Abstract
Systems pharmacology has evolved from a discipline that focuses on drug action at the organ level to a discipline that combines traditional pharmacokinetic and pharmacodynamic modeling with recent systems biology approaches. The integration of high-throughput data technologies with computational data analysis and modeling offers new opportunities to overcome the one disease, one target, one drug approach. Whole genomic or transcriptomic sequencing and proteomics allow qualitative, and sometimes quantitative, snapshots of the cellular state at any given condition (e.g., during disease or after drug treatment) that can be the basis for the development of whole cell models to predict drug responses. Networks of protein–protein interactions that were confirmed by experimental and computational analysis of the structure of the interaction partners can be combined with graph theory to identify modules regulating the cellular state. Dynamic modeling and sensitivity analysis allow the identification of robust and fragile nodes within these modules to identify putative drug targets for single or combinatorial drug treatment. Traditional pharmacokinetic and pharmacodynamic modeling complements these approaches by predicting drug concentration and target perturbation at the site of drug action. Such general systems pharmacology models are a great leap forward towards the development of patient-specific drug response models as a main component of precision medicine.
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Birtwistle, M.R. et al. (2016). Systems Pharmacology: An Overview. In: Mager, D., Kimko, H. (eds) Systems Pharmacology and Pharmacodynamics. AAPS Advances in the Pharmaceutical Sciences Series, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-44534-2_4
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