Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

Chan, Jinyan; Wang, Xuan; Turner, Jacob; Baldwin, Nicole; Gu, Jin

doi:10.1093/bioinformatics/btz006

Cited by 39 publications

(35 citation statements)

References 53 publications

(55 reference statements)

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“…Therefore, these data suggest that additional information or better predictors are required to be able to make use of CMap-based repurposing in the wet-lab. The scoring functions presented in this study already contribute to the workflows of repurposing tools such as Cogena [ 24 ], Dr Insight [ 9 ] and NFFinder [ 41 ], embedded in various kinds of pre- and/or post-processing, such as clustering or gene set enrichment analyses. One may of course ask what part of the workflow is contributing how much to the quality of the findings, and why?…”

Section: Resultsmentioning

confidence: 99%

Scoring functions for drug-effect similarity

Struckmann

Ernst

Fischer

et al. 2020

Briefings in Bioinformatics

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Motivation The difficulty to find new drugs and bring them to the market has led to an increased interest to find new applications for known compounds. Biological samples from many disease contexts have been extensively profiled by transcriptomics, and, intuitively, this motivates to search for compounds with a reversing effect on the expression of characteristic disease genes. However, disease effects may be cell line-specific and also depend on other factors, such as genetics and environment. Transcription profile changes between healthy and diseased cells relate in complex ways to profile changes gathered from cell lines upon stimulation with a drug. Despite these differences, we expect that there will be some similarity in the gene regulatory networks at play in both situations. The challenge is to match transcriptomes for both diseases and drugs alike, even though the exact molecular pathology/pharmacogenomics may not be known. Results We substitute the challenge to match a drug effect to a disease effect with the challenge to match a drug effect to the effect of the same drug at another concentration or in another cell line. This is welldefined, reproducible in vitro and in silico and extendable with external data. Based on the Connectivity Map (CMap) dataset, we combined 26 different similarity scores with six different heuristics to reduce the number of genes in the model. Such gene filters may also utilize external knowledge e.g. from biological networks. We found that no similarity score always outperforms all others for all drugs, but the Pearson correlation finds the same drug with the highest reliability. Results are improved by filtering for highly expressed genes and to a lesser degree for genes with large fold changes. Also a network-based reduction of contributing transcripts was beneficial, here implemented by the FocusHeuristics. We found no drop in prediction accuracy when reducing the whole transcriptome to the set of 1000 landmark genes of the CMap’s successor project Library of Integrated Network-based Cellular Signatures. All source code to re-analyze and extend the CMap data, the source code of heuristics, filters and their evaluation are available to propel the development of new methods for drug repurposing. Availability https://bitbucket.org/ibima/moldrugeffectsdb Contact steffen.moeller@uni-rostock.de Supplementary information Supplementary data are available at Briefings in Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Scoring functions for drug-effect similarity

Struckmann

Ernst

Fischer

et al. 2020

Briefings in Bioinformatics

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“…An effective drug treatment is one that reverts the aberrant gene expression in disease back to the normal level in health. DrInsight Package 17 (version: 0.1.1) was used for this purpose, and the outlier-sum (OS) statistic was retrieved, which models the overall disease-drug connectivity by aggregating disease transcriptome changes with drug perturbation. The Kolmogorov–Smirnov (K-S) test was then applied to the OS statistic, to show the significance level of one drug treatment relative to the background of all other drugs and compounds in the reference drug dataset.…”

Section: Methodsmentioning

confidence: 99%

Prediction of repurposed drugs for treating lung injury in COVID-19

Garmire

2020

F1000Res

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Background: Coronavirus disease (COVID-19) is an infectious disease discovered in 2019 and currently in outbreak across the world. Lung injury with severe respiratory failure is the leading cause of death in COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there still lacks efficient treatment for COVID-19 induced lung injury and acute respiratory failure. Methods: Inhibition of angiotensin-converting enzyme 2 (ACE2) caused by the spike protein of SARS-CoV-2 is the most plausible mechanism of lung injury in COVID-19. We performed drug repositioning analysis to identify drug candidates that reverse gene expression pattern in L1000 lung cell line HCC515 treated with ACE2 inhibitor. We confirmed these drug candidates by similar bioinformatics analysis using lung tissues from patients deceased from COVID-19. We further investigated deregulated genes and pathways related to lung injury, as well as the gene-pathway-drug candidate relationships. Results: We propose two candidate drugs, COL-3 (a chemically modified tetracycline) and CGP-60474 (a cyclin-dependent kinase inhibitor), for treating lung injuries in COVID-19. Further bioinformatics analysis shows that 12 significantly enriched pathways (P-value <0.05) overlap between HCC515 cells treated with ACE2 inhibitor and human COVID-19 patient lung tissues. These include signaling pathways known to be associated with lung injury such as TNF signaling, MAPK signaling and chemokine signaling pathways. All 12 pathways are targeted in COL-3 treated HCC515 cells, in which genes such as RHOA, RAC2, FAS, CDC42 have reduced expression. CGP-60474 shares 11 of 12 pathways with COL-3 and common target genes such as RHOA. It also uniquely targets other genes related to lung injury, such as CALR and MMP14. Conclusions: This study shows that ACE2 inhibition is likely part of the mechanisms leading to lung injury in COVID-19, and that compounds such as COL-3 and CGP-60474 have potential as repurposed drugs for its treatment.

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“…As a comparison, the methodology proposed by Chan et al . (2019) reported lower proportions of true positives than our approach for the BRCA and PRAD datasets with 21.42% and 15.94%, respectively (Supplementary Table 3) .…”

Section: Validation Of the Methodology And Comparison Against Equivalmentioning

confidence: 99%

Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures

Khatami

Mubeen

Bharadhwaj

et al. 2020

Preprint

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The utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and a novel scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for three different cancers. We also show how this approach can deconvolute a drugs’ mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs’ effect on a given patient.

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Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

Cited by 39 publications

References 53 publications

Scoring functions for drug-effect similarity

Scoring functions for drug-effect similarity

Prediction of repurposed drugs for treating lung injury in COVID-19

Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures

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