Replicate Designs and Average, Individual, and Population Bioequivalence
Replicate Designs and Average, Individual, and Population Bioequivalence
Scott D Patterson and Byron Jones
Abstract. Since the early 1990’s, 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 1980’s and since their use was proposed in United States Food and Drug Administration guidance in 1997. Guidance has since been proposed and finalised 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, 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 c -based approaches to the modelling of pharmacokinetic data from replicate design studies will be summarised. 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 characterised. Following review of previous research, properties of the restricted maximum likelihood and Method-of-Moments estimates of the IBE and PBE metrics will be explored. Inferential procedures in IBE and PBE will be described and assessed, and retrospective analysis of data is used to assess performance of the metrics.
It is concluded that IBE and (particularly) PBE are less stringent than ABE and will allow easier market access for new products having large differences in average exposure (and failing ABE), and we recommend that the PBE approach be discarded or modified significantly to protect public health. Estimates for the FDA’s IBE and PBE metric are asymptotically unbiased but positively biased (against sponsors) in small samples; however, the positive bias in small samples is not of practical significance when assessing IBE and PBE under proposed procedures for highly variable drugs. REML modelling may be used to assess ABE, IBE, and PBE, but statisticians should exercise caution when choosing the structure of the variance-covariance matrix. Last asymptotic and nonparametric percentile bootstrap based IBE and PBE inference appears similar to that of the Cornish-Fisher expansion recommended by FDA and provide practical alternatives to the FDA procedure when missing data and/or imbalance are present in the data.
