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Systems biology analysis unravels the complementary action of combined rosuvastatin and ezetimibe therapy

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Author: Verschuren, L. · Radonjic, M. · Wielinga, P.Y. · Kelder, T. · Kooistra, T. · Ommen, B. van · Kleemann, R.
Source:Pharmacogenetics and Genomics, 12, 22, 837-845
Identifier: 466432
doi: doi:10.1097/FPC.0b013e328359d274
Keywords: Biology · adverse effect · coamplified · combination therapy · inflammation · mouse model · systems biology · Biomedical Innovation · Healthy Living · Life · MSB - Microbiology and Systems Biology MHR - Metabolic Health Research · EELS - Earth, Environmental and Life Sciences


AIMS: Combination-drug therapy takes advantage of the complementary action of their individual components, thereby potentiating its therapeutic effect. Potential disadvantages include side effects that are not foreseen on basis of the data available from drug monotherapy. Here, we used a systems biology approach to understand both the efficacy and the side effects of a cholesterol-lowering drug-combination therapy on the basis of the biological pathways and molecular processes affected by each drug alone or in combination. METHODS AND RESULTS: ApoE*3Leiden transgenic mice, a mouse model with human-like cholesterol-lowering drug responses, were treated with rosuvastatin and ezetimibe, alone and in combination. Analyses included functional responses, viz. effects on cardiovascular risk factors, inflammation, and atherosclerosis, and measurement of global gene expression, and identification of enriched biological pathways and molecular processes. Combination therapy reduced plasma cholesterol, plasma inflammation markers, and atherosclerosis stronger than the single drugs did. Systems biology analysis at the level of biological processes shows that the therapeutic benefit of combined therapy is largely the result of additivity of the complementary mechanisms of action of the two single drugs. Importantly, combination therapy also exerted a significant effect on 16 additional and mostly NF-κB-linked signaling processes, 11 of which tended to be regulated in a similar direction with monotherapy. CONCLUSION: This study shows that gene expression analysis together with bioinformatics pathway analysis has the potential to help predict and identify drug combination-specific complementary and side effects. © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins.