Kiaei, A., Salari, N., Boush, M., Mansouri, K., Hosseinian Far, A., Ghasemi, H. and Mohammadi, M. (2022) Identification of suitable drug combinations for treating COVID-19 using a novel machine learning approach: The RAIN method. Life. 12(9) 2075-1729.
| Item Type: | Article |
|---|---|
| Abstract: | COVID-19 affects several human genes, each with its own p-value. The combination of drugs associated with these genes with small p-values may lead to an estimation of the combined p-value between COVID-19 and some drug combinations, thereby increasing the effectiveness of these combinations in defeating the disease. Based on human genes, we introduced a new machine learning method that offers an effective drug combination with low combined p-values between them and COVID-19. This study follows an improved approach to systematic reviews, called the Systematic Review and Artificial Intelligence Network Meta-Analysis (RAIN), registered within PROSPERO (CRD42021256797), in which, the PRISMA criterion is still considered. Drugs used in the treatment of COVID-19 were searched in the databases of ScienceDirect, Web of Science (WoS), ProQuest, Embase, Medline (PubMed), and Scopus. In addition, using artificial intelligence and the measurement of the p-value between human genes affected by COVID-19 and drugs that have been suggested by clinical experts, and reported within the identified research papers, suitable drug combinations are proposed for the treatment of COVID-19. During the systematic review process, 39 studies were selected. Our analysis shows that most of the reported drugs, such as azithromycin and hydroxyl-chloroquine on their own, do not have much of an effect on the recovery of COVID-19 patients. Based on the result of the new artificial intelligence, on the other hand, at a significance level of less than 0.05, the combination of the two drugs therapeutic corticosteroid + camostat with a significance level of 0.02, remdesivir + azithromycin with a significance level of 0.03, and interleukin 1 receptor antagonist protein + camostat with a significance level 0.02 are considered far more effective for the treatment of COVID-19 and are therefore recommended. Additionally, at a significance level of less than 0.01, the combination of interleukin 1 receptor antagonist protein + camostat + azithromycin + tocilizumab + oseltamivir with a significance level of 0.006, and the combination of interleukin 1 receptor antagonist protein + camostat + chloroquine + favipiravir + tocilizumab7 with corticosteroid + camostat + oseltamivir + remdesivir + tocilizumab at a significant level of 0.009 are effective in the treatment of patients with COVID-19 and are also recommended. The results of this study provide sets of effective drug combinations for the treatment of patients with COVID-19. In addition, the new artificial intelligence used in the RAIN method could provide a forward-looking approach to clinical trial studies, which could also be used effectively in the treatment of diseases such as cancer. |
| Uncontrolled Keywords: | /dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being |
| Creators: | Kiaei, Aliakbar, Salari, Nader, Boush, Mahnaz, Mansouri, Kamran, Hosseinian Far, Amin, Ghasemi, Hooman and Mohammadi, Masoud |
| Faculties, Divisions and Institutes: |
Faculties > Faculty of Business & Law > Business Systems & Operations University Faculties, Divisions and Research Centres - OLD > Research Centre > Centre for Sustainable Business Practices Research Centres > Centre for Sustainable Business Practices |
| Date: | 19 September 2022 |
| Date Type: | Publication |
| Journal or Publication Title: | Life |
| Volume: | 12 |
| Number: | 9 |
| Language: | English |
| DOI: | https://doi.org/10.3390/life12091456 |
| ISSN: | 2075-1729 |
| Status: | Published / Disseminated |
| Refereed: | Yes |
| Related URLs: | |
| URI: | http://nectar.northampton.ac.uk/id/eprint/17548 |
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