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

## 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.

## Key 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 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 fingerprint. Based
on these models, we prioritize compounds with favorable binding affinity
profiles, 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)
profiling, providing a multidimensional assessment of druggability
and safety. This study offers a generalizable approach for drug repurposing
in other complex diseases beyond opioid addiction.