Statistics in QbD Stats WS 09-06

8/16/2019 Statistics in QbD Stats WS 09-06

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Pharmaceutical

Development Using Quality-by-Design Approach – an

FDA Perspective

Chi-an Chen! Ph"D"Christine #oore! Ph"D"

$%ce of &e Drug Quality AssessmentCD'R(FDA

FDA()n*ustry Statistics +or,shop+ashington D"C"

September .-/! 001


2/22

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$utline

FDA initiatives for 2uality Pharmaceutical C3#Ps for the 4st Century

$&DQA5s PQAS 6he *esire* state Quality by *esign 7QbD8 an* *esign space 7)C9

Q:8

Application of statistical tools in QbD

Design of e;periments #o*el buil*ing < evaluation Statistical process control

FDA C#C Pilot Program Conclu*ing remar,s


3/22

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4st Century )nitiatives

Pharmaceutical C3#Ps for the 4st

Century – a ris,-base* approach7/(0=8 http>(("f*a"gov(c*er(gmp(gmp00=(3#P?@nalreport00="htm

$&DQA +hite Paper onPharmaceutical Quality AssessmentSystem 7PQAS8 http>(("f*a"gov(c*er(gmp(gmp00=(on*c?reorg"htm

http://www.fda.gov/cder/gmp/gmp2004/GMP_finalreport2004.htmhttp://www.fda.gov/cder/gmp/gmp2004/GMP_finalreport2004.htmhttp://www.fda.gov/cder/gmp/gmp2004/ondc_reorg.htmhttp://www.fda.gov/cder/gmp/gmp2004/ondc_reorg.htmhttp://www.fda.gov/cder/gmp/gmp2004/ondc_reorg.htmhttp://www.fda.gov/cder/gmp/gmp2004/ondc_reorg.htmhttp://www.fda.gov/cder/gmp/gmp2004/GMP_finalreport2004.htmhttp://www.fda.gov/cder/gmp/gmp2004/GMP_finalreport2004.htm


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6he Desire* State7anet +oo*coc,! $ctober 00B8

A maximally efcient,

agile, exible pharmaceuticalmanuacturing sector thatreliably produces high-

quality drug productswithout extensiveregulatory oversight

A mutual goal oindustry, society, and

regulator


5/22

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FDA5s )nitiative on Quality byDesign

)n a Quality-by-Design system> 6he pro*uct is *esigne* to meet patient

re2uirements 6he process is *esigne* to consistently meet

pro*uct critical 2uality attributes 6he impact of formulation components an* process

parameters on pro*uct 2uality is un*erstoo* Critical sources of process variability are i*enti@e*

an* controlle* 6he process is continually monitore* an* up*ate*

to assure consistent 2uality over time


6/22

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Quality

by

Design

FDA5s vie on QbD! #oheb &asr!


7/22

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Design Space 7)C9 Q:8

De@nition> 6he multi*imensional combination an*interaction of input variables 7e"g"! material

attributes8 an* process parameters that have been*emonstrate* to provi*e assurance of 2uality

+or,ing ithin the *esign space is not consi*ere*as a change" #ovement out of the *esign space isconsi*ere* to be a change an* oul* normally

initiate a regulatory post-approval change process" Design space is propose* by the applicant an* is

subect to regulatory assessment an* approval


8/22

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Current vs" QbD Approach toPharmaceutical Development

Current Approach QbD Approach

Quality assure* by testing an*inspection

Quality built into pro*uct <process by *esign! base* on

scienti@c un*erstan*ing

Data intensive submission –*isointe* information ithoutbig pictureE

nole*ge rich submission –shoing pro*uct ,nole*ge <process un*erstan*ing

Speci@cations base* on batch

history

Speci@cations base* on pro*uct

performance re2uirements

FroGen process!E *iscouragingchanges

Fle;ible process ithin *esignspace! alloing continuousimprovement

Focus on repro*ucibility – often

avoi*ing or ignoring variation

Focus on robustness –

un*erstan*ing an* controllingvariation


9/22

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Pharmaceutical Development< Pro*uct Hifecycle

Candidate

Selection

Product Design & Development

Process Design & Development

Manufacturing Development

Product

Approval

Continuous Improvement


10/22

Design ofExperiments

(DOE)

Model uildingAnd Evaluation

Process Design & Development!)nitial ScopingProcess CharacteriGationProcess $ptimiGationProcess Robustness

Statistical 6ool

Product Design & Development!)nitial ScopingPro*uct CharacteriGationPro*uct $ptimiGation

Manufacturing Developmentand Continuous Improvement!

Develop Control SystemsScale-up Pre*iction

6rac,ing an* tren*ing

StatisticalProcess Control

PharmaceuticalDevelopment < Pro*uct

Hifecycle


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Process 6erminology

Process Step

Input Materials Output Materials

(Product orIntermediate)

InputProcess

Parameters

MeasuredParametersor Attri"utes

Control #o*el

DesignSpace

Critical #ualit$ Attri"ute

ProcessMeasurements

and Controls


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Design Space Determination

First-principles approach combination of e;perimental *ata an*

mechanistic ,nole*ge of chemistry! physics!an* engineering to mo*el an* pre*ictperformance

Statistically *esigne* e;periments 7D$'s8 e%cient metho* for *etermining impact of

multiple parameters an* their interactions Scale-up correlation

a semi-empirical approach to translateoperating con*itions beteen *iIerent scales orpieces of e2uipment


13/22

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Design of ';periments 7D$'8

Structure*! organiGe* metho* for *etermining

the relationship beteen factors aIecting aprocess an* the response of that process Application of D$'s>

Scope out initial formulation or process *esign

$ptimiGe pro*uct or process Determine *esign space! inclu*ing multivariate

relationships


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D$' #etho*ology

(%) Coose experimental design 7e"g"! full factorial! *-optimal8 (') Conduct randomiedexperiments

() Create multidimensionalsurface model

7for optimiGation or control8

(*) Anal$e data

';periment

Factor A Factor J Factor C

4 K - -

- K -

L K K K= K - K

A

JC

"minitab"com


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#o*els for process *evelopment inetic mo*els – rates of reaction or *egra*ation

6ransport mo*els – movement an* mi;ing of mass orheat

#o*els for manufacturing *evelopment Computational Mui* *ynamics Scale-up correlations

#o*els for process monitoring or control Chemometric mo*els Control mo*els

All mo*els re2uire veri@cation through statisticalanalysis

#o*el Juil*ing < 'valuation -';amples


16/22

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Chemometrics is the science of relatingmeasurements ma*e on a chemical system or

process to the state of the system via applicationof mathematical or statistical metho*s 7)CS*e@nition8

Aspects of chemometric analysis> 'mpirical metho* Relates multivariate *ata to single or multiple responses UtiliGes multiple linear regressions

Applicable to any multivariate *ata> Spectroscopic *ata #anufacturing *ata

#o*el Juil*ing < 'valuation -Chemometrics


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Statistical Process Control -De@nitions

Statistical process control 7SPC8 is the applicationof statistical metho*s to i*entify an* control thespecial cause of variation in a process" Common cause variation – ran*om Muctuation of

response cause* by un,non factors Special cause variation – non-ran*om variation cause*

by a speci@c factor

Upper ControlHimit

Hoer ControlHimit

6arget

Upper Speci@cationHimit

Hoer Speci@cation

HimitSpecial cause variationN

Lσ


18/22

*Percent out of specification beyond the high risk specification limit.

σ3

)SL!min"pk

2.28%2σ0.7

15.9%1σ0.33

0.135%3σ1

0.003%4σ1.33

∼05σ1.7

∼06σ2

Expected Avg. OOS%*|X - SL|Cp

2.28%2σ0.7

15.9%1σ0.33

0.135%3σ1

0.003%4σ1.33

∼05σ1.7

∼06σ2

Expected Avg. OOS%*|X - SL|Cp

#ndustry Practice is to

consider processes $ith

"pk belo$ %.33 as &not

capable' of meeting

specifications.

"pk ( %.33 "pk ( .33

Process Capability )n*e; 7Cp,8


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Quality by Design < Statistics

Statistical analysis has multiple roles

in the Quality by Design approach Statistically *esigne* e;periments 7D$'s8 #o*el buil*ing < evaluation

Statistical process control Sampling plans 7not *iscusse* here8


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C#C Pilot Program

$bectives> to provi*e an opportunity for participating @rms to submit C#C information base* on QbD FDA to implement Q:! Q/! PA6! PQAS

6imeframe> began in fall 00BO to en* in spring 00: 3oal> 4 original or supplemental &DAs Status> 4 approve*O L un*er revieO . to be submitte* Submission criteria

#ore relevant scienti@c information *emonstrating use of QbDapproach! pro*uct ,nole*ge an* process un*erstan*ing! ris,assessment! control strategy


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C#C Pilot - Application ofQbD

All pilot &DAs to *ate containe* some elementsof QbD! inclu*ing use of appropriate statistical

tools D$'s for formulation or process optimiGation 7i"e"!

*etermining target con*itions8 D$'s for *etermining ranges of *esign space #ultivariate chemometric analysis for in-line(at-line

measurement using such technology as near-infrare*

Statistical *ata presentation an* usefulness Concise summary *ata acceptable for submission an*

revie 3enerally use* by revieers to un*erstan* ho

optimiGation or *esign space as *etermine*


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Conclu*ing Remar,s

Successful implementation of QbD ill re2uiremulti-*isciplinary an* multi-functional teams

Development! manufacturing! 2uality personnel 'ngineers! analysts! chemists! in*ustrial

pharmacists < statisticians or,ing together

FDA5s C#C Pilot Program provi*es anopportunity for applicants to share their QbD

approaches an* associate* statistical tools FDA loo,s forar* to or,ing ith in*ustry to

facilitate the implementation of QbD

Documents

Statistics in QbD Stats WS 09-06