title: Which compound to select in lead optimization? Prospectively validated proteochemometric models guide preclinical development.
abstract: In quite a few diseases, drug resistance due to target variability poses a serious problem in pharmacotherapy. This is certainly true for HIV, and hence, it is often unknown which drug is best to use or to develop against an individual HIV strain. In this work we applied 'proteochemometric' modeling of HIV Non-Nucleoside Reverse Transcriptase (NNRTI) inhibitors to support preclinical development by predicting compound performance on multiple mutants in the lead selection stage. Proteochemometric models are based on both small molecule and target properties and can thus capture multi-target activity relationships simultaneously, the targets in this case being a set of 14 HIV Reverse Transcriptase (RT) mutants. We validated our model by experimentally confirming model predictions for 317 untested compound-mutant pairs, with a prediction error comparable with assay variability (RMSE 0.62). Furthermore, dependent on the similarity of a new mutant to the training set, we could predict with high accuracy which compound will be most effective on a sequence with a previously unknown genotype. Hence, our models allow the evaluation of compound performance on untested sequences and the selection of the most promising leads for further preclinical research. The modeling concept is likely to be applicable also to other target families with genetic variability like other viruses or bacteria, or with similar orthologs like GPCRs.
authors: Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden, The Netherlands.
authors: Gerard J P
authors: GJ
authors: van Westen
authors: Jörg K
authors: JK
authors: Wegner
authors: Peggy
authors: P
authors: Geluykens
authors: Leen
authors: L
authors: Kwanten
authors: Inge
authors: I
authors: Vereycken
authors: Anik
authors: A
authors: Peeters
authors: Adriaan P
authors: AP
authors: Ijzerman
authors: Herman W T
authors: HW
authors: van Vlijmen
authors: Andreas
authors: A
authors: Bender
mesh_terms: D000595:Amino Acid Sequence; D001665:Binding Sites; D019992:Databases as Topic; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D054303:HIV Reverse Transcriptase / Q000037:antagonists & inhibitors / Q000737:chemistry; D006801:Humans; D008024:Ligands; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009154:Mutation / Q000235:genetics; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D018894:Reverse Transcriptase Inhibitors / Q000032:analysis* / Q000737:chemistry* / Q000494:pharmacology
chemical_list: D008024:Ligands; D018894:Reverse Transcriptase Inhibitors; D054303:HIV Reverse Transcriptase