Bioequivalenec of Highly Variable Drug Products

Bioequivalenec of Highly Variable Drug Products

Dr. Bhaswat S. Chakraborty

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Highly Variable Drug ProductsDefinition: 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 differentiationHighly variable drug substances, e.g. statins Highly variable drug products, e.g. enteric coated Sources of (high) variability Administration conditions, interactions with foodPhysiological factors (GE, transit, first-pass, ...), technical aspects, e.g. bioanalytical procedures3

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Usual Standards for Passing ABEAUC: 90% CI limits 80-125%Cmax: 90% CI limits 80-125%Data generated in a 2x2 crossover studyCriteria applied to drugs of low and high variability

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TwCounter-intuitive to the Concept of BETwo 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).

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ReferenceTestPI PIIPI PIIA better generic product gets penalized for high within-subject & within product variability of the reference!7

For HVDs and HVDPs, it may be almost impossible to show BE with a reasonable sample size.The common 22 cross-over design over assumes Independent Identically Distributions (IID), which may not holdIf 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 Variability8

Produces medical dilemma (Switchability for NTRs, Prescribability for nth genericIgnores distribution of Cmax and AUCWithin subject variation is not accurate Ignores correlated variances and subject-by-formulation interactionOne criteria irrespective of inherent patterns of product, drug or patient variationsAlthough rare, but may not be therapeutic equivalent

Other Limitations of a 2x2 Crossover Study9

HVDs & HVDPs are usually safe and of wide therapeutic range

ConcentrationTime10

Power to show BE with 40 subjects for CVintra 3050%

T/R 0.95, CVintra 30% power 0.816

T/R 1.00, CVintra 45% power 0.476

T/R 0.95, CVintra 50% n=98 (power 0.803)11

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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% unclear12

Reduce human experimentation (number of participants) in BE studiesProhibitive size of BE studies for some HVDs means no generic is available many patients go untreatedChanging criteria to reduce number of participants in BE studies on HVDs can be accomplished without compromising safety/efficacy80 125% BE criteria not universally implemented worldwideWhy a Different Set of Passing Criteria Needed for HVDs & HVDPs?13

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Approaches to SolutionUS-FDA:In favour of replicate design approachRejection of multiple dosing as less discriminativeIndividual BE:Prescribability" vs. switchability/interchangeabilityS*F interaction what does it mean therapeutically?Concept on trials for years, than dismissed Reference scaled procedureWidening of acceptance criteria due to scalingBased on Reference product related variability14

Highly VariableDrugsIncludes many therapeutic classesIncludes both newer and older productsPotential savings to patients in the billions of dollars if generics are approvedExamples: atorvastatin, esomeprazole, pantoprazole, clarithromycin, paroxetine (CR), risedronate, metaxalone, itraconazole, balsalazide, acitretin, verapamil, atovaquone, disulfiram, erythromycin, sulfasalazine, many delayed release and modified release products etc.

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Fed BE StudiesConfidence interval criteria now required for BE studies under fed conditionsGeneral paucity of information on variability under fed conditionsSome 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 appreciated16

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Hierarchy of DesignsThe 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 22 cross-over (RT | RT) Parallel (R | T)Full replicate: Total variance + within subjects (reference, test) Partial replicate: Total variance + within subjects (reference)Standard 22 cross-over: Total variance + incorrect within subjectParallel: Total variance17

Design of 4-period, Replicate StudiesSubjectsSequence 1Sequence 2TRPI

WASHOUT1Randomizaion

PII

PIIIPIV

WASHOUT2

WASHOUT3

RRRTTT18

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Replicate DesignsEach subject is randomly assigned to sequences, where at least one of the treatments is administered at least twiceNot only the global within-subject variability, but also the within-subject variability within product can be estimatedSmaller subject numbers compared to a standard 222 design but outweighed by an increased number of periodsTwo-sequence three-period TRT RTRTwo-sequence four-period (>2-sequence does not have any particular advantage)TRTR RTRT19

Conduct of Replicate StudiesGenerally 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 studiesAvoid 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 criteriaIf conducted in groups, for logistical reasons, ANOVA model should take the period effect of multiple groups into accountUse all data; if outliers are detected, make sure that they dont indicate product failure or strong subject-formulation interaction 20

Evaluation of BE: Replicate StudiesAny replicate design can be evaluated according to classical (unscaled) Average Bioequivalence (ABE)ABE mandatory if scaling not allowed FDA: sWR 30% is not caused by outliers; justification that the widened acceptance range is clinically irrelevant.26

Reference Scaled BE Criteria: USA & EMAThere is a difference between EMA and FDA scaling approachesUS FDA: Regulatory regulatory switching condition S 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 S avoids discontinuity

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Example 1: Data set 1RTRT | TRTR full replicate, 77 subjects, imbalanced, incomplete FDA: sWR 0.446 0.294 apply RSABE (CVWR 46.96%)a. critbound -0.0921 0 andb. PE 115.46% 80.00125.00% EMA: CVWR 46.96% apply ABEL (> 30%)Scaled Acceptance Range: 71.23140.40%Method A: 90% CI 107.11124.89% AR; PE 115.66%Method B: 90% CI 107.17124.97% AR; PE 115.73%

PE = Point estimate; AR = Acceptance range28

Example 2: Data set 2TRR | RTR | RRT partial replicate, 24 subjects, balanced, complete FDA: sWR 0.114 < 0.294 apply ABE (CVWR 11.43%)90% CI 97.05107.76 AR (CVintra 11.55%) EMA: CVWR 11.17% apply ABE ( 30%)Method A: 90% CI 97.32107.46% AR; PE 102.26%Method B: 90% CI 97.32107.46% AR; PE 102.26%A/B: CVintra 11.86%

PE = Point estimate; AR = Acceptance range29

Canadian BE Criteria for HVDPsThe 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 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.

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Analysis by SAS Proc Mixed

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Example 3: Inverika Data Set; Two Alverine Formulations; Intra-subject CV ~35%; n = 48

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Individual Bioequivalence (IBE) Metric

WhereWhereT = mean of the test productR = mean of the reference productD2 = variability due to the subject-by-formulation interactionWT2 = within-subject variability for the test productWR2 = within-subject variability for the reference productW02 = specified constant within-subject variability33

Population Bioequivalence (PBE) MetricWhereT = mean of the test productR = mean of the reference productTT2 = total variability (within- and between-subject) of the test productTR2 = total variability (within- and between-subject) of the reference product02 = specified constant total variance

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Example 3: Inverika Data Set; Two Alverine Formulations; Intra-subject CV ~35%; n = 48

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Issues with RSABEAdvantagesSometimes fewer subjects can be used to demonstrate BE for a HVDConcernsBorderline drugsSubmission of unscaled and reference-scaled BE statistics for same productWhat if T variability > R variabilityUnacceptably high or low T/R mean ratiosNumber of study subjects36

Questions?Comments?drb.chakraborty@cadilapharma.co.in37

Thank You Very Much38