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Review
. 2022 Sep 19;12(9):1456.
doi: 10.3390/life12091456.

Identification of Suitable Drug Combinations for Treating COVID-19 Using a Novel Machine Learning Approach: The RAIN Method

Aliakbar Kiaei  1 , Nader Salari  2 , Mahnaz Boush  3 , Kamran Mansouri  4 , Amin Hosseinian-Far  5 , Hooman Ghasemi  6 , Masoud Mohammadi  7
Affiliations
Review

Identification of Suitable Drug Combinations for Treating COVID-19 Using a Novel Machine Learning Approach: The RAIN Method

Aliakbar Kiaei et al. Life (Basel). .

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.

Keywords: COVID-19; RAIN method; drugs combinations; machine learning; network meta-analysis; treatment of patients.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
The flowchart on the stages of including the studies in the systematic review and meta-analysis (PRISMA 2009).
Figure 2
Figure 2
Boxplot of percent identity between COVID-19 proteins and similar nucleotides.
Figure 3
Figure 3
Structure of proposed method (p-NA).
Figure 4
Figure 4
Selecting association with the smallest combined p-value with the target.
Figure 5
Figure 5
Measure of combined p-value.
Figure 6
Figure 6
The 20 drugs with the smallest combined p-values between them and COVID-19 in parentheses.
Figure 7
Figure 7
(A) The circular bar plot of Table 5 p-value column, (B) the circular bar plot of scenario 2 column, (C) the circular bar plot of scenario 1 column, (D) the circular bar plot of scenario 3, (E) the circular bar plot of scenario 4, (F) the circular bar plot of scenario 5.
Figure 8
Figure 8
(A) The radar chart of scenario 1, (B) the radar chart of scenario 2, (C) the radar chart of scenario 3.
Figure 8
Figure 8
(A) The radar chart of scenario 1, (B) the radar chart of scenario 2, (C) the radar chart of scenario 3.
Figure 9
Figure 9
Network chart for p-values between COVID-19 and two drugs, with human genes as interface features.
See this image and copyright information in PMC

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