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Development Of An In Silico Model For Predicting Activation Of Promiscuous Drug Transporter HPXR | 3733
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Development of an in silico model for predicting activation of promiscuous drug transporter hPXR

2nd International Conference on Proteomics & Bioinformatics

Max K. Leong

ScientificTracks Abstracts: J Proteomics Bioinform

DOI: 10.4172/0974-276X.S1.060

Abstract
The nuclear receptor pregnane X receptor (PXR) is referred to as the �master� regulator of pathways involving the major metabolic enzymes and can be activated by a wide range of structurally diverse endogenous and xenobiotic molecules. In addition, the activation of PXR has significant impact on drug metabolism and efflux as well as drug-drug interactions. Identification of PXR activators is of critical importance in analyzing metabolism and pharmacokinetic profiles and detecting potential adverse drug-drug interactions in the process of drug discovery and development, which, however, cannot be accurately modeled without taking into account the promiscuous nature of hPXR. A predictive model was derived to predict the activation of hPXR using the novel pharmacophore ensemble/support vector machine (PhE/SVM) scheme. The derived PhE/SVM model is an accurate and robust predictive model as manifested by those samples in the training set, test set, and outlier set. Furthermore, the calculated results are consistent with the published hPXR-ligand cocomplex structure and the plasticity nature of hPXR is also revealed when compared with crystal structures.
Biography

Max K. Leong received the Ph.D. degree in Chemical Physics from University of Texas at Austin, USA. Afterward, he worked as a post-doc associate at the College of Pharmacy, University of Texas at Austin, USA. His career started from in silico drug discovery and later expanded to in silico ADME/ Tox. Currently, he is an editorial board member of Journal of Bioanalysis & Biomedicine and Journal of Bioequivalence & Bioavailability.

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