Q1E Q1E Sum umie e Yo Yosh shio ioka, a, P Ph. h. D D. - PowerPoint PPT Presentation

Evalua uation f for Sta tability ty data a Q1E Q1E Sum umie e Yo Yosh shio ioka, a, P Ph. h. D D. MHL HLW Nat ation onal al I Ins nstit itut ute e of of He Heal alth th S Scie ienc nces es

Q1E 1E pr prov ovid ides s re reco comme mend ndat ation ons s on on : :  How to to us use sta tabil ility d data ta gene nerat ated accord rding ng to Q Q1AR AR  When a and d how a re retest t per eriod o or a shel elf l life c can n be ex exten ended b beyo yond the pe perio iod cov overe red by y lon ong-ter erm d data Q1E 1E co cont ntai ains exampl ples s of st stati tistica cal a approa oache hes to o stabil ility ty data ta an analysi sis

 Extrapolation Extrapolation to toto o ex exten tend d re retes test t pe perio riod/ d/sh shelf elf lif ife  Statistical approaches Statistical approaches re reco comme mmend nded ed in in t the he gu guid idel eline ine

No Yes Significant change Accelerated condition

Where no significant change occurs at accelerated condition No Yes Little or no change Little or no variability Accelerated data & Long-term data

Where accelerated data show significant change No Significant Yes change Intermediate condition

No Yes Amenable? Performed? Statistical analysis

No Yes Available? Supporting data

Four outcomes passing through crossroads for Room Temperature Storage 12 month extension 6 month extension 3 month extension No extension

Outcome 1 12 month extension acceler erated d dat ata sho how no s signif ifica cant ch chang nge acc ccele lerated ed da data & & lon ong-term data litt ttle or or no no chan ange litt ttle or or no no vari riabi bility Outcome 4 no extension signif ifica cant ch chang nge at a accele lerat ated co condi dition n at i interm rmedi diate c cond ndition on

Stat tatist istic ical al an analy alysi sis longer retest period/shelf longer retest period/shelf life life (not necessarily required) (not necessarily required)

Where  Accelerated data show no significant change  Changes and variations in accelerated data long-term data No amenable? 6 month extension performed? with Supporting data Yes 12 month extension

Where Significant change at accelerated condition but not at intermediate condition No amenable? 3 month extension performed? with Supporting data Yes 6 month extension

St Stat atis isti tical al a ana nalys ysis is longer er re retest t per eriod/s /shel elf lif ife not al alway ays req equir ired Where  significant change at accelerated & intermediate conditions  variability in long-term data Statistical analysis can be appropriate to verify retest period/shelf life

Sta tatis isti tica cal a app ppro roach ches es re reco comm mmend nded ed i in t the he A Appe pend ndix ix  How to to an analyze ze lo long-term data for ap appro ropriat ate q quanti titat ative a attr tribute tes  How to to us use reg egres ession n ana nalysis is for re retes est per eriod od/shel elf l life e esti timatio ion  Exampl ples s of st stati tistica cal p proced edure res to det eterm rmine pool olabili lity of da data a from m differ erent nt batc tches es or f fact ctor co combi binatio ions

Regression analysis Est stabl blis ish h ret etes est t per erio iod/ d/she helf lf l life fe wit ith h a a hig igh h de degre ree e of of co conf nfid idenc nce Quan uanti tita tativ tive e at attri tribu bute te wi will ll r rema emain in wit ithi hin n acc ccep epta tance ce c cri riter eria ia for or a all ll fu futu ture re ba batc tche hes

Shelf-life Estimation with Upper and Lower Acceptance Criteria Based on Assay at 25C/60%RH 120 115 Assay (% of Label Claim) 110 105 Raw Data 100 Upper confidence limit Lower confidence limit 95 Regression line 90 Upper acceptance criterion: 105 85 Lower acceptance 80 criterion: 95 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 Time Point (Months)

Statistical approaches for determining whether data from different batches/factor combinations can be pooled  (Appro roach ch #1) Wheth ther r data a fro rom al all batche hes/f /factor or co combina natio ions su suppo port th the propos osed d perio iod #2 “ Poolabili test ” )  (Approach (Approach #2 Poolability ty test Wh Wheth ther da data a from m all ll batc tches es/fact ctor r combin inati tions c can n be co combi bined for ov overa rall es estim imate o of a a sing ngle e perio iod  (Alter ernat ative a appr proache hes)

Ap Appr proac oache hes s #1 #1 an and d #2 #2 ca can n als lso b be e ap appli lied ed t to d dat ata a ana naly lysi sis for or mu mult lti-fac acto tor r stu tudi dies es in incl cludi uding ng Bra racke keti ting ng & & Ma Matr trixi xing ng De Desi sign gns

Basic Principles  A shelf life is set based on long-term data  The extent of extrapolation will depend on accelerated (and if applicable, intermediate) data, as well as long-term data  Supporting data are useful in predicting long-term stability in primary batches

Basic Principles (cont’d)  Statistical analysis is not always necessary for setting a shelf life  A shelf life beyond the period covered by available long-term data can be proposed with supporting data, with or without statistical analysis  Where a statistical analysis is performed, longer extrapolation can be justified

MHLW Perspective - Q1E Before Q1E EU---12 month extrapolation with or without statistical analysis; US--- max 6 month extrapolation with statistical analysis; Japan--- no practical extrapolation  Q1E provides guidance on the extent of shelf life extrapolation in a variety of situations  Q1E clearly describes the role of accelerated data and of supporting data in shelf life estimation