Nicotinamide N-methyltransferase (NNMT) is a cytosolic enzyme that catalyzes the transfer of a methyl group from the co-factor S-adenosyl-L-methionine (SAM) onto the substrate, nicotinamide (NA) to form 1-methyl-nicotinamide (MNA). Higher NNMT expression and MNA concentrations have been associated with obesity and type-2 diabetes. Here we report a small molecule analog of NA, JBSNF-000088, that inhibits NNMT activity, reduces MNA levels and drives insulin sensitization, glucose modulation and body weight reduction in animal models of metabolic disease. In mice with high fat diet (HFD)-induced obesity, JBSNF-000088 treatment caused a reduction in body weight, improved insulin sensitivity and normalized glucose tolerance to the level of lean control mice. These effects were not seen in NNMT knockout mice on HFD, confirming specificity of JBSNF-000088. The compound also improved glucose handling in ob/ob and db/db mice albeit to a lesser extent and in the absence of weight loss. Co-crystal structure analysis revealed the presence of the N-methylated product of JBSNF-000088 bound to the NNMT protein. The N-methylated product was also detected in the plasma of mice treated with JBSNF-000088. Hence, JBSNF-000088 may act as a slow-turnover substrate analog, driving the observed metabolic benefits.

Generation of reliable pharmacophore models is a key strategy in drug design. The quality of a pharmacophore model is known to depend on several factors, with the quality of the conformer sets used perhaps being one of the most important. The goal of this study was to compare different conformational analysis methods to determine if one was superior to the others for pharmacophore generation using Catalyst/HypoGen. The five methods selected were Catalyst/Fast, Catalyst/Best, Omega, Chem-X and MacroModel. Data sets for which Catalysts models had previously been published were selected using defined quality measures. Hypotheses were generated for each of the data sets and the performance of the different conformational analysis methods was compared using both quantitative (cost and correlation coefficients) and qualitative measures (by comparing the hypotheses in terms of the features present and their spatial relationships). Two main conclusions emerged from the study. First, it was not always possible to replicate the literature results. The reasons for these failures are explored in detail, and a template for use in publications that apply the Catalyst methodology is proposed. Second, the faster rule-based methods for conformational analysis give pharmacophore models that are just as good as, and in some cases better than, the models generated using the slower, more rigorous approaches.

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