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Sci. Pharm.
Volume 82
Issue 1
10.3797/scipharm.1306-10
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Article

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

by
Majid ZANDKARIMI
1,*,
Mohammad SHAFIEI
1,
Farzin HADIZADEH
2,
Mohammad Ali DARBANDI
1 and
Kaveh TABRIZIAN
3
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.
Sci. Pharm. 2014, 82(1), 53-70; https://doi.org/10.3797/scipharm.1306-10
Submission received: 17 June 2013 / Accepted: 22 September 2013 / Published: 22 September 2013
Versions Notes

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

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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. https://doi.org/10.3797/scipharm.1306-10

AMA Style

ZANDKARIMI M, SHAFIEI M, HADIZADEH F, DARBANDI MA, TABRIZIAN K. Prediction of Pharmacokinetic Parameters Using a Genetic Algorithm Combined with an Artificial Neural Network for a Series of Alkaloid Drugs. Scientia Pharmaceutica. 2014; 82(1):53-70. https://doi.org/10.3797/scipharm.1306-10

Chicago/Turabian Style

ZANDKARIMI, Majid, Mohammad SHAFIEI, Farzin HADIZADEH, Mohammad Ali DARBANDI, and Kaveh TABRIZIAN. 2014. "Prediction of Pharmacokinetic Parameters Using a Genetic Algorithm Combined with an Artificial Neural Network for a Series of Alkaloid Drugs" Scientia Pharmaceutica 82, no. 1: 53-70. https://doi.org/10.3797/scipharm.1306-10

APA Style

ZANDKARIMI, M., SHAFIEI, M., HADIZADEH, F., DARBANDI, M. A., & TABRIZIAN, K. (2014). Prediction of Pharmacokinetic Parameters Using a Genetic Algorithm Combined with an Artificial Neural Network for a Series of Alkaloid Drugs. Scientia Pharmaceutica, 82(1), 53-70. https://doi.org/10.3797/scipharm.1306-10

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