Acyl(imidoyl)ketenes: Reactive Bidentate Oxa/Aza-Dienes for Organic Synthesis

Polyfunctional building blocks are essential for the implementation of diversity-oriented synthetic strategies, highly demanded in small molecule libraries’ design for modern drug discovery. Acyl(imidoyl)ketenes are highly reactive organic compounds, bearing both oxa- and aza-diene moieties, conjugated symmetrically to the ketene fragment, enabling synthesis of various skeletally diverse heterocycles on their basis. The highlights of reactions utilizing acyl(imidoyl)ketenes are high yields, short reaction time (about several minutes), high selectivity, atom economy, and simple purification procedures, which benefits the drug discovery. The present review focuses on the approaches to thermal generation of acyl(imidoyl)ketenes, patterns of their immediate transformations via intra- and intermolecular reactions, including the reactions of cyclodimerization, in which either symmetric or dissymmetric heterocycles can be formed. Recent advances in investigations on mechanisms, identifications of intermediates, and chemo- and regioselectivity of reactions with participation of acyl(imidoyl)ketenes are also covered.

Inhibiting Pyridoxal Kinase of Entamoeba histolytica Is Lethal for This Pathogen

Pyridoxal 5’-phosphate (PLP) functions as a cofactor for hundreds of different enzymes that are crucial to the survival of microorganisms. PLP-dependent enzymes have been extensively characterized and proposed as drug targets in Entamoeba histolytica. This pathogen is unable to synthesize vitamin B6via de-novo pathway and relies on the uptake of vitamin B6 vitamers from the host which are then phosphorylated by the enzyme pyridoxal kinase to produce PLP, the active form of vitamin B6. Previous studies from our lab shows that EhPLK is essential for the survival and growth of this protozoan parasite and its active site differs significantly with respect to its human homologue making it a potential drug target. In-silico screening of EhPLK against small molecule libraries were performed and top five ranked molecules were shortlisted on the basis of docking scores. These compounds dock into the PLP binding site of the enzyme such that binding of these compounds hinders the binding of substrate. Of these five compounds, two compounds showed inhibitory activity with IC50 values between 100-250 μM when tested in-vitro. The effect of these compounds proved to be extremely lethal for Entamoeba trophozoites in cultured cells as the growth was hampered by 91.5% and 89.5% when grown in the presence of these compounds over the period of 72 hours.

Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein–protein interaction-oriented library and machine learning

Protein–protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits. Recently, we developed a PPI-oriented library comprising 12,593 small-to-medium-sized newly synthesized molecules. This study validates a promising combined method using PPI-oriented library and ligand-based virtual screening (LBVS) to discover novel PPI inhibitory compounds for Kelch-like ECH-associated protein 1 (Keap1) and nuclear factor erythroid 2-related factor 2 (Nrf2). We performed LBVS with two random forest models against our PPI library and the following time-resolved fluorescence resonance energy transfer (TR-FRET) assays of 620 compounds identified 15 specific hit compounds. The high hit rates for the entire PPI library (estimated 0.56–1.3%) and the LBVS (maximum 5.4%) compared to a conventional screening library showed the utility of the library and the efficiency of LBVS. All the hit compounds possessed novel structures with Tanimoto similarity ≤ 0.26 to known Keap1/Nrf2 inhibitors and aqueous solubility (AlogP < 5). Reasonable binding modes were predicted using 3D alignment of five hit compounds and a Keap1/Nrf2 peptide crystal structure. Our results represent a new, efficient method combining the PPI library and LBVS to identify novel PPI inhibitory ligands with expanded chemical space.

Tuberculosis Drug Discovery: A Decade of Hit Assessment for Defined Targets

More than two decades have elapsed since the publication of the first genome sequence of Mycobacterium tuberculosis (Mtb) which, shortly thereafter, enabled methods to determine gene essentiality in the pathogen. Despite this, target-based approaches have not yielded drugs that have progressed to clinical testing. Whole-cell screening followed by elucidation of mechanism of action has to date been the most fruitful approach to progressing inhibitors into the tuberculosis drug discovery pipeline although target-based approaches are gaining momentum. This review discusses scaffolds that have been identified over the last decade from screens of small molecule libraries against Mtb or defined targets where mechanism of action investigation has defined target-hit couples and structure-activity relationship studies have described the pharmacophore.

Geometric Hashing and ΑΙ-Quantum Deep Learning functional similarities on Remdesivir, drug synergies to treat COVID-19 in Practice.

Νovel SARS coronavirus 2 (SARS-CoV-2) of the family Coronaviridae starting in China and spreading around the world is an enveloped, positive-sense, single-stranded RNA of the genus betacoronavirus encoding the SARS-COV-2 (2019-NCOV, Coronavirus Disease 2019. Remdesivir drug, or GS-5734 lead compound, first described in 2016 as a potential anti-viral agent for Ebola diseade and has also being researched as a potential therapeutic agent against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus that causes coronavirus disease 2019 (COVID-19). Computer-aided drug design (CADD), Structure and Ligand based Drug Repositioning strategies based on parallel docking methodologies have been widely used for both modern drug development and drug repurposing to find effective treatments against this disease. Quantum mechanics, molecular mechanics, molecular dynamics (MD), and combinations have shown superior performance to other drug design approaches providing an unprecedented opportunity in the rational drug development fields and for the developing of innovative drug repositioning methods. We tested 18 phytochemical small molecule libraries and predicted their synergies in COVID-19 (2019- NCOV), to devise therapeutic strategies, repurpose existing ones in order to counteract highly pathogenic SARS-CoV-2 infection. We anticipate that our geometry hashing driven quantum deep learing similarity approaches which is based on separated pairs of short consecutive matching fragments, can be used for the development of anticoronaviral drug combinations in large scale HTS screenings, and to maximize the safety and efficacy of the Remdesivir, Colchicine and Ursolic acid drugs already known to induce synergy with potential therapeutic value or drug repositioning to COVID-19 patients.

Quantum Deep Learning Functional Similarities on Remdesivir, Drug Synergies to Treat COVID-19 in Practice.

Νovel SARS coronavirus 2 (SARS-CoV-2) of the family Coronaviridae starting in China and spreading around the world is an enveloped, positive-sense, single-stranded RNA of the genus betacoronavirus encoding the SARS-COV-2 (2019-NCOV, Coronavirus Disease 2019. Remdesivir drug, or GS-5734 lead compound, first described in 2016 as a potential anti-viral agent for Ebola diseade and has also being researched as a potential therapeutic agent against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus that causes coronavirus disease 2019 (COVID-19). Computer-aided drug design (CADD), Structure and Ligand based Drug Repositioning strategies based on parallel docking methodologies have been widely used for both modern drug development and drug repurposing to find effective treatments against this disease. Quantum mechanics, molecular mechanics, molecular dynamics (MD), and combinations have shown superior performance to other drug design approaches providing an unprecedented opportunity in the rational drug development fields and for the developing of innovative drug repositioning methods. We tested 18 phytochemical small molecule libraries and predicted their synergies in COVID-19 (2019- NCOV), to devise therapeutic strategies, repurpose existing ones in order to counteract highly pathogenic SARS-CoV-2 infection and the BRD4- conserved residue associated COVID-19 pathology. We anticipate that our quantum deep learing similarity approaches can be used for the development of anticoronaviral drug combinations in large scale HTS screenings, and to maximize the safety and efficacy of the Remdesivir, Colchicine and Ursolic acid drugs already known to induce synergy with potential therapeutic value or drug repositioning to COVID-19 patients.

An OpenData portal to share COVID-19 drug repurposing data in real time

The National Center for Advancing Translational Sciences (NCATS) has developed an online open science data portal for its COVID-19 drug repurposing campaign – named OpenData – with the goal of making data across a range of SARS-CoV-2 related assays available in real-time. The assays developed cover a wide spectrum of the SARS-CoV-2 life cycle, including both viral and human (host) targets. In total, over 10,000 compounds are being tested in full concentration-response ranges from across multiple annotated small molecule libraries, including approved drug, repurposing candidates and experimental therapeutics designed to modulate a wide range of cellular targets. The goal is to support research scientists, clinical investigators and public health officials through open data sharing and analysis tools to expedite the development of SARS-CoV-2 interventions, and to prioritize promising compounds and repurposed drugs for further development in treating COVID-19.

A cell-based high-content screen identifies isocotoin as a small molecule inhibitor of the meiosis-specific MEIOB–SPATA22 complex†

MEIOB and SPATA22 are meiosis-specific proteins, interact with each other, and are essential for meiotic recombination and fertility. Aspartic acid 383 (D383) in MEIOB is critical for its interaction with SPATA22 in biochemical studies. Here we report that genetic studies validate the requirement of D383 for the function of MEIOB in mice. The MeiobD383A/D383A mice display meiotic arrest due to depletion of both MEIOB and SPATA22 proteins in the testes. We developed a cell-based bimolecular fluorescence complementation (BiFC) assay, in which MEIOB and SPATA22 are fused to split YFP moieties and their co-expression in cultured cells leads to the MEIOB–SPATA22 dimerization and reconstitution of the fluorophore. As expected, the interaction-disrupting D383A substitution results in the absence of YFP fluorescence in the BiFC assay. A high-throughput screen of small molecule libraries identified candidate hit compounds at a rate of 0.7%. Isocotoin, a hit compound from the natural product library, inhibits the MEIOB–SPATA22 interaction and promotes their degradation in HEK293 cells in a dose-dependent manner. Therefore, the BiFC assay can be employed to screen for small molecule inhibitors that disrupt protein–protein interactions or promote degradation of meiosis-specific proteins.