Next-generation drug repurposing using human genetics and network biology

Nabirotchkin, Serguei; Peluffo, Alex E.; Rinaudo, Philippe; Yu, Jinchao; Hajj, Rodolphe; Cohen, Daniel

doi:10.1016/j.coph.2019.12.004

Cited by 70 publications

(56 citation statements)

References 130 publications

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“…At this stage, our hope is that these results can be helpful in rapidly designing and implementing clinical trials to treat COVID-2019. We also hope that, in the near future, some of the prioritized drugs could be used in combination to provide an even better, and possibly personalized, treatment against COVID-2019 as it is known that combinatorial therapies can be more efficient against many types of diseases 6 , including viral diseases such as HIV 33 . In the bioinformatics field, while it is still a challenging task to predict synergistic therapies (some available methods have been recently studied for cancers) 34 , a simple approach is to combine drugs with different therapeutic effects or affecting different pathways or biological functions, and we hope that our study ( Figure 4) together with existing experimental and clinical results could provide helpful hints.…”

Section: Discussionmentioning

confidence: 99%

“…In silico methods offer a way to methodically and rapidly yield additional repurposing candidates 6 . For instance, when drug targets associated with the disease of interest are known, and when their protein structures or that of close homologs are available, it is possible to use structural bioinformatics to virtually screen (e.g., using molecular docking) a library of existing drugs against this known targets 7 .…”

Section: Introductionmentioning

confidence: 99%

“…There exist several ways to identify disease-related genes, whether using genomic data (e.g., Genome-Wide Association Studies), gene expression data (e.g., RNAseq differential expression analysis) or data directly collected from the scientific literature (e.g., text mining or expert curation, either analysed in-house or via recognised structured databases). Compared to virtual screening, where the candidate targets are known from the start, network biology methods can identify additional, unanticipated targets, which are part of the same molecular pathways than previously known targets for the disease of interest 6,10 .…”

Section: Introductionmentioning

confidence: 99%

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Network Bioinformatics Analysis Provides Insight into Drug Repurposing for COVID-2019

Li¹,

Yu²,

Zhang³

et al. 2020

Preprint

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The COVID-2019 disease caused by the SARS-CoV-2 virus (aka 2019-nCoV) has raised significant health concerns in China and worldwide. While novel drug discovery and vaccine studies are long, repurposing old drugs against the COVID-2019 epidemic can help identify treatments, with known preclinical, pharmacokinetic, pharmacodynamic, and toxicity profiles, which can rapidly enter Phase 3 or 4 or can be used directly in clinical settings. In this study, we presented a novel network based drug repurposing platform to identify potential drugs for the treatment of COVID-2019. We first analysed the genome sequence of SARS-CoV-2 and identified SARS as the closest disease, based on genome similarity between both causal viruses, followed by MERS and other human coronavirus diseases. Using our AutoSeed pipeline (text mining and database searches), we obtained 34 COVID-2019-related genes. Taking those genes as seeds, we automatically built a molecular network for which our module detection and drug prioritization algorithms identified 24 disease-related human pathways, five modules and finally suggested 78 drugs to repurpose. Following manual filtering based on clinical knowledge, we re-prioritized 30 potential repurposable drugs against COVID-2019 (including pseudoephedrine, andrographolide, chloroquine, abacavir, and thalidomide) . We hope that this data can provide critical insights into SARS-CoV-2 biology and help design rapid clinical trials of treatments against COVID-2019.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Network Bioinformatics Analysis Provides Insight into Drug Repurposing for COVID-2019

Li¹,

Yu²,

Zhang³

et al. 2020

Preprint

Self Cite

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“…This groundbreaking study paved new avenues to investigate novel therapeutic targets using a systems pharmacology approach. Undoubtedly, network biology presents a nextgeneration, integrative approach for drug repurposing that can predict individual or, more likely, combinatorial sets of drugs with high efficacy against SARS-CoV-2 30, 31 .…”

Section: Introductionmentioning

confidence: 99%

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis

Kumar

Mishra

Mehmood

et al. 2020

Preprint

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COVID-19 (Coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While the pathophysiology of this deadly virus is complex and largely unknown, we employ a network biology-fueled approach and integrated multiomics data pertaining to lung epithelial cells-specific coexpression network and human interactome to generate Calu-3-specific human-SARS-CoV-2 Interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 target central nodes of host-viral network that participate in core functional pathways. Network centrality analyses discover 28 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host modifying responses and cytokine storm. Overall, our network centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into SARS-CoV-2 pathogenicity.

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“…Besides, several pieces to the puzzle remain to be found to provide the full picture of a potential role of antihistamines in breast cancer, e.g., mode of action, role in inflammatory response, potential immunologic effects, optimal choice of drug, treatment time and period and interaction with other cancer treatments. These knowledge gaps call for interdisciplinary efforts and studies that integrate molecular aspects and network biology to define mechanisms of action, identify relevant tumor subsets, and develop response-predictive markers [7] will therefore be needed to answer outstanding issues and take the next steps toward design of clinical trials in repositioning antihistamines for breast cancer.…”

mentioning

confidence: 99%