Bioequivalence of Highly Variable Drug

Products

Dr. Bhaswat S. Chakraborty

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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

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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

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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).

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Reference Test

PI PII PI PII

A better generic product gets penalized for high within-subject & within product variability of the reference!

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• 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

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• 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

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HVDs & HVDPs are usually safe and of wide therapeutic range

Con

cent

rati

on

Time

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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)

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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% unclear

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• 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?

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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

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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.

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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

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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 subjectParallel: Total variance

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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 PIVW

A

S

H

O

U

T

2

W

A

S

H

O

U

T

3R

RR

TT

T

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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

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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

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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)

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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

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3-period Replicate 4-period Replicate

SAMPLE SIZES

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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)

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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

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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.

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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

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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 range

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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 range

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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.

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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

2 2 2 2

2 20

( ) ( )max( , )

T R D WT WRI

WR W

2

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(ln1.25)I

W

Where

WhereµT = mean of the test productµR = mean of the reference productσD

2 = variability due to the subject-by-formulation interactionσWT

2 = within-subject variability for the test productσWR

2 = within-subject variability for the reference productσW0

2 = specified constant within-subject variability

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Population Bioequivalence (PBE) Metric

WhereµT = mean of the test productµR = mean of the reference productσTT

2 = total variability (within- and between-subject) of the test product

σTR2 = total variability (within- and between-subject) of the reference product

σ02 = specified constant total variance

≤θP

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

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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

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Questions?Comments?

drb.chakraborty@cadilapharma.co.in

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Thank You Very Much