Ajaz Hussain, Ph.D. and David Horowitz, Esq. CDER, FDA Efficient Risk Based Regulatory Scrutiny for...

Ajaz Hussain, Ph.D. and David Horowitz, Esq.Ajaz Hussain, Ph.D. and David Horowitz, Esq.CDER, FDACDER, FDA

Efficient Risk Based Efficient Risk Based Regulatory Scrutiny for Regulatory Scrutiny for

Assuring Pharmaceutical Assuring Pharmaceutical Quality in the 21st CenturyQuality in the 21st Century

Arden House 2004, London

OutlineOutline

Challenging opportunities for Challenging opportunities for improving efficiency and improving efficiency and effectivenesseffectiveness Science, Risk, Quality Systems, Science, Risk, Quality Systems,

HarmonizationHarmonization Risk based CMC & Biopharm ReviewRisk based CMC & Biopharm Review

What type of “knowledge” may be most What type of “knowledge” may be most usefuluseful

Risk based GMP InspectionsRisk based GMP Inspections

Part I: What type of “knowledge” Part I: What type of “knowledge” may be most useful for risk may be most useful for risk

based CMC Reviewbased CMC Review

Evolution of Product DevelopmentEvolution of Product Development

Data base -to- Knowledge baseData base -to- Knowledge base

Empirical modelsEmpirical models

Mechanistic modelsMechanistic models

Regulatory utilityRegulatory utility

Efficient and Effective DesignEfficient and Effective Design Product quality and performance achieved Product quality and performance achieved

and assured by designand assured by design Dosage formDosage form Manufacturing process, ...Manufacturing process, ...

Specifications based on mechanistic Specifications based on mechanistic understanding of how formulation and understanding of how formulation and process factors impact product performanceprocess factors impact product performance

Continuous "real time" assurance of qualityContinuous "real time" assurance of quality Highest (pragmatic) level of process understandingHighest (pragmatic) level of process understanding

Design for Intended Use: Focus on ReliabilityDesign for Intended Use: Focus on Reliability

Mitigate RiskRisk of incorrect identityPoor product & process

Changes in clinical trial productInadequate Design Specifications

Critical to quality and performance?Risk of unqualified impurities

Risk of poor bioavailabilityRisk of incorrect expiry dateRisk of inadequate controls

Risk of SUPAC,..Risk of unrepresentative test

samples] Risk of Inadequate Facility and QS

Intended UseRoute of administration

Patient population…..

Product Design

Design Specifications(Customer requirements)

Manufacturing Processand its Control

RegulatorySpecs.

Tests

& C

on

trols

-R

isk M

itig

ati

on

Efficient Design: Different Efficient Design: Different StrategiesStrategies

Design strategies are generally Design strategies are generally company dependentcompany dependent

Regulatory process should only focus Regulatory process should only focus on the outcome of these effortson the outcome of these efforts

Certain information and knowledge Certain information and knowledge utilized during design justification utilized during design justification and development can be useful for and development can be useful for science based regulatory decisionsscience based regulatory decisions

Regulatory Utility of Regulatory Utility of Development KnowledgeDevelopment Knowledge

Structured knowledge that provides a Structured knowledge that provides a means for evaluating and predicting means for evaluating and predicting product performanceproduct performance Can be for certain aspects or be broad to Can be for certain aspects or be broad to

cover all aspectscover all aspects Fragmented/incomplete bits of data Fragmented/incomplete bits of data

may not be usefulmay not be useful Need a general understanding and criteria Need a general understanding and criteria

for the type of knowledge useful for for the type of knowledge useful for regulatory utility regulatory utility

Elements of Decision Making in PDElements of Decision Making in PD

Data - Information -Data - Information -(communication)-Knowledge (communication)-Knowledge -relationship in -relationship in PProduct roduct DDevelopmentevelopment

Significant reliance on Significant reliance on personal knowledge as personal knowledge as information source in PDinformation source in PD

Generation of memory and Generation of memory and experience is achieved over experience is achieved over a long period of timea long period of time

Court A W. Int. J. Info. Court A W. Int. J. Info. Management.. 17: 123, 1997Management.. 17: 123, 1997

Short-term memory

Long-term memory

Pharmaceutical Product Pharmaceutical Product Development Development

Multi-factorial Multi-factorial complex problemcomplex problem

Significant reliance on Significant reliance on personal knowledgepersonal knowledge

Historical data likely Historical data likely to have been to have been generated by a “Trial-generated by a “Trial-n-Error” approachn-Error” approach

Many choices for Many choices for achieving target achieving target specificationsspecifications

Without up-to-date Without up-to-date information, high information, high potential for: potential for: misjudgmentsmisjudgments ““reinventing the reinventing the

wheel”wheel” Mobile “institutional Mobile “institutional

memory”memory” Approved product Approved product

needs frequent needs frequent changes ..changes ..

Evolution of Product Evolution of Product Development ProcessDevelopment Process

““Art”Art” Science & Engineering based Science & Engineering based Trial-and-ErrorTrial-and-Error DOE DOE CADCAD Few creative options tested Few creative options tested

Many creative options testedMany creative options tested Dosage forms Dosage forms Drug Delivery Drug Delivery

Intelligent Drug Delivery Intelligent Drug Delivery CMC Review : Increased Reliance on CMC Review : Increased Reliance on

Pharmaceutics to optimize regulatory Pharmaceutics to optimize regulatory tests & filing req.tests & filing req.

Product Development Product Development KnowledgeKnowledge

HISTORICAL DATA DERIVED FROMTRIAL-N-ERROR EXPERIMENTATION

HEURISTIC RULES“Rules of Thumb”

EMPIRICAL MODELS

MECHANISTICMODELS

Level of Sophistication

HIGH

MEDIUM

LOW

Details Resolved

HIGH

MEDIUM

LOW

Rules

Artificial Intelligence (AI) and Artificial Intelligence (AI) and Information Technology (IT) can Information Technology (IT) can

Improve the Utility of Historical DataImprove the Utility of Historical Data

HISTORICAL DATA DERIVED FROMTRIAL-N-ERROR EXPERIMENTATION

HEURISTIC RULES“Rules of Thumb”

EMPIRICAL MODELS

MECHANISTICMODELS

Expert Systems (AI)

Fuzzy ES

Rules

ANN(IT)

Hypothesis Hypothesis (Hussain,1989):(Hussain,1989): “ANN Based “ANN Based Computer Aided Formulation Design”Computer Aided Formulation Design”

Many ChallengesMany Challenges Very complex systemsVery complex systems Subjective descriptions Subjective descriptions

of excipient of excipient “functionality”“functionality”

Historic process controls Historic process controls that do not necessarily that do not necessarily control critical attributes control critical attributes of in-process materialsof in-process materials

Subjective equipment Subjective equipment similarity descriptions similarity descriptions

Initial focus on Initial focus on Comparison to RSM Comparison to RSM Tool for improving Tool for improving

technical and marketing technical and marketing support functions of an support functions of an excipient supplier: Klucel excipient supplier: Klucel Net Net (Aqualon) (Aqualon)

Formulation and Formulation and marketing tool for a marketing tool for a propriety formulation propriety formulation technology: TIMERxtechnology: TIMERx

ANN Structure and FunctionANN Structure and Function

INP

UT

OU

TP

UT

Data/Information Ability to predict outcome(Knowledge)

Simulation tool; “What-If” analysisPattern visualization tool, Optimization, etc.

Feasibility of Predicting Drug Feasibility of Predicting Drug Dissolution From Multisource Dissolution From Multisource

Tablet FormulationsTablet Formulations

V.K. Tammara,V.K. Tammara,11 A.S. Carlin, A.S. Carlin,22 M.U. Mehta M.U. Mehta11 and and

A.S. Hussain A.S. Hussain 22

11 Division of Pharmaceutical Evaluation I, Office of Clinical Division of Pharmaceutical Evaluation I, Office of Clinical Pharmacology and Biopharmaceutics and Pharmacology and Biopharmaceutics and 22 Division of Product Division of Product Quality Research, Office of Testing and Research, CDER, FDAQuality Research, Office of Testing and Research, CDER, FDA

AAPS Annual Meeting 1999AAPS Annual Meeting 1999

Proposed SUPAC-IR NetworkProposed SUPAC-IR NetworkPhase III Phase I Phase II

In Vitro Dissolution

CompositionDrug attributesand dissolution test conditions

Equipment (D/OP)and Process

MethodsMethods

• Similarity between training (FDA/UMAB) and test (ANDA) formulations

FDA/UMAB 1 2 3 4 5 6 7 8 9

Binder X X

Diluent A X X X

Disint.

Diluent B X

Lubricant

Granulationmethod

X

TestFormulation

PredictionError % :

Q(10)

PredictionError % :

Q(30)ANDA 1 -29 -15

ANDA 2 -2 -2

ANDA 3 13 13

ANDA 4 - 6 - 4

ANDA 5 25 4

ANDA 6 7 2

ANDA 7 14 -5

ANDA 8 - 4 4

ANDA 9 - 14 7

Innovator 6 -7

9

Results and DiscussionResults and DiscussionComponent SUPAC

Reco.Limit

(Level 2)

Max change incomponent having noimpact on dissolution

(15% or less differencein dissolution)

Diluent A (IN) 10% 15%Diluent A (OUT) 10% 30%Diluent B 10% 70%Disintegrant (IN) 2% 7%Disintegrant(Out)

2% 3%

Binder 1% 5%Lubricant 0.5% 2%

Specifications Specifications

Product Product specifications based on mechanisticspecifications based on mechanistic understandingunderstanding of how formulation and process of how formulation and process factors impact product performancefactors impact product performance

Continuous "real time" assurance of qualityContinuous "real time" assurance of quality

Empirical Models: Experimental Empirical Models: Experimental Designs Designs (e.g., Mixture Designs)(e.g., Mixture Designs)

Set of combinations of the proportions (or Set of combinations of the proportions (or blends) used to collect observed response blends) used to collect observed response valuesvalues range of interest of the formulator (entire range of interest of the formulator (entire

simplex surface or partial surface)simplex surface or partial surface) Properties of the polynomials used to estimate Properties of the polynomials used to estimate

the response function depend on the the response function depend on the experimental designexperimental design

Factor SpaceFactor Space

x1 =1 x1=x2=0.5 x2 =1

x3 =1(0,0,1)

x1 =1(1,0,0)

x2 =1(0,1,0)

x1 =1(1,0,0,0)

x3 =1(0,0,1,0)

x2 =1(0,1,0,0)

x4 =1(0,0,0,1)

Response SurfaceResponse Surface

Choice of model to approximate the Choice of model to approximate the surface over the “region of interest”surface over the “region of interest”

Testing for adequacy of the modelTesting for adequacy of the model Suitable experimental designSuitable experimental design Assumption - existence of a continuous Assumption - existence of a continuous

functional relationship functional relationship

),.....,,( 21 qxxx

In Vitro Flux (Human Skin)

0.0 0.5 1.0W

0.0

0.5

1.00.0

0.5

1.0

Permeability (Human Skin)

0.0 0.5 1.0W

0.0

0.5

1.00.0

0.5

1.0

Lag-Time (Human Skin)

0.0 0.5 1.0W

0.0

0.5

1.00.0

0.5

1.0

Sathyan, G., Ritschel, W. A. and Hussain, A. S.: Transdermal delivery of tacrine, I: Identification of a suitable delivery vehicle. Int. J. Pharm. 114: 75‑83 (1995).

Drug-Polymer and Polymer #1- Drug-Polymer and Polymer #1- Polymer #2 RatiosPolymer #2 Ratios

Effect on drug release mechanism form a Effect on drug release mechanism form a hydrophilic matrix capsule containing hydrophilic matrix capsule containing HEC + Na.CMCHEC + Na.CMC

Drug + HEC + Na.CMC =1Drug + HEC + Na.CMC =1 Variables: Variables: Response: n, T50%Response: n, T50% Design: FactorialDesign: Factorial

CMCNaHEC

Drug

. CMCNa

HEC

.

nt tkM

M

Hussain, A. S., Johnson, R. D., Shivanand, P. and Zoglio, M. A.: Effects of blending a nonionic and an anionic cellulose ether polymer on drug release from hydrophilic matrix capsules. Drug. Dev. Ind. Pharm. 20: 2645‑2657 (1994).

Optimal FormulationsOptimal Formulations

Composition Formulation 1 Formulation 2

HEC 0.35 0.43

Na. CMC 0.46 0.44

Drug 0.19 0.13

Response Calc. Obs. Calc. Obs.

n 1 1 (0.13) 1 1(0.08)

T50% 7.5 7.8(0.73) 8 8.1(0.8)

Region of Interest (Mixture+Process)Region of Interest (Mixture+Process)

w1

w2

w1

w2

X3=1

X3=1X2=1

X2=1

X1=1

Z=+1

Z=-1

A move towards “Mechanism” A move towards “Mechanism” (macro-scale)(macro-scale)

The following slides provide a brief The following slides provide a brief summary of current research on topical summary of current research on topical microbicide vaginal productsmicrobicide vaginal products is an example of the “mechanistic” approachis an example of the “mechanistic” approach

• linking physics with physiology to identify critical linking physics with physiology to identify critical product attributes and explain how these attributes product attributes and explain how these attributes effect product performanceeffect product performance

INTROITUS

VAGINACERVIX

MUCUS

PROPHYLACTIC COATINGCONTRACEPTIVE COATING

Desired Distribution Profile ofDesired Distribution Profile ofCertain Vaginal FormulationsCertain Vaginal Formulations

InteractionsInteractions in the Vaginain the Vagina

MicrobicideMicrobicideFormulationFormulation

Anatomy,Anatomy,GeometryGeometry

SurfaceSurfacePropertiesProperties

FluidFluidContentsContents

MechanicalMechanicalPropertiesProperties

““SLIDING”SLIDING” gravity““SLIDING”SLIDING” gravity

““SQUEEZING”SQUEEZING” visceral contractions pressure tissue elasticity

““SQUEEZING”SQUEEZING” visceral contractions pressure tissue elasticity

““SEEPING”SEEPING” surface energies interfacial tensions

““SEEPING”SEEPING” surface energies interfacial tensions

rugae

mucus,

transudate

GELGEL

Pre-Coital Forces Acting on a Pre-Coital Forces Acting on a Bolus of Gel in VaginaBolus of Gel in Vagina

David Katz. Duke University

Mechanistic Analysis of Sub-Mechanistic Analysis of Sub-processes (Squeezing)processes (Squeezing)

R

F

2h

epithelial surfaces

vehicle

FORMULATION

• THEORYTHEORY • SIMULATIONSIMULATION

V

2

2E n 3 1 2 m

1

n 3n 6

2n 1

2V

n 2

2nt ho

3n 6

2n

2n

3n 6Solution for elastic surfaces (E, ); lubrication approximation; power law fluid (n, m); conserved bolus volume V = 2hoπ R2Area(t)=


SLIDING THEORYSLIDING THEORY

velocity depends upon…vagina properties

tilt angle tissue separation HHtottot

gel propertiesdensityrheology

velocityprofile

VVavgavg

HHtottot


Axial and Angular DependenceAxial and Angular Dependenceof Coating Thickness Distributionof Coating Thickness Distribution

Axial Dependence

0

500

1000

0 0.5 1

Axial Length

Th

ick

ne

ss

(

m)

AdvantageConceptrol

Angular DependenceThickness in microns

0

500

10000

45

90

135

180

225

270

315

Axial Dependence

0

500

1000

0 0.5 1

Axial Length

Th

ick

ne

ss

(

m)

Scaled to length of 71.11 mm

Angular DependenceThickness in microns

0

500

10000

45

90

135

180

225

270

315

Scaled to length of 51.67 mm

% Volume Distal to Fornix

39% 85%

% of Area Coated

36% 100%


Desired StateDesired State Regulatory policies tailored to recognize the level Regulatory policies tailored to recognize the level

of scientific of scientific knowledgeknowledge supporting product supporting product applications, process validation, and process applications, process validation, and process capability capability

Risk based regulatory scrutiny relate to the:Risk based regulatory scrutiny relate to the: level of scientific understandinglevel of scientific understanding of how formulation and of how formulation and

manufacturing process factors affect product quality manufacturing process factors affect product quality and performance, and and performance, and

the capability of the capability of process control strategies to prevent process control strategies to prevent or mitigate riskor mitigate risk of producing a poor quality product of producing a poor quality product

http://www.fda.gov/cder/gmp/21stcenturysummary.htm

Quality Risk ScenariosQuality Risk Scenarios Risk of unacceptable quality (examples)Risk of unacceptable quality (examples)

Releasing a unacceptable quality productReleasing a unacceptable quality product• Inadequate controls/specificationsInadequate controls/specifications

““New” impurities or degradation productsNew” impurities or degradation products Bio-in-equivalenceBio-in-equivalence

• Inadequate process validationInadequate process validation Process not understood, sampling not “representative”Process not understood, sampling not “representative”

Stability failureStability failure Unacceptable S&E profile, Bio-in-equivalenceUnacceptable S&E profile, Bio-in-equivalence Poor Process qualityPoor Process quality OthersOthers

AAPS Poster, November 2001

STABILITY PROFILES OF DRUG PRODUCTSEXTENDED BEYOND LABELED EXPIRATION

DATES

Jeb S. Taylor1, Robbe C. Lyon1, Hullahalli R. Prasanna1,Ajaz S. Hussain2

Center for Drug Evaluation and Research, FDA, 1Division of Product QualityResearch,Nicholson Research Center, Kensington, MD; 2Office of PharmaceuticalSciences, Rockville, MD

INTRODUCTION

The FDA administers the Shelf Life Extension Programfor the U.S. Military.

Selected drug products are tested to determine if theirshelf life can be extended past labeled expiration date.

Program probably contains the most extensive source ofstability data available.

The test attributes, methods of evaluation and analysisare described.

This report summarizes extended stability profiles for96 different drug products.

SUMMARY

Results from 1122 lots (96 drug products) were evaluated.

84% of the lots were extended for an average of 57 months pastthe original expiration date.

Of the 946 lots extended, 14% were eventually terminated due tofailure. The rest are still active or discontinued by the military.

22 Drug Products showed no signs of stability failure (at least 5lots of each tested).

10 Drug Products were unstable with most lots failing initialextension.

G r o u p 6 : U n s t a b l e P r o d u c t s :M o s t * L o t s F a i l I n i t i a l E x t e n s i o n

D r u g P r o d u c t D o s a g e F o r m# o f L o t s

T e s t e d# o f L o t s

E x t e n d e dI n i t i a l E x t e n s i o n

F a i l u r e sA l b u t e r o l A e r o s o l 2 0 A s s a y : S t r e s s ( 2 )B r o m p h e n i r a m i n e M a l e a t e -P h e n y l p r o p a n o l a m i n e H C l

S R T a b l e t s 4 0 A p p e a r a n c e : S t r e s s ( 4 )

D i p h e n h y d r a m i n e H C l S p r a y 2 0 A s s a y ( 2 )I s o p r o t e r e n o l H C l I n j e c t i o n 8 2 A s s a y ( 4 )

A s s a y : S t r e s s ( 2 )L e v a r t e r e n o l B i t a r t r a t e I n j e c t i o n 9 2 A s s a y : S t r e s s ( 7 )L i d o c a i n e H C l & E p i n e p h r i n e I n j e c t i o n 7 0 A s s a y ( 2 )

A s s a y : S t r e s s ( 5 )M e f l o q u i n e H C l T a b l e t s 1 9 4 D i s s o l u t i o n ( 1 5 )P e n i c i l l i n G P r o c a i n e P o w d e r 5 1 A s s a y ( 4 )P h e n o b a r b i t a l S o d i u m I n j C a r t r i d g e - N e e d l e 3 1 A s s a y ( 2 )P h y s o s t i g m i n e S a l i c y l a t e I n j e c t i o n 1 4 4 L o w p H ( 1 0 )

* 5 0 % O c c u r r e n c e

CONCLUSIONS

Actual Shelf Life may be much longer than indicated byExpiration Date on the label.

Stability Period is highly variable from lot to lot.

Continued testing and systematic evaluation is required toprovide assurance that stability and product quality ismaintained.

Dissolution Test & Dissolution Test & Bioequivalence: Risk Bioequivalence: Risk

AssessmentAssessmentDissolutiongenerally

“over-discriminating”

Dissolution failsto signal

bio-in-equi~ 30% (?)

NO YES

NO

YE

S

Bio

eq

uiv

ale

nt

Dissolution Specification

Why?

Appropriate Specification or “Over-Appropriate Specification or “Over-discrimination”discrimination”

All Bioequivalent to RLDAll Bioequivalent to RLDDISSOLUTION OF GENERIC & RESEARCH TABLETS

TIME IN MINUTES

0 5 10 15 20 25 30 35

% D

RU

G R

EL

EA

SE

D

0

10

20

30

40

50

60

70

80

90

100

110

ANDA1

ANDA2

ANDA3

ANDA4

ANDA5

ANDA6

ANDA7

ANDA8

ANDA8

74-217

UMAB-SLOW

UMAB-MEDIUM

UMAB-FAST

Failure to Discriminate Between Bio-in-Failure to Discriminate Between Bio-in-equivalent Products: Inappropriate equivalent Products: Inappropriate

Acceptance CriteriaAcceptance Criteria

0 10 20 30 40 50

% D

rug D

isso

lved

0

10

20

30

40

50

60

70

80

90

100

110

USP Specification

Product A

Product B

Time in Minutes

Product B was notbioequivalent to

Product A

Log(AUCinf): CI 94.6 - 123.6

Log(AUC): CI 89.1 - 130.0

Cmax: CI 105.3 - 164.2

(weak acid, rapid dissolution in SIF)

Time in Hours

0 1 2 3 4 5 6

Dru

g C

on

cen

trat

ion

in P

lasm

a (n

g/m

l)

0

200

400

600

800

1000

1200

1400

1600

1800Capsule (Ref.)

Tablet 1(wet-granulation - starch)

Tablet 2

(direct compression -

calcium phosphate)

USP Paddle 50rpm, Q 70% in 30 min

Failure to Discriminate Between Bio-in-Failure to Discriminate Between Bio-in-equivalent Products: Inappropriate Test equivalent Products: Inappropriate Test

Method?Method?

False Positives and False False Positives and False Negatives!!! Negatives!!!

15 min 30 min 45 min AUC CmaxRef 95 96 98 100 100B 96 97 97 104 95C 62 84 92 84 55D 82 94 95 88 87E 103 103 103 112 120F 13 35 53 100 102

Test/Ref. Mean

I. J. MacGilvery. Bioequivalence: A Canadian Regulatory Perspective. In, Pharmaceutical Bioequivalence. Eds. Welling, Tse, and Dighe. Marcel Dekker, Inc., New York, (1992)).

Failure of Dissolution Tests to Failure of Dissolution Tests to Signal Bio-in-equivalenceSignal Bio-in-equivalence

Inappropriate “acceptance criteria”Inappropriate “acceptance criteria” One point specificationOne point specification Set “too late”Set “too late”

Inappropriate test methodInappropriate test method media composition (pH,..)media composition (pH,..) media volumemedia volume hydrodynamicshydrodynamics

Excipients affect drug absorptionExcipients affect drug absorption Dissolution is NOT “RATE LIMITING”Dissolution is NOT “RATE LIMITING” Other reasons (many critical test variables)Other reasons (many critical test variables)

Is Dissolution Rate Limiting?Is Dissolution Rate Limiting?

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 4 8 12 16 20 24

Time

Con

cent

rati

on

Capsule

Solution

aaps Annual Meeting aaps Annual Meeting 4949

ICH Q6A DECISION TREES #7: SETTING ACCEPTANCE CRITERIA FOR DRUG PRODUCT DISSOLUTION

What specific test conditions and acceptance criteria are appropriate? [IR]

dissolution significantlyaffect BA?

Develop test conditions and acceptance distinguish batches with unacceptable BA

YES

NO

YES

NO

YES

NO

Do changes informulation or

manufacturing variables affect dissolution?

Are these changes controlledby another procedure

and acceptancecriterion?

Adopt appropriate test conditionsand acceptance criteria without

regard to discriminating power, topass clinically acceptable batches.

Adopt test conditions and acceptance criteria which can distinguish

these changes. Generally, single point acceptance criteria are acceptable.

Why?

How?

What?

ICH Q6A: Decision Tree #7 (1)

Modified release?

High solubility?

Rapid dissolution?

Relationship betweenDisintegration - Dissolution?

No

Yes

Yes

Generally single-point dissolution acceptance criteria

with a lower limit

Generally disintegrationacceptance criteria with

an upper limit

Establish drug releaseacceptance criteria:

ER: Multiple time pointMR: Two stage, parallel

or sequential

Yes

No

No

No

Yes

?

Disintegration - Dissolution Relationship

Total Dissolution = Ft + Fl + Fs + Fs*

Disintegration DisintegrationTime (DT)

TabletSurface (t)

LargeFragments (l)

SmallFragments (s)

Ft Fl Fs Fs*

After DTPrior to DT

10# screen

Fraction dissolved

Note: Disintegration and dissolution process in a dissolution apparatus may differ from that in a disintegration apparatus (different hydrodynamics and other conditions)

Cumulative Dissolution and Disintegration Data: Critical Formulation Variables

Time in Minutes

0 5 10 15 20 25 30 35

% D

ru

g D

isso

lved

0

20

40

60

80

100

120

Corresponding Disintegration Time

Data

MCC(-)SSG(+)MgS(-)

MCC(-)SSG(+)[MgS(-)]

DataDissolution and disintegration time data and the impact of

formulation variables on dissolution at various time points (Figure 1)

Li et al, Pharm. Develop. And Tech. 1: 343-355, 1996 and Rekhi et al., Pharm. Develop. and Tech. 2: 11-24, 1997

Predicting Dissolution of Experimental Furosemide TabletsUsing Near Infrared Spectroscopy

Everett H. Jefferson1, Alan S. Carlin1, Robbe C. Lyon1, Lawrence X. Yu2,Robert L. Hunt1, Jin T. Wang1, William N. Worsley1, Ajaz S. Hussain3

1Division of Product Quality Research, FDA, Kensington, MD

2Office of Generic Drugs, FDA, Rockville, MD

3Office of Pharmaceutical Science, FDA, Rockville, MD

Predicting Product Dissolution

Formulation-Processing

CasualLink

CasualLink

NIR SpectraNon-destructive Test

Dissolution(opportunity to consider

direct link to PK/PD)Destructive Test

Correlation

Develop Casual Links based on Critical Attributes

Dissolution - Formulation: Casual Link

Ex1, Ex2 = Excipients (USP/NF)P1, P2 = Process parameters (time, hardness ...)PS = Drug particle size (specification)

Dissolution is a function of processing variables:

Dissolution = f (Ex1, Ex2, P1, P2, PS…)

Use Multiple Linear Regression (MLR) to determine thevariables critical to dissolution:

y = 0 + 1 x1 + 2 x2 + 3 x3 + 12 x1 x2 + 13 x1 x3 + 23 x2 x3 + ...

Dissolution-Formulation Correlation:Direct Compression (%Diss at 15 min)

The critical formulation variables (p<0.05): disintegrant level disintegrant and filler interaction

Regression Model: %Diss_15 = 56.3 + 25.3 x2 - 15.1 x1 x2

FillerAcDiSol

MgSHardness

Filler*AcDiSolFiller*MgS

Filler* HardnessAcDiSol*MgS

AcDiSol*HardnessMgS*Hardness

x1 x2

x2

Within the design space only - e.g.,other variables held constant

NIR - Dissolution Correlation:Direct Compression (%Diss at 15 min)

Training Set (n=72)

R2 = 0.9771

Test Set (n=72)

R2 = 0.9465

0

20

40

60

80

100

0 20 40 60 80 100

Measured % Dissolution

Pre

dic

ted

% D

iss

olu

tio

n

Direct Com pression (Lots 121-132)PLS1 Model2nd Derivative Spectra

NIR - Formulation: Casual Link

1100 1300 1500 1700 1900 2100 2300 2500-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

3.0000

Wavelength

Inte

nsit

y

FurosemideAvicelMagnesium Stearate

1100 1300 1500 1700 1900 2100 2300 2500-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

3.0000

Wavelength

Inte

nsit

y

Lactose MonohydrateAc-Di-SolStarchPovidone

NIR Spectra of the Raw Materials

NIR - Formulation Correlations

Lactose Avicel

Manufacturing Training Set Test Set

Constituent Process n R2 n R2

Ac-Di-Sol Direct Compression 72 0.9897 72 0.9860Mg Stearate Direct Compression 72 0.9780 72 0.9781

Starch W et Granulation 48 0.9758 48 0.9539Povidone W et Granulation 24 0.9911 24 0.9873

Training Set (n=72)

R2 = 0.9790

Test Set (n=72)

R2 = 0.9786

-50

0

50

100

150

200

250

0 50 100 150 200 250

Measured Lactose (mg)

Pre

dic

ted

Lac

tose

(m

g)

Direct Com pressionPLS1 Model2nd Derivative Spectra

Training Set (n=72)

R2 = 0.9741

Test Set (n=72)

R2 = 0.9742

-50

0

50

100

150

200

250

0 50 100 150 200 250

Measured Avicel (mg)

Pre

dic

ted

Av

ice

l (m

g)

Direct Com pressionPLS1 Model2nd Derivative Spectra

Process Understanding

Risk(P/R)

CMC regulatory oversight

Company’sQuality system

cGMP regulatory oversight

Post approval change



Risk






Risk




CMC-GMP Risk ModelCMC-GMP Risk Model

Should allow us to recognize the level of process Should allow us to recognize the level of process understanding for a given productunderstanding for a given product

Provide a means to move towards a one time Provide a means to move towards a one time CMC review process for well understood products CMC review process for well understood products and processes (current system will remain for and processes (current system will remain for others)others) Exception - unless specifications need to changeException - unless specifications need to change Continuous improvement within a companies quality Continuous improvement within a companies quality

system system Share and have available knowledge throughout the Share and have available knowledge throughout the

organizationorganization

Risk Mitigation OpportunitiesRisk Mitigation Opportunities

• Advances in application of risk analysis and Advances in application of risk analysis and risk management (including IT revolution)risk management (including IT revolution)• More systematically applied outside More systematically applied outside

pharmaceutical sectorpharmaceutical sector Including data generated by advanced manufacturing Including data generated by advanced manufacturing

technologiestechnologies

• Risk management approaches to regulatory Risk management approaches to regulatory compliance gaining wider acceptance among compliance gaining wider acceptance among regulatorsregulators

Risk to QualityRisk to Quality

““Risk” to pharmaceutical quality is Risk” to pharmaceutical quality is associated with factors that relate to the associated with factors that relate to the probabilityprobability or or severityseverity of adverse effects of adverse effects on these attributes on these attributes Factors associated with compromising :Factors associated with compromising :

• e.g., identity, strength/potency, bioavailability, e.g., identity, strength/potency, bioavailability, purity, or clear/accurate labelingpurity, or clear/accurate labeling

Applying Risk Management to Drug Applying Risk Management to Drug Quality Regulation (cont’d)Quality Regulation (cont’d)

Tools and techniques for identifying and Tools and techniques for identifying and focusing on critical processes and focusing on critical processes and parameters potentially include:parameters potentially include: Failure Modes and Effects Analysis (FMEA)Failure Modes and Effects Analysis (FMEA) Hazard Analysis and Critical Control Points Hazard Analysis and Critical Control Points

(HACCP)(HACCP) Fault Tree Analysis (FTA)Fault Tree Analysis (FTA) Hazard and Operability Study (HAZOP)Hazard and Operability Study (HAZOP)

Applying Risk Management to Applying Risk Management to Drug Quality Regulation (cont’d)Drug Quality Regulation (cont’d) To support more focused regulatory To support more focused regulatory

scrutiny we need:scrutiny we need: Greater understanding of the sources of risk Greater understanding of the sources of risk

to product qualityto product quality• Including factors predictive of that risk, as well as Including factors predictive of that risk, as well as

those that are predictive of the successful those that are predictive of the successful mitigation of the risk; and mitigation of the risk; and

Enhanced hazard identification and risk Enhanced hazard identification and risk assessment capacityassessment capacity


Greater uncertainly requires greater Greater uncertainly requires greater regulatory scrutinyregulatory scrutiny Burden must rest largely with regulated Burden must rest largely with regulated

industry to demonstrate to regulators that industry to demonstrate to regulators that reduced regulatory scrutiny is justified by the reduced regulatory scrutiny is justified by the science/datascience/data

Regulators need to support industry efforts to Regulators need to support industry efforts to develop and provide the needed science/datadevelop and provide the needed science/data


Risks to pharmaceutical quality can be Risks to pharmaceutical quality can be ranked by identifying and weighting factors ranked by identifying and weighting factors associated with the associated with the probabilityprobability or or severityseverity of adverse effects on quality of adverse effects on quality attributes and surrogatesattributes and surrogates What processes and parameters are critical for What processes and parameters are critical for

identity, strength/potency, bioavailability, purity, or identity, strength/potency, bioavailability, purity, or clear/accurate labeling?clear/accurate labeling?

What factors will adversely (or positively) affect critical What factors will adversely (or positively) affect critical parameters/processes, increasing probability or parameters/processes, increasing probability or severity or impact?severity or impact?

Risk Management and Risk Management and Resource AllocationResource Allocation

FDA is developing a model using a FDA is developing a model using a Risk Ranking Risk Ranking and Filteringand Filtering technique to better focus technique to better focus inspectional resources based upon the risk of inspectional resources based upon the risk of manufacturing deficiencies that would reduce manufacturing deficiencies that would reduce drug qualitydrug quality systematically incorporate our current knowledge systematically incorporate our current knowledge

about drug quality risks and manufacturing sitesabout drug quality risks and manufacturing sites Prioritize sites for periodic systems-based inspectionsPrioritize sites for periodic systems-based inspections

Risk Management and Risk Management and Resource Allocation (cont’d)Resource Allocation (cont’d)

Starting in FY ‘03, shifted emphasis to Starting in FY ‘03, shifted emphasis to facilities making drugs perceived to be of facilities making drugs perceived to be of higher risk if subject to manufacturing higher risk if subject to manufacturing deficienciesdeficiencies

• Sterile drugsSterile drugs• Rx drugs (non-medical gas)Rx drugs (non-medical gas)• New registrantsNew registrants

New model under development for pilot New model under development for pilot implementation later this yearimplementation later this year

SITE RISK POTENTIAL

PRODUCT PROCESS FACILITY

A RISK-BASED FRAMEWORK FOR PRIORITIZING SITES FOR cGMP INSPECTION

Site potential risk score:

[wp Product weight] x [wPr Process weight] x [wfx Facility weight];

“w” is the relative weight assign to the individual module

Semi-quantitative weights

high, medium, low; yes/no; ordinal scales 1- 5

Risk Ranking Model: Risk Ranking Model: Product FactorsProduct Factors

What are the intrinsic properties of drug products What are the intrinsic properties of drug products such that deficiencies in quality, if any, would such that deficiencies in quality, if any, would have more adverse public health impact than have more adverse public health impact than others? others? sterilesterile Rx vs. OTCRx vs. OTC Route of administrationRoute of administration

Mining recall data can help weight product Mining recall data can help weight product factors (e.g., product or dosage form associated factors (e.g., product or dosage form associated with prevalence of serious recalls?)with prevalence of serious recalls?)

Risk Ranking Model:Risk Ranking Model:Facility FactorsFacility Factors

Are some manufacturing facilities (or Are some manufacturing facilities (or manufacturers) more likely to produce a product manufacturers) more likely to produce a product with quality problems?with quality problems? Effectiveness of quality systems and process Effectiveness of quality systems and process

capabilitycapability Inspectional record and compliance historyInspectional record and compliance history

• Exposure: volume produced at facilityExposure: volume produced at facility Product sales volumeProduct sales volume Special/sensitive populationsSpecial/sensitive populations

• Other characteristics?Other characteristics? New Registrants?New Registrants? Macher and Nickerson study will help identifyMacher and Nickerson study will help identify

Risk Ranking Model:Risk Ranking Model:Process FactorsProcess Factors

Are some manufacturing processes, for Are some manufacturing processes, for particular product classes, more likely to go particular product classes, more likely to go wrong than others?wrong than others? Are some process problems of greater public health Are some process problems of greater public health

significance? What are the consequences of process significance? What are the consequences of process problems?problems?

• Use Use expert elicitationexpert elicitation to identify risk factors and weightings to identify risk factors and weightings At the unit operation levelAt the unit operation level By product classesBy product classes

• Risk of contamination or mix-upsRisk of contamination or mix-ups• Maintaining state of control of the processMaintaining state of control of the process


Risk ranking and filtering technique may Risk ranking and filtering technique may be a useful tool for other aspects of drug be a useful tool for other aspects of drug quality regulationquality regulation Focus compliance programs? Focus compliance programs? Guidance development?Guidance development? Regulatory action decisions?Regulatory action decisions? Industry internal audits?Industry internal audits?


The product can only be as good as the The product can only be as good as the scientific/technical input/assumptions that scientific/technical input/assumptions that are used to develop the risk scoresare used to develop the risk scores

Multiple iterations and successive Multiple iterations and successive revisions will reflect growing knowledge revisions will reflect growing knowledge base and extensive input from internal and base and extensive input from internal and external expertsexternal experts

Ajaz Hussain, Ph.D. and David Horowitz, Esq. CDER, FDA Efficient Risk Based Regulatory Scrutiny for...

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Transcript of Ajaz Hussain, Ph.D. and David Horowitz, Esq. CDER, FDA Efficient Risk Based Regulatory Scrutiny for...