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. 2013 Sep 22;82(1):53-70.
doi: 10.3797/scipharm.1306-10. Print 2014 Jan-Mar.

Prediction of pharmacokinetic parameters using a genetic algorithm combined with an artificial neural network for a series of alkaloid drugs

Majid Zandkarimi  1 , Mohammad Shafiei  1 , Farzin Hadizadeh  2 , Mohammad Ali Darbandi  1 , Kaveh Tabrizian  3
Affiliations

Prediction of pharmacokinetic parameters using a genetic algorithm combined with an artificial neural network for a series of alkaloid drugs

Majid Zandkarimi et al. Sci Pharm. .

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: Alkaloid Drugs; Artificial Neural Network; Genetic Algorithm; Pharmacokinetic parameters; Structural Descriptors.

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Figures

Fig. 1
Fig. 1
The basic sources of failure in drug development [2].
Fig. 2
Fig. 2
General outline of the QSPkR method
Fig. 3
Fig. 3
Flowchart describing the steps used in selecting the best subset of descriptors by GA
Fig. 4
Fig. 4
Predicted vs. observed experimental pharmacokinetic values for optimum ANN models
See this image and copyright information in PMC

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References

    1. Prentis RA, Lis Y, Walker SR. Pharmaceutical innovation by the seven UK-owned pharmaceutical companies. Br J Clin Pharmacol. 1988;25:387–396. http://dx.doi.org/10.1111/j.1365-2125.1988.tb03318.x. - DOI - PMC - PubMed
    1. Kennedy T. Managing the drug discovery/development interface. J Drug Discov. 1997;2:436–444. http://dx.doi.org/10.1016/S1359-6446(9701099-4) - DOI
    1. Lin JH, Lu AY. Role of Pharmacokinetics and Metabolism in DrugDiscovery and Development. Pharmacol Rev. 1997;49:403–449. http://www.ncbi.nlm.nih.gov/pubmed/9443165. - PubMed
    1. van de Waterbeemd H. High-throughput and in silico techniques in drug metabolism and pharmacokinetics. Curr Opin Drug Disc. 2002;5:33–43. http://www.ncbi.nlm.nih.gov/pubmed/11865671. - PubMed
    1. Norris DA, Leesman GD, Sinko PJ, Grass GM. Development of predictive pharmacokinetic simulation models for drug discovery. J Control Release. 2000;65:55–62. http://dx.doi.org/10.1016/S0168-3659(99)00232-1. - DOI - PubMed

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