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Letters in Drug Design & Discovery
ISSN (Print): 1570-1808
ISSN (Online): 1875-628X
Epub Full Text Article
DOI: 10.2174/1570180811666140610213438      Price:  $95

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
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 modelling approach illustrated will find diverse applications in (QSAR, QSPR and QSTR) modelling for the virtual screening of large libraries.
ADME modeling, Caco-2 cell permeability, Genetic programming, MLP, SVR
Chemical Engineering Division CSIR-National Chemical Laboratory Pune - 411008 India.