An approach for widening the bioequivalence acceptance limits in the case of highly variable drugs.

PURPOSE: Highly variable drugs pose a problem in bioequivalence assessment because they often fail to meet current regulatory acceptance criteria for average bioequivalence (80-125%). This paper examines alternative approaches to establishing bioequivalence. METHODS: Suggested solutions have included alternate study designs, e.g., replicate and multiple dose studies, reducing the level of the confidence interval, and widening the acceptance limits. We focus on the latter approach. RESULTS: A rationale is presented for defining wider acceptance limits for highly variable drugs. Two previously described methods are evaluated, and a new method having more desirable properties is proposed. CONCLUSIONS: We challenge the "one size fits all" current definition of bioequivalence acceptance limits for highly variable drugs, proposing alternative limits or "goal posts" which vary in accordance with the intrasubject variability of the reference product.

An assessment of the 4-6-20 rule for acceptance of analytical runs in bioavailability, bioequivalence, and pharmacokinetic studies.

A recent conference report described a decision rule, hereafter referred to as the 4-6-20 rule, for acceptance/rejection of analytical runs in bioavailability, bioequivalence, and pharmacokinetic studies. This procedure requires that quality control specimens at three concentrations (low, medium, and high) be assayed in duplicate in each run. For run acceptance, at least four of the six assay values must be within +/- 20% of their respective nominal concentrations, and at least one of the two values at each concentration must be within these limits. An inherent flaw in this decision rule is that the risk of rejecting runs, when the assay performance has in fact not deteriorated, varies for each assay and is neither known nor controlled. In this paper simulation methods are used to evaluate the operating characteristics of the 4-6-20 rule in comparison to those of classical statistical quality control procedures.