amidon – Pfizer, Kalamazoo

CAMP – Consortium for the advanced manufacturing of pharmaceuticals

Abhay Gupta – graduate student in IPPH at Purdue

The Team - Headed by Jon Clark

risked-based development and cmc question-based review: asking the right questions for process...

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