Systems pharmacology: An overview

Marc R. Birtwistle, Jens Hansen, James M. Gallo, Sreeharish Muppirisetty, Peter Man Un Ung, Ravi Iyengar, Avner Schlessinger

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Scopus citations


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.

Original languageEnglish
Title of host publicationAAPS Advances in the Pharmaceutical Sciences Series
PublisherSpringer Verlag
Number of pages28
StatePublished - 2016

Publication series

NameAAPS Advances in the Pharmaceutical Sciences Series
ISSN (Print)2210-7371
ISSN (Electronic)2210-738X


  • Bioinformatics
  • Cytochrome P450
  • Druggability
  • Genbank
  • Genomic variation
  • Interactome
  • Networks-based
  • Nodes
  • Personalized medicine
  • Pharmacogenomics
  • Protein-protein interaction (PPI)


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