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Molecules 2018, 23(7), 1820; https://doi.org/10.3390/molecules23071820

Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme

1
Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 97401, Taiwan
2
Department of Life Science and Institute of Biotechnology, National Dong Hwa University, Shoufeng, Hualien 97401, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Xiangxiang Zeng, Alfonso Rodríguez-Patón and Quan Zou
Received: 7 July 2018 / Revised: 19 July 2018 / Accepted: 20 July 2018 / Published: 22 July 2018
(This article belongs to the Special Issue Molecular Computing and Bioinformatics)
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Abstract

P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood–brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure–activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r2 = 0.96, qCV2 = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q2 = 0.80–0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development. View Full-Text
Keywords: P-glycoprotein; efflux ratio; in silico; machine learning; hierarchical support vector regression; absorption; distribution; metabolism; excretion; toxicity P-glycoprotein; efflux ratio; in silico; machine learning; hierarchical support vector regression; absorption; distribution; metabolism; excretion; toxicity
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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).

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Chen, C.; Lee, M.-H.; Weng, C.-F.; Leong, M.K. Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme. Molecules 2018, 23, 1820.

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