Building a MCHR1 homology model provides insight into the receptor–antagonist contacts that are important for the development of new anti-obesity agents

Systematic Data Mining Reveals Synergistic H3R/MCHR1 Ligands

In this study, we report a ligand-centric data mining approach that guided the identification of suitable target profiles for treating obesity. The newly developed method is based on identifying target pairs for synergistic positive effects and also encompasses the exclusion of compounds showing a detrimental effect on obesity treatment (off-targets). Ligands with known activity against obesity-relevant targets were compared using fingerprint representations. Similar compounds with activities to different targets were evaluated for the mechanism of action since activation or deactivation of drug targets determines the pharmacological effect. In vitro validation of the modeling results revealed that three known modulators of melanin-concentrating hormone receptor 1 (MCHR1) show a previously unknown submicromolar affinity to the histamine H3 receptor (H₃R). This synergistic activity may present a novel therapeutic option against obesity.

Profiling the interaction mechanism of quinoline/quinazoline derivatives as MCHR1 antagonists: an in silico method

Melanin concentrating hormone receptor 1 (MCHR1), a crucial regulator of energy homeostasis involved in the control of feeding and energy metabolism, is a promising target for treatment of obesity. In the present work, the up-to-date largest set of 181 quinoline/quinazoline derivatives as MCHR1 antagonists was subjected to both ligand- and receptor-based three-dimensional quantitative structure–activity (3D-QSAR) analysis applying comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The optimal predictable CoMSIA model exhibited significant validity with the cross-validated correlation coefficient (Q²) = 0.509, non-cross-validated correlation coefficient (R²_ncv) = 0.841 and the predicted correlation coefficient (R²_pred) = 0.745. In addition, docking studies and molecular dynamics (MD) simulations were carried out for further elucidation of the binding modes of MCHR1 antagonists. MD simulations in both water and lipid bilayer systems were performed. We hope that the obtained models and information may help to provide an insight into the interaction mechanism of MCHR1 antagonists and facilitate the design and optimization of novel antagonists as anti-obesity agents.

Improvement of the Prediction Power of the CoMFA and CoMSIA Models on Histamine H3 Antagonists by Different Variable Selection Methods

The aim of this study is to enhance the predictivity power of CoMFA and CoMSIA models by means of different variable selection algorithms. The genetic algorithm (GA), successive projection algorithm (SPA), stepwise multiple linear regression (SW-MLR), and the enhanced replacement method (ERM) were used and tested as variable selection algorithms. Then, the selected variables were used to generate a simple and predictive model by the multilinear regression algorithm. A set of 74 histamine H₃ antagonists were split into 40 compounds as a training set, and 17 compounds as a test set, by the Kennard-Stone algorithm. Before splitting the data, 17 compounds were randomly selected from the pool of the whole data set as an evaluation set without any supervision, pretreatment, or visual inspection. Among applied variable selection algorithms, ERM had noticeable improvement on the statistical parameters. The r² values of training, test, and evaluation sets for the ERM-MLR model using CoMFA fields were 0.9560, 0.8630, and 0.8460 and using the CoMSIA fields were 0.9800, 0.8521, and 0.9080, respectively. In this study, the principles of organization for economic cooperation and development (OECD) for regulatory acceptability of QSARs are considered.

Discovery, synthesis, and structure-activity relationship of 6-aminomethyl-7,8-dihydronaphthalenes as human melanin-concentrating hormone receptor 1 antagonists

Human melanin-concentrating hormone receptor 1 (hMCHR1) antagonists are promising targets for obesity treatment. We identified the tetrahydronaphthalene derivative 1a with modest binding affinity for hMCHR1 by screening an in-house G protein-coupled receptor (GPCR) ligand library. We synthesized a series of 6-aminomethyl-5,6,7,8-tetrahydronaphthalenes and evaluated their activity as hMCHR1 antagonists. Modification of the biphenylcarbonylamino group revealed that the biphenyl moiety played a crucial role in the interaction of the antagonist with the receptor. The stereoselective effect of the chiral center on binding affinity generated the novel 6-aminomethyl-7,8-dihydronaphthalene scaffold without a chiral center. Optimization of the amino group led to the identification of a potent antagonist 2s (4'-fluoro-N-[6-(1-pyrrolidinylmethyl)-7,8-dihydro-2-naphthalenyl][1,1'-biphenyl]-4-carboxamide), which significantly inhibited the nocturnal food intake in rats after oral administration. Pharmacokinetic analysis confirmed that 2s had good oral bioavailability and brain penetrance. This antagonist appears to be a viable lead compound that can be used to develop a promising therapy for obesity.