Prediction of Pharmacokinetic Parameters Using a Genetic Algorithm Combined with an Artificial Neural Network for a Series of Alkaloid Drugs

Abstract

An important goal for drug development within the pharmaceutical industry is the application of simple methods to determine human pharmacokinetic parameters. Effective computing tools are able to increase scientists’ ability to make precise selections of chemical compounds in accordance with desired pharmacokinetic and safety profiles. This work presents a method for making predictions of the clearance, plasma protein binding, and volume of distribution for alkaloid drugs. The tools used in this method were genetic algorithms (GAs) combined with artificial neural networks (ANNs) and these were applied to select the most relevant molecular descriptors and to develop quantitative structure-pharmacokinetic relationship (QSPkR) models. Results showed that three-dimensional structural descriptors had more influence on QSPkR models. The models developed in this study were able to predict systemic clearance, volume of distribution, and plasma protein binding with normalized root mean square error (NRMSE) values of 0.151, 0.263, and 0.423, respectively. These results demonstrate an acceptable level of efficiency of the developed models for the prediction of pharmacokinetic parameters.

Quality-by-design in pharmaceutical development: From current perspectives to practical applications

“…Furthermore, ANNs have been successfully used for in vitro release kinetics prediction of active substances from hydrophilic matrix formulations (94,95). Moreover, in the past years, ANNs were combined with various computational tools, e.g., gene algorithms, for the evaluation of the pharmacokinetic parameters of alkaloid drugs (96). RNN has been applied in MPC of CQAs in continuous pharmaceutical manufacturing with favourable results (97).…”