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Int J Mol Sci. 2012;13(6):6924-43. doi: 10.3390/ijms13066924. Epub 2012 Jun 7.

Development of classification models for identifying "true" P-glycoprotein (P-gp) inhibitors through inhibition, ATPase activation and monolayer efflux assays.

Author information

1
Department of Pharmaceutical Sciences, University of Pisa, Via Bonanno 6, Pisa 56126, Italy; E-Mail: simona.rapposelli@farm.unipi.it.

Abstract

P-glycoprotein (P-gp) is an efflux pump involved in the protection of tissues of several organs by influencing xenobiotic disposition. P-gp plays a key role in multidrug resistance and in the progression of many neurodegenerative diseases. The development of new and more effective therapeutics targeting P-gp thus represents an intriguing challenge in drug discovery. P-gp inhibition may be considered as a valid approach to improve drug bioavailability as well as to overcome drug resistance to many kinds of tumours characterized by the over-expression of this protein. This study aims to develop classification models from a unique dataset of 59 compounds for which there were homogeneous experimental data on P-gp inhibition, ATPase activation and monolayer efflux. For each experiment, the dataset was split into a training and a test set comprising 39 and 20 molecules, respectively. Rational splitting was accomplished using a sphere-exclusion type algorithm. After a two-step (internal/external) validation, the best-performing classification models were used in a consensus predicting task for the identification of compounds named as "true" P-gp inhibitors, i.e., molecules able to inhibit P-gp without being effluxed by P-gp itself and simultaneously unable to activate the ATPase function.

KEYWORDS:

MDR1 ligands; P-glicoprotein; P-gp inhibitors; classification model; consensus model; decision trees

PMID:
22837672
PMCID:
PMC3397504
DOI:
10.3390/ijms13066924
[Indexed for MEDLINE]
Free PMC Article
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