Line-Walking Method for Predicting Inhibition of P450 Drug Metabolism

Matthew G Hudelson; Jeffrey P Jones

doi:10.1021/jm0601553

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Line-Walking Method for Predicting Inhibition of P450 Drug Metabolism

Journal article

Open access

Peer reviewed

Line-Walking Method for Predicting Inhibition of P450 Drug Metabolism

Matthew G Hudelson and Jeffrey P Jones

Journal of medicinal chemistry, Vol.49(14), pp.4367-4373

07/13/2006

DOI: https://doi.org/10.1021/jm0601553

Handle:

https://hdl.handle.net/2376/115843

PMCID: PMC2871544

PMID: 16821796

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https://doi.org/10.1021/jm0601553View

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Abstract

Drug metabolism

P450

support vector machines

QSAR

Drug Design

recursive partitioning

A new method, called line-walking recursive partitioning (LWRP), for partitioning diverse structures based on chemical properties that uses only nine descriptors of the shape, polarizability, and charge of the molecule is described. We use a training set of over 600 compounds, and a validation set of 100 compounds for the cytochrome P450 enzymes 2C9, 2D6 and 3A4. The LWRP algorithm itself incorporates elements from support vector machines (SVM) and recursive partitioning, while circumventing the need for linear or quadratic programming methods required in SVM. We compare LWRP with a many-descriptor SVM model, using the same dataset as described in the literature 1 . The line-walking method, using nine descriptors, predicted the validation set with about 84-90 % accuracy, a success rate comparable to the SVM method. Furthermore, line-walking was able to find errors in the assignment of inhibitor values within the validation set for the 2C9 inhibitors. When these errors are corrected, the model predicts with an even higher level of accuracy. While this method has been applied to P450 enzymes it should be of general use in partitioning molecules based on function.

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Title: Line-Walking Method for Predicting Inhibition of P450 Drug Metabolism
Creators: Matthew G Hudelson - Department of Mathematics, Washington State University, P.O. Box 643113, Pullman, Washington 99164-3113, 509-335-3125, Facsimile Machine 509-335-1188
Jeffrey P Jones - Department of Chemistry, Washington State University, P.O. Box 644630, Pullman, Washington 99164-4630
Publication Details: Journal of medicinal chemistry, Vol.49(14), pp.4367-4373
Academic Unit: Chemistry, Department of; Mathematics and Statistics, Department of
Identifiers: 99900548030601842
Language: English
Resource Type: Journal article