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The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

A Novel Approach for Predicting P-Glycoprotein (ABCB1) Inhibition Using Molecular Interaction Fields

Open access
Broccatelli, Fabio, Carosati, Emanuele, Neri, Annalisa, Frosini, Maria, Goracci, Laura, Oprea, Tudor I., Cruciani, Gabriele·2011·Journal of Medicinal Chemistry

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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Crossref · 10.1021/jm101421d

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Metadata

Authors
Broccatelli, F., Carosati, E., Neri, A., Frosini, M., Goracci, L., Oprea, T. I., & Cruciani, G.
Venue
Journal of Medicinal Chemistry
Published
2011-02-22
Volume
54
Issue
6
Pages
1740-1751
ISSN
  • 0022-2623 (ISSN-L)
  • 1520-4804
OpenAlex
W2040651998
Cited by
176
References
2
FWCI
11.36
Type
journal article
Language
en
Open access
green

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.