A Multiple Classifier System Identifies Novel Cannabinoid CB2 Receptor Ligands

open access articleDrugs have become an essential part of our lives due to their ability to improve people’s health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have undesirable side effects, making the pharmaceutical industry strive to discover new drugs and active compounds. The development of drugs is an expensive process, which typically starts with the detection of candidate molecules (screening) for an identified protein target. To this end, the use of high-performance screening techniques has become a critical issue in order to palliate the high costs. Therefore, the popularity of computer-based screening (often called virtual screening or in-silico screening) has rapidly increased during the last decade. A wide variety of Machine Learning (ML) techniques has been used in conjunction with chemical structure and physicochemical properties for screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently (iii) Multiple Classifier Systems (MCS). In this work, we apply an MCS for virtual screening (D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine (1.834.362 compounds), was virtually screened to identify 48.432 potential active molecules using D2-MCS. This list was subsequently clustered based on circular fingerprints and from each cluster, the most active compound was maintained. From these, the top 60 were kept, and 21 novel compounds were purchased. Experimental validation confirmed six highly active hits (>50% displacement at 10 μM and subsequent Ki determination) and an additional five medium active hits (>25% displacement at 10 μM). D2-MCS hence provided a hit rate of 29% for highly active compounds and an overall hit rate of 52%

Synthesis and SAR evaluation of coumarin derivatives as potent cannabinoid receptor agonists

We report the development and extensive structure-activity relationship evaluation of a series of modified coumarins as cannabinoid receptor ligands. In radioligand, and [S-35]GTP gamma S binding assays the CB receptor binding affinities and efficacies of the new ligands were determined. Furthermore, we used a ligand-based docking approach to validate the empirical observed results. In conclusion, several crucial structural requirements were identified. The most potent coumarins like 3-butyl-7-(1-butylcyclopentyl)-5-hydroxy-2H-chromen-2-one (36b, K-i CB2 13.7 nM, EC50 18 nM), 7-(1-butylcyclohexyl)-5-hydroxy-3-propyl-2H-chromen-2-one (39b, K-i CB2 6.5 nM, EC50 4.51 nM) showed a CB2 selective agonistic profile with low nanomolar affinities. (C) 2021 Published by Elsevier Masson SAS.Peer reviewe

A multiple classifier system identifies novel cannabinoid CB2 receptor ligands

Abstract Drugs have become an essential part of our lives due to their ability to improve people’s health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have undesirable side effects, making the pharmaceutical industry strive to discover new drugs and active compounds. The development of drugs is an expensive process, which typically starts with the detection of candidate molecules (screening) after a protein target has been identified. To this end, the use of high-performance screening techniques has become a critical issue in order to palliate the high costs. Therefore, the popularity of computer-based screening (often called virtual screening or in silico screening) has rapidly increased during the last decade. A wide variety of Machine Learning (ML) techniques has been used in conjunction with chemical structure and physicochemical properties for screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently (iii) Multiple Classifier Systems (MCS). Here, we apply an MCS for virtual screening (D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine (1,834,362 compounds), was virtually screened to identify 48,232 potential active molecules using D2-MCS. Identified molecules were ranked to select 21 promising novel compounds for in vitro evaluation. Experimental validation confirmed six highly active hits (> 50% displacement at 10 µM and subsequent Ki determination) and an additional five medium active hits (> 25% displacement at 10 µM). Hence, D2-MCS provided a hit rate of 29% for highly active compounds and an overall hit rate of 52%.Dutch Scientific Council | Ref. VENI 14410Xunta de Galicia | Ref. ED431C2018/55-GR

Text-mined orthosteric and allosteric compound dataset

Compound dataset retrieved from the ChEMBL database using text mining. Text-mined orthosteric and allosteric binding types annotations are included in the dataset

Design, Synthesis and Pharmacological Profile of LUF7746, a Novel Covalent Partial Agonist for the Adenosine A1 Receptor

Partial agonists for G protein-coupled receptors (GPCRs) provide opportunities for novel pharmacotherapies with enhanced on-target safety compared to full agonists. For the human adenosine A1 receptor (hA1AR) this has led to the discovery of capadenoson, which has been in phase IIa clinical trials for heart failure. Accordingly, the design and profiling of novel hA1AR partial agonists has become an important research focus. In this study, we report on LUF7746, a capadenoson derivative bearing an electrophilic fluorosulfonyl moiety, as an irreversibly binding hA1AR modulator. Meanwhile, a nonreactive ligand bearing a methylsulfonyl moiety, LUF7747, was designed as a control probe in our study. In a radioligand binding assay, LUF7746’s apparent affinity increased to nanomolar range with longer pre-incubation time, suggesting an increasing level of covalent binding over time. Moreover, compared to the reference full agonist CPA, LUF7746 was a partial agonist in a hA1AR-mediated G protein activation assay and resistant to blockade with an antagonist/inverse agonist. An in silico structure-based docking study combined with site-directed mutagenesis of the hA1AR demonstrated that amino acid Y2717.36 was the primary anchor point for the covalent interaction. Additionally, a label-free whole-cell assay was set up to identify LUF7746’s irreversible activation of an A1 receptor-mediated cell morphological response. These results led us to conclude that LUF7746 is a novel covalent hA1AR partial agonist and a valuable chemical probe for further mapping the receptor activation process. It may also serve as a prototype for a therapeutic approach in which a covalent partial agonist may cause less on-target side effects, conferring enhanced safety compared to a full agonist.<br /

Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling

Abstract Sodium-dependent glucose co-transporter 1 (SGLT1) is a solute carrier responsible for active glucose absorption. SGLT1 is present in both the renal tubules and small intestine. In contrast, the closely related sodium-dependent glucose co-transporter 2 (SGLT2), a protein that is targeted in the treatment of diabetes type II, is only expressed in the renal tubules. Although dual inhibitors for both SGLT1 and SGLT2 have been developed, no drugs on the market are targeted at decreasing dietary glucose uptake by SGLT1 in the gastrointestinal tract. Here we aim at identifying SGLT1 inhibitors in silico by applying a machine learning approach that does not require structural information, which is absent for SGLT1. We applied proteochemometrics by implementation of compound- and protein-based information into random forest models. We obtained a predictive model with a sensitivity of 0.64 ± 0.06, specificity of 0.93 ± 0.01, positive predictive value of 0.47 ± 0.07, negative predictive value of 0.96 ± 0.01, and Matthews correlation coefficient of 0.49 ± 0.05. Subsequent to model training, we applied our model in virtual screening to identify novel SGLT1 inhibitors. Of the 77 tested compounds, 30 were experimentally confirmed for SGLT1-inhibiting activity in vitro, leading to a hit rate of 39% with activities in the low micromolar range. Moreover, the hit compounds included novel molecules, which is reflected by the low similarity of these compounds with the training set (< 0.3). Conclusively, proteochemometric modeling of SGLT1 is a viable strategy for identifying active small molecules. Therefore, this method may also be applied in detection of novel small molecules for other transporter proteins

Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors

Kinases are frequently studied in the context of anticancer drugs. Their involvement in cell responses, such as proliferation, differentiation, and apoptosis, makes them interesting subjects in multitarget drug design. In this study, a workflow is presented that models the bioactivity spectra for two panels of kinases: (1) inhibition of RET, BRAF, SRC, and S6K, while avoiding inhibition of MKNK1, TTK, ERK8, PDK1, and PAK3, and (2) inhibition of AURKA, PAK1, FGFR1, and LKB1, while avoiding inhibition of PAK3, TAK1, and PIK3CA. Both statistical and structure-based models were included, which were thoroughly benchmarked and optimized. A virtual screening was performed to test the workflow for one of the main targets, RET kinase. This resulted in 5 novel and chemically dissimilar RET inhibitors with remaining RET activity of &lt;60% (at a concentration of 10 mu M) and similarities with known RET inhibitors from 0.18 to 0.29 (Tanimoto, ECFP6). The four more potent inhibitors were assessed in a concentration range and proved to be modestly active with a pIC(50) value of 5.1 for the most active compound. The experimental validation of inhibitors for RET strongly indicates that the multitarget workflow is able to detect novel inhibitors for kinases, and hence, this workflow can potentially be applied in polypharmacology modeling. We conclude that this approach can identify new chemical matter for existing targets. Moreover, this workflow can easily be applied to other targets as well