Systems Pharmacology-Guided Identification of Novel Drug Combinations for Complex Disease Treatment
Keywords:
systems pharmacology; drug combinations; network medicine; computational infrastructure; governance; clinical translationAbstract
The treatment of complex diseases such as cancer, metabolic syndrome, and neurodegenerative disorders increasingly requires therapeutic strategies that move beyond single-target pharmacology. Systems pharmacology provides a multiscale framework for understanding how drugs perturb interconnected molecular, cellular, and physiological networks, and it has become central to the rational identification of synergistic drug combinations. This paper examines the architecture, infrastructure, and governance dimensions of systems pharmacology-guided combination discovery. It considers how network-based representations, pharmacogenomic data resources, and integrative computational workflows are organized into modular discovery pipelines. The analysis emphasizes structural trade-offs among model interpretability, predictive accuracy, scalability, and reproducibility. Attention is given to community-based network methods that identify functional modules and candidate combinations from complex systems, including herbal medicine resources. The paper further discusses deployment challenges in clinical translation, including regulatory evaluation, real-world evidence integration, algorithmic fairness, and institutional sustainability. By treating combination discovery as a socio-technical infrastructure rather than a narrow informatics task, the paper argues that future progress depends on coordinated governance of data, models, and evidence. It concludes with forward-looking perspectives on how systems pharmacology can support robust, equitable, and sustainable therapeutic innovation.
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