The traditional Mongolian medicine Hunqile-7 (HQL-7), which is mainly used to relieve pain in clinic, has certain toxicity. Therefore, toxicological investigation of HQL-7 is of great significance to its safety assessment. In this study, the toxic mechanism of HQL-7 was explored based on a combination of metabolomics and intestinal flora metabolism. UHPLC-MS was used to analyze the serum, liver and kidney samples of rats after intragastric administration of HQL-7. The decision tree and K Nearest Neighbor (KNN) model were established based on the bootstrap aggregation (bagging) algorithm to classify the omics data. After samples were extracted from rat feces, the high-throughput sequencing platform was used to analyze the 16s rRNA V3-V4 region of bacteria. The experimental results confirm that the bagging algorithm improved the classification accuracy. The toxic dose, toxic intensity, and toxic target organ of HQL-7 were determined in toxicity tests. Seventeen biomarkers were identified and the metabolism dysregulation of these biomarkers may be responsible for the toxicity of HQL-7 in vivo. Several kinds of bacteria was demonstrated to be closely related to the physiological indices of renal and liver function, indicating liver and kidney damage induced by HQL-7 may be related to the disturbance of these intestinal bacteria. Overall, the toxic mechanism of HQL-7 was revealed in vivo, which not only provides a scientific basis for the safe and rational clinical use of HQL-7, but also opens up a new field of research on big data for Mongolian medicine.
Keywords: UHPLC–MS; bagging algorithm; intestinal flora; metabonomics; toxic mechanism; “Hunqile-7”.
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