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Open Access Highly Accessed Research article

A Systems Biology Strategy for Predicting Similarities and Differences of Drug Effects: Evidence for Drug-specific Modulation of Inflammation in Atherosclerosis

Robert Kleemann1*, Svetlana Bureeva3, Ally Perlina3, Jim Kaput45, Lars Verschuren12, Peter Y Wielinga1, Eva Hurt-Camejo6, Yuri Nikolsky3, Ben van Ommen2 and Teake Kooistra1

Author affiliations

1 Metabolic Health Research, TNO, Zernikedreef 9, Leiden, 2333 CK, The Netherlands

2 Microbiology and Systems Biology, TNO, Utrechtseweg 48, Zeist, 3704 HE, The Netherlands

3 GeneGo, Inc., 5901 Priestly Drive, Carlsbad, CA 92008, USA

4 Division of Personalized Nutrition and Medicine FDA, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR 72079, USA

5 Nestle Institute of Health Sciences, EPFL Campus Bâtiment G at Quartier de l'Innovation, Lausanne, 1015, Switzerland

6 Cardiovascular & Gastrointestinal Innovative Medicines Unit, AstraZeneca, Pepparedsleden 1, Mölndal, 43183, Sweden

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Citation and License

BMC Systems Biology 2011, 5:125  doi:10.1186/1752-0509-5-125

Published: 12 August 2011

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.

Conclusion

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.