Benztropine for COVID-19

AbstractRecently, COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, caused > 6 million deaths. Symptoms included respiratory strain and complications, leading to severe pneumonia. SARS-CoV-2 attaches to the ACE-2 receptor of the host cell membrane to enter. Targeting the SARS-CoV-2 entry may effectively inhibit infection. Acid sphingomyelinase (ASMase) is a lysosomal protein that catalyzes the conversion of sphingolipid (sphingomyelin) to ceramide. Ceramide molecules aggregate/assemble on the plasma membrane to form “platforms” that facilitate the viral intake into the cell. Impairing the ASMase activity will eventually disrupt viral entry into the cell. In this review, we identified the metabolism of sphingolipids, sphingolipids' role in cell signal transduction cascades, and viral infection mechanisms. Also, we outlined ASMase structure and underlying mechanisms inhibiting viral entry 40 with the aid of inhibitors of acid sphingomyelinase (FIASMAs). In silico molecular docking analyses of FIASMAs with inhibitors revealed that dilazep (S = − 12.58 kcal/mol), emetine (S = − 11.65 kcal/mol), pimozide (S = − 11.29 kcal/mol), carvedilol (S = − 11.28 kcal/mol), mebeverine (S = − 11.14 kcal/mol), cepharanthine (S = − 11.06 kcal/mol), hydroxyzin (S = − 10.96 kcal/mol), astemizole (S = − 10.81 kcal/mol), sertindole (S = − 10.55 kcal/mol), and bepridil (S = − 10.47 kcal/mol) have higher inhibition activity than the candidate drug amiodarone (S = − 10.43 kcal/mol), making them better options for inhibition.

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.

AbstractSARS-CoV-2 emerged in China at the end of 2019 and has rapidly become a pandemic with roughly 2.7 million recorded COVID-19 cases and greater than 189,000 recorded deaths by April 23rd, 2020 (www.WHO.org). There are no FDA approved antivirals or vaccines for any coronavirus, including SARS-CoV-2. Current treatments for COVID-19 are limited to supportive therapies and off-label use of FDA approved drugs. Rapid development and human testing of potential antivirals is greatly needed. A quick way to test compounds with potential antiviral activity is through drug repurposing. Numerous drugs are already approved for human use and subsequently there is a good understanding of their safety profiles and potential side effects, making them easier to fast-track to clinical studies in COVID-19 patients. Here, we present data on the antiviral activity of 20 FDA approved drugs against SARS-CoV-2 that also inhibit SARS-CoV and MERS-CoV. We found that 17 of these inhibit SARS-CoV-2 at a range of IC50 values at non-cytotoxic concentrations. We directly follow up with seven of these to demonstrate all are capable of inhibiting infectious SARS-CoV-2 production. Moreover, we have evaluated two of these, chloroquine and chlorpromazine, in vivo using a mouse-adapted SARS-CoV model and found both drugs protect mice from clinical disease.

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.