Drug combinations proposed by machine learning on genes/proteins to improve the efficacy of Tecovirimat in the treatment of Monkeypox: A Systematic Review and Network Meta-analysis.

Abstract

BackgroundThe World Health Organization (WHO) describes Monkeypox as a viral zoonosis, or an animal-to-human virus transmission, with symptoms comparable to those of past smallpox patients but clinically less severe. This studys objective is to assess the results of previous investigations on the best drug combinations for treating Monkeypox. MethodThe pharmacological combinations used to treat monkeypox sickness have been researched in two stages for this systematic review and network meta-analysis. To begin with, a certain machine learning technique is used to extract the medication combinations from the researched articles offered on science databases, including Scopus, PubMed, Web of Science (ISI), Science Direct, Embase, and Google Scholar. Second, the tested medicine combinations will have been proven. ResultsThe results of this study show that the p-value between the proposed drug combination and Monkeypox for scenarios 1 to 5 were 0.108, 0.042, 0.023, 0.018, and 0.015, respectively. Scenario i is the combination of the first i suggested drugs for treating Monkeypox. This has led to a 720 percent increase in the proposed drug combinations efficacy in treating Monkeypox. ConclusionThe suggested drug combination decreases the p-value between MonkeyPox and the genes as potential targets for Monkeypox progression, which leads to an improvement in the treatment of Monkeypox. Therefore, using the right combination of drugs is important in improving the communitys health and reducing per capita treatment costs.