Approach to the specificity and selectivity between D2 and D3 receptors by mutagenesis and binding experiments part I: Expression and characterization of D2 and D3 receptor mutants.

D3/D2 sub-specificity is a complex problem to solve. Indeed, in the absence of easy structural biology of the G-protein coupled receptors, and despite key progresses in this area, the systematic knowledge of the ligand/receptor relationship is difficult to obtain. Due to these structural biology limitations concerning membrane proteins, we favored the use of directed mutagenesis to document a rational towards the discovery of markedly specific D3 ligands over D2 ligands together with basic binding experiments. Using our methodology of stable expression of receptors in HEK cells, we constructed the gene encoding for 24 mutants and 4 chimeras of either D2 or D3 receptors and expressed them stably. Those cell lines, expressing a single copy of one receptor mutant each, were stably constructed, selected, amplified and the membranes from them were prepared. Binding data at those receptors were obtained using standard binding conditions for D2 and D3 dopamine receptors. We generated 26 new molecules derived from D2 or D3 ligands. Using 8 reference compounds and those 26 molecules, we characterized their binding at those mutants and chimeras, exemplifying an approach to better understand the difference at the molecular level of the D2 and D3 receptors. Although all the individual results are presented and could be used for minute analyses, the present report does not discuss the differences between D2 and D3 data. It simply shows the feasibility of the approach and its potential.

Deep generative models for ligand-based de novo design applied to multi-parametric optimization.

Multi-parameter optimization (MPO) is a major challenge in new chemical entity (NCE) drug discovery. Recently, promising results were reported for deep learning generative models applied to de novo molecular design, but, to our knowledge, until now no report was made of the value of this new technology for addressing MPO in an actual drug discovery project. In this study, we demonstrate the benefit of applying AI technology in a real drug discovery project. We evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the obtention of lead compounds meeting 11 different biological activity objectives simultaneously. Using the initial dataset of the project, we built QSAR models for all the 11 objectives, with moderate to high performance (precision between 0.67 and 1.0 on an independent test set). Our DL-based AI de novo design algorithm, combined with the QSAR models, generated 150 virtual compounds predicted as active on all objectives. Eleven were synthetized and tested. The AI-designed compounds met 9.5 objectives on average (i.e., 86% success rate) versus 6.4 (i.e., 58% success rate) for the initial molecules measured on all objectives. One of the AI-designed molecules was active on all 11 measured objectives, and two were active on 10 objectives while being in the error margin of the assay for the last one. The AI algorithm designed compounds with functional groups, which, although being rare or absent in the initial dataset, turned out to be highly beneficial for the MPO.

Design and synthesis of novel N-sulfonyl-2-indoles that behave as 5-HT6 receptor ligands with significant selectivity for D3 over D2 receptors.

All clinically-used antipsychotics display similar affinity for both D2 (D2R) and D3 (D3R) receptors, and they likewise act as 5-HT2A receptor antagonists. They provide therapeutic benefit for positive symptoms, but no marked or consistent improvement in neurocognitive, social cognitive or negative symptoms. Since blockade of D3 and 5-HT6 (5-HT6R) receptors enhances neurocognition and social cognition, and potentially improves negative symptoms, a promising approach for improved treatment for schizophrenia would be to develop drugs that preferentially act at D3R versus D2R and likewise recognize 5-HT6R. Starting from the high affinity 5-HT6R ligands I and II, we identified compounds 11a and 14b that behave as 5-HT6R ligands with significant selectivity for D3R over D2R.

Present understanding of the relationship between exercise and arrhythmogenic right ventricular dysplasia/cardiomyopathy.

Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) is an inherited cardiomyopathy that is characterized clinically by ventricular arrhythmias and an increased risk of sudden death and pathologically by fibrofatty replacement of the myocardium. Soon after the initial description of the disease, investigators and clinicians caring for patients with ARVD/C recognized a link between exercise and ARVD/C. In this article, we review the considerable body of epidemiologic data, basic research, and clinical research supporting the link between exercise and the development and outcomes of ARVD/C. Based on these data, we now advise that patients with ARVD/C avoid participation in competitive athletics and limit exercise as much as possible.

New ligands at the melatonin binding site MT(3).

The third melatonin binding site, MT3 is a non-classical one since it is not a seven transmembrane domains receptor, but an enzyme, quinone reductase 2. A major concern for the study of the physiological role of this site is the lack of specific ligands, permitting to more accurately dissect the pathways linked to the activation of MT3. Indeed, in the course of finding new ligands, we identified a new series of compounds with affinity to the binding site in the nM range, particularly 2,3-dimethoxy 7-hydroxy 10-methyl 5H 10H indeno(1,2-b)indol-10-one (DMHMIO), with a Ki of 190 pM. Based on slightly different and novel synthons compared to most of the compounds used in melatonin pharmacology studies, these compounds offer new perspective for the description of the melatonin pathways, so much more by not having any affinity towards the MT1 and MT2 'classical' melatonin receptors.