Drug repositioning by merging active subnetworks validated in cancer and COVID-19
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment.
Article activity feed
-
-
SciScore for 10.1101/2021.05.13.21257140: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Cell Lines Sentences Resources In [Lucchetta, 2020], we employed the gene co-expression network made available by the DREAM challenge [Choobdar et al., 2019], and DEG for seven different datasets: two inflammatory disease microarray experiments (asthma and rheumatoid arthritis), two cancer RNA-Seq studies (prostate and colorectal cancer), and three COVID-19 datasets called BALF (bronchoalveolar lavage fluid RNA-Seq), PBMC (infected patient peripheral blood mononuclear cells) and COVID19 cells (human adenocarcinomic alveolar basal epithelial A549 cells). COVID19suggested: NoneA549suggested: NCI-DTP Cat# A549, …SciScore for 10.1101/2021.05.13.21257140: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Cell Lines Sentences Resources In [Lucchetta, 2020], we employed the gene co-expression network made available by the DREAM challenge [Choobdar et al., 2019], and DEG for seven different datasets: two inflammatory disease microarray experiments (asthma and rheumatoid arthritis), two cancer RNA-Seq studies (prostate and colorectal cancer), and three COVID-19 datasets called BALF (bronchoalveolar lavage fluid RNA-Seq), PBMC (infected patient peripheral blood mononuclear cells) and COVID19 cells (human adenocarcinomic alveolar basal epithelial A549 cells). COVID19suggested: NoneA549suggested: NCI-DTP Cat# A549, RRID:CVCL_0023)Software and Algorithms Sentences Resources In this work, we use the five active subnetworks detected by all five methods as inputs of DrugMerge. DrugMergesuggested: NoneDrugMatrix, CMAP, and L1000) using the enrichR R/Bioconductor package [Kuleshov et al., 2016], we obtain a listing of drugs enriched in AN ranked by their adjusted p-value in reverse order (from the smallest to the largest). enrichRsuggested: (Enrichr, RRID:SCR_001575)R/Bioconductorsuggested: None.stats of SciPy.org. SciPysuggested: (SciPy, RRID:SCR_008058)Ambiguous cases were checked by hand against the pharmacological literature and DrugBank records (https://go.drugbank.com/). 3.9 CMAP algorithm: To compare the DrugMerge performance in the four benchmark diseases (asthma, rheumatoid arthritis, prostate cancer, and colorectal cancer), we used the PharmacoGx [Smirnov et al., 2016] R package, which performs the Connectivity Map (CMAP) analysis [Lamb et al., 2006]. DrugBanksuggested: (DrugBank, RRID:SCR_002700)Since we are looking for disease treatments or drug repurposing, the CMAP drugs should be anti-correlated with disease signatures. CMAPsuggested: (CMAP, RRID:SCR_009034)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-
