ArticleScientia pharmaceutica
Prediction of pharmacokinetic parameters using a genetic algorithm combined with an artificial neural network for a series of alkaloid drugs.
Article in Scientia pharmaceutica. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Design of Highly Efficient COMolecules (Basel, Switzerland) · 2026Article
- In Silico ADME Methods Used in the Evaluation of Natural Products.Pharmaceutics · 2025Review
- The Applications of Genetic Algorithms in Medicine.Oman medical journal · 2015Review
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5 authors.
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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.
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