The use of GA-RxODE (Genetics Algorithms and Running simulations from Ordinary Differential Equations-based model) method to optimize bioequivalence studies.

Abstract

Bioequivalence (BE) studies are prerequisite in generic products approval. Normally, they are quite simple in design and expensive in execution, and sometimes suffer ethical questioning. Genetics Algorithms and Running simulations from Ordinary Differential Equations‐based model (GA‐RxODE) is a multipurpose method used in pharmacokinetic (PK) optimization. It can be used to complete concentration–time (C–T) missing data. In this investigation, GA‐RxODE was applied in BE field. For this purpose, three BE studies were selected as a source data comprising formulations of metformin, alprazolam and clonazepam. From them, five blood samples values per volunteer‐round from specific preset times were chosen as if BE study was carried out with five instead of the classic 10–20 samples. With the five values of each volunteer a complete C–T curve was simulated by GA‐RxODE and certain PK estimation parameters (as maximum concentration, C max, and area under C–T curve from zero to infinite, AUCinf) were elicited. Finally, with these modeled parameters, a BE analysis was performed according to certain regulatory agencies guidances. Some results, expressed as geometric mean ratios of compared formulations and their 90% confidence intervals (CI90), were as follows: Metformin C max = 0.954 (0.878–1.035), AUCinf = 0.949 (0.881–1.022); Alprazolam C max = 1.063 (0.924–1.222), AUCinf = 1.036 (0.857–1.249), Clonazepam C max = 0.927 (0.831–1.034), and AUCinf = 1.021 (0.931–1.119). All CI90 were inside the 0.8–1.25 BE range. In summary, the simulated data were bioequivalent and non‐significantly different from original studies’ data. This raises the opportunity to perform more economic BE studies to build reliable PK estimation parameters from a few samples per volunteer.