An integrated strategy by using target tissue metabolomics biomarkers as pharmacodynamic surrogate indices to screen antipyretic components of Qingkaikling injection

Abstract

Traditional Chinese medicine (TCM) treatment can be valuable therapeutic strategies. However, the active components and action mechanisms that account for its therapeutic effects remain elusive. Based on the hypothesis that the components of a formula which exert effect would be measurable in target tissue, a target tissue metabolomics-based strategy was proposed for screening of antipyretic components in Qingkaikling injection (QKLI). First, we detected the components of QKLI which could reach its target tissue (hypothalamus) by determining the hypothalamus microdialysate and discovered that only baicalin and geniposide could be detected. Then, by conducting hypothalamus metabolomics studies, 14 metabolites were screened as the potential biomarkers that related to the antipyretic mechanisms of QKLI and were used as its pharmacodynamic surrogate indices. Subsequently, the dynamic concentration of baicalin and geniposide in hypothalamus microdialysates and biomarkers in hypothalamus were measured and correlated with each other. The results indicated that only baicalin shown a good correlation with these biomarkers. Finally, a network pharmacology approach was established to validate the antipyretic activity of baicalin and the results elucidated its antipyretic mechanisms as well. The integrated strategy proposed here provided a powerful means for identifying active components and mechanisms contributing to pharmacological effects of TCM.

Figure 1

The flow diagram of identifying antipyretic components and mechanisms of QKLI by using target tissue metabolomics biomarkers as pharmacodynamic surrogate indices.

Figure 2

Hypothalamus microdialysis concentration-time profiles of baicalin and geniposidein in CG and MG rats after intravenous administration of QKLI.

Figure 3

PCA scores plot among CG, MG, and TG rats at different sampling time points. The plot showed a time dependent trajectory of hypothalamus metabolites which clustered at different spatial positions. (a) At positive ion mode. (b) At negative ion mode.

Figure 4

PLS-DA score plot obtained from CG (), MG (), and TG at 3 h (). (a) At positive ion mode. (b) At negative ion mode.

Figure 5

Hypothalamus microdialysates concentration−time courses of baicalin and geniposide in TG rats after intravenous administration of QKLI.

Figure 6

The metabolic network of baicalin (Yellow indicates the baicalin-related targets, green indicates the verified biomarkers and red indicates the fever-related molecules).

Figure 7

The metabolic pathway of baicalin-related targets relate to verified biomarkers and fever-related molecules. (a) The metabolic pathway of baicalin-related target CASP3 relate to fever-related molecule PGF2α (Yellow indicates CASP3, green indicates IMP and N6-(1,2-dicarboxyethyl)-AMP and red indicates PGF2α); (b) Metabolic pathway of baicalin-related target CASP3 relate to fever-related molecule NO (Yellow indicates CASP3, green indicates IMP and N6-(1,2-dicarboxyethyl)-AMP and blue indicates fever-related molecule NO); (c) The metabolic pathway of baicalin-related target HIF-1α relate to fever-related small molecule cAMP (Yellow indicates HIF-1α, green indicates N6-(1,2-dicarboxyethyl)-AMP and azure indicates cAMP); (d) Metabolic pathway of baicalin-related target HIF-1α to fever-related molecule PGE2, PGF2α (Yellow indicates HIF-1α, green indicates IMP and N6-(1,2-dicarboxyethyl)-AMP and red indicates PGE2, PGF2α).

Figure 8

Metabolic pathways of ATP and the verified biomarkers (Yellow indicates ATP, blue indicates UDP-D-galactose, rose red indicates ADP–D-ribose, pink indicates IMP and GMP and green indicates N6-(1,2-dicarboxyethyl)-AMP).