An efficient multistep ligand-based virtual screening approach for GPR40 agonists

Perspectives on current approaches to virtual screening in drug discovery

INTRODUCTION

For the past two decades, virtual screening (VS) has been an efficient hit finding approach for drug discovery. Today, billions of commercially accessible compounds are routinely screened, and many successful examples of VS have been reported. VS methods continue to evolve, including machine learning and physics-based methods.

AREAS COVERED

The authors examine recent examples of VS in drug discovery and discuss prospective hit finding results from the critical assessment of computational hit-finding experiments (CACHE) challenge. The authors also highlight the cost considerations and open-source options for conducting VS and examine chemical space coverage and library selections for VS.

EXPERT OPINION

The advancement of sophisticated VS approaches, including the use of machine learning techniques and increased computer resources as well as the ease of access to synthetically available chemical spaces, and commercial and open-source VS platforms allow for interrogating ultra-large libraries (ULL) of billions of molecules. An impressive number of prospective ULL VS campaigns have generated potent and structurally novel hits across many target classes. Nonetheless, many successful contemporary VS approaches still use considerably smaller focused libraries. This apparent dichotomy illustrates that VS is best conducted in a fit-for-purpose way choosing an appropriate chemical space. Better methods need to be developed to tackle more challenging targets.

Physicochemistry shapes bioactivity landscape of pan-ABC transporter modulators: Anchor point for innovative Alzheimer's disease therapeutics

Alzheimer's disease (AD) is a devastating neurological disorder characterized by the pathological accumulation of macromolecular Aβ and tau leading to neuronal death. Drugs approved to treat AD may ameliorate disease symptoms, however, no curative treatment exists. Aβ peptides were discovered to be substrates of adenosine triphosphate-(ATP)-binding cassette (ABC) transporters. Activators of these membrane-bound efflux proteins that promote binding and/or translocation of Aβ could revolutionize AD medicine. The knowledge about ABC transporter activators is very scarce, however, the few molecules that were reported contain substructural features of multitarget (pan-)ABC transporter inhibitors. A cutting-edge strategy to obtain new drug candidates is to explore and potentially exploit the recently proposed multitarget binding site of pan-ABC transporter inhibitors as anchor point for the development of innovative activators to promote Aβ clearance from the brain. Molecular associations between functional bioactivities and physicochemical properties of small-molecules are key to understand these processes. This study provides an analysis of a recently reported unique multitarget dataset for the correlation between multitarget bioactivity and physicochemistry. Six novel pan-ABC transporter inhibitors were validated containing substructural features of ABC transporter activators, which underpins the relevance of the multitarget binding site for the targeted development of novel AD diagnostics and therapeutics.

Ligand-based G Protein Coupled Receptor pharmacophore modeling: Assessing the role of ligand function in model development

Integral membrane proteins in the G Protein-Coupled Receptor (GPCR) class are attractive drug development targets. However, computational methods applicable to ligand discovery for many GPCR targets are restricted by limited numbers of known ligands. Pharmacophore models can be developed using variously sized training sets and applied in database mining to prioritize candidate ligands for subsequent validation. This in silico study assessed the impact of key pharmacophore modeling decisions that arise when known ligand numbers for a target of interest are low. GPCR included in this study are the adrenergic alpha-1A, 1D and 2A, adrenergic beta 2 and 3, kappa, delta and mu opioid, serotonin 1A and 2A, and the muscarinic 1 and 2 receptors, all of which have rich ligand data sets suitable to assess the performance of protocols intended for application to GPCR with limited ligand data availability. Impact of ligand function, potency and structural diversity in training set selection was assessed to define when pharmacophore modeling targeting GPCR with limited known ligands becomes viable. Pharmacophore elements and pharmacophore model selection criteria were also assessed. Pharmacophore model assessment was based on percent pharmacophore model generation failure, as well as Güner-Henry enrichment and goodness-of-hit scores. Three of seven pharmacophore element schemes evaluated in MOE 2018.0101, Unified, PCHD, and CHD, showed substantially lower failure rates and higher enrichment scores than the others. Enrichment and GH scores were used to compare construction protocol for pharmacophore models of varying purposes- such as function specific versus nonspecific ligand identification. Notably, pharmacophore models constructed from ligands of mixed functions (agonists and antagonists) were capable of enriching hitlists with active compounds, and therefore can be used when available sets of known ligands are limited in number.

Recent Updates on Free Fatty Acid Receptor 1 (GPR-40) Agonists for the Treatment of Type 2 Diabetes Mellitus

BACKGROUND

The global incidence of type 2 diabetes mellitus (T2DM) has enthused the development of new antidiabetic targets with low toxicity and long-term stability. In this respect, free fatty acid receptor 1 (FFAR1), which is also recognized as a G protein-coupled receptor 40 (GPR40), is a novel target for the treatment of T2DM. FFAR1/GPR40 has a high level of expression in β-cells of the pancreas, and the requirement of glucose for stimulating insulin release results in immense stimulation to utilise this target in the medication of T2DM.

METHODS

The data used for this review is based on the search of several scienctific databases as well as various patent databases. The main search terms used were free fatty acid receptor 1, FFAR1, FFAR1 agonists, diabetes mellitus, G protein-coupled receptor 40 (GPR40), GPR40 agonists, GPR40 ligands, type 2 diabetes mellitus and T2DM.

RESULTS

The present review article gives a brief overview of FFAR1, its role in T2DM, recent developments in small molecule FFAR1 (GPR40) agonists reported till now, compounds of natural/plant origin, recent patents published in the last few years, mechanism of FFAR1 activation by the agonists, and clinical status of the FFAR1/GPR40 agonists.

CONCLUSION

The agonists of FFAR1/GRP40 showed considerable potential for the therapeutic control of T2DM. Most of the small molecule FFAR1/GPR40 agonists developed were aryl alkanoic acid derivatives (such as phenylpropionic acids, phenylacetic acids, phenoxyacetic acids, and benzofuran acetic acid derivatives) and thiazolidinediones. Some natural/plant-derived compounds, including fatty acids, sesquiterpenes, phenolic compounds, anthocyanins, isoquinoline, and indole alkaloids, were also reported as potent FFAR1 agonists. The clinical investigations of the FFAR1 agonists demonstrated their probable role in the improvement of glucose control. Though, there are some problems still to be resolved in this field as some FFAR1 agonists terminated in the late phase of clinical studies due to "hepatotoxicity." Currently, PBI-4050 is under clinical investigation by Prometic. Further investigation of pharmacophore scaffolds for FFAR1 full agonists as well as multitargeted modulators and corresponding clinical investigations will be anticipated, which can open up new directions in this area.

Bayesian machine learning to discover Bruton's tyrosine kinase inhibitors

Bruton's tyrosine kinase (BTK) has a crucial role in multiple cell signaling pathways including B-cell antigen receptor (BCR) and Fc receptor (FcR) signaling cascades, which has attracted much attention to find BTK inhibitors to treat autoimmune diseases. In this work, we constructed a Bayesian classification model for virtually seeking novel BTK inhibitors, which showed good performance in terms of screening efficiency and accuracy. Through searching for several chemical libraries including Chembl_17 (1,317,484 compounds), Chembridge (103,473 compounds), and Chemdiv (700,000 compounds) using this model followed by molecular docking and activity prediction, 52 compounds with novel scaffolds were acknowledged as potential BTK inhibitors, which could be promising starting points for further exploration. This study also provided a guide to construct an efficient and effective protocol for virtual screening by integrating machine learning methods.

Discovery of potent apoptosis signal-regulating kinase 1 inhibitors via integrated computational strategy and biological evaluation

Apoptosis signal-regulating Kinase 1 (ASK1) has been confirmed as a potential therapeutic target for the treatment of non-alcoholic steatohepatitis (NASH) disorder and the discovery of ASK1 inhibitors has attracted increasing attention. In this work, a series of in silico methods including pharmacophore screening, docking binding site analysis, protein-ligand interaction fingerprint (PLIF) similarity investigation and molecular docking were applied to find the potential hits from commercial compound databases. Five compounds with potential inhibitory activity were purchased and submitted to biological activity validation. Thus, one hit compound was discovered with micromolar IC₅₀ value (10.59 μM) against ASK1. Results demonstrated that the integration of computation methods and biological test was quite reliable for the discovery of potent ASK1 inhibitors and the strategy could be extended to other similar targets of interest.

Discovery of Potential Inhibitors of Squalene Synthase from Traditional Chinese Medicine Based on Virtual Screening and In Vitro Evaluation of Lipid-Lowering Effect

Squalene synthase (SQS), a key downstream enzyme involved in the cholesterol biosynthetic pathway, plays an important role in treating hyperlipidemia. Compared to statins, SQS inhibitors have shown a very significant lipid-lowering effect and do not cause myotoxicity. Thus, the paper aims to discover potential SQS inhibitors from Traditional Chinese Medicine (TCM) by the combination of molecular modeling methods and biological assays. In this study, cynarin was selected as a potential SQS inhibitor candidate compound based on its pharmacophoric properties, molecular docking studies and molecular dynamics (MD) simulations. Cynarin could form hydrophobic interactions with PHE54, LEU211, LEU183 and PRO292, which are regarded as important interactions for the SQS inhibitors. In addition, the lipid-lowering effect of cynarin was tested in sodium oleate-induced HepG2 cells by decreasing the lipidemic parameter triglyceride (TG) level by 22.50%. Finally. cynarin was reversely screened against other anti-hyperlipidemia targets which existed in HepG2 cells and cynarin was unable to map with the pharmacophore of these targets, which indicated that the lipid-lowering effects of cynarin might be due to the inhibition of SQS. This study discovered cynarin is a potential SQS inhibitor from TCM, which could be further clinically explored for the treatment of hyperlipidemia.

Discovery of Potential Orthosteric and Allosteric Antagonists of P2Y1R from Chinese Herbs by Molecular Simulation Methods

P2Y1 receptor (P2Y1R), which belongs to G protein-coupled receptors (GPCRs), is an important target in ADP-induced platelet aggregation. The crystal structure of P2Y1R has been solved recently, which revealed orthosteric and allosteric ligand-binding sites with the details of ligand-protein binding modes. And it suggests that P2Y1R antagonists, which recognize two distinct sites, could potentially provide an efficacious and safe antithrombotic profile. In present paper, 2D similarity search, pharmacophore based screening, and molecular docking were used to explore the potential natural P2Y1R antagonists. 2D similarity search was used to classify orthosteric and allosteric antagonists of P2Y1R. Based on the result, pharmacophore models were constructed and validated by the test set. Optimal models were selected to discover potential P2Y1R antagonists of orthosteric and allosteric sites from Traditional Chinese Medicine (TCM). And the hits were filtered by Lipinski's rule. Then molecular docking was used to refine the results of pharmacophore based screening and analyze the binding mode of the hits and P2Y1R. Finally, two orthosteric and one allosteric potential compounds were obtained, which might be used in future P2Y1R antagonists design. This work provides a reliable guide for discovering natural P2Y1R antagonists acting on two distinct sites from TCM.

Identification of a novel selective inhibitor of mutant isocitrate dehydrogenase 1 at allosteric site by docking-based virtual screening

Optimal docking was employed to screen SPECS compound library, followed by cellular assays of mutant and wild type of IDH1.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.