title: Clarifying off-target effects for torcetrapib using network pharmacology and reverse docking approach.
abstract: BACKGROUND
  Torcetrapib, a cholesteryl ester transfer protein (CETP) inhibitor which raises high-density lipoprotein (HDL) cholesterol and reduces low-density lipoprotein (LDL) cholesterol level, has been documented to increase mortality and cardiac events associated with adverse effects. However, it is still unclear the underlying mechanisms of the off-target effects of torcetrapib.
  
  
  RESULTS
  In the present study, we developed a systems biology approach by combining a human reassembled signaling network with the publicly available microarray gene expression data to provide unique insights into the off-target adverse effects for torcetrapib. Cytoscape with three plugins including BisoGenet, NetworkAnalyzer and ClusterONE was utilized to establish a context-specific drug-gene interaction network. The DAVID functional annotation tool was applied for gene ontology (GO) analysis, while pathway enrichment analysis was clustered by ToppFun. Furthermore, potential off-targets of torcetrapib were predicted by a reverse docking approach. In general, 10503 nodes were retrieved from the integrative signaling network and 47660 inter-connected relations were obtained from the BisoGenet plugin. In addition, 388 significantly up-regulated genes were detected by Significance Analysis of Microarray (SAM) in adrenal carcinoma cells treated with torcetrapib. After constructing the human signaling network, the over-expressed microarray genes were mapped to illustrate the context-specific network. Subsequently, three conspicuous gene regulatory networks (GRNs) modules were unearthed, which contributed to the off-target effects of torcetrapib. GO analysis reflected dramatically over-represented biological processes associated with torcetrapib including activation of cell death, apoptosis and regulation of RNA metabolic process. Enriched signaling pathways uncovered that IL-2 Receptor Beta Chain in T cell Activation, Platelet-Derived Growth Factor Receptor (PDGFR) beta signaling pathway, IL2-mediated signaling events, ErbB signaling pathway and signaling events mediated by Hepatocyte Growth Factor Receptor (HGFR, c-Met) might play decisive characters in the adverse cardiovascular effects associated with torcetrapib. Finally, a reverse docking algorithm in silico between torcetrapib and transmembrane receptors was conducted to identify the potential off-targets. This screening was carried out based on the enriched signaling network analysis.
  
  
  CONCLUSIONS
  Our study provided unique insights into the biological processes of torcetrapib-associated off-target adverse effects in a systems biology visual angle. In particular, we highlighted the importance of PDGFR, HGFR, IL-2 Receptor and ErbB1tyrosine kinase might be direct off-targets, which were highly related to the unfavorable adverse effects of torcetrapib and worthy of further experimental validation.
authors.affiliation: State Key Laboratory of Natural and Biomimetic Drugs, Department of Pharmacology, School of Basic Medical Sciences, Peking University, Beijing, China.
authors.forename: Shengjun
authors.initials: S
authors.lastname: Fan
authors.forename: Qiang
authors.initials: Q
authors.lastname: Geng
authors.forename: Zhenyu
authors.initials: Z
authors.lastname: Pan
authors.forename: Xin
authors.initials: X
authors.lastname: Li
authors.forename: Lu
authors.initials: L
authors.lastname: Tie
authors.forename: Yan
authors.initials: Y
authors.lastname: Pan
authors.forename: Xuejun
authors.initials: X
authors.lastname: Li
mesh_terms: D066246:ErbB Receptors / Q000378:metabolism; D053263:Gene Regulatory Networks / Q000187:drug effects; D006801:Humans; D007376:Interleukin-2 / Q000378:metabolism; D062105:Molecular Docking Simulation; D011487:Protein Conformation; D019859:Proto-Oncogene Proteins c-met / Q000378:metabolism; D011804:Quinolines / Q000009:adverse effects* / Q000378:metabolism*; D011948:Receptors, Antigen, T-Cell / Q000378:metabolism; D015375:Receptors, Interleukin-2 / Q000737:chemistry / Q000378:metabolism; D017479:Receptors, Platelet-Derived Growth Factor / Q000378:metabolism; D015398:Signal Transduction / Q000187:drug effects; D049490:Systems Biology / Q000379:methods*; D059467:Transcriptome / Q000187:drug effects / Q000235:genetics
chemical_list: D007376:Interleukin-2; D011804:Quinolines; D011948:Receptors, Antigen, T-Cell; D015375:Receptors, Interleukin-2; C483909:torcetrapib; C512478:EGFR protein, human; D066246:ErbB Receptors; C491743:MET protein, human; D019859:Proto-Oncogene Proteins c-met; D017479:Receptors, Platelet-Derived Growth Factor