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.
Highlights
Many studies on pharmacokinetics report that most of the key causes of costly failures in drug development are because of poor pharmacokinetics and lack of efficacy (Fig. 1)
It is known that a larger data set for training artificial neural networks (ANNs) models leads to models with better efficiency [37]
These conditions made it reasonable to choose a combined technique of genetic algorithms (GAs)-ANN for use in the study
Summary
Many studies on pharmacokinetics report that most of the key causes of costly failures in drug development are because of poor pharmacokinetics and lack of efficacy (Fig. 1). Screening through pharmacokinetic properties and toxicity is usually performed in vitro using animal models. Considerable research has been done on pharmacokinetic predictions for new drugs and these are performed without any further in vitro or in vivo experiments. Constructing prediction models involves taking known pharmacokinetic data from a set of drugs already in use that are closely related in terms of their physicochemical properties. The model that is subsequently constructed is used to predict unknown pharmacokinetic parameters of the new entities. Despite recent progress in this field, more research and development is still needed to increase the precision of such predictions
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