Drug repurposing through pathway perturbation dynamics: A systems biology approach for precision oncology

Drug repurposing offers a cost-efficient strategy to discover new therapeutic applications for approved drugs. While current computational strategies prioritize candidates by targeting disease-related pathways, they often fail to quantitatively model pathway perturbation dynamics-a critical gap that limits mechanistic interpretability.

To address this issue, we presented PathPertDrug, a novel framework that systematically identifies cancer drug candidates by quantifying functional antagonism between drug-induced and disease-associated pathway perturbations (activation/ inhibition). By integrating drug-induced gene expression, disease-related gene expression, and pathway information, PathPertDrug evaluated pathway-level functional reversals, enabling precise prediction of drug-disease associations.

Our method demonstrated superior predictive accuracy and robustness across pan-cancer benchmarks. It achieved a higher median AUROC (0.62 vs. 0.42-0.53) and a substantial improvement in AUPR (3-23 %) over existing methods. The consistent AUPR enhancement, particularly under class imbalance, underscores the robustness of our model in reliably prioritizing true positive associations. Validated by the comparative toxicogenmics database, PathPertDrug rediscovered 83 % of literature-supported cancer drugs (e.g., fulvestrant (Fulvestrant) for colorectal cancer) and predicted novel candidates (e.g., rifabutin-lung cancer).

This pathway-centric approach bridged mechanistic insights with translational applications, providing a paradigm shift for precision oncology drug discovery.