The purpose of the present work is to develop a non-binary biopharmaceutical classification system the so called AB Gamma system. The original mathematical model used for the development of BCS, appropriately modified, was applied to estimate the limiting values of drug solubility and permeability when the fraction of dose absorbed, Fa, was 0.90 or 0.20. The AB Gamma system is based on the fraction of dose absorbed and relies on permeability, solubility plane. The first category (A, alpha) includes drugs with Fa = 0.90, whereas the B (beta) category consists of drugs with Fa = 0.20. The area lying between the two boundaries of A and B defines the third category (gamma), Gamma, (0.20 < Fa < 0.90). For comparative purposes, the BCS classes I-IV were co-plotted together with the AB Gamma system. Most of the BCS classes II and III are included in category Gamma which mainly consists of drugs with properties like moderate or low solubility and permeability. Due to the dynamic character of dissolution and uptake processes, category A is expanded toward BCS Class II. The AB Gamma system allows the classification of all compounds into three categories (A, B, Gamma) in terms of the fraction of dose absorbed. (C) 2014 Elsevier B. V. All rights reserved.

Introduction Generic products of antiepileptic drugs (AEDs) are currently a controversial topic as neurologists and patients are reluctant to switch from brand products to generics and to switch between generics. Objective The aim of this study was to provide enlightenment on issues of bioequivalence (BE) and interchangeability of AED products. Methods Monte Carlo simulations of the classic 2 x 2 BE studies were performed to study the effect of sample size, within-subject variability, and the true difference in pharmacokinetic values of the products under comparison on BE acceptance of generic AED products. Simulations were extended to study the comparative performance of two generic AED products against the same innovative product. The simulated results are compared with literature data on AEDs. Results The question with regard to bioavailability (BA) is whether two formulations are different, while for BE the question is whether two formulations are sufficiently similar in terms of extent and rate of absorption. Therefore, the criteria for BA and BE and the statistical analysis involved in their analysis are different. Two generic formulations that meet regulatory approval requirements for generics by being bioequivalent to the same innovative AED may not be bioequivalent to one another and therefore should not be regarded as equal or as therapeutically equivalent products. A switch from a standard or an immediate-release formulation to a modified-release product, which comprises extended-release or delayed-release formulations, should not be regarded as a switch between generics, but rather as a switch between different formulation types. Discussions Switches between bioequivalent generic AED products could potentially lead to larger changes in plasma levels and exposure than the brand-to-generic switch. The simulation work verified the clinical findings that not all generic AED products bioequivalent to the same innovative product are bioequivalent to one another. Conclusions Two generic formulations that meet regulatory approval requirements for generics, by being bioequivalent to the innovative AED, may not be bioequivalent to one another. Additional BE criteria are needed for a formulation switch, particularly in epilepsy, where a breakthrough seizure may change a patient's status from seizure-free to refractory.

ObjectivesTwo-stage clinical designs are currently recommended by the regulatory authorities for the assessment of bioequivalence (BE). A specific statistical methodology was recently proposed by the European Medicines Agency (EMA). The aims of this article are to elaborate on the suggested statistical design from the EMA and to compare it with the existing statistical methods reported in the literature. MethodsMonte Carlo simulations were used to simulate the conditions of a two-stage BE design. The starting sample size was either 24 or 48, whereas the coefficient of variation of the within-subject variability was equal to 20% and 40%. Several geometric mean ratio levels of the BE metric were considered. Under each condition, 1000000 studies were simulated. Key findingsThe overall performance, in terms of percentage of BE acceptance, is identical. The additional term, sequencexstage', suggested in the EMA method is in most cases nonsignificant. The same results were obtained regardless of the type (fixed or random) of the effect applied to the subjects' term. ConclusionsAny BE study either finished or in progress which relies on the existing literature methodology leads to the same percentage of BE acceptance as if it was analysed with the recently proposed EMA method.