Next Article in Journal
QSAR and Molecular Docking Studies of Oxadiazole-Ligated Pyrrole Derivatives as Enoyl-ACP (CoA) Reductase Inhibitors
Previous Article in Journal
Pharmacophore Elucidation and Molecular Docking Studies on 5-Phenyl- 1-(3-pyridyl)-1H-1,2,4-triazole-3-carboxylic Acid Derivatives as COX-2 Inhibitors
Article Menu

Article Versions

Export Article

Open AccessArticle
Sci. Pharm. 2014, 82(1), 53-70; doi:10.3797/scipharm.1306-10

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

1
Department of Pharmaceutical Sciences, School of Pharmacy, Zabol University of Medical Sciences, Zabol, Iran
2
Department of Medicinal Chemistry, School of Pharmacy, Mashad University of Medical Sciences, Mashad, Iran
3
Department of Pharmacology and Toxicology, School of Pharmacy, Zabol University of Medical Sciences, Zabol, Iran
*
Author to whom correspondence should be addressed.
Received: 17 June 2013 / Accepted: 22 September 2013 / Published: 22 September 2013
Download PDF [317 KB, uploaded 27 September 2016]

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.
Keywords: Genetic Algorithm; Artificial Neural Network; Structural Descriptors; Alkaloid Drugs; Pharmacokinetic parameters Genetic Algorithm; Artificial Neural Network; Structural Descriptors; Alkaloid Drugs; Pharmacokinetic parameters
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

ZANDKARIMI, M.; SHAFIEI, M.; HADIZADEH, F.; DARBANDI, M.A.; TABRIZIAN, K. Prediction of Pharmacokinetic Parameters Using a Genetic Algorithm Combined with an Artificial Neural Network for a Series of Alkaloid Drugs. Sci. Pharm. 2014, 82, 53-70.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sci. Pharm. EISSN 2218-0532 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top