Integrative Multi-Scale Network Simulation for Precision Drug Repurposing with PETS

Drug repurposing offers a promising strategy to accelerate therapeutic discovery for complex diseases; however, conventional approaches often overlook the multi-scale, dynamic nature of drug-induced perturbations. In this study, we introduce PETS (Predictive Evaluation of Therapeutic Simulations), an innovative in silico framework that synergistically integrates curated pathway models with multi-layer network propagation, adaptive dampening, and memory mechanisms to simulate drug-gene interactions with high fidelity. By propagating perturbations through tissue- and cell-specific protein-protein interaction networks via iterative state updates, PETS robustly captures both proximal and distal regulatory feedback, enabling the identification and ranking of drug candidates capable of reversing disease-specific gene expression signatures. Validation against the SigCom L1000 dataset demonstrates that PETS achieves lower mean squared errors and higher predictive consistency compared to existing methods across models of Alzheimers disease, AD-expressing neuroblastoma, and glioblastoma multiforme. Moreover, PETS concurrently quantifies on-target therapeutic efficacy and potential off-target toxicity, providing mechanistic insights that are critical for candidate prioritization. Looking ahead, the integration of patient-specific multi-omics data and dynamic network topologies is expected to further enhance the translational impact of PETS. Our framework represents a significant advancement in the computational prediction of drug-gene perturbation effects, positioning it as a cornerstone tool for precision medicine and preclinical drug discovery.