Fang, Junwei; Wang, Liping; Wang, Yang; Qiu, Mingfeng; Zhang, Yongyu published the artcile< Metabolomics combined with pattern recognition and bioinformatics analysis methods for the development of pharmacodynamic biomarkers on liver fibrosis>, Related Products of 112-63-0, the main research area is liver fibrosis pharmacodynamic biomarker metabolomics bioinformatics analysis method.
The major obstacle for the development of targeted therapies is the lack of pharmacodynamic (PD) biomarkers to provide an early readout of biol. activities. As the modulation of metabolites may reflect the biol. changes occurring 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 i.p. injection of CCl4 twice weekly for 6 wk, the treatment of total aglycon extracts of Scutellaria baicalensis (TAES) was begun 4 wk after the modeling, and gas chromatog.-mass spectrometry (GC-MS) based metabolomics combined with pattern recognition and network anal. were carried out for the research on PD biomarkers of TAES on liver fibrosis. After 2 wk of treatment, TAES shows pos. effects on CCl4-induced liver fibrosis. In the metabolomics study, 63 urinary metabolites contributing 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. By topol. anal., 6 metabolites with high centrality in the metabolic sub-network were selected as potential PD biomarkers. Within 24 h of the final administration, the 6 identified urine metabolic biomarkers with response to time variation 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 pharmacol. effect of target drugs, and the information from PD biomarkers can be combined with pharmacokinetics to select the optimal dose and a schedule of administration for the drugs.
Molecular BioSystems published new progress about Aglycons Role: BSU (Biological Study, Unclassified), BIOL (Biological Study). 112-63-0 belongs to class esters-buliding-blocks, and the molecular formula is C19H34O2, Related Products of 112-63-0.
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