Jump to main content
Jump to site search


Metabolomics Combined with Pattern Recognition and Bioinformatics Analysis Methods for the Pharmacodynamic Biomarkers Development on Liver Fibrosis

Abstract

The major obstacle for the development of targeted therapies is lacking of pharmacodynamics (PD) biomarkers to provide an early read out of biological activity. As the modulation of metabolites may reflect the biological changes occurred in the targets, metabolomics is promising to be an efficient way to explore PD biomarkers. In the present study, a liver fibrosis rat model was established by intraperitoneal injection of CCl4 twice weekly for 6 weeks, treatment of total aglycone extracts of scutellaria baicalensis (TAES) was began 4 weeks after the modeling, gas chromatography-mass spectrometry (GC-MS) based metabolomics combined with pattern recognition and network analysis were guided for searching of PD biomarkers of TAES on liver fibrosis. After 2 weeks of treatment, TAES shows positive effects on CCl4-induced liver fibrosis. In metabolomics study, 63 urinary metabolites contributed to liver fibrosis were identified. Six metabolic pathways significantly enriched in metabolomics data were mapped onto a network to determine global patterns of metabolic alterations in liver fibrosis. And by topological analysis, 6 metabolites with high centrality in metabolic sub-network were selected as potential PD biomarkers. Within 24 h after the final administration, the 6 identified urine metabolic biomarkers with the response to the time-varying of TAES were validated as PD biomarkers. This integrative study presents an attractive strategy to explore PD biomarkers which may give insight into the actual pharmacological effect of target drugs, and the information from PD biomarkers can be combined with pharmacokinetics to select optimal dose and schedule of administration of the drugs.

Back to tab navigation
Please wait while Download options loads

Supplementary files

Publication details

The article was received on 16 Feb 2017, accepted on 30 Apr 2017 and first published on 11 May 2017


Article type: Paper
DOI: 10.1039/C7MB00093F
Citation: Mol. BioSyst., 2017, Accepted Manuscript
  •   Request permissions

    Metabolomics Combined with Pattern Recognition and Bioinformatics Analysis Methods for the Pharmacodynamic Biomarkers Development on Liver Fibrosis

    J. Fang, L. Wang, Y. Wang, M. Qiu and Y. Zhang, Mol. BioSyst., 2017, Accepted Manuscript , DOI: 10.1039/C7MB00093F

Search articles by author