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SARS-CoV-2: An Update on Potential Antivirals in Light of SARS-CoV Antiviral Drug Discoveries.

ABSTRACT: Coronaviruses (CoVs) are a group of RNA viruses that are associated with different diseases in animals, birds, and humans. Human CoVs (HCoVs) have long been known to be the causative agents of mild respiratory illnesses. However, two HCoVs associated with severe respiratory diseases are Severe Acute Respiratory Syndrome-CoV (SARS-CoV) and Middle East Respiratory Syndrome-CoV (MERS-CoV). Both viruses resulted in hundreds of deaths after spreading to several countries. Most recently, SARS-CoV-2 has emerged as the third HCoV causing severe respiratory distress syndrome and viral pneumonia (known as COVID-19) in patients from Wuhan, China, in December 2019. Soon after its discovery, SARS-CoV-2 spread to all countries, resulting in millions of cases and thousands of deaths. Since the emergence of SARS-CoV, many research groups have dedicated their resources to discovering effective antivirals that can treat such life-threatening infections. The rapid spread and high fatality rate of SARS-CoV-2 necessitate the quick discovery of effective antivirals to control this outbreak. Since SARS-CoV-2 shares 79% sequence identity with SARS-CoV, several anti-SARS-CoV drugs have shown promise in limiting SARS-CoV-2 replication in vitro and in vivo. In this review, we discuss antivirals described for SARS-CoV and provide an update on therapeutic strategies and antivirals against SARS-CoV-2. The control of the current outbreak will strongly depend on the discovery of effective and safe anti-SARS-CoV-2 drugs.

SUBMITTER: Elshabrawy HA

PROVIDER: S-EPMC7350231 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The COVID-19 pandemic has accelerated the need to identify new antiviral therapeutics at pace, including through drug repurposing. We employed a Quadratic Unbounded Binary Optimization (QUBO) model, to search for compounds similar to Remdesivir, the first antiviral against SARS-CoV-2 approved for human use, using a quantum-inspired device. We modelled Remdesivir and compounds present in the DrugBank database as graphs, established the optimal parameters in our algorithm and resolved the Maximum Weighted Independent Set problem within the conflict graph generated. We also employed a traditional Tanimoto fingerprint model. The two methods yielded different lists of lead compounds, with some overlap. While GS-6620 was the top compound predicted by both models, the QUBO model predicted BMS-986094 as second best. The Tanimoto model predicted different forms of cobalamin, also known as vitamin B12. We then determined the half maximal inhibitory concentration (IC50) values in cell culture models of SARS-CoV-2 infection and assessed cytotoxicity. We also demonstrated efficacy against several variants including SARS-CoV-2 Strain England 2 (England 02/2020/407073), B.1.1.7 (Alpha), B.1.351 (Beta) and B.1.617.2 (Delta). Lastly, we employed an in vitro polymerization assay to demonstrate that these compounds directly inhibit the RNA-dependent RNA polymerase (RdRP) of SARS-CoV-2. Together, our data reveal that our QUBO model performs accurate comparisons (BMS-986094) that differed from those predicted by Tanimoto (different forms of vitamin B12); all compounds inhibited replication of SARS-CoV-2 via direct action on RdRP, with both models being useful. While Tanimoto may be employed when performing relatively small comparisons, QUBO is also accurate and may be well suited for very complex problems where computational resources may limit the number and/or complexity of possible combinations to evaluate. Our quantum-inspired screening method can therefore be employed in future searches for novel pharmacologic inhibitors, thus providing an approach for accelerating drug deployment.

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SARS-CoV-2: An Update on Potential Antivirals in Light of SARS-CoV Antiviral Drug Discoveries.

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