1. Highly Variable Drugs & Drug Products-A Rationale for Solution of a Persistent Problem
Kamal K. Midha C.M., Ph.D, D.Sc
College of Pharmacy and Nutrition,
University of Saskatchewan &
Pharmalytics, Inc. Saskatoon Canada

2. Outline
Highly variable drugs (HVD) and highly variable drug products (HVDP)
Examples: Studies from our archives
Widening the bioequivalence (BE) limits
Arbitrary preset wider BE limits
Scaling
Conclusions

3. What are Highly Variable Drugs?
Drugs with high within-subject variabilities in Cmax and/or AUC are called ‘highly variable drugs’ (HVDs)
ANOVA-CV ≥ 30%
HVDPs are products in which the drug is not highly variable, but the product is of poor pharmaceutical quality
high within-formulation variability

4. The Width of the 90%CI The width depends on:-
the 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 %
Highly Variable Drugs are a problem

5. 90%CIs & BE Limits Green Low WSV (~15%) Narrow 90%CI Passes Red
High WSV (~35%)
Wide 90%CI
Lower bound <80%
Fails
125%
100%
80%
GMR & the # subjects same in both cases

6. When Will a Drug Formulation Pass or Fail the BE Criteria
When Will a Drug Formulation Pass or Fail the BE Criteria? Experience from 1200 Studies M. Tanguay et al., AAPS Abstract, November 2002 (Data from 800 fasting studies)
Intra-individual CV%z
Studies Failing (%)
< 10% 6%
10-20%
10%
20-30%
26%
>30%
62%

7. BE Requirements for HVD/Ps
At present, there are no set specific acceptance criteria for HVD/Ps
We shall apply 90%CIs to both Cmax and AUC in this presentation for acceptance in order to stimulate discussion

8. Some Examples
Product A
Product B
Product C

9. Study Design and Data Analysis
ABE1: Non-replicated study design
Using two or more period data
ANOVA 1
ABE3: Partially replicated study design
Using three period Data
Reference product is replicated
ANOVA 2
ABE4: Fully replicated study design
Using four period data
Both test and reference products are replicated
ANOVA 3

10. Residual Variance (ABE1)
ANOVA 1:
Contains several variance components
WSV in ADME, plus a component of analytical variability
Within formulation variability (WFV)
Subject by formulation interaction (S*F)
Unexplained random variability

11. Replicate Designs (ABE3 or ABE4)
ANOVA-2:
Formulation
Period
Subject
Subject by Formulation Interaction
Residual Variance (approx = WSV)
Can separate test and reference variances

12. Product A: ANOVA-CV% Study 1a Study 2b Study 3c ln Cmax 42.3 39.9 37.2
ln AUClast
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)
Different analytical methods
different analysts
CV% consistent between studies
The drug is HVD
No evidence of HVD/P

13. Cmax
AUClast
Ref-1
Ref-2 6 13 27 7 16 20
This plot is the actual data from the study
High lighted are some subjects with the greatest variability between doses. The same subjects contribute to the high WSV of Cmax and AUC
The next slide shows these 6 subjects only

14. Cmax
AUClast
Ref-1
Ref-2 6 13 27 7 16 20
Note these data are for the reference formulation so this is not an example of S*F.
The test formulation was given only once so there are no data for it

15. Product A (ABE3) 3 x 37 Subjects
Measure GMR% CV% %CI
ln Cmax
ln AUClast
ANOVA-2 (GLM)
The study would pass in Canada because the GMR of Cmax fell within %, but failed in the US because US-FDA requires a 90%CI on Cmax

16. Product A: 90%CIs Measure T v R1 T v R2 R1v R2
ANOVA-1 (GLM)
Measure T v R T v R R1v R2
ln Cmax
ln AUClast
Statisticians don’t like this approach, but it does illustrate that the failures of CIs for Cmax to fall within BEL are due to high within-subject variability
This study design did not distinguish variability due to ADME from that due to the Ref formulation. In this instance, however, we do have some solution data available from a pharmacokinetic study. Next slide

17. Product B (ABE4) 22 healthy volunteers
2-Formulation, 4-Period, 4-Sequence Cross-Over design
Washout period, 2 weeks
17 plasma samples collected over 96 hours

18. Product B (Cmax) Ref-1 Ref-2 Test-1 Test- 2 9 17 27
This plot is the actual data from the study
REML gave sigmaD of 0.09 whereas earlier methods showed no evidence of interaction
Some bootstrap samples will have interaction in the present treatment

19. Product B (Cmax) Ref-1 Ref-2 Test-1 Test- 2 9 17 27
This plot is the actual data from the study
REML gave sigmaD of 0.09 whereas earlier methods showed no evidence of interaction
Some bootstrap samples will have interaction in the present treatment

20. Product B (AUC) Ref-1 Ref-2 Test-1 Test- 2 9 17 27
This plot is the actual data from the study
REML gave sigmaD of 0.09 whereas earlier methods showed no evidence of interaction
Some bootstrap samples will have interaction in the present treatment

21. Product B (ABE4) Test versus Ref
ANOVA-3: (MIXED)
Measure GMR% CV% %CI
ln Cmax
ln AUClast

22. Product B (ABE4) Test-1 versus Test-2
Measure GMR% CV% %CI
ln Cmax
ln AUClast
ANOVA-1: (GLM)

23. Product B (ABE4) Ref-1 versus Ref-2
Measure GMR% CV% %CI
ln Cmax
ln AUClast
ANOVA-1: (GLM)

24. Product C (ABE4) 37 healthy volunteers
2-Formulation, 4-Period, 4-Sequence Cross-Over design
Washout period, 1 week
15 plasma samples collected over 13.5 hr

25. Product C (Cmax)
Test
Reference
ln Cmax
37 Subjects in Numerical Order

26. Product C (AUClast) ln Cmax 37 Subjects in Numerical Order Test
Reference
ln Cmax
37 Subjects in Numerical Order

27. Product C (ABE4) Test versus Ref
Measure GMR% CV% %CI
ln Cmax
ln AUClast
ANOVA-3: (MIXED)

28. Product C (ABE4) Test-1 versus Test-2
Measure GMR% CV% %CI
ln Cmax
ln AUClast
ANOVA-1: (GLM)

29. Product C (ABE4) Ref-1 versus Ref-2
Measure GMR% CV% %CI
ln Cmax
ln AUClast
ANOVA-1: (GLM)

30. Dealing with HVDs HVDs are generally safe drugs
High WSV of Cmax is often the problem
A 90%CI is not required for Cmax in the case of ‘uncomplicated drugs’...
a potential solution for HVD/Ps?

31. Suggested Approaches*
BE Study
Multiple dose study
BE on the basis of metabolite
Area correction method to reduce intra-subject variability
Application of stable isotope technique
* From Published Literature

32. Suggested Approaches*
Statistical Considerations
Scaled ABE criteria
GMR-dependent scale ABE limits
Individual Bioequivalence (IBE)
BE Study Design
Replicate Design
Group sequential design
*From Published Literature

33. Other Possible Approaches Relaxing the Criteria
Widening the BE limits from ± 20% (80-125% on the log scale) to ± 30% (70-143% on the log scale)?
CPMP Guidelines permit a sponsor to justify prospectively widening the BE Limits to, say, %, for Cmax
Lowering the confidence level, e.g., from 90% to 80%

34. Widen the BE Limits for HVDs
The BE Limits can be scaled to WSV
2-Period design: scale to the residual SD
Replicate design: scale to the within-subject SD of the reference formulation

35. Widening the BE Limits -0.223 = ln0.80 +0.223 = ln1.25
sw0 is the SD at which the BE limits are permitted to be widened (set by an agency)
swr is either the residual SD (ABE2) or the SD of the ref product (replicate design)

36. Reference Scaling of BE Limits%
The Black Box
Sw0=0.20
Sw0=0.25
Swr
80%
125%

37. Some Acceptance Limits for BE (%)
Swr Sw0=0.20 Sw0=0.25

38. Scaling of ABE limits Conclusions
ABE is insensitive to S*F
Unscaled ABE is very sensitive to differences between the means
Scaled ABE is much less sensitive to differences between the means

39. Replicate Designs
Give a measure of pharmaceutical quality of each formulation in terms of variances
Allows scaling to the WSV of the reference product
reduces the number of subjects required to achieve adequate statistical power

40. Disadvantages of Reference Scaling
Scaling can allow the GMR to rise to unacceptably high levels
A constraint on GMR would be appropriate
to be set by an agency
e.g., within %

41. Disadvantages of Reference Scaling
Potentially different BE limits for different studies on the same drug
A poor quality study might give exaggerated variances and wider BE limits
might encourage sloppy studies
unlikely to occur with GLP in place during the conduct of the entire study

42. Conclusion
If Ref scaled ABE is to be considered, we suggest that sw0 = 0.25 seems reasonable
Scaling can lead the GMR to rise to unacceptably high levels
Therefore a constraint on GMR can be considered

43. Acknowledgements Gordon McKay John W. Hubbard Rabi Patnaik
Maureen J. Rawson
Gordon McKay
John W. Hubbard
Rabi Patnaik

44. Sample Size and Study Design – ABE 2 Period Crossover and Replicate Designs
Number of Subjects*
CVw%
2-period crossover 0% Deviation
Replicate design 0% Deviation
2-period crossover 5% Deviation
Replicate design 5% Deviation
2-period crossover 10% Deviation
Replicate design 10% Deviation 30 40 22 54 28
112 56 45 84 44
230
116 60
140 70
184 92
384
192 75
200
100
264
134
554
278
%Deviation: %Deviation in true BA
*Assumes negligible S*F
90% power
(Patterson et al. Eur J Clin Pharmacol, 2001)