# Presentation: 232694

## EVALUATION OF BIOEQUIVALENCE

**FOR HIGHLY-VARIABLE DRUGS**
**AND DRUG PRODUCTS**
- _Laszlo Endrenyi_
- University of Toronto
- _Laszlo Tothfalusi_
- Semmelweis University of Budapest

- FDA/CDER
- Advisory Committee on
-    Pharmaceutical Sciences
- Rockville, MD
- April 14, 2004

## SYNOPSIS

- Introduction
_*Several questions*_ to be raised for the
-      for the consideration of the Advisory
-      Committee and CDER
- -  Is there a problem?
- -  Which study condition?
- -  Which study design?
- -  Which method of evaluation?
- -  What is the limiting variation for HVD/P?
- -  Is test/estimation for the ratio of
-         	variations needed?
- -  Is a secondary BE test needed/useful?

## USUAL REGULATORY CRITERION

- _Record_:		Parameters (AUC and Cmax) of N 		subjects for the Test (T) and 			Reference (R) products
- _Calculate:_	Averages of the logarithmic 			parameters for both formulations
- 		By taking antilogs, get geometric 		means for the two formulations
- 		Take the ratio (T/R) of the two 			geometric means (GMR)
- 		Calculate the 90% confidence limits 		of GMR
- _Criterion:_	The confidence limits for GMR 			should be between 0.80 and 1.25.

## THE PROBLEM OF HIGHLY-VARIABLE DRUGS AND DRUG PRODUCTS (HVD/P)

- _Criterion:_	The confidence limits for GMR should be
- 			between 0.80 and 1.25
- _Problem_:	With large variation (wide confidence 		limits):
- 			*it is very difficult to satisfy the regulatory **	**	**criterion*,
- 			unless the number of subjects (N) is very 		large
- 			Problem especially with Cmax
- 			which often has higher variation than AUC
- _Definition:_	Highly-variable drug
- 			if coefficient of variation CV _>_ 30%

## IS THERE A PROBLEM WITH BE FOR HVD/P?

_**IS THERE A PROBLEM WITH BE FOR HVD/P?**_
_**- BIOEQUIVALENT WITH ITSELF?**_

- Administer the same HVD formulation twice:
- - generally can not demonstrate  BE
- Example: oral  administration, on two occasions, of IsoptinSR 240 mg  tablets
-     Y.-C. Tsang  et al., Pharm. Res. 3: 846 - 850(1996)

- Lack of bioequivalence
- Apparent Subject*Period  interaction

##    Failing BE criteria: statistics on 1300 studies

***   ******Failing BE criteria: statistics on 1300 studies***

- Diane Potvin, MDS Pharma Services

_**IS THERE A PROBLEM WITH BE FOR HVD/P?**__**FAILURE RATE OF BE STUDIES INCREASES WITH C.V.**_

## IS THERE A PROBLEM WITH BE FOR HVD/P?FAILURE RATE OF BE STUDIES INCREASES WITH C.V.

_**IS THERE A PROBLEM WITH BE FOR HVD/P?**__**FAILURE RATE OF BE STUDIES INCREASES WITH C.V.**_

- Failure rate is high and increases with C.V.
- Fewer failures for AUC than for Cmax
-     but still a substantial number

- Diane Potvin, MDS Pharma, Montréal

## WHICH STUDY CONDITION?

- - Single dosing
- - Steady state

## WHICH STUDY CONDITION?STEADY-STATE STUDIES

- Comparative parameters, especially of Cmax, have often (but not always) smaller variation in steady-state studies than following single oral administration
- Theoretical:
- 	A.A. El-Tahtawy et al., Pharm. Res. 11:1330-1336 (1994)
- 			      12:1634-1641 (1995)
- 			      15: 98-104 (1998)
- 	J. Zha and L. Endrenyi, Biopharm. Stat. 7:191-204 (1997)
- Observations:
- 	H.H. Blume et al., “BioInternational 2”, pp. 117-122 (1995)
- 	B. Schug et al., “BioInternational 96”, pp. 101-106 (1996)
-      Coefficients of variation (%) after single and multiple dosing
- 		(Blume, Schug, et al.)

## WHICH STUDY CONDITION?STEADY-STATE STUDIES

**- Single dosing**
**- Steady state**
- Often (but not always) lower variability
- But: reduction of variability is
- - Poorly defined (large, small, negative)
- - Arbitrary (changes with accumulation)
- Estimated Cmax has positive bias
-        L.Tothfalusi and L. Endrenyi, J.Pharmacokin.
-        Pharmacodyn. 30:363-385 (2003)
- In Europe but not in U.S.
- In Canada, for modified release
- (if accumulation)

## WHICH STUDY DESIGN?

**- 2x2 crossover**
**	**2 periods
- 	2 sequences
**- Replicate design**
- 	3 or 4 periods
- 	2 sequences (recommended)
- 	Example:
- 		T R T R
- 		R T R T

## WHICH STUDY DESIGN?REPLICATE DESIGN

**Advantage:**
**	**Get clear, directly estimated
- 	     - Within-subject variations
- 		  (sWT2, sWR2)
- 	     - Subject-by-formulation interaction
- 		  (sS*F2)
- 	_Note_: Favored by K.K. Midha
**Comment:**
- 	Longer duration
- 		Subject dropouts, greater expense
**Issue:**
- 	Various possible methods of evaluation 		E.g.:	ANOVA
- 		Linear mixed-effect model
- 		       Maximum likelihood
- 		       Restricted max. likelihood
- 		Variance component estim’n
**Question:**
- 	- *IS A TEST COMPARING T AND R **	**	**  VARIATIONS NEEDED?   *
*	**- OR AN ESTIMATE?*

## WHICH STUDY DESIGN?2x2 CROSSOVER

**Advantages:**
-       Simple execution
-       Simple evaluation
-       Available studies can be evaluated retrospectively
**Comment::**
-       _Can_ estimate ratio of within-subject variabilities
- 	Guilbaud, 1993, 1999; Gould, 2000
- 	Yi  = Ti + Ri       (i-th subject)
- 	Xi  = Ti  - Ri
- 	Regression of Y *vs.* X
- 	     Slope = b
- 	Ratio of within-subject variances:
- 	     sWT2/sWR2  =  (1+b)/(1-b)
**Issue:**
-      Features of the method have not been studied

## WHICH METHOD OF EVALUATION?

**-  Unscaled average BE**
-         The customary procedure,
-           with BE limits of GMR = 0.80-1.25
**-  Unscaled average BE **
**    ****with expanded BE limits**
**        **
**        ***** Preset BE limits**
- 	    e.g. GMR = 0.75-1.33
- 	            GMR = 0.70-1.43
-         *** BE limits proportional **
**	****to estimated variation**
- 	      Boddy et al., Pharm.Res. 12:1865-1868 (1995)
**Scaled average BE**
- 	Schall, BioInternational 2, 91-106 (1995)
- 	Tothfalusi et al., Pharm.Res. 18:728-733 (2001)
- 	Tothfalusi and Endrenyi, Pharm.Res. 20:382-389 (2003
**-  (Scaled individual BE)**
**	**For comparisons only

## WHICH METHOD OF EVALUATION?UNSCALED AVERAGE BE WITH EXPANDED LIMITS  -   PRESET LIMITS  

**Unscaled average BE**
- 	1/BEL  GMR  BEL
- 	- lgBEL  log(GMR)  lgBEL
- 	- lgBEL  mT - mR  lgBEL
- 	     BEL:  BE limit
- 	     lgBEL: logarithm of BEL
- 	     GMR: Ratio of geometric means
- 	     mT, mR: Estimated logarithmic means
- 	For example:
- 	     0.75  GMR  1.33
- 	     - 0.288  mT - mR  0.288
- 	instead of:
- 	     0.80  GMR  1.25
- 	     - 0.223  mT - mR  0.223
**Advantage:**
- 	Simple
**Disadvantage:**
- 	Arbitrary
- 	Only partial reduction of sample size
- 	     Not for higher variabilities

## WHICH METHOD OF EVALUATION?UNSCALED AVERAGE BE WITH EXPANDED LIMITS-PROPORTIONAL TO ESTIMATED VARIATION

- Confidence interval of log(GMR) is proportional
- to estimated variation
-        A.W. Boddy et al. Pharm. Res. 12:1865-1868 (1995)
-       - lgBELA*s  mT - mR  lgBELA*s
-       Proportionality factor, lgBELA = 1.0 suggested
**Advantages:**
-       - Can apply the usual two one-sided t-tests procedure
- 	(However, see below)
-       - Statistical power is independent of sample size
-       - Statistical power is, with same sample size,
- 	much higher than of unscaled average BE
**Comments:**
-       - The estimated limits are random variables
- 	(lgBELA*s)
-        - Therefore, application of the two one-sided tests
- 	procedure is not correct
-       	(However, approximately correct with large  N)

## WHICH METHOD OF EVALUATION?SCALED AVERAGE BE

- Difference between logarithmic means is normalized
- by estimated variation
-        R. Schall, BioInternational 2, 91-106 (1995)
-        L.Tothfalusi et al., Pharm.Res. 18:728-733 (2001)
-        L. Tothfalusi and L. Endrenyi, Pharm.Res. 20:382-389 (2003)
-       - lgBELA  (mT - mR)/s  lgBELA
-       General procedure suggested for setting BE limits
**Advantages:**
-       - Statistical power is independent of variation
-       - Statistical power is, with same sample size,
- 	much higher than of unscaled average BE
-       - Interpretation: Compare expected change due
- 	to switching with expected difference
- 	between replicate administrations
-       - Interpretation: Standardized effect size, as in
- 	clinical comparisons
**Comment:**
-       Confidence limits estimated by:
- 	- Noncentral t-test (2x2 crossover)
- 	- Hyslop’s procedure (replicate or 2x2 		        crossover)

## WHICH METHOD OF EVALUATION?SCALED AVERAGE BE

- Comparative performance of method
- Simulations
- 	4 periods
- 	N = 24
- 	CVWT = CVWR = 40%

- Scaled average BE is much less permissive than
-       scaled individual BE (lower GMR’s)
- Scaled average BE, at large variations, has much higher
-       statistical power than unscaled average BE

## WHAT IS THE LIMITING VARIATION FOR HVD/P?

- Subject to regulatory decision
-       (except with unscaled average BE
-        with preset limits)
- For unscaled average BE with expanding limits,
-       scaled average BE:
-       *Mixed regulatory model*:
- 	Unscaled average BE if   o  ,
- 	HVD/P procedure if  > o
- 	        (o: limiting variation)
- Alternatives for limiting variation:
-          L. Tothfalusi and L. Endrenyi, Pharm.Res. 20:382-389 (2003)
-       CVo  = 20%  (as for individual BE)
-       CVo  = 25%  (as in Boddy et al., 1995;
- 		      preferred by K. Midha)
-       CVo  = 30%  (usual definition of HVD/P)
- Sponsor’s intention for using HVD/P procedure
- to be stated in protocol

## IS A SECONDARY CRITERION NEEDED?

- Concern about possibly large deviations
- between estimated logarithmic means
-      [i.e., about log(GMR)]
-      L. Benet, AAPS Workshop on Individual BE, 1999.
- Concern arose due to features of individual BE
**Comments:**
-      *Individual BE (IBE) *_*vs*_*. HVD/P*
-      - With HVD/P the potential deviation is
-        much smaller than with IBE
-      - Differing sources of the deviation
- 	* IBE: expanded, more permissive
- 	       regulatory criterion
- 	* HVD/P: Arises as a natural, direct
- 	       consequence of variability

## IS A SECONDARY CRITERION NEEDED?

- HVD/P:  Larger deviation between the      	   (logarithmic) means arises as a 	   natural, direct consequence of the 	   higher variability

- Larger deviations occur
- at higher variations

## IS A SECONDARY CRITERION USEFUL?

**Illustrative example:**
-       Effect of secondary criterion on the
-       determination of (scaled) individual BE

- 	GMR limit:  Constraint of GMR 1.25 alone
- 	scIBE4:  Scaled individual BE
- 	scGIBE4:  Combination of the two criteria
- Acceptance by the combined criterion does not
-       exceed acceptances by either separate criterion
*Strong constraint on GMR could remove the *
*      **combined criterion from the BE criterion*

## SUMMARY

_**Several questions**_ for the consideration
-      of the Advisory Committee and CDER
- -  Is there a problem?
- -  Which study condition?
- -  Which study design?
- -  Which method of evaluation?
- -  What is the limiting variation for HVD/P?
- -  Is test/estimation for the ratio of
-         	variations needed?
- -  Is a secondary BE test needed/useful?
_*Further investigation*_ of designs and features
-       	of methods would be very useful