Novel tricyclic small molecule inhibitors of Nicotinamide N-methyltransferase for the treatment of metabolic disorders.

Nicotinamide N-methyltransferase (NNMT) is a metabolic regulator that catalyzes the methylation of nicotinamide (Nam) using the co-factor S-adenosyl-L-methionine to form 1-methyl-nicotinamide (MNA). Overexpression of NNMT and the presence of the active metabolite MNA is associated with a number of diseases including metabolic disorders. We conducted a high-throughput screening campaign that led to the identification of a tricyclic core as a potential NNMT small molecule inhibitor series. Elaborate medicinal chemistry efforts were undertaken and hundreds of analogs were synthesized to understand the structure activity relationship and structure property relationship of this tricyclic series. A lead molecule, JBSNF-000028, was identified that inhibits human and mouse NNMT activity, reduces MNA levels in mouse plasma, liver and adipose tissue, and drives insulin sensitization, glucose modulation and body weight reduction in a diet-induced obese mouse model of diabetes. The co-crystal structure showed that JBSNF-000028 binds below a hairpin structural motif at the nicotinamide pocket and stacks between Tyr-204 (from Hairpin) and Leu-164 (from central domain). JBSNF-000028 was inactive against a broad panel of targets related to metabolism and safety. Interestingly, the improvement in glucose tolerance upon treatment with JBSNF-000028 was also observed in NNMT knockout mice with diet-induced obesity, pointing towards the glucose-normalizing effect that may go beyond NNMT inhibition. JBSNF-000028 can be a potential therapeutic option for metabolic disorders and developmental studies are warranted.

Novel Inhibitors of Nicotinamide-N-Methyltransferase for the Treatment of Metabolic Disorders.

Nicotinamide-N-methyltransferase (NNMT) is a cytosolic enzyme catalyzing the transfer of a methyl group from S-adenosyl-methionine (SAM) to nicotinamide (Nam). It is expressed in many tissues including the liver, adipose tissue, and skeletal muscle. Its expression in several cancer cell lines has been widely discussed in the literature, and recent work established a link between NNMT expression and metabolic diseases. Here we describe our approach to identify potent small molecule inhibitors of NNMT featuring different binding modes as elucidated by X-ray crystallographic studies.

Machine learning-based QSAR models to predict sodium ion channel (Nav 1.5) blockers.

Aim: Conventional experimental approaches used for the evaluation of the proarrhythmic potential of compounds in the drug discovery process are expensive and time consuming but an integral element in the safety profile required for a new drug to be approved. The voltage-gated sodium ion channel 1.5 (Nav 1.5), a target known for arrhythmic drugs, causes adverse cardiac complications when the channel is blocked. Results: Machine learning classification and regression models were built to predict the possibility of blocking these channels by small molecules. The finalized models tested with balanced accuracies of 0.88, 0.93 and 0.94 at three thresholds (1, 10 and 30 µmol, respectively). The regression model built to predict the pIC50 of compounds had q2 of 0.84 (root-mean-square error = 0.46). Conclusion: The machine learning models that have been built can act as effective filters to screen out the potentially toxic compounds in the early stages of drug discovery.

Discovery and optimization of novel phenyldiazepine and pyridodiazepine based Aurora kinase inhibitors.

Aurora B plays critical role in the process of chromosome condensation and chromosome orientation during the regulation of mitosis. The overexpression of Aurora B has been observed in several tumor types. As a part of our ongoing effort to develop Aurora B inhibitors, herein, we described the design, synthesis and evaluation of phenyl/pyridine diazepine analogs. The diazepane aniline pyrimidine (4a) was identified as an initial hit (Aurora B IC50 6.9 µM). Molecular modeling guided SAR optimization lead to the identification of 8-fluorobenzodiazepine (6c) with single digit nM potency (Aurora B IC50 8 nM). In the antiproliferation assay 6c showed activity across the cell lines with IC50 of 0.57, 0.42, and 0.69 µM for MCF-7, MDA-MB 231, and SkoV3 respectively. In the in vivo PK profile. 6c has shown higher bioavailability (73%) along with good exposure (AUC of 1360 ng.h/mL).

A classification model for blood brain barrier penetration.

Traditional experimental approaches to evaluate the Blood-Brain Barrier (BBB) permeability of a drug are expensive and time consuming. Hence, several computational models have been developed over time to estimate propensities of compounds to penetrate the BBB. In this study, we aimed to build improved BBB classification models using a large curated dataset of 605 compounds with two classification thresholds (threshold-1: Brain/Plasma ≥ 0.6 as BBB+ and Brain/Plasma<0.6 as BBB- and threshold-2: Brain/Plasma>0.6 as BBB+ and Brain/Plasma<0.3 as BBB-). This dataset was split into a training set of 479 compounds for threshold-1, 432 compounds for threshold-2 and a test set of 126 compounds for threshold-1 and 110 compounds for threshold-2. A single model could not predict similar results for each dataset in case of two thresholds. Hence, consensus model building was employed on the modelling set that gave similar results for each of the datasets for two thresholds. The consensus model performed better on overall prediction datasets (a test set with 126 compounds and a WDI dataset with 1425 compounds for threshold-1 and a test set with 110 compounds and the WDI dataset for threshold-2), with accuracies of 86% and 87% for threshold-1 and threshold-2, respectively. The prediction performance of our consensus model was better than other existing models, by the criteria of percent accuracy, Matthew's correlation coefficient, sensitivity, specificity and Correct Classification Ratio. An analysis of substructure moieties among BBB + compounds showed a list of moieties that were present more among BBB + compounds than among BBB- compounds. These findings corroborate with the results of similar analyses reported earlier. The BBB prediction models developed in this study would be quite useful for screening compounds very early on in drug discovery projects.

A small molecule inhibitor of Nicotinamide N-methyltransferase for the treatment of metabolic disorders.

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.

Novel nicotinamide analog as inhibitor of nicotinamide N-methyltransferase.

Nicotinamide N-methyltransferase (NNMT) has been linked to obesity and diabetes. We have identified a novel nicotinamide (NA) analog, compound 12 that inhibited NNMT enzymatic activity and reduced the formation of 1-methyl-nicotinamide (MNA), the primary metabolite of NA by ∼80% at 2 h when dosed in mice orally at 50 mg/kg.

Crystal structures of monkey and mouse nicotinamide N-methyltransferase (NNMT) bound with end product, 1-methyl nicotinamide.

Nicotinamide N-methyltransferase (NNMT) is a S-adenosyl-l-methionine (SAM)-dependent enzyme that catalyzes N-methylation of nicotinamide (NA) and other pyridines to form N-methyl pyridinium ions. Here we report the first ternary complex X-ray crystal structures of monkey NNMT and mouse NNMT in bound form with the primary endogenous product, 1-methyl nicotinamide (MNA) and demethylated cofactor, S-adenosyl-homocysteine (SAH) determined at 2.30 Å and 1.88 Å respectively. The structural fold of these enzymes is identical to human NNMT. It is known that the primary endogenous product catalyzed by NNMT, MNA is a specific inhibitor of NNMT. Our data clearly indicates that the MNA binds to the active site and it would be trapped in the active site due to the formation of the bridge between the pole (long helix, α3) and long C-terminal loop. This might explain the mechanism of MNA acting as a feedback inhibitor of NNMT.

TRPV1 antagonism by piperazinyl-aryl compounds: A Topomer-CoMFA study and its use in virtual screening for identification of novel antagonists.

Transient Receptor Potential Vanilloid, member 1 (TRPV1), is a non-selective cation channel belonging to the transient receptor potential (TRP) family of ion channels. It occurs in the peripheral and central nervous system, activated by a variety of exogenous and endogenous stimuli, thus playing a key role in transmission of pain. This has been a target for chronic pain since more than a decade and a number of antagonists that progressed into clinical trials have failed due to the unexpected side effect of core body temperature rise, thus halting progress in this field. Of late, there has been an upsurge in research on this target, with the rat TRPV1 structure being determined, many new antagonists discovered that are temperature-neutral and many new therapeutic avenues being discovered for TRPV1, including diseases of respiratory and digestive systems, skin and bladder. Towards identifying diverse compounds to decipher the role of this target in various indications, here we report a 3D-QSAR model built using the new topomer-CoMFA methodology on a series of piperazinyl-aryl TRPV1 antagonists and the use of this model, along with a pharmacophore model and the shape of one of the potent compounds of this series, to virtually screen a subset of the ZINC database to find novel and diverse hits. These can serve as starting points to develop modality-selective antagonists for chronic pain and to elucidate the critical role of TRPV1 in the various new therapeutic areas.

A New Series of Orally Bioavailable Chemokine Receptor 9 (CCR9) Antagonists; Possible Agents for the Treatment of Inflammatory Bowel Disease.

Chemokine receptor 9 (CCR9), a cell surface chemokine receptor which belongs to the G protein-coupled receptor, 7-trans-membrane superfamily, is expressed on lymphocytes in the circulation and is the key chemokine receptor that enables these cells to target the intestine. It has been proposed that CCR9 antagonism represents a means to prevent the aberrant immune response of inflammatory bowel disease in a localized and disease specific manner and one which is accessible to small molecule approaches. One possible reason why clinical studies with vercirnon, a prototype CCR9 antagonist, were not successful may be due to a relatively poor pharmacokinetic (PK) profile for the molecule. We wish to describe work aimed at producing new, orally active CCR9 antagonists based on the 1,3-dioxoisoindoline skeleton. This study led to a number of compounds that were potent in the nanomolar range and which, on optimization, resulted in several possible preclinical development candidates with excellent PK properties.

3D-QSAR analysis of TRPV1 inhibitors reveals a pharmacophore applicable to diverse scaffolds and clinical candidates.

TRPV1 (Transient Receptor Potential Vanilloid Type 1) receptor, a member of Transient Receptor Potential Vanilloid subfamily of ion channels, occurs in the peripheral and central nervous system, and plays a key role in transmission of pain. Consequently, this has been the target for discovery of several pain relieving agents which have undergone clinical trials. Though several TRPV1 antagonists have progressed to become clinical candidates, many are known to cause temperature elevation in humans, halting their further advancement, and signifying the need for new chemotypes. Different chemical classes of TRPV1 antagonists share three important features: an amide or an isostere flanked by an aromatic (or fused aromatic) ring with polar substitutions on one side, and a hydrophobic group on the other. Recent work identified new series of compounds with these and additional features, leading to improvement of properties, and development of clinical candidates. Herein, we describe a 3D-QSAR model (n=62; R(2)=0.9 and Q(2)=0.75) developed from the piperazinyl-aryl series of compounds and a novel 5-point pharmacophore model is shown to fit several diverse scaffolds, six clinical candidates, five pre-clinical candidates and three lead compounds. The pharmacophore model can aid in finding new chemotypes as starting points that can be developed further.

Comparison of conformational analysis techniques to generate pharmacophore hypotheses using catalyst.

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.