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ABE IBE PBE

The document discusses bioequivalence in pharmaceuticals, focusing on average, individual, and population bioequivalence definitions, concerns, and methodologies. It emphasizes the limitations of the conventional average bioequivalence approach and presents individual and population bioequivalence as alternatives that allow for better switchability and prescribability of generic drugs. Recommendations for study designs and statistical methods to ensure reliable results in bioequivalence studies are provided, along with examples and analysis of variability.

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Average, Individual and Population Bioequivalence – Understanding the True Variability of a Generic Product 1st Annual Generic Medicine Middle East Conference April 14-15th, Crown Plaza, Yas Island, Abu Dhabi Dr. Bhaswat S. Chakraborty Sr. Vice President, R&D, Cadila Pharmaceuticals
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 Gen 2 Brand? Gen 1 • Prescribability – Can a physician have an equal Brand ? choice of prescribing Brand or Generic N to drug-naïve patients? Gen 3 Gen N • Switchability – Can a patient stabilized on Generic1 be switched to Generic N?
Design of 2-product, 2-period, crossover studies Period I W Period II A Sequence 1 Test S Reference H Subjects Randomizaion O U Sequence 2 Reference T Test
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 ( µT − µ R ) 2 + σ D + (σ WT − σ WR ) 2 2 2 ≤ θI max(σ WR , σ W 0 ) 2 2 Where (ln1.25) 2 +ε θI = σ2 0 W 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 ≤θP 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
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 recommended TRTR or TRT RTRT RTR • Estimation of sWR2, sWT2, 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 W W W PI PII PIII PIV A A A S S S Sequence 1 T R T R H H H O O O Subjects Randomizaion U U U Sequence 2 R T T T R T T 1 2 3
Sample Size for IBE Minimum 12 Source: US FDA Guidelines for Industry
Sample Size for PBE Minimum 18 Source: US FDA Guidelines for Industry
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  E ( yR − yT ) 2 − E ( yR − y R ) 2 '  if E ( yR − yR )2 / 2 ≥ σ 02 '  E ( yR − yR ) 2 / 2 ' θ =  E ( yR − yT ) − E ( yR − y R ) 2 ' 2 if E ( yR − yR )2 / 2 < σ 02 '   σ02 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
Declaring IBE and PBE H0: θ ≥ θI or θP; HA: < θI or θP 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%.
Analysis by SAS Proc Mixed
Example: Two Cyclosporine Formulations Test: open circles; Ref.: closed circles; n = 20 Canafax et al.(1999) Pharmacology 59:78–88
ABE – Two Cyclosporine Formulations Test: open circles; Ref.: closed circles; n = 20 Canafax et al.(1999) Pharmacology 59:78–88
IBE – Two Cyclosporine Formulations Test: open circles; Ref.: closed circles; n = 20 <θI εI=0.04-0.05;Constant Scaled σ W0 2 = 0.2; θI = 2.245; IBE declared Canafax et al.(1999) Pharmacology 59:78–88
Another Example: Two Alverine Formulations Highly variable drug, intra-subject CV ~35%; n = 48 Chakraborty et al.(2010) Unpublished Data
ABE, IBE & PBE: Two Alverine Formulations Highly 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 σWT2 – 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 C max 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.
Acknowledgements: Priyanka Kocheta Somnath Sakore Thank you very much