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Clarifying off-target effects for torcetrapib using network pharmacology and reverse docking approach
Shengjun Fan1, Qiang Geng, Zhenyu Pan
1State Key Laboratory of Natural and Biomimetic Drugs, Department of Pharmacology, School of Basic Medical Sciences, Peking University, Beijing, China.
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.
Insights
Torcetrapib, a CETP inhibitor, causes adverse effects. Systems biology identified Platelet-Derived Growth Factor Receptor and Hepatocyte Growth Factor Receptor as potential off-targets, warranting further study.
Area of Science:
- Pharmacology
- Systems Biology
- Bioinformatics
Background:
- Torcetrapib, a cholesteryl ester transfer protein (CETP) inhibitor, increases HDL cholesterol and decreases LDL cholesterol.
- Torcetrapib has been linked to increased mortality and cardiac events.
- The precise mechanisms behind torcetrapib's off-target adverse effects remain unclear.
Purpose of the Study:
- To elucidate the underlying mechanisms of torcetrapib's off-target adverse effects.
- To identify potential molecular targets responsible for torcetrapib's toxicity.
- To utilize a systems biology approach for comprehensive analysis.
Main Methods:
- Developed a systems biology approach integrating human signaling networks and gene expression data.
- Utilized Cytoscape with plugins (BisoGenet, NetworkAnalyzer, ClusterONE) for network construction.
- Applied DAVID and ToppFun for gene ontology and pathway enrichment analysis, and reverse docking for off-target prediction.
Main Results:
- Constructed a large-scale human signaling network (10503 nodes, 47660 relations).
- Identified 388 significantly up-regulated genes in torcetrapib-treated adrenal carcinoma cells.
- Uncovered three key gene regulatory network modules contributing to adverse effects.
- Functional analysis revealed over-representation in cell death, apoptosis, and RNA metabolic processes.
- Pathway analysis implicated PDGFR, HGFR, IL-2 Receptor, and ErbB signaling in adverse cardiovascular effects.
Conclusions:
- Systems biology provides novel insights into torcetrapib's off-target adverse effects.
- Platelet-Derived Growth Factor Receptor (PDGFR), Hepatocyte Growth Factor Receptor (HGFR), IL-2 Receptor, and ErbB tyrosine kinase are highlighted as potential direct off-targets.
- These identified targets require further experimental validation for their role in torcetrapib's toxicity.
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