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.
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ABSTRACT: The purpose of this work was to develop a mathematical model of the drug dissolution () from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling of versus extrudate diameter () and the time variable () and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations’ parameters. Two inputs were found important for the drug dissolution: and . The extrudates length () was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling of versus and resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs’ black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies.Computational and Mathematical Methods in Medicine 01/2015; 2015:1-9. DOI:10.1155/2015/863874 · 1.02 Impact Factor
- Journal of Bioequivalence & Bioavailability 11/2012; 5(1):006-015. DOI:10.4172/jbb.1000128