Systems pharmacology: network analysis to identify multiscale mechanisms of drug action

• Published in: Annual review of pharmacology and toxicology Vol. 52; p. 505
• Main Authors: Zhao, Shan, Iyengar, Ravi
• Format: Journal Article
• Language: English
• Published: United States 01.01.2012
• Subjects: Animals; Databases, Factual; Drug Delivery Systems - methods; Drug Discovery; Humans; Metabolomics - methods; Pharmaceutical Preparations; Pharmacokinetics; Proteomics - methods; Systems Biology - methods
• ISSN: 1545-4304
• DOI: 10.1146/annurev-pharmtox-010611-134520

Systems approaches have long been used in pharmacology to understand drug action at the organ and organismal levels. The application of computational and experimental systems biology approaches to pharmacology allows us to expand the definition of systems pharmacology to include network analyses at multiple scales of biological organization and to explain both therapeutic and adverse effects of drugs. Systems pharmacology analyses rely on experimental "omics" technologies that are capable of measuring changes in large numbers of variables, often at a genome-wide level, to build networks for analyzing drug action. A major use of omics technologies is to relate the genomic status of an individual to the therapeutic efficacy of a drug of interest. Combining pathway and network analyses, pharmacokinetic and pharmacodynamic models, and a knowledge of polymorphisms in the genome will enable the development of predictive models of therapeutic efficacy. Network analyses based on publicly available databases such as the U.S. Food and Drug Administration's Adverse Event Reporting System allow us to develop an initial understanding of the context within which molecular-level drug-target interactions can lead to distal effectors in a process that results in adverse phenotypes at the organ and organismal levels. The current state of systems pharmacology allows us to formulate a set of questions that could drive future research in the field. The long-term goal of such research is to develop polypharmacology for complex diseases and predict therapeutic efficacy and adverse event risk for individuals prior to commencement of therapy.