Highly Variable Drugs & Drug Products-A Rationale for Solution of a Persistent Problem

1. Highly Variable Drugs & Drug Products-A Rationale for Solution of a Persistent Problem Kamal K. Midha C.M., Ph.D, D.Sc College of Pharmacy and Nutrition, University of Saskatchewan & Pharmalytics, Inc. Saskatoon Canada

2. Outline • Highly variable drugs (HVD) and highly variable drug products (HVDP) • Examples: Studies from our archives • Widening the bioequivalence (BE) limits • Arbitrary preset wider BE limits • Scaling • Conclusions

3. What are Highly Variable Drugs? • Drugs with high within-subject variabilities in Cmax and/or AUC are called ‘highly variable drugs’ (HVDs) • ANOVA-CV ≥ 30% • HVDPs are products in which the drug is not highly variable, but the product is of poor pharmaceutical quality • high within-formulation variability

4. The Width of the 90%CI • The width depends on:- • the Within-Subject Variability (WSV) • the number of subjects in the study • The wider the 90%CI, the more likely it is to fall outside the limits of 80-125% • Highly Variable Drugs are a problem

5. Green Low WSV (~15%) Narrow 90%CI Passes Red High WSV (~35%) Wide 90%CI Lower bound <80% Fails GMR & the # subjects same in both cases 125% 100% 80% 90%CIs & BE Limits

6. When Will a Drug FormulationPass or Fail the BE Criteria? Experience from 1200 StudiesM. Tanguay et al., AAPS Abstract, November 2002(Data from 800 fasting studies)

7. BE Requirements for HVD/Ps • At present, there are no set specific acceptance criteria for HVD/Ps • We shall apply 90%CIs to both Cmax and AUC in this presentation for acceptance in order to stimulate discussion

8. Some Examples • Product A • Product B • Product C

9. Study Design and Data Analysis • ABE1: Non-replicated study design • Using two or more period data • ANOVA 1 • ABE3: Partially replicated study design • Using three period Data • Reference product is replicated • ANOVA 2 • ABE4: Fully replicated study design • Using four period data • Both test and reference products are replicated • ANOVA 3

10. Residual Variance (ABE1) • ANOVA 1: • Contains several variance components • WSV in ADME, plus a component of analytical variability • Within formulation variability (WFV) • Subject by formulation interaction (S*F) • Unexplained random variability

11. Replicate Designs(ABE3 or ABE4) • ANOVA-2: • Formulation • Period • Subject • Subject by Formulation Interaction • Residual Variance (approx = WSV) • Can separate test and reference variances

12. Study 1a Study 2b Study 3c • ln Cmax 42.3 39.9 37.2 • ln AUClast 34.8 36.6 33.0 aBioequivalence study, n=37 (3-period study) bPharmacokinetic study n=11 (solution, 3-period study) cPharmacokinetic study, n=9, CPZ with & without quinidine (2-period study) Product A:ANOVA-CV%

13. Ref-1 Ref-2 Ref-1 Ref-2 6 6 7 6 7 16 20 13 13 16 20 16 6 13 20 7 16 13 20 27 7 27 27 27 Cmax AUClast

14. Ref-1 Ref-2 Ref-1 Ref-2 6 6 7 6 7 16 20 13 13 16 20 16 6 13 20 7 16 13 20 27 7 27 27 27 Cmax AUClast

15. Measure GMR% CV% 90%CI ln Cmax 115 42.399-133 ln AUClast 110 34.8 97-124 ANOVA-2 (GLM) Product A (ABE3)3 x 37 Subjects

16. Measure T v R1 T v R2 R1v R2 ln Cmax 103 - 146 89 - 12672 - 102 ln AUClast 97 - 128 94 - 125 85 - 112 ANOVA-1 (GLM) Product A: 90%CIs

17. Product B (ABE4) • 22 healthy volunteers • 2-Formulation, 4-Period, 4-Sequence Cross-Over design • Washout period, 2 weeks • 17 plasma samples collected over 96 hours

18. Ref-1 Test-1 Ref-2 Test- 2 17 9 9 27 9 17 27 17 27 9 17 27 Product B (Cmax)

19. Ref-1 Test-1 Ref-2 Test- 2 17 9 9 27 9 17 27 17 27 9 17 27 Product B (Cmax)

20. Ref-1 Test-1 Ref-2 Test- 2 9 17 9 9 27 27 27 17 9 17 17 27 Product B (AUC)

21. Measure GMR% CV% 90%CI ln Cmax 112 36.7 95-131 ln AUClast 113 28.0101-126 ANOVA-3: (MIXED) Product B (ABE4)Test versus Ref

22. Measure GMR% CV% 90%CI ln Cmax 97 26.0 84-111 ln AUClast 97 18.7 87-107 ANOVA-1: (GLM) Product B (ABE4)Test-1 versus Test-2

23. Measure GMR% CV% 90%CI ln Cmax 87 49.966-113 ln AUClast 87 39.2 71-108 ANOVA-1: (GLM) Product B (ABE4)Ref-1 versus Ref-2

24. Product C (ABE4) • 37 healthy volunteers • 2-Formulation, 4-Period, 4-Sequence Cross-Over design • Washout period, 1 week • 15 plasma samples collected over 13.5 hr

25. Product C (Cmax) Test Reference ln Cmax 37 Subjects in Numerical Order

26. Product C (AUClast) Test Reference ln Cmax 37 Subjects in Numerical Order

27. Measure GMR% CV% 90%CI ln Cmax 104 41.792-117 ln AUClast 103 35.8 93-114 ANOVA-3: (MIXED) Product C (ABE4)Test versus Ref

28. Measure GMR% CV% 90%CI ln Cmax 99 29.6 87-111 ln AUClast 92 32.5 81-106 ANOVA-1: (GLM) Product C (ABE4)Test-1 versus Test-2

29. Measure GMR% CV% 90%CI ln Cmax 107 33.794-123 ln AUClast 109 27.1 97-122 ANOVA-1: (GLM) Product C (ABE4)Ref-1 versus Ref-2

30. Dealing with HVDs • HVDs are generally safe drugs • High WSV of Cmax is often the problem • A 90%CI is not required for Cmax in the case of ‘uncomplicated drugs’... • a potential solution for HVD/Ps?

31. Suggested Approaches* • BE Study • Multiple dose study • BE on the basis of metabolite • Area correction method to reduce intra-subject variability • Application of stable isotope technique * From Published Literature

32. Suggested Approaches* • Statistical Considerations • Scaled ABE criteria • GMR-dependent scale ABE limits • Individual Bioequivalence (IBE) • BE Study Design • Replicate Design • Group sequential design *From Published Literature

33. Other Possible ApproachesRelaxing the Criteria • Widening the BE limits from ± 20% (80-125% on the log scale) to ± 30% (70-143% on the log scale)? • CPMP Guidelines permit a sponsor to justify prospectively widening the BE Limits to, say, 75-133%, for Cmax • Lowering the confidence level, e.g., from 90% to 80%

34. Widen the BE Limits for HVDs • The BE Limits can be scaled to WSV • 2-Period design: scale to the residual SD • Replicate design: scale to the within-subject SD of the reference formulation

35. Widening the BE Limits • -0.223 = ln0.80 +0.223 = ln1.25 • sw0 is the SD at which the BE limits are permitted to be widened (set by an agency) • swr is either the residual SD (ABE2) or the SD of the ref product (replicate design)

36. Sw0=0.20 Sw0=0.25 125% % The Black Box 80% Swr Reference Scaling of BE Limits

37. Swr Sw0=0.20 Sw0=0.25 0.30 71.6-139.8 76.5-130.7 0.40 64.0-156.3 70.0-142.9 0.50 57.2-174.7 64.0-156.3 Some Acceptance Limits for BE (%)

38. Scaling of ABE limitsConclusions • ABE is insensitive to S*F • Unscaled ABE is very sensitive to differences between the means • Scaled ABE is much less sensitive to differences between the means

39. Replicate Designs • Give a measure of pharmaceutical quality of each formulation in terms of variances • Allows scaling to the WSV of the reference product • reduces the number of subjects required to achieve adequate statistical power

40. Disadvantages of Reference Scaling • Scaling can allow the GMR to rise to unacceptably high levels • A constraint on GMR would be appropriate • to be set by an agency • e.g., within 80-125%

41. Disadvantages of Reference Scaling • Potentially different BE limits for different studies on the same drug • A poor quality study might give exaggerated variances and wider BE limits • might encourage sloppy studies • unlikely to occur with GLP in place during the conduct of the entire study

42. Conclusion • If Ref scaled ABE is to be considered, we suggest that sw0 = 0.25 seems reasonable • Scaling can lead the GMR to rise to unacceptably high levels • Therefore a constraint on GMR can be considered

43. Acknowledgements Maureen J. Rawson Gordon McKay John W. Hubbard Rabi Patnaik

44. Sample Size and Study Design – ABE2 Period Crossover and Replicate Designs %Deviation: %Deviation in true BA *Assumes negligible S*F 90% power (Patterson et al. Eur J Clin Pharmacol, 2001)