Identification, biological characterization and pharmacophoric analysis of a new potent and selective NK1 receptor antagonist clinical candidate.

The last two decades have provided a large weight of preclinical data implicating the neurokinin-1 receptor (NK1) and its cognate ligand substance P (SP) in a broad range of both central and peripheral disease conditions. However, to date, only the NK1 receptor antagonist aprepitant has been approved as a therapeutic and this is to prevent chemotherapy-induced nausea & vomiting (CINV). The belief remained that the full therapeutic potential of NK1 receptor antagonists had yet to be realized; therefore clinical evidence that NK1 receptor antagonists may be effective in major depression disorder, resulted in a significant further investment in discovering novel CNS penetrant druggable NK1 receptor antagonists to address this condition. At GlaxoSmithKline after the discovery of casopitant, that went on to demonstrate efficacy as a novel antidepressant in the clinic, additional novel analogues of this NK1 receptor antagonist were designed to further enhance its drug developability characteristics. Herein, we therefore describe the discovery process and the vivo pharmacological and pharmacokinetic profile of the new NK1 receptor antagonist 3a (also called orvepitant), selected as clinical candidate and further progressed into clinical studies for major depressive disorder. Moreover, molecular modeling studies enabled us to improve the pharmacophore model of the NK1 receptor antagonists with the identification of a region able to accommodate a variety of heterocycle moieties.

5-Aryl-4-carboxamide-1,3-oxazoles: potent and selective GSK-3 inhibitors.

5-Aryl-4-carboxamide-1,3-oxazoles are a novel, potent and selective series of GSK-3 inhibitors. The optimization of the series to yield compounds with cell activity and brain permeability is described.

Deep Learning Applied to Ligand-Based De Novo Drug Design.

In the latest years, the application of deep generative models to suggest virtual compounds is becoming a new and powerful tool in drug discovery projects. The idea behind this review is to offer an updated view on de novo design approaches based on artificial intelligent (AI) algorithms, with a particular focus on ligand-based methods. We start this review by reporting a brief overview of the most relevant de novo design approaches developed before the use of AI techniques. We then describe the nowadays most common neural network architectures employed in ligand-based de novo design, together with an up-to-date list of more than 100 deep generative models found in the literature (2017-2020). In order to show how deep generative approaches are applied into drug discovery context, we report all the now available studies in which generated compounds have been synthetized and their biological activity tested. Finally, we discuss what we envisage as beneficial future directions for further application of deep generative models in de novo drug design.

Identification of 2-(4-pyridyl)thienopyridinones as GSK-3ß inhibitors.

The discovery of a novel series of 2-(4-pyridyl)thienopyridinone GSK-3ß inhibitors is reported. X-ray crystallography reveals its binding mode and enables rationalization of the SAR. The initial optimization of the template for improved cellular activity and predicted CNS penetration is also presented.

2-Methyl-3-furanyl-4H-1,2,4-triazol-3-ylthioamides: a new class of selective orexin 2 antagonists.

A new class of selective orexin 2 antagonist was identified among commercial products. Initial SAR was obtained using commercial derivatives only prior to starting ad hoc medicinal chemistry activities.

Pyrrolo[1,2-a]pyrazine and pyrazolo[1,5-a]pyrazine: novel, potent, and selective series of Vasopressin 1b receptor antagonists.

Novel series of pyrrole-pyrazinone and pyrazole-pyrazinone have been identified as potent and selective Vasopressin(1b) receptor antagonists. Exploration of the substitution pattern around the core of these templates allowed generation of compounds with high inhibitory potency at the Vasopressin(1b) receptor, including examples that showed good selectivity with respect to Vasopressin(1a), Vasopressin(2), and Oxytocin receptor subtypes.

QM Calculations in ADMET Prediction.

In recent years, there has been an increase in the application of quantum mechanics (QM) methods to describe properties related to the ADMET profile of small molecules. The application of these methods allows calculating useful descriptors and physiochemical properties contributing to ADMET prediction. Considering that QM methods are the only one that describe the electronic state of a molecules, such methods are particularly useful for studying the metabolism of drugs; furthermore, the introduction of mixed QM and molecular mechanics (QM/MM) is also increasing the understanding of drug interaction with cytochromes from a mechanistic point of view. Finally, combining the increase number of experimental data with machine learning algorithms and QM-derived descriptors allowed the creation of an end-user software capable of affecting the drug discovery process.

Molecular descriptors and methods for ligand based virtual high throughput screening in drug discovery.

The aim of virtual high throughput screening is the identification of biologically relevant molecules amongst either tangible or virtual (large) collections of compounds. Amongst the various virtual screening approaches, those that are ligand based are becoming very popular due to the possibility to screen millions of molecules in a timely way. Descriptors and methods are briefly introduced and reviewed with more emphasis for those approaches that are based on fingerprint descriptors and that seems to be more utilized during the drug discovery process.

Stereoselective synthesis of 3-substituted tetrahydropyrazinoisoquinolines via intramolecular cyclization of enantiomerically enriched dihydro-2H-pyrazines.

The preparation of 3-substituted tetrahydropyrazinoisoquinolines using the tributyltin hydride mediated intramolecular radical cyclization of suitably protected 2-substituted 3,4-dihydropyrazines is reported. The compounds are obtained as single enantiomers, as the relative configuration of the new generated stereogenic center is driven by the stereochemistry of the 2-substituted carbon in the starting materials, which is in turn derived from naturally occurring amino acids.

A novel ketolide class: Synthesis and antibacterial activity of a lead compound.

Synthesis and antibacterial activity of a new class of ketolide antibiotics, exemplified by the prototype GW680788X (1), are described. The structure of (1) has been elucidated by spectroscopic analysis. The good antibacterial activity shown by (1) in comparison with clarithromycin prompted us to consider this compound as a lead molecule for further exploration.

Generation of a focused set of GSK compounds biased toward ligand-gated ion-channel ligands.

A "data mining" methodology based on substructural analysis and standard 1024 Daylight fingerprints as descriptors was applied to a set of known antagonists of a subfamily of ligand-gated ion channels comprising nicotinic acetylcholine receptors (nAChR's), 5-hydroxytryptamine, gamma-amino butyric acid-A, and glycine receptors. The derived scoring function was used to generate a focused set that was screened for alpha7 nAChR, resulting in the identification of novel alpha7 ligands easily amenable to chemical modification. Finally, the same scoring function was applied retrospectively to other in-house sets screened for the same target in the same assay. The results and performance of the method are described in detail.