Meta-Analysis and Topological Perturbation in Interactomic Network for Antiopioid Addiction Drug Repurposing

Area of Science:

• Computational Biology and Bioinformatics
• Pharmacology and Drug Discovery
• Neuroscience and Addiction Research

Background:

• The opioid crisis necessitates rapid development of new therapeutic strategies.
• Existing drug discovery pipelines are often lengthy and costly.
• Repurposing existing drugs offers a faster route to clinical application.

Purpose of the Study:

• To develop a novel computational approach for identifying repurposable drugs for opioid addiction.
• To bridge the gap between transcriptomic data analysis and practical drug discovery.
• To provide a generalizable framework applicable to other complex diseases.

Main Methods:

• Meta-analysis of seven transcriptomic datasets for opioid addiction using differential gene expression (DGE) analysis.
• Application of multiscale topological differentiation with persistent Laplacians on protein-protein interaction (PPI) networks derived from DEGs.
• Functional validation, data curation, DrugBank cross-referencing, predictive modeling (NLP embeddings, molecular fingerprints), molecular docking, and ADMET profiling.

Main Results:

• Identification of 1,865 high-confidence gene targets implicated in opioid addiction.
• Compilation of a repurposing candidate drug list based on target interactions and binding affinity.
• Prioritization of compounds through predictive modeling, molecular docking, and comprehensive ADMET profiling for druggability and safety.

Conclusions:

• The study presents a robust, generalizable computational methodology for drug repurposing.
• This approach effectively identifies potential therapeutic agents for opioid addiction.
• The framework facilitates accelerated drug discovery for complex diseases.