Identification of Atovaquone and Mebendazole as Repurposed Drugs with Antiviral Activity against SARS-CoV-2 (Version 5)

Abstract

Given the continuing heavy toll of the COVID-19 pandemic, therapeutic options for treatment are urgently needed. Here, we adopted a repositioning approach using in silico molecular modeling to screen FDA-approved drugs with established safety profiles for potential inhibitory effects against SARS-CoV-2. We used structure-based drug design to screen more than 2000 FDA approved drugs against SARS-CoV-2 main protease enzyme (Mpro) substrate-binding pocket. We additionally screened the top hits from both sites for potential covalent binding via nucleophilic thiol attack of Cys 145. High-scoring candidates were then screened for antiviral activity against infectious SARS-CoV-2 in a cell-based viral replication assay, and counter screened for toxicity. Promising candidates included atovaquone, mebendazole, ouabain, dronedarone, and entacapone, although atovaquone and mebendazole were the only two candidates with IC50s that fall within their therapeutic plasma concentration. In addition, we performed Mpro assays on the top hits, which demonstrated inhibition of Mpro by dronedarone (IC50 18 M), mebendazole (IC50 19 M) and entacapone (IC50 9 M). Atovaquone showed only modest Mpro inhibition, and thus we explored other potential antiviral mechanisms. Although atovaquone is a known DHODH inhibitor, we did not observe inhibition of DHODH by atovaquone at concentrations relevant to the SARS-CoV-2 IC50. Interestingly, metabolomic profiling of atovaquone treated cells demonstrated marked dysregulation of metabolites in the purine metabolism pathway. In summary, a number of our top hits from the in-silico screen demonstrated Mpro inhibitory activity associated with antiviral effects. Atovaquone and mebendazole are the most promising candidates targeting SARS-CoV-2 from our screen, however atovaquone did not significantly inhibit Mpro at therapeutically meaningful concentrations but may inhibit SARS-CoV-2 viral replication by targeting host purine metabolism.

Efficient Deep Model Ensemble Framework for Drug-Target Interaction Prediction

“…5 Mahmoud et al utilized molecular modeling to screen over 2,000 FDAapproved drugs, discovering that atovaquone, mebendazole, and ouabain exhibit high resistance to SARS-CoV-2. 6 Additionally, Xia et al's findings suggest that Melphalan may activate HMOX1, inhibit CDC20, and interact with MYC, aligning with prior empirical evidence. 7 These instances illustrate the high efficacy of computational methods like molecular docking and virtual screening in drug development, substantially reducing the scope of wet-lab experiments.…”

Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients

“…The antiviral effects of nucleoside analogues is believed to result from their incorporation specifically by viral polymerases leading to defects in viral replication or through their antimetabolite activity where they compete with cellular enzymes for their natural ligands [ 39 ]. We did not assess rosuvastatin in our in vitro screens of viral inhibition; however, in work by Ahmed et al, their structure-based drug repositioning approach for drugs with potential inhibitory effects on COVID-19 virus predicted anti-viral drugs and rosuvastatin among their top six hits and demonstrated the inhibitory effects on SARS-CoV-2 replication by rosuvastatin and other drugs in VeroE6 cells [ 44 ]. The physical properties of rosuvastatin, in particular its structural similarity to nucleoside analogues, may confer this statin with additional antiviral properties not had by the other statins that we assessed, and could explain why our EHR analysis found rosuvastatin as associated with a reduction in the relative risk of death in patients with COVID-19; whereas, NeMoCAD did not predict this drug.…”

Robust and Adaptive Deep Model Ensemble Framework Fine-tuned by Structural Information for Drug-Target Interactions

“…For example, Beck et al [6] employed computational methods to sift through extensive datasets, predicting that Azanavir, Remdesivir, and Kaletra could inhibit SARS-CoV-2. Mahmoud and colleagues [7] utilized computational molecular modeling to screen over 2000 FDA-approved drugs with established safety profiles and found that Atovaquone, Methylene Blue, and Valinomycin exhibited significant antiviral activities against SARS-CoV-2. Xia et al [8] conducted a case study on Melphalan (a key drug in anti-tumor therapy) using deep learning, revealing that Melphalan might activate HMOX1, inhibit CDC20, and interact with MYC, consistent with previous research findings.…”