Bioequivalence and the pharmaceutical industry

Abstract

Since the early 1990s, average bioequivalence (ABE) studies have served as the international regulatory standard for demonstrating that two formulations of drug product will provide the same therapeutic benefit and safety profile when used in the marketplace. Population (PBE) and individual (IBE) bioequivalence have been the subject of intense international debate since methods for their assessment were proposed in the late 1980s and since their use was proposed in United States Food and Drug Administration guidance in 1997.Guidance has since been proposed and finalized by the Food and Drug Administration for the implementation of such techniques in the pioneer and generic pharmaceutical industries. The current guidance calls for the use of replicate design and of cross-over studies (cross-overs with sequences TRTR, RTRT, where T is the test and R is the reference formulation) for selected drug products, and proposes restricted maximum likelihood and method-of-moments techniques for parameter estimation. In general, marketplace access will be granted if the products demonstrate ABE based on a restricted maximum likelihood model. Study sponsors have the option of using PBE or IBE if the use of these criteria can be justified to the regulatory authority. Novel and previously proposed SAS 1 -based approaches to the modelling of pharmacokinetic data from replicate design studies will be summarized.Restricted maximum likelihood and method-of-moments modelling results are compared and contrasted based on the analysis of data available from previously performed replicate design studies, and practical issues involved in the application of replicate designs to demonstrate ABE are characterized.It is concluded that replicate designs may be used effectively to demonstrate ABE for highly variable drug products. Statisticians should exercise caution in the choice of modelling procedure.

Comparison of models for average bioequivalence in replicated crossover designs

“…They advise that this potential subject  formulation interaction is a concern and that a replicated crossover design can provide an assessment of this interaction. We made no evaluation of the relevance of subject  formulation interaction or of the performance of estimates of this interaction; for a thorough discussion of this topic we refer the reader to the papers of Patterson and Jones [3,6] and to the literature that they cite. However, our results show that, with regard to ABE assessment, the EM analysis model (our Model 3) performs better than a simpler model that does not account for this interaction (Model 1) and than one alternative model (Model 4) that accounts for this interaction in a different way.…”

“…CV=30% or more) and/or when the true mean ratio is assumed to fall within the equivalence limits, but to deviate from 100% by more than 10%. A recent review of much of the literature in the area of BE assessment can be found in [3].…”

“…one term each for within-and between-subject variability and a term for subject  formulation interaction. Patterson and Jones [3,6] propose a random slopes and intercepts (RIS) model that incorporates 4 variance components, i.e. one term for betweensubject variance, a within-subject variability term for each formulation and a term for subject  formulation interaction.…”

Simulation assessments of statistical aspects of bioequivalence in the pharmaceutical industry

“…In situations with missing data at random, one traditional approach to estimation and inference for d is to model the data using restricted maximum likelihood (REML) [9]. Approaches to the REML modelling of data in such studies are described by Patterson and Jones [10]. Unconstrained REML procedures may yield estimates which are negative; furthermore, it is known that estimates of variance are model-dependent (see [10] for more details).…”

Comparison of Parametric and Bootstrap Method in Bioequivalence Test

“…Based on the two-one sided test (TOST) proposed by Shuirmann (1987) (FDA, 2001), two formulations are claimed to be bioequivalent when the 90% confidence intervals (CIs) of mean log (AUC) differences and log (Cmax) differences fall within the regulatory acceptance limits (log (0.8) to log (1.25)). The mean differences in log (AUC) or log (Cmax) between the test and the reference formulation represent the formulation effect, a key parameter in the ABE test (Patterson and Jones, 2002).…”