Meta-Analysis and Topological Perturbation in Interactomic Network for Antiopioid Addiction Drug Repurposing
Chunhuan Zhang, Sean Cottrell, Benjamin Jones, Yueying Zhu, Huahai Qiu, Bengong Zhang, Tianshou Zhou, Jian Jiang
TL;DR
This study uses gene expression data and network analysis to find existing drugs that could treat opioid addiction, offering a new approach for drug repurposing.
Contribution
The novel multiscale topological differentiation method identifies key genes in PPI networks for drug repurposing.
Findings
A meta-analysis of seven transcriptomic datasets identified 1,865 high-confidence targets for opioid addiction.
Drug repurposing candidates were prioritized using molecular docking and ADMET profiling for safety and druggability.
The approach is generalizable for drug repurposing in other complex diseases.
Abstract
The ongoing opioid crisis highlights the urgent need for novel therapeutic strategies that can be rapidly deployed. This study presents a novel approach to identify potential repurposable drugs for the treatment of opioid addiction, aiming to bridge the gap between transcriptomic data analysis and drug discovery. Specifically, we perform a meta-analysis of seven transcriptomic data sets related to opioid addiction by differential gene expression (DGE) analysis and propose a novel multiscale topological differentiation to identify key genes from a protein–protein interaction (PPI) network derived from DEGs. This method uses persistent Laplacians to accurately single out important nodes within the PPI network through a multiscale manner to ensure high reliability. Subsequent functional validation by pathway enrichment and rigorous data curation yields 1,865 high-confidence targets…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsBioinformatics and Genomic Networks · Computational Drug Discovery Methods · Receptor Mechanisms and Signaling