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Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors

Authors
  • Sutherland, Jeffrey J.1
  • Weaver, Donald F.2, 2
  • 1 Queen's University, Department of Chemistry, Kingston, Ontario, K7L 3N6, Canada , Kingston
  • 2 Dalhousie University, Halifax, Departments of Medicine (Neurology) and Chemistry and School of Biomedical Engineering, Nova Scotia, B3H 4J3, Canada , Nova Scotia
Type
Published Article
Journal
Journal of Computer-Aided Molecular Design
Publisher
Springer-Verlag
Publication Date
May 01, 2004
Volume
18
Issue
5
Pages
309–331
Identifiers
DOI: 10.1023/B:JCAM.0000047814.85293.da
Source
Springer Nature
Keywords
License
Yellow

Abstract

Three-dimensional quantitative structure-activity relationship (3D-QSAR) modelling using comparative molecular similarity indices analysis (CoMSIA) was applied to a series of 406 structurally diverse dihydrofolate reductase (DHFR) inhibitors from Pneumocystis carinii (pc) and rat liver (rl). X-ray crystal structures of three inhibitors bound to pcDHFR were used for defining the alignment rule. For pcDHFR, a QSAR model containing 6 components was selected using leave-10%-out cross-validation (n=240, q2=0.65), while a 4-component model was selected for rlDHFR (n=237, q2=0.63); both include steric, electrostatic and hydrophobic contributions. The models were validated using a large test set, designed to maximise its diversity and to verify the predictive accuracy of models for extrapolation. The pcDHFR model has r2=0.60 and mean absolute error (MAE) = 0.57 for the test set after removing 4 outliers, and the rlDHFR model has r2=0.60 and MAE = 0.69 after removing 4 test set outliers. In addition, classification models predicting selectivity for pcDHFR over rlDHFR were developed using soft independent modelling by class analogy (SIMCA), with a selectivity ratio of 2 (IC50,rlDHFR/ IC50,pcDHFR) used for delimiting classes. A 5-component model including steric and electrostatic contributions has cross-validated and test set classification rates of 0.67 and 0.68 for selective inhibitors, and 0.85 and 0.72 for unselective inhibitors. The predictive accuracy of models, together with the identification of important contributions in QSAR and classification models, offer the possibility of designing potent selective inhibitors and estimating their activity prior to synthesis.

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