title: Drug effects viewed from a signal transduction network perspective.
abstract: Understanding how drugs affect cellular network structures and how resulting signals are translated into drug effects holds the key to the discovery of medicines. Herein we examine this cause-effect relationship by determining protein network structures associated with the generation of specific in vivo drug-effect patterns. Medicines having similar in vivo pharmacology have been identified by a comparison of drug-effect profiles of 1320 medicines. Protein network positions reached by these medicines were ascertained by examining the coinvestigation frequency of these medicines and 1179 protein network constituents in millions of scientific investigations. Interestingly, medicine associations obtained by comparing by drug-effect profiles mirror those obtained by comparing drug-protein coinvestigation frequency profiles, demonstrating that these drug-protein reachability profiles are relevant to in vivo pharmacology. By using protein associations obtained in these investigations and independent, curated protein interaction information, drug-mediated protein network topology models can be constructed. These protein network topology models reveal that drugs having similar pharmacology profiles reach similar discrete positions in cellular protein network systems and provide a network view of medicine cause-effect relationships.
authors.affiliation: Pfizer Global Research and Development, CT, USA.
authors.forename: Anton F
authors.initials: AF
authors.lastname: Fliri
authors.forename: William T
authors.initials: WT
authors.lastname: Loging
authors.forename: Robert A
authors.initials: RA
authors.lastname: Volkmann
mesh_terms: D016000:Cluster Analysis; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D010600:Pharmacology / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D015398:Signal Transduction / Q000187:drug effects
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins