Individual Bioequivalence:Background and Concepts

Mei-Ling Chen, Ph.D.Associate Director

Office of Pharmaceutical Science

Advisory Committee for Pharmaceutical Science

November 28-29, 2001Rockville, MD

Individual Bioequivalence Advantages

Compares both population means and variances

Considers subject-by-formulation interaction

Establishes goalposts based on the reference variability for highly variable drug products

Creates incentive for both innovators and generic sponsors to manufacture less variable products

Encourages use of heterogeneous subjects that are more representative of the general population

Individual Bioequivalence Assessment Distance Concept

Individual Difference Ratio (IDR)

Difference between T and R IDR = -------------------------------------

Difference between R and R’

Both T and R are administered to the same individual

Goal: IDR not substantially greater than 1

Individual Bioequivalence (Average Difference)2 + Variance Terms ------------------------------------------------------ BE Limit

Reference Variance

T - R)2 + D2 + (WT

2 - WR2)

------------------------------------------- I WR

2

Variance Terms

- Subject-by-formulation interaction ( D2

)- Within-subject variances (WT

2, WR2)

Subject-by-Formulation Interaction The S x F interaction is a measure of the extent to which the individual

mean differences, Tj - Rj, between the T and R products are likely to differ.

D2 = variance of (µTj - µRj)

= (BT - BR)2 + 2 (1- ) BT BR

µTj, µRj : individual-specific means

BT, BR : between-subject standard deviations

: correlation coefficient between µTj & µRj

D2 : S x F interaction variance component

Sources of S x F Interaction

D2 = var (µTj - µRj)

= (BT - BR)2 + 2 (1- ) BT BR

Changes in between-subject variability for T and R formulation

Lack of congruence in individual means between T and R formulation

Verapamil Immediate Release Age-based SxF Interaction

MEASURE Subject Test/Reference Ratio Generic 1/Reference Generic 2/Reference

AUC0-12 Elderly 1.43 1.04AUC0-12 Young 0.998 0.995

AUC0-24 Elderly 1.46 1.06AUC0-24 Young 1.00 0.97

Cmax Elderly 1.77 1.04Cmax Young 0.93 1.05

*SxF interaction occurred to Generic 1, but not Generic 2.

Reference: Carter BL et al., Pharmacotherapy, 13, 1993

Calcium Channel Blocker Gender-based S x F Interaction

Mean Cmax (%CV) Mean AUC (%CV)Males

Product T 129 ng/ml 3102 ng x hr/mlProduct R 147 2953Overall % CV 32.1 34.5

T/R Ratio 0.92 1.11

FemalesProduct T 129 ng/ml 2785 ngxhr/mlProduct R 201 3549

Overall % CV 19.8 14.4

T/R Ratio 0.62 0.77

Interpretation of S x F Interaction

Approach 1

Percentage of individuals whose average T/R ratios lie outside the range of 80-125%

Approach 2

Presence of a subgroup(s) with a different average T/R ratio(s) from the remaining subjects of the population

Approach 1

0102030405060

%

0.1 0.15 0.2 0.25 0.3 0.35 0.4

Sigma D Value

Proportion of Individuals With T/R Ratio Outside 80-125% (Assuming T/R mean ratio is 1.0)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 0.1 0.2 0.3 0.4 0.5

Proportion of Subjects in Subgroup

1.2 1.3 1.4 1.5 1.75 2

Sigm a D

Ratio of Means in Subgroup:

Approach 2 S x F Interaction Due to Subgroups

Individual Bioequivalence Criterion

(T - R)2 + D2 + (WT

2 - WR2)

------------------------------------------- I WR

2

Reference Scaling

Away from the “one-size-fits-all” approach

Goalposts adjusted for highly variable drug products

Mixed-Scaling Approach

Reference-Scaled (if WR > W0 )

(T - R)2 + D2 + (WT

2 - WR2)

------------------------------------------- I WR

2

Constant-Scaled (if WR W0 )

(T - R)2 + D2 + (WT

2 - WR2)

------------------------------------------- I W0

2

Constraint on Mean Difference

(T - R)2 + D2 + (WT

2 - WR2)

------------------------------------------- I WR

2

Incentives for manufacturing less variable formulations

Mean-variance tradeoff

Possibility of a large mean difference

Guidance recommendation - Further constraint on the point estimate of geometric T/R mean ratio to be within 80-125%

Replicate Design Studies:NDA’s and FDA Database

Mei-Ling Chen, Ph.D.Associate Director

Office of Pharmaceutical Science

Advisory Committee for Pharmaceutical Science

November 28-29, 2001Rockville, MD

27

28

9

13

Old Data - FDA Old Data - Industry

New Data - NDAs New Data - ANDAs

Replicate Design Data NDAs and ANDAs

Old Database

Industry Data

0

10

20

30

40

50

% D

ata

Sets

AUCinf 18 25

Cmax 39 29

SxF > 0.15T/R WS SD

>1.20

FDA Data

0

10

20

30

40

50

% D

ata

Sets

AUCinf 19 33

Cmax 33 30

SxF > 0.15T/R WS SD

>1.20

9

13

335

65

33

6

3

02468

101214

NDAs ANDAs ContractStudies

# of

Dat

a Se

ts

Total MR IR SLR HVD

N= 17- 93

New Data

Sample T/R Mean S x F Within- Subject Std. Deviation

Dataset Size Measure Ratio (%) Interaction Test Reference T/R Ratio ABE IBE

1 93 AUCI 100.6 0 0.58 0.45 1.28 PASS PASSCMAX 96.7 0 0.60 0.52 1.15 PASS PASS

2 28 AUCt 93.8 0 0.24 0.31 0.78 PASS PASS(10F) AUCI 95.6 0 0.23 0.31 0.73 PASS PASS

CMAX 103.5 0 0.29 0.32 0.91 PASS PASS

3 36 AUCt 96.1 0.13 0.12 0.17 0.69 PASS PASS(10F) AUCI 94.8 0.08 0.12 0.12 0.99 PASS PASS

CMAX 74.3 0.23 0.28 0.31 0.89 FAIL FAIL

Modified Release Products - NDAs

Sample T/R Mean SxF Within- Subject Std. Deviation Dataset Size Measure Ratio (%) Interaction Test Reference T/R Ratio ABE IBE

1 40 AUCt 99.0 0 0.06 0.11 0.58 PASS PASSAUCI 99.1 0 0.06 0.11 0.55 PASS PASSCMAX 93.9 0.03 0.28 0.37 0.75 PASS PASS

2 40 AUCt 107.7 0.44 0.07 0.23 0.30 PASS FAILAUCI 107.6 0.42 0.07 0.21 0.35 PASS FAILCMAX 100.3 0.23 0.39 0.47 0.82 PASS PASS

3 34 AUCt 96.1 0.14 0.41 0.34 1.20 PASS PASS

(14F) AUCI 93.0 0.30 0.35 0.40 0.86 FAIL PASSCMAX 93.8 0.06 0.31 0.33 0.92 PASS PASS

Immediate Release Products - NDAs

Cmax

0

2500

5000

7500

10000

12500

Test . 7580 11700 .

Ref. 357 . . 1260

Period 1 Period 2 Period 3 Period 4

AUCinf

0

5000

10000

15000

20000

25000

Test . 23819 24710 .

Ref. 727 . . 3680

Period 1 Period 2 Period 3 Period 4

IR - Data Set #2

Subject #9

FDA Contract Studies

Replicated-Crossover Design

Conducted at the University of Tennessee

Ranitidine

Metoprolol

Methylphenidate

Interplay of Drugs and Excipients

R

Excipient 1Sorbitol

Low permeability Osmotic pressure

GI transit time

DrugsRanitidine Metoprolol(Low P) (High P)

Excipient 2Sucrose

High permeability

Bioavailability? SxF Interaction?

Excipient Effect Hypothesis

The bioavailability of a low permeability drug (e.g., ranitidine) is more likely to be affected by an excipient such as sorbitol that reduces the gastrointestinal transit time.

Subject-by-formulation interactions may occur when two syrup formulations contain different sweetening agents, e.g., sorbitol versus sucrose.

Hussain AS, et al., AAPS Annual Meeting, 2000.

Ranitidine Levels

Time in Hours

0 2 4 6 8 10 12

Ra

nit

idin

e C

on

c.

(ng

/ml)

0

100

200

300

400

500

Sucrose Solution

Sorbitol Solution

Failed ABE and IBE AUCinf, T/R = 0.56 Cmax, T/R = 0.49

(reference)

(test)

Hussain AS, et al., AAPS Annual Meeting, 2001.

Metoprolol Levels

Time in Hours

0 2 4 6 8 10 12 14

Me

top

rolo

l C

on

c.

(ng

/ml)

0

10

20

30

40

50

60

70 Sucrose Solution

Sorbitol Solution

(reference)

AUCinf, T/R = 0.93 Cmax, T/R = 0.77 AUCinf Passed ABE & IBE Cmax Failed Both

(test)

Hussain AS, AAPS Annual Meeting, 2000.

Ranitidine in Sucrose vs. Sorbitol Solution

Subject-by-Formulation Interaction

AU

CI

0

1000

2000

3000

4000

5000

0

1000

2000

3000

4000

5000

Sucrose Sorbitol

Reduction of between-subject variability from sucrose to sorbitol resulted in an SxF interaction (D = 0.15)

BR= 0.24 BT= 0.13

Methylphenidate

MEASURE MEAN SxF INTRASUBJECT STD. DEV.a ABE IBE T/R Ratio Test Ref. T/R Ratio

(%)

Cmax 109.6 0.14 0.259 0.175 1.480 Pass Fail

AUC0-t 106.8 0 0.224 0.191 1.171 Pass Pass

AUC0- 108.1 0 0.228 0.184 1.234 Pass Pass

aAll analyses were conducted using ln-transformed data, and thestandard deviation approximated the coefficient of variation (% CV)on the original scale.

Ref: Meyer MC. et al., Pharmaceutical Research. 17:381-384, 2000