ArticleCell death discovery2023
Repositioning linifanib as a potent anti-necroptosis agent for sepsis.
Article in Cell death discovery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 28 citations in OpenAlex.
- Cell death signaling and immune regulation: new perspectives on targeted therapy for sepsis.Cellular & molecular biology letters · 2025Review
- Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction.BMC biology · 2025Article
- Classification of Acid and Alkaline Enzymes Based on Normalized Van der Waals Volume Features.Proteomics. Clinical applications · 2025Article
- FDA-approved phensuximide inhibits RIPK1-dependent immunogenic cell death.Cell death & disease · 2025Article
- The V-shaped association between the ratio of neutrophil counts to prognostic nutritional index and 30-, 60-, and 90-day mortality in elderly critically ill patients aged 65 and older with sepsis: a retrospective study based on the MIMIC database.Frontiers in nutrition · 2025Article
- HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction.Journal of chemical information and modeling · 2024Article
- Article
- DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model.BMC biology · 2024Article
- Repurposing of Ibrutinib and Quizartinib as potent inhibitors of necroptosis.Communications biology · 2023Article
- The VEGFR/PDGFR tyrosine kinase inhibitor, ABT-869, blocks necroptosis by targeting RIPK1 kinase.The Biochemical journal · 2023Article
- Accurately identifying hemagglutinin using sequence information and machine learning methods.Frontiers in medicine · 2023Article
- Cell death proteins in sepsis: key players and modern therapeutic approaches.Frontiers in immunology · 2023Review
- ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree.Frontiers in genetics · 2023Article
- Computational prediction of promotors inFrontiers in microbiology · 2023Article
- StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach.IET systems biologyArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a systemic inflammatory syndrome (SIRS) caused by acute microbial infection, and it has an extremely high mortality rate. Tumor necrosis factor-α (TNF-α)-induced necroptosis contributes to the pathophysiology of sepsis, so inhibiting necroptosis might be expected to improve clinical outcomes in septic patients. Here we predicted candidate drugs for treating sepsis in silico by combining genes differentially expressed in septic patients and controls combined with interrogation of the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 perturbation database. Sixteen candidate drugs were screened out through bioinformatics analysis, and the top candidate linifanib was validated in cellular and mouse models of TNF-α-induced necroptosis. Cell viability was measured using a luminescent ATP assay, while the effects of linifanib on necroptosis were investigated by western blotting, immunoprecipitation, and RIPK1 kinase assays. Linifanib effectively protected cells from necroptosis and rescued SIRS mice from TNF-α-induced shock and death. In vitro, linifanib directly suppressed RIPK1 kinase activity. In vivo, linifanib effectively reduced overexpressed IL-6, a marker of sepsis severity, in the lungs of SIRS mice. Our preclinical evidence using an integrated in silico and experimental drug repositioning approach supports the potential clinical utility of linifanib in septic patients. Further clinical validation is now warranted.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.