Anisomycin for COVID-19

Abstract The prediction of virus–drug associations (VDAs) is crucial for drug repositioning, contributing to the identification of latent antiviral drugs. In this study, we developed a graph-based integrated Gaussian kernel similarity (GiGs) method for predicting potential VDAs in drug repositioning. The GiGs model comprises three components: (i) collection of experimentally validated VDA information and calculation virus sequence, drug chemical structure, and drug side effect similarity; (ii) integration of viruses and drugs similarity based on the above information and Gaussian interaction profile kernel (GIPK); and (iii) utilization of similarity-constrained weight graph normalization matrix factorization to predict antiviral drugs. The GiGs model enhances correlation matrix quality through the integration of multiple biological data, improves performance via similarity constraints, and prevents overfitting and predicts missing data more accurately through graph regularization. Extensive experimental results indicated that the GiGs model outperforms five other advanced association prediction methods. A case study identified broad-spectrum drugs for treating highly pathogenic human coronavirus infections, with molecular docking experiments confirming the model’s accuracy.

SummaryA novel coronavirus, named SARS-CoV-2, emerged in 2019 from Hubei region in China and rapidly spread worldwide. As no approved therapeutics exists to treat Covid-19, the disease associated to SARS-Cov-2, there is an urgent need to propose molecules that could quickly enter into clinics. Repurposing of approved drugs is a strategy that can bypass the time consuming stages of drug development. In this study, we screened the Prestwick Chemical Library® composed of 1,520 approved drugs in an infected cell-based assay. 90 compounds were identified. The robustness of the screen was assessed by the identification of drugs, such as Chloroquine derivatives and protease inhibitors, already in clinical trials. The hits were sorted according to their chemical composition and their known therapeutic effect, then EC50 and CC50 were determined for a subset of compounds. Several drugs, such as Azithromycine, Opipramol, Quinidine or Omeprazol present antiviral potency with 2<EC50<20µM. By providing new information on molecules inhibiting SARS-CoV-2 replication in vitro, this study could contribute to the short-term repurposing of drugs against Covid-19.

Summary ParagraphThe current COVID-19 pandemic is caused by SARS-coronavirus 2 (SARS-CoV-2). There are currently no therapeutic options for mitigating this disease due to lack of a vaccine and limited knowledge of SARS-CoV-2 biology. As a result, there is an urgent need to create new disease models to study SARS-CoV-2 biology and to screen for therapeutics using human disease-relevant tissues. COVID-19 patients typically present with respiratory symptoms including cough, dyspnea, and respiratory distress, but nearly 25% of patients have gastrointestinal indications including anorexia, diarrhea, vomiting, and abdominal pain. Moreover, these symptoms are associated with worse COVID-19 outcomes1. Here, we report using human pluripotent stem cell-derived colonic organoids (hPSC-COs) to explore the permissiveness of colonic cell types to SARS-CoV-2 infection. Single cell RNA-seq and immunostaining showed that the putative viral entry receptor ACE2 is expressed in multiple hESC-derived colonic cell types, but highly enriched in enterocytes. Multiple cell types in the COs can be infected by a SARS-CoV-2 pseudo-entry virus, which was further validated in vivo using a humanized mouse model. We used hPSC-derived COs in a high throughput platform to screen 1280 FDA-approved drugs against viral infection. Mycophenolic acid and quinacrine dihydrochloride were found to block the infection of SARS-CoV-2 pseudo-entry virus in COs both in vitro and in vivo, and confirmed to block infection of SARS-CoV-2 virus. This study established both in vitro and in vivo organoid models to investigate infection of SARS-CoV-2 disease-relevant human colonic cell types and identified drugs that blocks SARS-CoV-2 infection, suitable for rapid clinical testing.

AbstractIdentification of host factors contributing to replication of viruses and resulting disease progression remains a promising approach for development of new therapeutics. Here, we evaluated 6710 clinical and preclinical compounds targeting 2183 host proteins by immunocytofluorescence-based screening to identify SARS-CoV-2 infection inhibitors. Computationally integrating relationships between small molecule structure, dose-response antiviral activity, host target and cell interactome networking produced cellular networks important for infection. This analysis revealed 389 small molecules, >12 scaffold classes and 813 host targets with micromolar to low nanomolar activities. From these classes, representatives were extensively evaluated for mechanism of action in stable and primary human cell models, and additionally against Beta and Delta SARS-CoV-2 variants and MERS-CoV. One promising candidate, obatoclax, significantly reduced SARS-CoV-2 viral lung load in mice. Ultimately, this work establishes a rigorous approach for future pharmacological and computational identification of novel host factor dependencies and treatments for viral diseases.

AbstractThe ongoing pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), necessitates strategies to identify prophylactic and therapeutic drug candidates for rapid clinical deployment. Here, we describe a screening pipeline for the discovery of efficacious SARS-CoV-2 inhibitors. We screen a best-in-class drug repurposing library, ReFRAME, against two high-throughput, high-content imaging infection assays: one using HeLa cells expressing SARS-CoV-2 receptor ACE2 and the other using lung epithelial Calu-3 cells. From nearly 12,000 compounds, we identify 49 (in HeLa-ACE2) and 41 (in Calu-3) compounds capable of selectively inhibiting SARS-CoV-2 replication. Notably, most screen hits are cell-line specific, likely due to different virus entry mechanisms or host cell-specific sensitivities to modulators. Among these promising hits, the antivirals nelfinavir and the parent of prodrug MK-4482 possess desirable in vitro activity, pharmacokinetic and human safety profiles, and both reduce SARS-CoV-2 replication in an orthogonal human differentiated primary cell model. Furthermore, MK-4482 effectively blocks SARS-CoV-2 infection in a hamster model. Overall, we identify direct-acting antivirals as the most promising compounds for drug repurposing, additional compounds that may have value in combination therapies, and tool compounds for identification of viral host cell targets.

IntroductionWith the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease. Computational models have the ability to quickly predict potential reusable drug candidates to treat diseases.MethodsIn this study, two matrix decomposition-based methods, i.e., Matrix Decomposition with Heterogeneous Graph Inference (MDHGI) and Bounded Nuclear Norm Regularization (BNNR), were integrated to predict anti-viral drugs. Moreover, global leave-one-out cross-validation (LOOCV), local LOOCV, and 5-fold cross-validation were implemented to evaluate the performance of the proposed model based on datasets of DrugVirus that consist of 933 known associations between 175 drugs and 95 viruses.ResultsThe results showed that the area under the receiver operating characteristics curve (AUC) of global LOOCV and local LOOCV are 0.9035 and 0.8786, respectively. The average AUC and the standard deviation of the 5-fold cross-validation for DrugVirus datasets are 0.8856 ± 0.0032. We further implemented cross-validation based on MDAD and aBiofilm, respectively, to evaluate the performance of the model. In particle, MDAD (aBiofilm) dataset contains 2,470 (2,884) known associations between 1,373 (1,470) drugs and 173 (140) microbes. In addition, two types of case studies were carried out further to verify the effectiveness of the model based on the DrugVirus and MDAD datasets. The results of the case studies supported the effectiveness of MHBVDA in identifying potential virus-drug associations as well as predicting potential drugs for new microbes.

ABSTRACTVaccines and drugs are two effective medical interventions to mitigate SARS-CoV-2 infection. Three SARS-CoV-2 inhibitors, remdesivir, paxlovid, and molnupiravir, have been approved for treating COVID-19 patients, but more are needed, because each drug has its limitation of usage and SARS-CoV-2 constantly develops drug resistance mutations. In addition, SARS-CoV-2 drugs have the potential to be repurposed to inhibit new human coronaviruses, thus help to prepare for future coronavirus outbreaks. We have screened a library of microbial metabolites to discover new SARS-CoV-2 inhibitors. To facilitate this screening effort, we generated a recombinant SARS-CoV-2 Delta variant carrying the nano luciferase as a reporter for measuring viral infection. Six compounds were found to inhibit SARS-CoV-2 at the half maximal inhibitory concentration (IC50) below 1 μM, including the anthracycline drug aclarubicin that markedly reduced viral RNA-dependent RNA polymerase (RdRp)-mediated gene expression, whereas other anthracyclines inhibited SARS-CoV-2 by activating the expression of interferon and antiviral genes. As the most commonly prescribed anti-cancer drugs, anthracyclines hold the promise of becoming new SARS-CoV-2 inhibitors.IMPORTANCEMicrobial metabolites are a rich source of bioactive molecules. The best examples are antibiotics and immunosuppressants that have transformed the practice of modern medicine and saved millions of lives. Recently, some microbial metabolites were reported to have antiviral activity, including the inhibition of Zika virus and Ebola virus. In this study, we discovered several microbial metabolites that effectively inhibit SARS-CoV-2 infection, including anthracyclines that have also been shown to inhibit other viruses including Ebola virus through enhancing interferon responses, which indicates potentially broad antiviral properties of these microbial metabolites and can lead to the discovery of pan-antiviral molecules.