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Predicting P-glycoprotein substrates by a quantitative structure-activity relationship model.
Vijay K Gombar1, Joseph W Polli, Joan E Humphreys
1Department of Drug Metabolism and Pharmacokinetics, Metabolic and Viral Diseases' Center of Excellence for Drug Discovery, GlaxoSmithKline, Research Triangle Park, North Carolina 27709, USA.
Journal of Pharmaceutical Sciences
|March 5, 2004
Summary
A new quantitative structure-activity relationship (QSAR) model predicts P-glycoprotein (Pgp) substrate potential. This in silico tool aids compound selection by identifying potential Pgp substrates and non-substrates.
Area of Science:
- Pharmacology
- Computational Chemistry
- Drug Discovery
Background:
- P-glycoprotein (Pgp) is a key efflux transporter implicated in multidrug resistance.
- Accurate prediction of Pgp substrate potential is crucial for drug development.
- Existing methods for Pgp substrate identification can be time-consuming and resource-intensive.
Purpose of the Study:
- To develop and validate a quantitative structure-activity relationship (QSAR) model for predicting P-glycoprotein (Pgp) substrate status.
- To identify key structural features that confer Pgp substrate activity.
- To provide an in silico screening tool to prioritize compounds for experimental evaluation.
Main Methods:
- Development of a two-group linear discriminant QSAR model using 95 compounds with known Pgp substrate/non-substrate classification.
- Utilized 27 statistically significant structural descriptors.
- Validated the model using jackknifed cross-validation and an independent test set of 58 compounds.
Main Results:
- The QSAR model achieved 100% sensitivity and 90.6% specificity in cross-validation.
- Prediction accuracy on the test set was 86.2%.
- Key structural attributes identified include membrane partitioning, molecular bulk, and hydride electrotopological values.
Conclusions:
- The developed Pgp QSAR model effectively predicts substrate potential with high accuracy.
- The model aids in understanding structural determinants of Pgp interaction.
- This in silico approach can streamline compound selection and optimize in vitro assay prioritization in drug discovery.