Average, Individual and Population Bioequivalence – Understanding the True Variability of a Generic Product

Dr. Bhaswat S. ChakrabortySr. Vice President, R&D, Cadila Pharmaceuticals

1st Annual Generic Medicine Middle East Conference April 14-15th, Crown Plaza, Yas Island, Abu Dhabi

Outline

• Bioequivalence and Average Bioequivalence (ABE)

• Main concerns with ABE

• Design and limitations of ABE studies

• Individual and Population Bioequivalence (IBE and PBE)

• Metrics

• Design and sample size of replicate studies for IBE and PBE

• Conduct and analysis

• Example

• Issues

• Conclusion

Bioequivalent Drug Products

• Pharmaceutical Equivalent– Same dose and dosage form, ideally same assay and

content uniformity

– Could be pharmaceutical alternative dose or form

• Bioequivalent– Statistical and pharmacokinetic equivalent

– Equivalent rate and extent of absorption• 90% CI of relative mean Cmax and AUC: 80-125%

• Interpretation: Therapeutic equivalence

Currently Practiced Bioequivalence

• For almost all generic drus today, the regulatory standard is “average bioequivalence (IBE)”

• Concluded from 2-product, 2-period, crossover studies with fixed effects

• That means– An average patient (volunteer) will have– An average Cmax and AUC

– From an average reference and test product– That are not significantly different

• Problem: cannot individualize or generalize for population

Three Main Concerns with ABE• Safety

– Generic N– as safe as the Brand?

• Prescribability– Can a physician have an equal

choice of prescribing Brand or Generic N to drug-naïve patients?

• Switchability– Can a patient stabilized on

Generic1 be switched to Generic N?

Brand

Gen 1Gen 2

Gen 3 Gen N

?

Design of 2-product, 2-period, crossover studies

Subjects

Sequence 1

Sequence 2

Test

Reference

Reference

Test

Period I W

A

S

H

O

U

T

Randomizaion

Period II

Limitations of ABE

• Produces medical dilemma

• 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 Choices in BE and their Conditions

• Individual Bioequivalence (IBE)

– Addresses switchability

• Population Bioequivalence (PBE)

– Addresses prescribability

• Design and statistics of IBE & PBE

– Take into account both population mean and variance

– Address switchability and thereby subject-fomulation interaction

– Provide same level of confidence (consumer’s risk of 5%) and power

– Accept formulations with reduced within subject variability

Individual Bioequivalence (IBE) Metric

2 2 2 2

2 20

( ) ( )

max( , )T R D WT WR

IWR W

µ µ σ σ σ θσ σ

− + + − ≤

2

20

(ln1.25)I

W

εθσ

+=Where

Where

µT = mean of the test product

µR = mean of the reference product

σD2 = variability due to the subject-by-formulation interaction

σWT2 = within-subject variability for the test product

σWR2 = within-subject variability for the reference product

σW02 = specified constant within-subject variability

Population Bioequivalence (PBE) Metric

Where

µT = mean of the test product

µR = mean of the reference product

σTT2 = total variability (within- and between-subject) of the test product

σTR2 = total variability (within- and between-subject) of the reference product

σ02 = specified constant total variance

≤θP

FDA Recommended Designs of IBE/PBE studies

• 2-product, 2-period, crossover studies with fixed effects are not recommended

• 3- or 4-period, replicate designs with restricted sequences are recommendedTRTR or TRTRTRT RTR

• Estimation of sWR2, sWT

2, and sD2 are required along with

response means

• One can use either reference or constant scaling (without much concern about increased Type I error due to multiple testing)

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 PIVW

A

S

H

O

U

T

2

W

A

S

H

O

U

T

3

R

RR

TT

T

Sample Size for IBE

Source: US FDA Guidelines for IndustryMinimum 12

Sample Size for PBE

Source: US FDA Guidelines for IndustryMinimum 18

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

Standards for IBE and PBE

2 ' 2

' 2

2 ' 2

20

( ) ( )

( ) / 2

( ) ( )

R T R R

R R

R T R R

E y y E y y

E y y

E y y E y yθ

σ

− − − −=

− − −

' 2 20( ) / 2R RE y y σ− ≥

' 2 20( ) / 2R RE y y σ− <

Where σ0 is constant variability.

For IBE, YT, YR and YR’ are PK responses from the test and

two reference formulations to the same individual

For PBE, YT, YR and YR’ are PK responses from the test and two reference formulations to the different individuals

if

if

Declaring IBE and PBE

IBE or PBE is claimed when 95% confidence upper bound of of θ is less than θI or θP and the observed ratio of geometric

means is within bioequivalence limits of 80 – 125%.

H0: θ ≥ θI or θP; HA: < θI or θP

Analysis by SAS Proc Mixed

Example: Two Cyclosporine FormulationsTest: open circles; Ref.: closed circles; n = 20

Canafax et al.(1999) Pharmacology 59:78–88

ABE – Two Cyclosporine FormulationsTest: open circles; Ref.: closed circles; n = 20

Canafax et al.(1999) Pharmacology 59:78–88

IBE – Two Cyclosporine FormulationsTest: open circles; Ref.: closed circles; n = 20

Canafax et al.(1999) Pharmacology 59:78–88

εI=0.04-0.05;Constant Scaled σW02 = 0.2; θI = 2.245; IBE declared

<θI

Another Example: Two Alverine FormulationsHighly variable drug, intra-subject CV ~35%; n = 48

Chakraborty et al.(2010) Unpublished Data

ABE, IBE & PBE: Two Alverine FormulationsHighly variable drug, intra-subject CV ~35%; n = 48

Chakraborty et al.(2010) Unpublished Data

Issues

• Primary– Justification and need for an IBE criterion– Financial and human resource burden of conducting replicate study

design– appropriateness of the statistical methodology

• Advanced– Mean-variance trade-off (if the term σWT

2 – σWR2 ) is sufficiently negative,

the test product could be deemed BE with a large difference in the averages of a BE metric; resolved difference cannot exceed 80-125%)

– Extra-reference 2x3 designs (TRR,RTR)• Advantages, disadvantages

– ABE from replicate studies• CR products, highly variable drugs

Conclusions• ABE serves well for wide therapeutic index and

uncomplicated PK drugs• ABE has limitations

– Ignores switchability and prescribability; ignores distributions of Cmax and AUC; estimate of within subject variation is not accurate; ignores correlated variances and subject-by-formulation interaction….

• IBE provides for switchability and PBE for prescribability• Replicate studies are required for IBE and PBE• Analysis of variability is done by a mixed effects model• NTR, HVDP, CR formulations can be assured for IBE and

PBE• There are methodological issues for IBE and PBE which must

be discussed and resolved

References

• US FDA (1999). In Vivo Bioequivalence Studies Based on Population and Individual Bioequivalence Approaches. Food and Drug Administration, Rockville, Maryland, August, 1999.

• US FDA (2001). Guidance for Industry: Statistical Approaches to Establishing Bioequivalence. Food and Drug Administration, Rockville, Maryland, January, 2001.

Thank you very much

Acknowledgements:Priyanka KochetaSomnath Sakore