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Transmembranal G Protein-Coupled Receptors (GPCRs) transduce extracellular chemical signals to the cell, via conformational change from a resting (inactive) to an active (canonically bound to a G-protein) conformation. Receptor activation is normally modulated by extracellular ligand binding, but mutations in the receptor can also shift this equilibrium by stabilizing different conformational states. In this work, we built structure-energetic relationships of receptor activation based on original thermodynamic cycles that represent the conformational equilibrium of the prototypical A2A adenosine receptor (AR). These cycles were solved with efficient free energy perturbation (FEP) protocols, allowing to distinguish the pharmacological profile of different series of A2AAR agonists with different efficacies. The modulatory effects of point mutations on the basal activity of the receptor or on ligand efficacies could also be detected. This methodology can guide GPCR ligand design with tailored pharmacological properties, or allow the identification of mutations that modulate receptor activation with potential clinical implications.

Lymphoblast cells from four individuals of a family of two genetically unrelated parents and their monozygotic twins were used to generate integration-free human induced pluripotent stem cells (hiPSCs). Reprogramming factors were delivered by co-electroporation of three episomal-based plasmids expressing OCT3/4, SOX2, KLF4, L-MYC and LIN28. The hiPSCs showed a normal karyotype, expressed pluripotency-associated markers, displayed the capacity for in vitro differentiation into the three germ layers and were Epstein Barr virus-free. These hiPSC lines offer the possibility to compare genetically unrelated and genetically identical tissues from different individuals and to study genotype-specific effects, which are particularly relevant for toxicology testing.

Abstract Long‐QT syndrome (LQTS) is an arrhythmogenic disorder characterised by prolongation of the QT interval in the electrocardiogram, which can lead to sudden cardiac death. Pharmacological treatments are far from optimal for congenital forms of LQTS, while the acquired form, often triggered by drugs that (sometimes inadvertently) target the cardiac hERG channel, is still a challenge in drug development because of cardiotoxicity. Current experimental models in vitro fall short in predicting proarrhythmic properties of new drugs in humans. Here, we leveraged a series of isogenically matched, diseased and genetically engineered, human induced pluripotent stem cell‐derived cardiomyocytes (hiPSC‐CMs) from patients to test a novel hERG allosteric modulator for treating congenital LQTS, drug‐induced LQTS or a combination of the two. By slowing IKr deactivation and positively shifting IKr inactivation, the small molecule LUF7346 effectively rescued all of these conditions, demonstrating in a human system that allosteric modulation of hERG may be useful as an approach to treat inherited and drug‐induced LQTS. Furthermore, our study provides experimental support of the value of isogenic pairs of patient hiPSC‐CMs as platforms for testing drug sensitivities and performing safety pharmacology.

Infection with HIV cannot currently be cured; however it can be controlled by combination treatment with multiple anti-retroviral drugs. Given different viral genotypes for virtually each individual patient, the question now arises which drug combination to use to achieve effective treatment. With the availability of viral genotypic data and clinical phenotypic data, it has become possible to create computational models able to predict an optimal treatment regimen for an individual patient. Current models are based only on sequence data derived from viral genotyping; chemical similarity of drugs is not considered. To explore the added value of chemical similarity inclusion we applied proteochemometric models, combining chemical and protein target properties in a single bioactivity model. Our dataset was a large scale clinical database of genotypic and phenotypic information (in total ca. 300,000 drug-mutant bioactivity data points, 4 (NNRTI), 8 (NRTI) or 9 (PI) drugs, and 10,700 (NNRTI) 10,500 (NRTI) or 27,000 (PI) mutants). Our models achieved a prediction error below 0.5 Log Fold Change. Moreover, when directly compared with previously published sequence data, derived models PCM performed better in resistance classification and prediction of Log Fold Change (0.76 log units versus 0.91). Furthermore, we were able to successfully confirm both known and identify previously unpublished, resistance-conferring mutations of HIV Reverse Transcriptase (e.g. K102Y, T216M) and HIV Protease (e.g. Q18N, N88G) from our dataset. Finally, we applied our models prospectively to the public HIV resistance database from Stanford University obtaining a correct resistance prediction rate of 84% on the full set (compared to 80% in previous work on a high quality subset). We conclude that proteochemometric models are able to accurately predict the phenotypic resistance based on genotypic data even for novel mutants and mixtures. Furthermore, we add an applicability domain to the prediction, informing the user about the reliability of predictions.

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.