Computer-based evolutionary search for a nonlinear conversion function for establishing in vitro-in vivo correlation (IVIVC) of oral drug formulations.

Abstract

Establishment of in vitro-in vivo correlation (IVIVC) accelerates optimization of desirable drug formulations and/or modification of the manufacturing processes in the scale-up and post-approval periods. This article presents a method of finding the optimal conversion function for establishing Level A point-to-point IVIVC, based on a computer-based evolutionary search technique. Gene expression programming (GEP) is a technique for optimizing a mathematical expression tree with the help of a genetic algorithm. A parameter optimization routine, which minimizes the number of parameters in the mathematical expression trees and estimates the best-fit parameter values, was implemented in the GEP algorithm. Feasibility of the computer program was investigated using the in vitro and in vivo data for sustained release diltiazem formulations. It provided a mathematical equation that, from their in vitro dissolution profiles, successfully predicts the plasma concentration profiles of three different formulations of diltiazem following oral administration. Because the present approach does not use intravenous injection data like conventional IVIVC analyses, it is widely applicable to the evaluation of various oral formulations.