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  3. Mechanism-based prediction of drug synergy via network controllability analysis of therapeutic pathways in intractable diseases

Mechanism-based prediction of drug synergy via network controllability analysis of therapeutic pathways in intractable diseases

  • iScience. 2026 Mar 11;29(4):115339. doi: 10.1016/j.isci.2026.115339.
Satoko Namba 1,  Mitsuhiro Goda 2 3,  Yurika Kuniki 4,  Keisuke Ishizawa 3 4 5,  Midori Iida 6,  Jun-Ichi Takeshita 7,  Yoshihiro Yamanishi 1 8
Affiliations

Affiliations

  • 1 Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya 464-8601, Japan.
  • 2 Department of Clinical Pharmacology and Therapeutics, Graduate School of Biomedical and Health Sciences, Hiroshima University, Kasumi 1-2-3, Minami-ku, Hiroshima 734-8553, Japan.
  • 3 Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima 770-8503, Japan.
  • 4 Department of Pharmacy, Tokushima University Hospital, Kuramoto-Cho, Tokushima 770- 8503, Japan.
  • 5 Clinical Research Center for Developmental Therapeutics, Tokushima University Hospital, Kuramoto-cho, Tokushima 770-8503, Japan.
  • 6 Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan.
  • 7 Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8569, Japan.
  • 8 Division of Interdisciplinary Research and Development (R&D), Aichi Cancer Center Research Institute, Nagoya 464-8681, Japan.
PMID: 41952985 DOI: 10.1016/j.isci.2026.115339
Abstract

Identifying effective drug combinations for multidrug therapies in intractable diseases remains challenging, and drug synergy depends on regulatory mechanisms. Herein, we introduce a mechanistic framework that redefines drug combination strategies into two distinct synergy paradigms-"boost" and "complement"-and present a network-based method called SYNERGIE to predict associated combinations across diseases. For diseases lacking known therapeutic targets, we introduced network controllability analysis to estimate therapeutic pathways and target molecules. SYNERGIE integrates drug-disease and drug-drug interactions across multiomics layers using Bayesian optimization to prioritize disease-state-specific combinations. SYNERGIE outperformed existing methods across 14 diseases, and identified both doublets and triplets. Therapeutic effects of triplets predicted for colorectal Cancer were validated through in vitro experiments and multi-resolution omics analyses of human cohort data at bulk and single-cell levels. SYNERGIE offers a mechanism-aware approach to identifying clinically viable combination therapies, and is expected to support stratified treatment of intractable diseases.

Keywords

pharmacoinformatics; pharmacology; systems medicine.

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