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WHO Prequalification Program Workshop, Kiev, Ukraine, June 25-27,2007 Visit FDA website: www.fda.gov Factors to Consider for Bioequivalence Requires a Comparator Product Parent Compound Metabolite(s)? P’dynamic Response SAMENESS VARIABILITY Single Dose Multiple Dose ? Highly Variable Drugs Food effects Narrow Therapeutic Index Drugs w ith Long T50 Statistical Test Set at 5% Industry Risk Consumer Risk Comparator Product • Innovator Pharmaceutical Product ( Safety and efficacy) • A generic product should not be a comparator as long as an innovator product is available. • Selection should be made at the national level by the drug regulatory agency – National Innovator – WHO comparator product ( quality-safety-efficacy and has reference to manufacturing site) – ICH or associated country comparator product In The Case that Innovator Product cannot be identified • Important Criteria for Selection – Product is in the WHO list – Approval in an ICH – Associate Country- Pre-qualified by WHO – Extensive documented use in clinical trials reported in peer-reviewed scientific journals – Long unproblematic post-market surveillance (“well selected comparator”) A product approved based on comparison with A non domestic comparator product may not be interchangeable with currently marketed domestic products Set at 5% Stavchansky’s Recommendation: FDA should pressure the Innovator Companies to put forward a Confidence Interval for their HVP GE = PE + TE Therapeutic Equivalence of Multisource Product Therapeutic Equivalence can be assured when the multisource product is: pharmaceutically equivalent and bioequivalent. TE = PE + BE The concept of interchangeability applies to: 1. - the dosage form and 2. - the indications and instruction for use. AVERAGE BIOEQUIVALENCE A GLOBAL STANDARD OF PHARMACEUTICAL QUALITY ? Origin of ABE • A survey of physicians suggested that for most drugs, a difference of up to 20% in dose between two treatments would have no clinical significance Average Bioequivalence • two drug products are Bioequivalent ‘on the average’ when the (1-2α) confidence interval around the Geometric Mean Ratio falls entirely within 80-125% (regulatory control of specified limit) AVERAGE BIOEQUIVALENCE Comapre the population average response of the log-transformed Bioavailability Parameters after administration of the Test and Reference products. Pr1 f (T , R ) 2 1 P Test Confidence Interval Reference The same Mean different Variances ? What to do? Who decides the goal post? Clinical Judgement CMS Variability of Reference Product Population vs Individual Dose -Response curves BA metrics and which distribution Which parameters must meet criteria width of the interval The The assigned assurance probability Average Response test within 80 -125% 67 Ln0.8 ( T R ) Ln1.25 80 125 111 NTI 90 digoxin, phenytoin, warfarin, theophylline, lithium 150 Some International Criteria Country/Region AUC 90% CI Criteria Cmax 90% CI Criteria Canada (most drugs) 80 – 125% none (point estimate only) Europe (some drugs) 80 – 125% 75 – 133% South Africa (most drugs) 80 – 125% 75 – 133% (or broader if justified) Japan (some drugs) 80 – 125% Some drugs wider than 80 – 125% Worldwide (WHO) 80 – 125% “acceptance range for Cmax may be wider than for AUC” Geometric90%CI 100. exp ( LSM A LSM b t d f , 0.0 5SEa b ) Least Square Means from ANOVA t-statistic with 0.05 in one tail Standard Error Limitations of 2-Period Designs • The intra subject variance associated with the Test and Ref products may not be the same • A poor pharmaceutical product may have inflated intrasubject variance because of high within formulation variability • The residual variance in 2-period designs averages the intrasubject variance of the two products – The Test and Ref intrasubject variance cannot be separated REP L ICAT ED CRO S S O V ER DES IG NS F O R T W O F O RM UL AT IO NS O P T IM AL F O R CARRYO V ER ES T IM AT IO N S EQ UENCE 1 2 3 4 1 T R T R P ERIO D 2 1 2 3 T T R R R R T T R T 1 T R T R S W IT CHBACK DES IG NS S EQ UENCE 1 2 1 T R 2 3 R T T R 1 2 3 4 A B A B B A B A 2 T R R T 3 R T R T 4 R T T R Replicate Designs • Yields information on the Intrasubject Variance • Ideally, intrasubject variance of the Test product should be similar to the intrasubject variance of the Reference product What do we learn from ANOVA Analysis • The sources of variance in the model are – Product – Period – Sequence – Subject (Sequence) – Residual variance Source: Modified from K. Midha These account for all the inter-subject variability This estimates Intra-subject variability ‘Fixed Effects” in ANOVA • Product • Period • Sequence These fixed effects usually are not significant in the f-test • Subject nested within sequence is usually significant (f-test) because of large variability between subjects Source: Modified from K. Midha The Residual Variance (SW2) • Sources of Variability – – – – Intra-subject variance in Pharmacokinetics Analytical variability Subject by formulation interaction Unexplained random variation ANOVA CV Residual Variance 100% ANOVA CV WSV Source: Modified from K. Midha Example using ANOVA results T , obs R , obs t 0 . 95 ( ) S T, obs = 24.7 ng/ml R, obs = 23.7 ng/ml v = 22 t 0.95(v) = 1.7171 s = 5.693 n = 24 s*sqrt 2/n = 1.543 24.7 – 23.7 +/- 1.717 (1.643) ng/ml 1 +/- 2.82 ng/ml -1.82 ng/ml; 3.82 ng/ml The lower CI limit = 23.7 – 1.82 / 23.7 = 92.3 % The Upper CI limit = 23.7 + 3.83 / 23.7 = 116% 2 N The ‘ANOVA-CV’ • The ANOVA-CV which is easily calculated from the residual variance is an estimate of WSV ANOVA CV Residual Variance 100% ANOVA CV WithinSubj ectVariance(WSV ) Variability • It is well known the Between Subject Variance (BSV) can be very high – Biological variation – Within Subject Variance (WSV) contributes to BSV • WSV can also be high e.g. highly variable drugs and highly variable drug products • Drugs with an ANOVA-CV 30% are defined as ‘highly variable drugs’ Thank you Muchas Gracias