Pharmacokinetic Modeling of Caco-2 Cell Permeability Using Genetic Programming (GP) Method

ISSN: 1875-628X (Online)
ISSN: 1570-1808 (Print)


Volume 11, 10 Issues, 2014


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Letters in Drug Design & Discovery

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Pharmacokinetic Modeling of Caco-2 Cell Permeability Using Genetic Programming (GP) Method

Author(s): Renu Vyas, Purva Goel, M. Karthikeyan, S.S. Tambe and B.D. Kulkarni

Affiliation: Chemical Engineering Division, CSIR-National Chemical Laboratory, Pune – 411008 India.

Abstract

An accurate prediction of the pharmacokinetic properties of orally administered drugs is of paramount importance in pharmaceutical industry. Caco-2 cell permeability is a well established parameter for assessing the drug absorption profiles of lead molecules. Due to the restrictions on animal testing, prohibitive in situ models and ethical issues, the development of predictive models is essential. Genetic programming (GP) is an artificial intelligence (AI)-based exclusively data driven modeling paradigm. Given an example input-output data, it searches and optimizes, both the structure and parameters of a well fitting linear/non-linear input-output model. Despite this novelty, GP has not been widely exploited in drug design. Accordingly, in this study we propose a GP based approach for the in silico prediction of Caco-2 cell permeability using a diverse set of molecules. The predictions yielded a high magnitude for the training and test set correlation coefficient with low RMSE, indicating accurate Caco-2 permeability prediction and generalization performance by the GP model. The predictions were better or comparable to artificial neural networks (ANN) and support vector regression (SVR) methods. The GP based modeling approach illustrated will find diverse applications in (QSAR, QSPR and QSTR) modeling for the virtual screening of large libraries.




Keywords: ADME modeling, Caco-2 cell permeability, genetic programming, MLP, SVR.

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Article Details

Volume: 11
Issue Number: 9
First Page: 1112
Last Page: 1118
Page Count: 7
DOI: 10.2174/1570180811666140610213438
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