Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking.

BACKGROUND: The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2. METHODS AND FINDINGS: We perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we found that 3 of the drugs fulfils the criterions well among which the antiretroviral drug Amprenavir (DrugBank ID-DB00701) would probably be the most effective drug based on the selected criterions. CONCLUSIONS: Our study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.

Physiological Relevance of Angiotensin Converting Enzyme 2 As a Metabolic Linker and Therapeutic Implication of Mesenchymal Stem Cells in COVID-19 and Hypertension.

Severe acute respiratory syndrome corona virus - 2 (SARS-CoV-2) is a single stranded RNA virus and responsible for infecting human being. In many cases the individual may remain asymptomatic. Some recently reported studies revealed that individuals of elderly age group and with pre-existing medical conditions such as hypertension, diabetes mellitus had severe consequences, even may lead to death. However, it is not clearly delineated whether hypertension itself or associated comorbidities or antihypertensive therapy contributes to the grave prognosis of COVID-19 infections. This review is aimed to decipher the exact mechanisms involved at molecular level from existing evidence and as reported. It has been reported that SARS-CoV-2 enters into the host cell through interaction between conserved residues of viral spike protein and angiotensin converting enzyme 2 (ACE2) receptor which is highly expressed in host's cardiac and pulmonary cells and finally transmembrane protease, serine-2 (TMPRSS2), helps in priming of the surface protein. Subsequently, symptom related to multi organ involvement is primarily contributed by cytokine storm. Although various clinical trials are being conducted on renin- angiotensin- system inhibitor, till to date there is no standard treatment protocol approved for critically ill COVID-19 positive cases with pre-existing hypertension. Recently, several studies are carried out to document the safety and efficacy outcome of mesenchymal stem cell transplantation based on its immunomodulatory and regenerative properties. Therefore, identification of future novel therapeutics in the form of mesenchymal stem cell either alone or in combination with pharmacological approach could be recommended for combating SARS-CoV-2 which might be dreadful to debilitating elderly people. Graphical Abstract.