Metabolomics combined with pattern recognitionrnand bioinformatics analysis methods for the development of pharmacodynamic biomarkers onrnliver fibrosis

摘要

The major obstacle for the development of targeted therapies is the lack of pharmacodynamic (PD)rnbiomarkers to provide an early readout of biological activities. As the modulation of metabolites mayrnreflect the biological changes occurring in the targets, metabolomics is promising to be an efficientrnway to explore PD biomarkers. In the present study, a liver fibrosis rat model was established byrnintraperitoneal injection of CCl4 twice weekly for 6 weeks, the treatment of total aglycone extracts ofrnScutellaria baicalensis (TAES) was begun 4 weeks after the modeling, and gas chromatography-massrnspectrometry (GC-MS) based metabolomics combined with pattern recognition and network analysisrnwere carried out for the research on PD biomarkers of TAES on liver fibrosis. After 2 weeks of treatment,rnTAES shows positive effects on CCl4-induced liver fibrosis. In the metabolomics study, 63 urinaryrnmetabolites contributing to liver fibrosis were identified. Six metabolic pathways significantly enriched inrnmetabolomics data were mapped onto a network to determine global patterns of metabolic alterationsrnin liver fibrosis. By topological analysis, 6 metabolites with high centrality in the metabolic sub-networkrnwere selected as potential PD biomarkers. Within 24 h of the final administration, the 6 identifiedrnurine metabolic biomarkers with response to time variation of TAES were validated as PD biomarkers.rnThis integrative study presents an attractive strategy to explore PD biomarkers, which may give insightrninto the actual pharmacological effect of target drugs, and the information from PD biomarkers canrnbe combined with pharmacokinetics to select the optimal dose and a schedule of administration forrnthe drugs.