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Bioequivalence of Highly Variable Drug Products

High variability in PK can be a characteristic of certain drug products which require different from ordinary strategies and study designs for establishing bioequivalence.

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Bioequivalence of Highly Variable Drug Products

  1. 1. Bioequivalence of Highly Variable Drug Products Dr. Bhaswat S. Chakraborty
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  3. 3. Highly Variable Drug Products • Definition: BIO-international '92 [2001] "Drugs which exhibit intra "Drugs which exhibit intra-subject variabilities >30 % (CV from ANOVA) are to be classified as highly variable …" • Essential differentiation • Highly variable drug substances, e.g. statins • Highly variable drug products, e.g. enteric coated • Sources of (high) variability • Administration conditions, interactions with food • Physiological factors (GE, transit, first-pass, ...), technical aspects, e.g. bioanalytical procedures 3
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  5. 5. Usual Standards for Passing ABE • AUC: 90% CI limits 80-125% • Cmax: 90% CI limits 80-125% • Data generated in a 2x2 crossover study • Criteria applied to drugs of low and high variability 5
  6. 6. TwCounter-intuitive to the Concept of BE • Two formulations with a large difference in Means: Bioequivalent (if variances are low) • Two formulations with a small difference in Means: Not Bioequivalent (if variances are high). 6
  7. 7. Reference Test PI PII PI PII A better generic product gets penalized for high within- subject & within product variability of the reference! 7
  8. 8. • For HVDs and HVDPs, it may be almost impossible to show BE with a reasonable sample size. • The common 2×2 cross-over design over assumes Independent Identically Distributions (IID), which may not hold • If e.g., the variability of the reference is higher than the one of the test, one obtains a high common (pooled) variance and the test will be penalized for the ‘bad’ reference (previous slide) Impact of High Variability 8
  9. 9. • Produces medical dilemma (Switchability for NTRs, Prescribability for nth generic • Ignores distribution of Cmax and AUC • Within subject variation is not accurate • Ignores correlated variances and subject-by-formulation interaction • One criteria irrespective of inherent patterns of product, drug or patient variations • Although rare, but may not be therapeutic equivalent Other Limitations of a 2x2 Crossover Study 9
  10. 10. HVDs & HVDPs are usually safe and of wide therapeutic range Concentration Time 10
  11. 11. Power to show BE with 40 subjects for CVintra 30–50% μT/μR 0.95, CVintra 30% → power 0.816 μT/μR 1.00, CVintra 45% → power 0.476 μT/μR 0.95, CVintra 50% → n=98 (power 0.803) 11
  12. 12. US FDA ANDAs: 2003 – 2005 Example (1010 studies, 180 drugs) • 31% (57/180) highly variable (CV ≥30%) • of these HVDs/HVDPs, • 60% due to PK (e.g., first pass metabolism) • 20% formulation performance • 20% unclear 12
  13. 13. • Reduce human experimentation (number of participants) in BE studies • Prohibitive size of BE studies for some HVDs means no generic is available – many patients go untreated • Changing criteria to reduce number of participants in BE studies on HVDs can be accomplished without compromising safety/efficacy • 80 – 125% BE criteria not universally implemented worldwide Why a Different Set of Passing Criteria Needed for HVDs & HVDPs? 13
  14. 14. Approaches to Solution • US-FDA: • In favour of replicate design approach • Rejection of multiple dosing as less discriminative • Individual BE: • “Prescribability" vs. “switchability/interchangeability” • S*F interaction – what does it mean therapeutically? • Concept on trials for years, than dismissed • Reference scaled procedure • Widening of acceptance criteria due to scaling • Based on Reference product related variability 14
  15. 15. Highly VariableDrugs • Includes many therapeutic classes • Includes both newer and older products • Potential savings to patients in the billions of dollars if generics are approved • Examples: atorvastatin, esomeprazole, pantoprazole, clarithromycin, paroxetine (CR), risedronate, metaxalone, itraconazole, balsalazide, acitretin, verapamil, atovaquone, disulfiram, erythromycin, sulfasalazine, many delayed release and modified release products etc. 15
  16. 16. Fed BE Studies • Confidence interval criteria now required for BE studies under fed conditions • General paucity of information on variability under fed conditions • Some drugs show much more variability under fed conditions than fasting conditions, making them HVDs (e.g., esomeprazole, pantoprazole, tizanidine) • May be more HVDs than generally appreciated 16
  17. 17. Hierarchy of Designs • The more ‘sophisticated’ a design is, the more information can be extracted. • Hierarchy of designs: • Variances which can be estimated: Full replicate (TRTR | RTRT or TRT | RTR) Partial replicate (TRR | RTR | RRT) Standard 2×2 cross-over (RT | RT) Parallel (R | T) Full replicate: Total variance + within subjects (reference, test) Partial replicate: Total variance + within subjects (reference) Standard 2×2 cross-over: Total variance + incorrect within subject Parallel: Total variance 17
  18. 18. Design of 4-period, Replicate Studies Subjects Sequence 1 Sequence 2 T R PI W A S H O U T 1 Randomizaion PII PIII PIV W A S H O U T 2 W A S H O U T 3 R RR TT T 18
  19. 19. Replicate Designs • Each subject is randomly assigned to sequences, where at least one of the treatments is administered at least twice • Not only the global within-subject variability, but also the within-subject variability within product can be estimated • Smaller subject numbers compared to a standard 2×2×2 design – but outweighed by an increased number of periods • Two-sequence three-period TRT RTR • Two-sequence four-period (>2-sequence does not have any particular advantage) TRTR RTRT 19
  20. 20. Conduct of Replicate Studies • Generally dosing, environmental control, blood sampling scheme and duration, diet, rest and sample preparation for bioanalysis are all the same as those for 2-period, crossover studies • Avoid first-order carryover (from preceding formulation) & direct-by- carryover (from current and preceding formulation) effects • Unlikely when the study is single dose, drug is not endogenous, washout is adequate, and the results meet all the criteria • If conducted in groups, for logistical reasons, ANOVA model should take the period effect of multiple groups into account • Use all data; if outliers are detected, make sure that they don’t indicate product failure or strong subject-formulation interaction 20
  21. 21. Evaluation of BE: Replicate Studies • Any replicate design can be evaluated according to ‘classical’ (unscaled) Average Bioequivalence (ABE) • ABE mandatory if scaling not allowed FDA: sWR <0.294 (CVWR <30%); different models depend on design (e.g., SAS Proc MIXED for full replicate and SAS Proc GLM for partial replicate) • EMA: CVWR ≤30%; all fixed effects model according to 2011’s Q&A-document preferred (e.g., SAS Proc GLM) • Even if scaling is not intended, replicate design give more information about formulation(s) 21
  22. 22. Sample size and Ethics: Replicate Studies • 4-period replicate designs: • Sample size = ~½ of 2×2 study’s sample size • 3-period replicate designs: • Sample size = ~¾ of 2×2 study’s sample size • Number of treatments (and biosamples) • Same asconventional 2×2 cross-over • Allow for a safety margin – expect a higher number of drop-outs due to additional period(s). • More time commitment from subjects; Consider increased blood loss; improved Bioanalytical method required 22
  23. 23. 23 3-period Replicate 4-period Replicate SAMPLE SIZES
  24. 24. Unscaled & Scaled ABE from Replicate Studies • Common to EMA and FDA • ABE: • Scaled ABE model: • Regulatory switching condition θS is derived from the regulatory standardized variation σ0 (proportionality between acceptance limits in ln-scale and σW in the highly variable region) 24
  25. 25. Reference Scaling • A general objective in assessing BE is to compare the log-transformed BA measure after administration of the T and R products • An expected squared distance between the T and R formulations to the expected squared distance between two administrations of the R formulation • An acceptable T formulation is one where the T-R distance is not substantially greater than the R-R distance • When comparison of T happens in central tendencies and also to the reference variance, this is referred to as scaling to the reference variability 25
  26. 26. Reference Scaled BE Criteria • Highly Variable Drugs / Drug Products with CVWR >30 % • USA: Recommended in API specific guidances; scaling for AUC and/or Cmax acceptable, • GMR 0.80 – 1.25; ≥24 subjects • EU: Widening of acceptance range (only Cmax ) to maximum of 69.84% – 143.19%), GMR 0.80 – 1.25; Demonstration that CVWR >30% is not caused by outliers; justification that the widened acceptance range is clinically irrelevant. 26
  27. 27. Reference Scaled BE Criteria: USA & EMA • There is a difference between EMA and FDA scaling approaches • US FDA: Regulatory regulatory switching condition θS is set to 0.893, which would translate into • RSABE is allowed only if CVWR ≥ 30% (sWR ≥ 0.294), which explains to the discontinuity at 30% • EMA: Regulatory regulatory switching condition θS avoids discontinuity 27
  28. 28. Example 1: Data set 1 • RTRT | TRTR full replicate, 77 subjects, imbalanced, incomplete • FDA: sWR 0.446 ≥ 0.294 → apply RSABE (CVWR 46.96%) • a. critbound -0.0921 ≤ 0 and • b. PE 115.46% ⊂ 80.00–125.00% • EMA: CVWR 46.96% → apply ABEL (> 30%) • Scaled Acceptance Range: 71.23–140.40% • Method A: 90% CI 107.11–124.89% ⊂ AR; PE 115.66% • Method B: 90% CI 107.17–124.97% ⊂ AR; PE 115.73% PE = Point estimate; AR = Acceptance range28
  29. 29. Example 2: Data set 2 • TRR | RTR | RRT partial replicate, 24 subjects, balanced, complete • FDA: sWR 0.114 < 0.294 → apply ABE (CVWR 11.43%) • 90% CI 97.05–107.76 ⊂ AR (CVintra 11.55%) • EMA: CVWR 11.17% → apply ABE (≤ 30%) • Method A: 90% CI 97.32–107.46% ⊂ AR; PE 102.26% • Method B: 90% CI 97.32–107.46% ⊂ AR; PE 102.26% • A/B: CVintra 11.86% PE = Point estimate; AR = Acceptance range29
  30. 30. Canadian BE Criteria for HVDPs • The 90% confidence interval of the relative mean AUC of the test to reference product should be within the following limits: • 80.0%-125.0%, if sWR ≤0.294 (i.e., CV ≤30.0%); • [exp(-0.76sWR) × 100.0%]-[exp(0.76sWR) × 100.0%] if 0.294 <sWR ≤0.534 (i.e., 30.0% <CV ≤57.40%); or, • 66.7%-150.0%, if sWR >0.534 (i.e., CV >57.4%). • The relative mean AUC of the test to reference product should be within 80.0% and 125.0% inclusive; • The relative mean maximum concentration (Cmax) of the test to reference product should be between 80.0% and 125.0% inclusive. 30
  31. 31. Analysis by SAS Proc Mixed 31
  32. 32. Example 3: Inverika Data Set; Two Alverine Formulations; Intra-subject CV ~35%; n = 48 32
  33. 33. Individual Bioequivalence (IBE) Metric 2 2 2 2 2 2 0 ( ) ( ) max( , ) T R D WT WR I WR W              2 2 0 (ln1.25) I W      Where Where µT = mean of the test product µR = mean of the reference product σD 2 = variability due to the subject-by-formulation interaction σWT 2 = within-subject variability for the test product σWR 2 = within-subject variability for the reference product σW0 2 = specified constant within-subject variability 33
  34. 34. Population Bioequivalence (PBE) Metric Where µT = mean of the test product µR = mean of the reference product σTT 2 = total variability (within- and between-subject) of the test product σTR 2 = total variability (within- and between-subject) of the reference product σ0 2 = specified constant total variance ≤θP 34
  35. 35. Example 3: Inverika Data Set; Two Alverine Formulations; Intra-subject CV ~35%; n = 48 35
  36. 36. Issues with RSABE • Advantages • Sometimes fewer subjects can be used to demonstrate BE for a HVD • Concerns • Borderline drugs • Submission of unscaled and reference-scaled BE statistics for same product • What if T variability > R variability • Unacceptably high or low T/R mean ratios • Number of study subjects 36
  37. 37. Questions? Comments? drb.chakraborty@cadilapharma.co.in 37
  38. 38. Thank You Very Much 38

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