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Pre-qualification Program: Priority Medicines Interchangeability of Multi Source Drug Products

Pre-qualification Program: Priority Medicines Interchangeability of Multi Source Drug Products. Interchangeability of Multisource Drug Products Containing Highly Variable Drugs .

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Pre-qualification Program: Priority Medicines Interchangeability of Multi Source Drug Products

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

  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 • Whatassumptionsare we willing to make? • Multisource products are interchangeable with brand products • Howsure do you want to be? • How to protect the consumer and the industry?

  4. 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

  5. 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

  6. 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

  7. 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 !

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. Is present FDA’s approach suitable for HVD? Source: Barbara M. Davit AAPS/FDA Workshop 5/22/2077, Rockville, MD

  15. 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

  16. 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

  17. 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

  18. 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) Chlorpromazine:ANOVA-CV%

  19. 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

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

  21. 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) Chlorpromazine: 90%CIs

  22. 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

  23. 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

  24. 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.

  25. 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

  26. Methods Variables tested: • Impact of increasing within subject variability • Use of point estimate constraint (80-125%) • σw0: 0.2 vs. 0.25 vs. 0.294 • Sample size: 24 vs. 36

  27. Methods Statistical Analysis: • Modified Hyslop model* • Number of simulations: 1 million (106)/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

  28. Impact of Within Subject Variability • 15% CV • 30% CV • 60% CV

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

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

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

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

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

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

  35. Impact of σW0 σW0 = 0.2 σW0 = 0.25 σW0 = 0.294

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

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

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

  39. 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 σW0 = 0.25 demonstrates a good balance between a conservative approach, and a practical one

  40. 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

  41. 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

  42. FDA Proposal-continued • BE criteria scaled to reference variability (Cmax & AUC) • Where σw0 = 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

  43. Use of reference average BE for HVD • BE criteria scaled to reference variability • 90% upper confidence bound for: Ho: (µT-µR)2 – θσ2wr must be ≤0 Where θ = scaled average BE limit and θ = (ln Δ)2/ σ2wo Where σwo = 0.25 Use a point estimate constraint Both Cmax and AUC must meet criteria

  44. Advantages of scaled BEreference scaled • Test product will benefit if: • T variability < R variability • The test product will not benefit if: • T variability > R variability

  45. 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

  46. 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

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