1. Bioequivalence of Highly
Variable Drug Products
Dr. Bhaswat S. Chakraborty

2. 2

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

4. 4

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. 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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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. • 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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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. HVDs & HVDPs are usually safe and of wide
therapeutic range
Concentration
Time
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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. 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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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. 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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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.
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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
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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
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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
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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
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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
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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)
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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
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23. 23
3-period Replicate 4-period Replicate
SAMPLE SIZES

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)
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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
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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.
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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
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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. 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. 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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31. Analysis by SAS Proc Mixed
31

32. Example 3: Inverika Data Set;
Two Alverine Formulations; Intra-subject CV ~35%; n = 48
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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
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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
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35. Example 3: Inverika Data Set;
Two Alverine Formulations; Intra-subject CV ~35%; n = 48
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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
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37. Questions?
Comments?
drb.chakraborty@cadilapharma.co.in
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38. Thank You Very
Much
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