1. Pre-qualification Program: Priority Medicines Interchangeability of Multi Source Drug Products SALOMON STAVCHANSKY, PH.D. ALCON CENTENNIAL PROFESSOR OF PHARMACEUTICS THE UNIVERSITY OF TEXAS AT AUSTIN COLLEGE OF PHARMACY AUSTIN, TEXAS 78712 stavchansky@mail.utexas.edu Kiev, Ukraine, June 25-27, 2007 Interchangeability of Multisource Drug Products Containing Highly Variable Drugs

2. Outline Background –What is a highly variable drug? –Present bioequivalence BE study approach –Disadvantages of present approach Bioequivalence Example of Highly Variable Drugs Reference – scaled average BE approach –Widening the bioequivalence limits –scaling Simulation Studies Summary and Conclusions

3. Questions Why? –Have you ever had or heard of a therapeutic failure Where do we want to be? –No therapeutic failures and no adverse events What assumptions are we willing to make? –Multisource products are interchangeable with brand products How sure do you want to be? –How to protect the consumer and the industry?

5. Highly Variable Drug Characteristics Drugs with high within subject variability (CVwr) in bioavailability parameters AUC and/or Cmax ≥ 30% Non narrow therapeutic index drugs Represent about 10% of the drugs studied in vivo and reviewed by the OGD-FDA

6. HVD Drug Products Highly Variable Drug Products in which the drug is not highly variable, but the product is of poor pharmaceutical quality –High within-formulation variability

7. Variability Due to Drug Substance and/or Drug Product Drug Substance –Variable absorption rate, extent –Low extent of absorption –Extensive pre-systemic metabolism Drug product –Formulation Inactive ingredient effects Manufacturing effects –Effects of Bioequivalence Study Conduct Bioanalytical Assay Sensitivity Suboptimal PK Sampling

8. Summary of the issues High Probability that the BE parameters will vary when the same subject receives a highly variable drug on different occasions Because of high variability the risk is to reject a product that in reality is bioequivalent -- Industry Risk !

9. FDA Study to Characterize Highly Variable Drugs in BE Studies: methods Collected data from 1127 acceptable BE studies, submitted –In 524 ANDAs –From 2003-2005 (3 years) Most sponsors used 2-way crossover studies –Used ANOVA Root Mean Square Error to estimate within-subject variance Drug was classified as highly variable if RMSE ≥ 0.3 or 30% Source: Barbara M. Davit AAPS/FDA Workshop 5/22/2077, Rockville, MD

10. FDA Study to Characterize Highly Variable Drugs in BE Studies: results BE studies of HVD enrolled more study subjects than studies of drugs with low variability –Average N in studies of HVD = 47 –Average N in studies of drugs with lower variability = 33 Range 18 – 73 subjects Source: Barbara M. Davit AAPS/FDA Workshop 5/22/2077, Rockville, MD

11. FDA Study to Characterize Highly Variable Drugs in BE Studies: results 10% of studies evaluated were HVD; of these: –52% of studies were consistently HVD –16% were borderline RMSE was slightly above or below 0.3 Average across all studies –For the remaining 32%, high variability occurred sporadically Not HVD in most BE studies

12. Reasons for Inconsistent Variability in BE Studies Differences in formulations Bioanalytical assay sensitivity Demographic characteristics of subjects Subjects with irregular plasma concentrations Number of study subjects Whether subjects were fasted or fed Source: Barbara M. Davit AAPS/FDA Workshop 5/22/2077, Rockville, MD

13. Present FDA Approach for BE of HVD ANDAs for HVD use the same study design for drugs with lower variability Two way crossover design Replicate study design Firms are encouraged to use sequential designs

14. Present FDA Approach for BE of HVD HVD must meet same acceptance criteria as drugs with lower variability 90% CI of AUC and Cmax test/reference (T/R ratios) must fall within: 0.8-1.25 (80-125%) Statistical adjustment necessary if a sequential study design is used

15. Is present FDA’s approach suitable for HVD? ApproachDisadvantage Enrolled adequate # of subjects (N) to show BE in 2 way crossover study Study may require larger N If study underpowered must do new study Replicate design ( 4- period) study High dropout rate; may need to enroll larger N EXPENSIVE Group sequential designMust specify in protocol a priori Statistical Adjustment Source: Barbara M. Davit AAPS/FDA Workshop 5/22/2077, Rockville, MD

16. Background for NEW approach ACPS Meeting, April 14, 2004: Discussion on Highly Variable Drugs Different approaches were considered, e.g., expansion of bioequivalence limits, and scaled average bioequivalence Committee favored scaled average bioequivalence over other approaches FDA working group was created; a research project to evaluate scaling was initiated ACPS = Advisory Committee for Pharmaceutical Science

17. The Width of the 90% Confidence Interval The width depends on: –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

18. 90%CIs & BE Limits Green –Low WSV (~15%) –Narrow 90%CI –Passes Red –High WSV (~35%) –Wide 90%CI –Lower bound <80% –Fails 125% 100% 80% GMR & the # subjects same in both cases

19. Chlorpromazine: ANOVA-CV% Study 1 a Study 2 b Study 3 c ln Cmax 42.3 39.9 37.2 ln AUClast 34.8 36.6 33.0 a Bioequivalence study, n=37 (3-period study) b Pharmacokinetic study n=11 (solution, 3-period study) c Pharmacokinetic study, n=9, CPZ with & without quinidine (2-period study)

20. Ref-1 Ref-2 Ref-1 Ref-2 6 6 6 6 13 27 7 7 7 7 16 20 CmaxAUClast

21. Chlorpromazine (ABE3) 3 x 37 Subjects Measure GMR% CV% 90%CI ln Cmax115 42.3 99-133 ln AUClast110 34.8 97-124 ANOVA-2 (GLM)

22. ANOVA-1 (GLM) Measure T v R 1 T v R 2 R 1 v R 2 ln Cmax103 - 14689 - 12672 - 102 ln AUClast 97 - 12894 - 12585 - 112 Chlorpromazine: 90%CIs

23. Background ACPS Meeting, October 6, 2006  Preliminary results of simulation study were presented  Committee was in favor of using a point estimate constraint with scaled average BE  Most members favored a minimum sample size of 24 ACPS = Advisory Committee for Pharmaceutical Science

24. Research Project Highly Variable Drugs (HVD) working group evaluated different scaling approaches and study designs. Outcome: –Scaled average bioequivalence, based on within subject variability of reference* * Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD

25. Objective Determine the impact of scaled average bioequivalence on the power (percent of studies passing) at different levels of within subject variability (CV%), and under different conditions.

26. Methods Study design: 3-way crossover, e.g., R T R Sample sizes tested: 24 and 36 Within subject variability: 15% - 60% CV Geometric mean ratio: 1 – 1.7

27. Methods Variables tested: Impact of increasing within subject variability Use of point estimate constraint (80- 125%) σ w0 : 0.2 vs. 0.25 vs. 0.294 Sample size: 24 vs. 36

28. Methods Statistical Analysis: Modified Hyslop model* Number of simulations: 1 million (10 6 )/test Percent of studies passing was determined using average bioequivalence (80-125% limits), and scaled average bioequivalence (limits determined as a function of reference within subject variability) Test performed under different conditions *Hyslop et al. Statist. Med. 2000; 19:2885-2897. Hyslop’s model was modified by Donald Schuirmann Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD

29. Impact of Within Subject Variability 15% CV 30% CV 60% CV

30. Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD

33. Impact of Point Estimate Constraint Lower variability (30% CV) Higher variability (60% CV)

34. Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD

36. Impact of σ W0 σ W0 = 0.2 σ W0 = 0.25 σ W0 = 0.294

40. Impact of Different Point Estimate Constraints Point estimate constraint = ±15% Point estimate constraint = ± 20%

41. Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD

43. Summary  Partial replicate, 3-way crossover design appears to work well  A point estimate constraint has little impact at lower variability (~30%); more significant effect at greater variability (~60%)  A σ W0 = 0.25 demonstrates a good balance between a conservative approach, and a practical one

44. Conclusion  Scaled ABE presents a reasonable option for evaluating BE of highly variable drugs  Practical value, reduction in sample size: Potentially decreasing cost and unnecessary human testing (without increase in patient risk)  Use of point estimate constraint addresses concerns that products with large GMR differences may be judged bioequivalent

45. FDA Proposal * : Scaled Average BE for HVA Drugs Three-period, partial replicate design –Reference product (R) is administered twice –Test product (T) is administered once –Sequences = RTR, TRR, RRT Sample size: Determined by sponsor (adequate power) –minimum is 24 subjects * Currently under evaluation Source: Sam H. Haidar: AAPS/FDA Workshop 5/22/2077, Rockville, MD

46. FDA Proposal- continued BE criteria scaled to reference variability (C max & AUC) –Where σ w0 = 0.25 The point estimate (test/reference geometric mean ratio) must fall within [0.80-1.25] Both conditions must be passed by the test product to conclude BE to the reference product

47. Use of reference average BE for HVD BE criteria scaled to reference variability 90% upper confidence bound for: Ho: (µ T- µ R ) 2 – θ σ 2 wr must be ≤ 0 Where θ = scaled average BE limit and θ = (ln Δ) 2 / σ 2 wo Where σ wo = 0.25 Use a point estimate constraint Both Cmax and AUC must meet criteria

48. Advantages of scaled BE reference scaled Test product will benefit if: –T variability < R variability The test product will not benefit if: –T variability > R variability

49. Concerns with Proposed Approach Firms will conduct a replicate design study and submit results to FDA –If within subject variability ≥ 30%, FDA will use the reference- scaled average BE approach –If within subject variability ≤ 30%, FDA will use the unscaled average BE approach What if the drug is characterized as a borderline HV drug? –FDA simulations showed that study outcome will be the same whether the scaled or unscaled approach is used Scaling can allow AUC and Cmax GMR to be unacceptably high or low –Acceptance criteria will include a point estimate constraint

50. Concerns with Proposed Approach What if high variability results from formulations problems or poor study conduct? –If T variability > R variability, no benefit in using scaled approach –The burden is on the applicant to convince FDA that product is a HVD

53. спасибо Thank you