Meta-analysis and Topological Perturbation in Interactomic Network for Anti-opioid Addiction Drug Repurposing
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916965301354496 |
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| author | Zhang, Chunhuan Cottrell, Sean Jones, Benjamin Zhu, Yueying Qiu, Huahai Zhang, Bengong Zhou, Tianshou Jiang, Jian |
| author_facet | Zhang, Chunhuan Cottrell, Sean Jones, Benjamin Zhu, Yueying Qiu, Huahai Zhang, Bengong Zhou, Tianshou Jiang, Jian |
| contents | 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. Speciffcally, we perform a meta-analysis of seven transcriptomic datasets 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 yield 1,865 high-conffdence targets implicated in opioid addiction, which are cross-referenced with DrugBank to compile a repurposing candidate list. To evaluate drug-target interactions, we construct predictive models utilizing two natural language processing-derived molecular embeddings and a conventional molecular ffngerprint. Based on these models, we prioritize compounds with favorable binding afffnity proffles, and select candidates that are further assessed through molecular docking simulations to elucidate their receptor-level interactions. Additionally, pharmacokinetic and toxicological evaluations are performed via ADMET (absorption, distribution, metabolism, excretion, and toxicity) proffling, providing a multidimensional assessment of druggability and safety. This study offers a generalizable approach for drug repurposing in other complex diseases beyond opioid addiction. Keywords: Opioid addiction; Interactomic network; Topological perturbation; Differentially expressed gene; Drug repurposin |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19410 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Meta-analysis and Topological Perturbation in Interactomic Network for Anti-opioid Addiction Drug Repurposing Zhang, Chunhuan Cottrell, Sean Jones, Benjamin Zhu, Yueying Qiu, Huahai Zhang, Bengong Zhou, Tianshou Jiang, Jian Molecular Networks Quantitative Methods 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. Speciffcally, we perform a meta-analysis of seven transcriptomic datasets 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 yield 1,865 high-conffdence targets implicated in opioid addiction, which are cross-referenced with DrugBank to compile a repurposing candidate list. To evaluate drug-target interactions, we construct predictive models utilizing two natural language processing-derived molecular embeddings and a conventional molecular ffngerprint. Based on these models, we prioritize compounds with favorable binding afffnity proffles, and select candidates that are further assessed through molecular docking simulations to elucidate their receptor-level interactions. Additionally, pharmacokinetic and toxicological evaluations are performed via ADMET (absorption, distribution, metabolism, excretion, and toxicity) proffling, providing a multidimensional assessment of druggability and safety. This study offers a generalizable approach for drug repurposing in other complex diseases beyond opioid addiction. Keywords: Opioid addiction; Interactomic network; Topological perturbation; Differentially expressed gene; Drug repurposin |
| title | Meta-analysis and Topological Perturbation in Interactomic Network for Anti-opioid Addiction Drug Repurposing |
| topic | Molecular Networks Quantitative Methods |
| url | https://arxiv.org/abs/2509.19410 |