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"
$%ce of &e Drug Quality AssessmentCD'R(FDA
FDA()n*ustry Statistics +or,shop+ashington D"C"
September .-/! 001
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$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
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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
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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
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Quality
by
Design
FDA5s vie on QbD! #oheb &asr!
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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
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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
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Pharmaceutical Development< Pro*uct Hifecycle
Candidate
Selection
Product Design & Development
Process Design & Development
Manufacturing Development
Product
Approval
Continuous Improvement
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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$'s8 e%cient metho* for *etermining impact of
multiple parameters an* their interactions Scale-up correlation
a semi-empirical approach to translateoperating con*itions beteen *iIerent scales orpieces of e2uipment
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Design of ';periments 7D$'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
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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
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*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 ¬
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