A machine learning method for the identification and characterization of novel COVID-19 drug targets.

In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. In order to contribute to this effort, we present GuiltyTargets-COVID-19 ( https://guiltytargets-covid.eu/ ), a machine learning supported web tool to identify novel candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with a lung tissue specific protein-protein interaction network, we demonstrate that GuiltyTargets-COVID-19 is capable of (i) prioritizing meaningful target candidates and assessing their druggability, (ii) unraveling their linkage to known disease mechanisms, (iii) mapping ligands from the ChEMBL database to the identified targets, and (iv) pointing out potential side effects in the case that the mapped ligands correspond to approved drugs. Our example analyses identified 4 potential drug targets from the datasets: AKT3 from both the bulk and single cell RNA-Seq data as well as AKT2, MLKL, and MAPK11 in the single cell experiments. Altogether, we believe that our web tool will facilitate future target identification and drug development for COVID-19, notably in a cell type and tissue specific manner.

Easy access to α-ketoamides as SARS-CoV-2 and MERS Mpro inhibitors via the PADAM oxidation route.

SARS-CoV-2 caused worldwide the current outbreak called COVID-19. Despite multiple countermeasures implemented, there is an urgent global need for new potent and efficient antiviral drugs against this pathogen. In this context, the main protease (Mpro) of SARS-CoV-2 is an essential viral enzyme and plays a pivotal role in viral replication and transcription. Its specific cleavage of polypeptides after a glutamine residue has been considered as a key element to design novel antiviral drugs. Herein, we reported the design, synthesis and structure-activity relationships of novel α-ketoamides as covalent reversible inhibitors of Mpro, exploiting the PADAM oxidation route. The reported compounds showed µM to nM activities in enzymatic and in the antiviral cell-based assays against SARS-CoV-2 Mpro. In order to assess inhibitors' binding mode, two co-crystal structures of SARS-CoV-2 Mpro in complex with our inhibitors were solved, which confirmed the covalent binding of the keto amide moiety to the catalytic Cys145 residue of Mpro. Finally, in order to interrogate potential broad-spectrum properties, we assessed a selection of compounds against MERS Mpro where they showed nM inhibitory potency, thus highlighting their potential as broad-spectrum coronavirus inhibitors.

Comprehensive Fragment Screening of the SARS‐CoV‐2 Proteome Explores Novel Chemical Space for Drug Development

SARS‐CoV‐2 (SCoV2) and its variants of concern pose serious challenges to the public health. The variants increased challenges to vaccines, thus necessitating for development of new intervention strategies including anti‐virals. Within the international Covid19‐NMR consortium, we have identified binders targeting the RNA genome of SCoV2. We established protocols for the production and NMR characterization of more than 80 % of all SCoV2 proteins. Here, we performed an NMR screening using a fragment library for binding to 25 SCoV2 proteins and identified hits also against previously unexplored SCoV2 proteins. Computational mapping was used to predict binding sites and identify functional moieties (chemotypes) of the ligands occupying these pockets. Striking consensus was observed between NMR‐detected binding sites of the main protease and the computational procedure. Our investigation provides novel structural and chemical space for structure‐based drug design against the SCoV2 proteome.

Comprehensive Fragment Screening of the SARS-CoV-2 Proteome Explores Novel Chemical Space for Drug Development.

SARS-CoV-2 (SCoV2) and its variants of concern pose serious challenges to the public health. The variants increased challenges to vaccines, thus necessitating for development of new intervention strategies including anti-virals. Within the international Covid19-NMR consortium, we have identified binders targeting the RNA genome of SCoV2. We established protocols for the production and NMR characterization of more than 80 % of all SCoV2 proteins. Here, we performed an NMR screening using a fragment library for binding to 25 SCoV2 proteins and identified hits also against previously unexplored SCoV2 proteins. Computational mapping was used to predict binding sites and identify functional moieties (chemotypes) of the ligands occupying these pockets. Striking consensus was observed between NMR-detected binding sites of the main protease and the computational procedure. Our investigation provides novel structural and chemical space for structure-based drug design against the SCoV2 proteome.

Cytopathic SARS-CoV-2 screening on VERO-E6 cells in a large-scale repurposing effort.

Worldwide, there are intensive efforts to identify repurposed drugs as potential therapies against SARS-CoV-2 infection and the associated COVID-19 disease. To date, the anti-inflammatory drug dexamethasone and (to a lesser extent) the RNA-polymerase inhibitor remdesivir have been shown to be effective in reducing mortality and patient time to recovery, respectively, in patients. Here, we report the results of a phenotypic screening campaign within an EU-funded project (H2020-EXSCALATE4COV) aimed at extending the repertoire of anti-COVID therapeutics through repurposing of available compounds and highlighting compounds with new mechanisms of action against viral infection. We screened 8702 molecules from different repurposing libraries, to reveal 110 compounds with an anti-cytopathic IC50 < 20 µM. From this group, 18 with a safety index greater than 2 are also marketed drugs, making them suitable for further study as potential therapies against COVID-19. Our result supports the idea that a systematic approach to repurposing is a valid strategy to accelerate the necessary drug discovery process.

Natural Compounds Inhibit SARS-CoV-2 nsp13 Unwinding and ATPase Enzyme Activities.

SARS-CoV-2 infection is still spreading worldwide, and new antiviral therapies are an urgent need to complement the approved vaccine preparations. SARS-CoV-2 nps13 helicase is a validated drug target participating in the viral replication complex and possessing two associated activities: RNA unwinding and 5'-triphosphatase. In the search of SARS-CoV-2 direct antiviral agents, we established biochemical assays for both SARS-CoV-2 nps13-associated enzyme activities and screened both in silico and in vitro a small in-house library of natural compounds. Myricetin, quercetin, kaempferol, and flavanone were found to inhibit the SARS-CoV-2 nps13 unwinding activity at nanomolar concentrations, while licoflavone C was shown to block both SARS-CoV-2 nps13 activities at micromolar concentrations. Mode of action studies showed that all compounds are nsp13 noncompetitive inhibitors versus ATP, while computational studies suggested that they can bind both nucleotide and 5'-RNA nsp13 binding sites, with licoflavone C showing a unique pattern of interaction with nsp13 amino acid residues. Overall, we report for the first time natural flavonoids as selective inhibitors of SARS-CoV-2 nps13 helicase with low micromolar activity.

High-throughput drug screening allowed identification of entry inhibitors specifically targeting different routes of SARS-CoV-2 Delta and Omicron/BA.1.

The Severe Acute Respiratory Syndrome Coronavirus type 2 (SARS-CoV-2) has continuously evolved, resulting in the emergence of several variants of concern (VOCs). To study mechanisms of viral entry and potentially identify specific inhibitors, we pseudotyped lentiviral vectors with different SARS-CoV-2 VOC spike variants (D614G, Alpha, Beta, Delta, Omicron/BA.1), responsible for receptor binding and membrane fusion. These SARS-CoV-2 lentiviral pseudoviruses were applied to screen 774 FDA-approved drugs. For the assay we decided to use CaCo2 cells, since they equally allow cell entry through both the direct membrane fusion pathway mediated by TMPRSS2 and the endocytosis pathway mediated by cathepsin-L. The active molecules which showed stronger differences in their potency to inhibit certain SARS-CoV-2 VOCs included antagonists of G-protein coupled receptors, like phenothiazine-derived antipsychotic compounds such as Chlorpromazine, with highest activity against the Omicron pseudovirus. In general, our data showed that the various VOCs differ in their preferences for cell entry, and we were able to identify synergistic combinations of inhibitors. Notably, Omicron singled out by relying primarily on the endocytosis pathway while Delta preferred cell entry via membrane fusion. In conclusion, our data provide new insights into different entry preferences of SARS-CoV-2 VOCs, which might help to identify new drug targets.

The blood-brain barrier is dysregulated in COVID-19 and serves as a CNS entry route for SARS-CoV-2.

Neurological complications are common in COVID-19. Although SARS-CoV-2 has been detected in patients' brain tissues, its entry routes and resulting consequences are not well understood. Here, we show a pronounced upregulation of interferon signaling pathways of the neurovascular unit in fatal COVID-19. By investigating the susceptibility of human induced pluripotent stem cell (hiPSC)-derived brain capillary endothelial-like cells (BCECs) to SARS-CoV-2 infection, we found that BCECs were infected and recapitulated transcriptional changes detected in vivo. While BCECs were not compromised in their paracellular tightness, we found SARS-CoV-2 in the basolateral compartment in transwell assays after apical infection, suggesting active replication and transcellular transport of virus across the blood-brain barrier (BBB) in vitro. Moreover, entry of SARS-CoV-2 into BCECs could be reduced by anti-spike-, anti-angiotensin-converting enzyme 2 (ACE2)-, and anti-neuropilin-1 (NRP1)-specific antibodies or the transmembrane protease serine subtype 2 (TMPRSS2) inhibitor nafamostat. Together, our data provide strong support for SARS-CoV-2 brain entry across the BBB resulting in increased interferon signaling.

SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data.

One of the impacts of the coronavirus disease 2019 (COVID-19) pandemic has been a push for researchers to better exploit synthetic data and accelerate the design, analysis, and modeling of clinical trials. The unprecedented clinical efforts caused by COVID-19's emergence will certainly boost future robust and innovative approaches of statistical sciences applied to clinical fields. Here, we report the development of SASC, a simple but efficient approach to generate COVID-19-related synthetic clinical data through a web application. SASC takes basic summary statistics for each group of patients and attempts to generate single variables according to internal correlations. To assess the "reliability" of the results, statistical comparisons with Synthea, a known synthetic patient generator tool, and, more importantly, with clinical data of real COVID-19 patients are provided. The source code and web application are available on GitHub, Zenodo, and Mendeley Data.

Corrigendum to "Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases"[Artificial Intelligence in Life Sciences] 1(2021), 100020.

[This corrects the article DOI: 10.1016/j.ailsci.2021.100020.].

Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases.

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center 'Lean European Open Survey on SARS-CoV-2-infected patients' (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

Structural and Biochemical Analysis of the Dual Inhibition of MG-132 against SARS-CoV-2 Main Protease (Mpro/3CLpro) and Human Cathepsin-L.

After almost two years from its first evidence, the COVID-19 pandemic continues to afflict people worldwide, highlighting the need for multiple antiviral strategies. SARS-CoV-2 main protease (Mpro/3CLpro) is a recognized promising target for the development of effective drugs. Because single target inhibition might not be sufficient to block SARS-CoV-2 infection and replication, multi enzymatic-based therapies may provide a better strategy. Here we present a structural and biochemical characterization of the binding mode of MG-132 to both the main protease of SARS-CoV-2, and to the human Cathepsin-L, suggesting thus an interesting scaffold for the development of double-inhibitors. X-ray diffraction data show that MG-132 well fits into the Mpro active site, forming a covalent bond with Cys145 independently from reducing agents and crystallization conditions. Docking of MG-132 into Cathepsin-L well-matches with a covalent binding to the catalytic cysteine. Accordingly, MG-132 inhibits Cathepsin-L with nanomolar potency and reversibly inhibits Mpro with micromolar potency, but with a prolonged residency time. We compared the apo and MG-132-inhibited structures of Mpro solved in different space groups and we identified a new apo structure that features several similarities with the inhibited ones, offering interesting perspectives for future drug design and in silico efforts.

Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments.

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of Minimal Information for Chemosensitivity Assays (MICHA), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies as well as six recently conducted COVID-19 studies. With the MICHA web server and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

Identification of Inhibitors of SARS-CoV-2 3CL-Pro Enzymatic Activity Using a Small Molecule in Vitro Repurposing Screen.

Compound repurposing is an important strategy for the identification of effective treatment options against SARS-CoV-2 infection and COVID-19 disease. In this regard, SARS-CoV-2 main protease (3CL-Pro), also termed M-Pro, is an attractive drug target as it plays a central role in viral replication by processing the viral polyproteins pp1a and pp1ab at multiple distinct cleavage sites. We here report the results of a repurposing program involving 8.7 K compounds containing marketed drugs, clinical and preclinical candidates, and small molecules regarded as safe in humans. We confirmed previously reported inhibitors of 3CL-Pro and have identified 62 additional compounds with IC50 values below 1 µM and profiled their selectivity toward chymotrypsin and 3CL-Pro from the Middle East respiratory syndrome virus. A subset of eight inhibitors showed anticytopathic effect in a Vero-E6 cell line, and the compounds thioguanosine and MG-132 were analyzed for their predicted binding characteristics to SARS-CoV-2 3CL-Pro. The X-ray crystal structure of the complex of myricetin and SARS-Cov-2 3CL-Pro was solved at a resolution of 1.77 Å, showing that myricetin is covalently bound to the catalytic Cys145 and therefore inhibiting its enzymatic activity.

A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization.

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

A Blueprint for High Affinity SARS-CoV-2 Mpro Inhibitors from Activity-Based Compound Library Screening Guided by Analysis of Protein Dynamics.

The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ∼30 000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ∼200 virtual screenings of compound libraries on selected protein structures, we redefine the protein's druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

A SARS-CoV-2 cytopathicity dataset generated by high-content screening of a large drug repurposing collection.

SARS-CoV-2 is a novel coronavirus responsible for the COVID-19 pandemic, in which acute respiratory infections are associated with high socio-economic burden. We applied high-content screening to a well-defined collection of 5632 compounds including 3488 that have undergone previous clinical investigations across 600 indications. The compounds were screened by microscopy for their ability to inhibit SARS-CoV-2 cytopathicity in the human epithelial colorectal adenocarcinoma cell line, Caco-2. The primary screen identified 258 hits that inhibited cytopathicity by more than 75%, most of which were not previously known to be active against SARS-CoV-2 in vitro. These compounds were tested in an eight-point dose response screen using the same image-based cytopathicity readout. For the 67 most active molecules, cytotoxicity data were generated to confirm activity against SARS-CoV-2. We verified the ability of known inhibitors camostat, nafamostat, lopinavir, mefloquine, papaverine and cetylpyridinium to reduce the cytopathic effects of SARS-CoV-2, providing confidence in the validity of the assay. The high-content screening data are suitable for reanalysis across numerous drug classes and indications and may yield additional insights into SARS-CoV-2 mechanisms and potential therapeutic strategies.