title: A systems biology strategy for predicting similarities and differences of drug effects: evidence for drug-specific modulation of inflammation in atherosclerosis.
abstract: BACKGROUND
  Successful drug development has been hampered by a limited understanding of how to translate laboratory-based biological discoveries into safe and effective medicines. We have developed a generic method for predicting the effects of drugs on biological processes. Information derived from the chemical structure and experimental omics data from short-term efficacy studies are combined to predict the possible protein targets and cellular pathways affected by drugs.
  
  
  RESULTS
  Validation of the method with anti-atherosclerotic compounds (fenofibrate, rosuvastatin, LXR activator T0901317) demonstrated a great conformity between the computationally predicted effects and the wet-lab biochemical effects. Comparative genome-wide pathway mapping revealed that the biological drug effects were realized largely via different pathways and mechanisms. In line with the predictions, the drugs showed differential effects on inflammatory pathways (downstream of PDGF, VEGF, IFNγ, TGFβ, IL1β, TNFα, LPS), transcriptional regulators (NFκB, C/EBP, STAT3, AP-1) and enzymes (PKCδ, AKT, PLA2), and they quenched different aspects of the inflammatory signaling cascade. Fenofibrate, the compound predicted to be most efficacious in inhibiting early processes of atherosclerosis, had the strongest effect on early lesion development.
  
  
  CONCLUSIONS
  Our approach provides mechanistic rationales for the differential and common effects of drugs and may help to better understand the origins of drug actions and the design of combination therapies.
authors.affiliation: Metabolic Health Research, TNO, Zernikedreef 9, Leiden, The Netherlands. robert.kleemann@tno.nl
authors.forename: Robert
authors.initials: R
authors.lastname: Kleemann
authors.forename: Svetlana
authors.initials: S
authors.lastname: Bureeva
authors.forename: Ally
authors.initials: A
authors.lastname: Perlina
authors.forename: Jim
authors.initials: J
authors.lastname: Kaput
authors.forename: Lars
authors.initials: L
authors.lastname: Verschuren
authors.forename: Peter Y
authors.initials: PY
authors.lastname: Wielinga
authors.forename: Eva
authors.initials: E
authors.lastname: Hurt-Camejo
authors.forename: Yuri
authors.initials: Y
authors.lastname: Nikolsky
authors.forename: Ben
authors.initials: B
authors.lastname: van Ommen
authors.forename: Teake
authors.initials: T
authors.lastname: Kooistra
mesh_terms: D000818:Animals; D000893:Anti-Inflammatory Agents / Q000494:pharmacology* / Q000627:therapeutic use; D050197:Atherosclerosis / Q000188:drug therapy*; D002317:Cardiovascular Agents / Q000494:pharmacology* / Q000627:therapeutic use; D015195:Drug Design; D011345:Fenofibrate; D005464:Fluorobenzenes; D005786:Gene Expression Regulation / Q000187:drug effects*; D006801:Humans; D006845:Hydrocarbons, Fluorinated; D051379:Mice; D046228:Microarray Analysis; D008954:Models, Biological; D011743:Pyrimidines; D050436:Regulatory Elements, Transcriptional / Q000187:drug effects; D000068718:Rosuvastatin Calcium; D013329:Structure-Activity Relationship; D013449:Sulfonamides; D049490:Systems Biology / Q000379:methods*; D005466:Fluorocarbons; D000096926:Benzenesulfonamides
chemical_list: D000893:Anti-Inflammatory Agents; D002317:Cardiovascular Agents; D011345:Fenofibrate; D005464:Fluorobenzenes; D006845:Hydrocarbons, Fluorinated; D011743:Pyrimidines; D000068718:Rosuvastatin Calcium; D013449:Sulfonamides; C423915:T0901317; D005466:Fluorocarbons; D000096926:Benzenesulfonamides