Literature DB >> 21341745

A novel approach for predicting P-glycoprotein (ABCB1) inhibition using molecular interaction fields.

Fabio Broccatelli1, Emanuele Carosati, Annalisa Neri, Maria Frosini, Laura Goracci, Tudor I Oprea, Gabriele Cruciani.   

Abstract

P-glycoprotein (Pgp or ABCB1) is an ABC transporter protein involved in intestinal absorption, drug metabolism, and brain penetration, and its inhibition can seriously alter a drug's bioavailability and safety. In addition, inhibitors of Pgp can be used to overcome multidrug resistance. Given this dual purpose, reliable in silico procedures to predict Pgp inhibition are of great interest. A large and accurate literature collection yielded more than 1200 structures; a model was then constructed using various molecular interaction field-based technologies, considering pharmacophoric features and those physicochemical properties related to membrane partitioning. High accuracy was demonstrated internally with two different validation sets and, moreover, using a number of molecules, for which Pgp inhibition was not experimentally available but was evaluated in-house. All of the validations confirmed the robustness of the model and its suitability to help medicinal chemists in drug discovery. The information derived from the model was rationalized as a pharmacophore for competitive Pgp inhibition.

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Year:  2011        PMID: 21341745      PMCID: PMC3069647          DOI: 10.1021/jm101421d

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  99 in total

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Review 4.  Three-dimensional pharmacophore methods in drug discovery.

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6.  Passive permeability and P-glycoprotein-mediated efflux differentiate central nervous system (CNS) and non-CNS marketed drugs.

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Review 9.  Effects of drug transporters on volume of distribution.

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  43 in total

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3.  Ligand Promiscuity between the Efflux Pumps Human P-Glycoprotein and S. aureus NorA.

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Review 4.  Molecular Modeling of Drug-Transporter Interactions-An International Transporter Consortium Perspective.

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5.  BDDCS class prediction for new molecular entities.

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6.  Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors.

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Journal:  J Comput Aided Mol Des       Date:  2013-12-10       Impact factor: 3.686

Review 7.  The significance of acid/base properties in drug discovery.

Authors:  David T Manallack; Richard J Prankerd; Elizabeth Yuriev; Tudor I Oprea; David K Chalmers
Journal:  Chem Soc Rev       Date:  2013-01-21       Impact factor: 54.564

8.  Pharmacophore-based discovery of inhibitors of a novel drug/proton antiporter in human brain endothelial hCMEC/D3 cell line.

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9.  Prediction of P-glycoprotein inhibitors with machine learning classification models and 3D-RISM-KH theory based solvation energy descriptors.

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10.  Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions.

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