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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-SAB July 20 th , 2004.

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slide1

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-SAB

July 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 data with filing and could share it prior to the filing

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

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

Current vs.QBDDesiredState
risk based development a simple concept
Use sound scientific principles in the design of the DF and Process

Identify the critical attributes (CAs) for the raw materials

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

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

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?

Risk Based Development: a simple concept
risked based development rbd
Risked Based Development - RBD

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

  • Exploring the characteristics of the RMs, and possible variability in RM and processing that are
    • expected to impact on required DF performance
  • Deciding on a DF based on #1 (+ business case) and selection of possible processes
  • Deciding what data are necessary to access the probable success of #2 (1st,principles, lit, DOE)
  • Collect and analyze the data (here comes PAT)
    • Gap analysis - refining models as development proceeds
  • Continuous improvement
example solid oral dosage forms
Example: Solid Oral Dosage Forms

Does it work?

Can we make it?

how realistic is rbd
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!
slide7
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’
variability is the enemy
Variability is the Enemy

Variability

RM Input

Product

Process

variable

???

FIXED!!

Adjustable!

You CANNOT have a constant output from a

fixed process and variable input - KRM

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

slide9
Example: CMC-API Selection Rationale/Process for DF DevelopmentHow 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.
an example q6a polymorph decision tree
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
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
an example con t q6a polymorph decision tree16
An Example con’t: Q6A polymorph decision 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?
an example con t q6a polymorph decision tree17
An Example con’t: Q6A polymorph decision tree

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
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

slide19

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 decompression the importance of bi
Phenacetin - fracture on decompression 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 excipients drugs

0.040

0.025

0.035

0.030

0.020

0.025

Bonding Index

0.020

0.015

Bonding Index

0.015

0.010

0.010

0.005

0.005

0.000

Sucrose

Mannitol

SD Lactose

CaSO4*H2O

Hyd. Lactose

MCC PH101

MCC PH102

MCC PH103

0.000

Aspirin

PNU-A

PNU-B

PNU-E

PNU-C

PNU-D

Flurbip.

Acetamin.

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

Bonding Index of: Excipients Drugs

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

erythromycin fracture on ejection the importance of the bfi
Erythromycin - fracture on ejection the importance of the BFI

BFI = 0.7 (High)

BI = 0.03 (High)

SI = 0.04 (High)

BFI = 0.5[(σT/ σTo)-1] (<0.20)

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

brittle fracture index of excipients at a solid fraction of 0 9

1.0

0.9

0.8

0.7

0.6

0.5

Brittle Fracture Index

0.4

0.3

0.2

0.1

0.0

Sorbitol

Sucrose

Povidone

Mannitol

SD Lactose

Corn Starch

MCC, coarse

MCC, medium

MCC, med, RM

Croscarmellose

Ca SO4 di-H2O

Hydrous Lactose

Brittle Fracture Index of Excipients at a solid fraction of 0.9

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

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

slide24

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

1.0

0.8

0.6

Brittle Fracture Index

0.4

0.2

0.0

0

20

40

60

80

100

% Drug Mixed with Excipient

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.

empirical modeling of a binary mixture

Hmix

C1

Component

C2

Empirical Modeling of a Binary Mixture

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

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

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

risk based development cmc questions
Use sound scientific principles in the design of the DF and Process

Identify the critical attributes (CAs) for the raw materials

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

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

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

Were the principles appropriately applied?

How were the CAs identified and the formula designed?

Risk Based Development-CMC questions
pccps and scale up with monitoring
PCCPs and Scale up with Monitoring

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

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

3 point beam bending

E = F l3/ 4x h3b

Roll speed: 4 - 12 rpm

VFS Speed: 200 rpm

HFS Speed: 30 rpm

Roll Pressure: 6560

average nir spectrum n 13
Average NIR Spectrum (n = 13)

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

dry granulation by roller compaction
Dry Granulation by Roller Compaction
  • 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.

dry granulation by roller compaction32
Dry Granulation by Roller Compaction
  • The particle sizes of the milled material is also manifest in the slope of the NIR signal (as predicted)

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

real time setup
Real-Time Setup
  • Similar trends (as seen before) were observed for Thickness, Width, Force at break and Young’s Modulus

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

on line vs off line slope data and post milling ps
On-line vs. Off-line Slope Data and Post Milling PS

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

scale up 10 tolmetin compacts
Scale Up: 10% Tolmetin Compacts
  • Alexanderwerk’s WP 120 x 40
    • Formulations:100% Avicel® PH200 (MCC), 10% Tolmetin, 30% DiTab®, 60% MCC
  • 8 Compactor settings studied prepared with and without vacuum

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

risk based development cmc questions36
Use sound scientific principles in the design of the DF and Process

Identify the critical attributes (CAs) for the raw materials

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

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

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

Were the principles appropriately applied?

How were the CAs identified and the formula designed?

Ditto for PCCPs

What were the bases for the analyses selection?

What are the supporting data for all of the above?

Product Development History

Risk Based Development-CMC questions
summary pat gmps rbd pccp
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
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
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