Using the Variable-Nearest Neighbor Method To Identify P-Glycoprotein Substrates and Inhibitors.
No Thumbnail Available
Date
0000-00-00
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Permeability glycoprotein Pgp is an essential membrane bound transporter that efficiently extracts compounds from a cell As such it is a critical determinant of the pharmacokinetic properties of drugs Multidrug resistance in cancer is often associated with overexpression of Pgp which increases the efflux of chemotherapeutic agents from the cell This in turn may prevent an effective treatment by reducing the effective intracellular concentrations of such agents Consequently identifying compounds that can either be transported out of the cell by Pgp substrates or impair Pgp function inhibitors is of great interest Herein using publically available data we developed quantitative structure activity relationship QSAR models of Pgp substrates and inhibitors These models employed a variable nearest neighbor v NN method that calculated the structural similarity between molecules and hence possessed an applicability domain that is they used all nearest neighbors that met a minimum similarity constraint The performance characteristics of these v NN based models were comparable or at times superior to those of other model constructs The best v NN models for identifying either Pgp substrates or inhibitors showed overall accuracies of 80 and values of 0 60 when tested on external data sets with candidate Pgp substrates and inhibitors The v NN prediction model with a well defined applicability domain gave accurate and reliable results The v NN method is computationally efficient and requires no retraining of the prediction model when new assay information becomes available an important feature when keeping QSAR models up to date and maintaining their performance at high levels