Which Compound to Select in Lead Optimization? Prospectively Validated Proteochemometric Models Guide Preclinical Development

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

Resistance Mutations in Human Immunodeficiency Virus Type 1 Integrase Selected with Elvitegravir Confer Reduced Susceptibility to a Wide Range of Integrase Inhibitors▿

Integration of viral DNA into the host chromosome is an essential step in the life cycle of retroviruses and is facilitated by the viral integrase enzyme. The first generation of integrase inhibitors recently approved or currently in late-stage clinical trials shows great promise for the treatment of human immunodeficiency virus (HIV) infection, but virus is expected to develop resistance to these drugs. Therefore, we used a novel resistance selection protocol to follow the emergence of resistant HIV in the presence of the integrase inhibitor elvitegravir (GS-9137). We find the primary resistance-conferring mutations to be Q148R, E92Q, and T66I and demonstrate that they confer a reduction in susceptibility not only to elvitegravir but also to raltegravir (MK-0518) and other integrase inhibitors. The locations of the mutations are highlighted in the catalytic sites of integrase, and we correlate the mutations with expected drug-protein contacts. In addition, mutations that do not confer reduced susceptibility when present alone (H114Y, L74M, R20K, A128T, E138K, and S230R) are also discussed in relation to their position in the catalytic core domain and their proximity to known structural features of integrase. These data broaden the understanding of antiviral resistance against integrase inhibitors and may give insight facilitating the discovery of second-generation compounds