Systems Pharmacology of Human Oncoviruses: Network-Based Drug Discovery for Virus-Driven Cancers

Human oncoviruses, including high-risk HPV, EBV, HBV, HCV, MCPyV, KSHV, and HTLV-1, account for approximately 15%-20% of global human malignancies. Rather than acting through isolated pathways, viral oncoproteins systematically rewire host regulatory networks, rendering conventional single-target oncology paradigms vulnerable to rapid resistance and clinical failure. Systems pharmacology offers a transformative, network-centric framework that views virus-driven cancers as systemic perturbations of host protein-protein interaction networks. This review synthesises computational methodology, network topology, and drug discovery strategies across major human oncoviruses. We examine how graph centrality algorithms, quantitative network proximity metrics (Z-score), and complementary exposure synergy modelling (S_AB) uncover hidden network vulnerabilities and guide targeted drug repurposing. Despite distinct genomic structures and tissue tropisms, oncogenic viruses converge on shared host interactome hubs, notably TP53, AKT1, BCL2, MYC, STAT3, and VEGFA, to drive cell cycle dysregulation, apoptotic suppression, and microenvironmental remodelling. We evaluate repositioned synthetic pharmaceuticals (such as kinase inhibitors and host ion channel blockers) and multi-target bioactive formulations, detailing their mechanisms in targeting viral host dependencies. Finally, we address key translational bottlenecks, including disparities between in silico binding predictions and in vivo pharmacokinetics, extensive plasma protein binding, tumour penetration barriers, and viral latency reservoirs. Overcoming these hurdles will require integrating deep graph learning, single-cell spatial multi-omics, and patient-derived organoid models to accelerate the clinical realization of network-targeted therapies in viral oncology.