TRAINING WORKSHOP ON PHARMACEUTICAL QUALITY, GOOD MANUFACTURING PRACTICE & BIOEQUIVALENCE

1. TRAINING WORKSHOP ON PHARMACEUTICAL QUALITY, GOOD MANUFACTURING PRACTICE & BIOEQUIVALENCE Statistical Considerations for Bioequivalence Studies Prepared byJohn Gordon, Ph.D. Presented by Hans Kemmler White Sands, 23 August 2006 e-mail: john_gordon@hc-sc.gc.ca Kyiv, 2005-10-06

2. Introduction • Performance will never be identical • Two formulations • Two batches of the same formulation? • Two tablets within a batch? • Purpose of bioequivalence (BE) • Demonstrate that performance is not “significantly” different • Same therapeutic effect • What constitutes a ‘significant’ difference? Kyiv, 2005-10-06

3. Introduction cont. • Agencies must define a standard consisting of the following: • Bioavailability metrics • One or more acceptance criteria for each metric • Number and type of metrics may vary • Dependent on drug formulation Kyiv, 2005-10-06

4. Metrics for BE studies • Concentration vs. time profiles • Area under the curve (AUC) • Maximal concentration (Cmax) • Time to Cmax (Tmax) • Statistical measures of BE metrics • Mean • Variance Kyiv, 2005-10-06

5. Logarithmic Transformations • Distribution of BE metrics • Skewed to the right • Consistent with lognormal distribution • Proportionate effects Kyiv, 2005-10-06

6. Example • What would be the expected drop in AUC if a patient received 20% less drug? • Subject 1 • Original AUC = 100 units • 20% drop = 20 units • Subject 2 • Original AUC = 1000 units • 20% drop = 200 units Kyiv, 2005-10-06

7. Example cont. • Log transformation • Absolute intrasubject differences become independent of patient’s AUC • Log(80) – log(100) = log(800) – log(1000) • Log transformation for concentration dependent measures • Accepted by regulatory agencies Kyiv, 2005-10-06

8. Analysis of Variance • ANOVA • Most common technique of analysis and estimation • Lognormal distribution • Raw data must be log transformed • Comparison of means and variances of transformed data • Geometric mean • Results reported in original scale Kyiv, 2005-10-06

9. ANOVAHypothesis Testing • Null hypothesis test • No formulation difference • Convey little detail • Statistically significant difference • Clinically significant? • Imprecise estimates (high variability) • No statistically significant difference Kyiv, 2005-10-06

10. Confidence Intervals (CI) • Inference from study to wider world • Range of values within which we can have a chosen confidence that the population value will be found • Study findings expressed in scale of original data measurement Kyiv, 2005-10-06

11. Confidence Intervals cont. • Width of CI indication of (im)precision of sample estimates • Width partially dependent on: • Sample size • Variability of characteristic being measured • Between subjects • Within subjects • Measurement error • Other error Kyiv, 2005-10-06

12. Confidence Intervals cont. • Degree of confidence required • More confidence = wider interval • In other words, width of CI dependent on: • Standard error (SE) • Standard deviation, sample size • Degree of confidence required Kyiv, 2005-10-06

13. Confidence Intervals cont. • Statistical analysis of pharmacokinetic measures • Confidence intervals • Two one-sided tests Kyiv, 2005-10-06

14. Typical BEAssessment Criteria • 90% confidence interval • Ratio of geometric means • Acceptance criteria: 80 – 125% • Log transformed AUCT & Cmax Kyiv, 2005-10-06

15. Statistical Approaches for BE • Average bioequivalence • Population bioequivalence • Individual bioequivalence Kyiv, 2005-10-06

16. Statistical approaches cont. • Average BE • Conventional method • Compares only population averages • Does not compare products variances • Does not assess subject x formulation interaction Kyiv, 2005-10-06

17. Statistical approaches cont. • Population and individual BE • Include comparisons of means and variances • Population BE • Assesses total variability of the measure in the population • Individual BE • Assesses within subject variability • Assesses subject x formulation interaction Kyiv, 2005-10-06

18. Design Considerations • Non-replicated designs • Most common • Crossover designs • Two-formulation, two-period, two-sequence, crossover design • Average or population BE approaches • Parallel designs Kyiv, 2005-10-06

19. Design Considerations • Replicated designs • Can be used for all approaches • Critical for individual BE approach • Suggested replicated design • Two-formulation, four-period, two-sequence • T R T R • R T R T Kyiv, 2005-10-06

20. Statistical effects in model • Sequence effect • Subject (SEQ) effect • Formulation effect • Period effect • Carryover effect • Residual Kyiv, 2005-10-06

21. Outliers • Statistical outliers • Valid clinical/physiological justification • Re-testing? Kyiv, 2005-10-06

22. Add-on designs • All studies should be powered appropriately • If study fails the standard • Reformulate • Undertake larger study • Add-on study • Consistency testing • Group-sequential designs • Penalty for ‘peeking’ at results Kyiv, 2005-10-06