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**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

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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

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**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

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**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

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**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

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**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%

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**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

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Some Examples Product A Product B Product C

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**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

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**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

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**Replicate Designs (ABE3 or ABE4)**

ANOVA-2: Formulation Period Subject Subject by Formulation Interaction Residual Variance (approx = WSV) Can separate test and reference variances

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**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

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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

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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

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**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

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**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

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**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

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**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

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**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

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**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

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**Product B (ABE4) Test versus Ref**

ANOVA-3: (MIXED) Measure GMR% CV% %CI ln Cmax ln AUClast

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**Product B (ABE4) Test-1 versus Test-2**

Measure GMR% CV% %CI ln Cmax ln AUClast ANOVA-1: (GLM)

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**Product B (ABE4) Ref-1 versus Ref-2**

Measure GMR% CV% %CI ln Cmax ln AUClast ANOVA-1: (GLM)

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**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

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

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**Product C (AUClast) ln Cmax 37 Subjects in Numerical Order Test**

Reference ln Cmax 37 Subjects in Numerical Order

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**Product C (ABE4) Test versus Ref**

Measure GMR% CV% %CI ln Cmax ln AUClast ANOVA-3: (MIXED)

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**Product C (ABE4) Test-1 versus Test-2**

Measure GMR% CV% %CI ln Cmax ln AUClast ANOVA-1: (GLM)

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**Product C (ABE4) Ref-1 versus Ref-2**

Measure GMR% CV% %CI ln Cmax ln AUClast ANOVA-1: (GLM)

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**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?

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**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

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**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

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**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%

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**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

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**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)

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**Reference Scaling of BE Limits**

% The Black Box Sw0=0.20 Sw0=0.25 Swr 80% 125%

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**Some Acceptance Limits for BE (%)**

Swr Sw0=0.20 Sw0=0.25

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**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

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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

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**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 %

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**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

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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

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**Acknowledgements Gordon McKay John W. Hubbard Rabi Patnaik**

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

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**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)

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