A community effort in SARS-CoV-2 drug discovery.

peer reviewedThe COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.R-AGR-3826 - COVID19-14715687-CovScreen (01/06/2020 - 31/01/2021) - GLAAB Enric

How good are publicly available web services that predict bioactivity profiles for drug repurposing?^$

<p>Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely available computational tools are available via the Internet, which predict multitarget profiles of drug-like compounds. They are based on chemical similarity assessment (ChemProt, SuperPred, SEA, SwissTargetPrediction and TargetHunter) or machine learning methods (ChemProt and PASS). To compare their performance, this study has created two evaluation sets, consisting of (1) 50 well-known repositioned drugs and (2) 12 drugs recently patented for new indications. In the first set, sensitivity values varied from 0.64 (TarPred) to 1.00 (PASS Online) for the initial indications and from 0.64 (TarPred) to 0.98 (PASS Online) for the repurposed indications. In the second set, sensitivity values varied from 0.08 (SuperPred) to 1.00 (PASS Online) for the initial indications and from 0.00 (SuperPred) to 1.00 (PASS Online) for the repurposed indications. Thus, this analysis demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications.</p

Antibacterial activity of griseofulvin analogues as an example of drug repurposing

Griseofulvin is a well-known antifungal drug that was launched in 1962 by Merck & Co. for the treatment of dermatophyte infections. However, according to predictions using the Way2Drug computational drug repurposing platform, it may also have antibacterial activity. As no confirmation of this prediction was found in the published literature, this study estimated in-silico antibacterial activity for 42 griseofulvin derivatives. Antibacterial activity was predicted for 33 of the 42 compounds, which led to the conclusion that this activity might be considered as typical for this chemical series. Therefore, experimental testing of antibacterial activity was performed on a panel of Gram-positive and Gram-negative micro-organisms. Antibacterial activity was evaluated using the microdilution method detecting the minimal inhibitory concentration (MIC) and the minimal bactericidal concentration (MBC). The tested compounds exhibited potent antibacterial activity against all the studied bacteria, with MIC and MBC values ranging from 0.0037 to 0.04 mg/mL and from 0.01 to 0.16 mg/mL, respectively. Activity was 2.5–12 times greater than that of ampicillin and 2–8 times greater than that of streptomycin, which were used as the reference drugs. Similarity analysis for all 42 compounds with the (approximately) 470,000 drug-like compounds indexed in the Clarivate Analytics Integrity database confirmed the significant novelty of the antibacterial activity for the compounds from this chemical class. Therefore, this study demonstrated that by using computer-aided prediction of biological activity spectra for a particular chemical series, it is possible to identify typical biological activities which may be used for discovery of new applications (e.g. drug repurposing)

Etoposide-Induced Apoptosis in Cancer Cells Can Be Reinforced by an Uncoupled Link between Hsp70 and Caspase-3

The Hsp70 chaperone binds and inhibits proteins implicated in apoptotic signaling including Caspase-3. Induction of apoptosis is an important mechanism of anti-cancer drugs, therefore Hsp70 can act as a protective system in tumor cells against therapeutic agents. In this study we present an assessment of candidate compounds that are able to dissociate the complex of Hsp70 with Caspase-3, and thus sensitize cells to drug-induced apoptosis. Using the PASS program for prediction of biological activity we selected a derivative of benzodioxol (BT44) that is known to affect molecular chaperones and caspases. Drug affinity responsive target stability and microscale thermophoresis assays indicated that BT44 bound to Hsp70 and reduced the chaperone activity. When etoposide was administered, heat shock accompanied with an accumulation of Hsp70 led to an inhibition of etoposide-induced apoptosis. The number of apoptotic cells increased following BT44 administration, and forced Caspase-3 processing. Competitive protein&ndash;protein interaction and immunoprecipitation assays showed that BT44 caused dissociation of the Hsp70&ndash;Caspase-3 complex, thus augmenting the anti-tumor activity of etoposide and highlighting the potential role of molecular separators in cancer therapy

A community effort to discover small molecule SARS-CoV-2 inhibitors

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of a community effort, the “Billion molecules against Covid-19 challenge”, to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 potentially active molecules, which were subsequently ranked to find ‘consensus compounds’. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (Nsp12 domain), and (alpha) spike protein S. Overall, 27 potential inhibitors were experimentally confirmed by binding-, cleavage-, and/or viral suppression assays and are presented here. All results are freely available and can be taken further downstream without IP restrictions. Overall, we show the effectiveness of computational techniques, community efforts, and communication across research fields (i.e., protein expression and crystallography, in silico modeling, synthesis and biological assays) to accelerate the early phases of drug discovery