The hypolipidemic mechanism ofAlismatis rhizomawas exploredviaan integrated strategy combining network pharmacology with metabolomics and lipidomics for the first time.

Metabolic reprogramming is a common phenomenon in cancer, with aerobic glycolysis being one of its important characteristics. Hypoxia-inducible factor-1

Purpose: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML) model for predicting the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children. Methods: A retrospective study was performed on the clinical data and therapeutic drug monitoring (TDM) results of children initially treated for tuberculosis at the affiliated Changsha Central Hospital of University of South China. After the features were screened by univariate risk factor analysis, AutoML technology was used to establish predictive models. The area under the receiver operating characteristic curve (AUC) was used to evaluate model’s performance, and then the TreeShap algorithm was employed to interpret the variable contributions. Results: A total of 184 children were enrolled in this study, of whom 19 (10.33%) developed ATB-DILI. Univariate analysis showed that seven variables were risk factors for ATB-DILI, including the plasma peak concentration (C_max) of rifampicin, body mass index (BMI), alanine aminotransferase, total bilirubin, total bile acids, aspartate aminotransferase and creatinine. Among the numerous predictive models constructed by the “H2O” AutoML platform, the gradient boost machine (GBM) model exhibited the superior performance with AUCs of 0.838 and 0.784 on the training and testing sets, respectively. The TreeShap algorithm showed that C_max of rifampicin and BMI were important features that affect the AutoML model’s performance. Conclusion: The GBM model established by AutoML technology shows high predictive accuracy and interpretability for ATB-DILI in children. The prediction model can assist clinicians to implement timely interventions and mitigation strategies, and formulate personalized medication regimens, thereby minimizing potential harm to high-risk children of ATB-DILI.

Alismatis Rhizoma (AR) is widely used in Chinese medicine, and its major bioactive components, triterpenes, reportedly possess various pharmacological activities. Therefore, it is very important to study the metabolism of triterpenes in vivo. However, the metabolism of AR triterpene extract has not been comprehensively elucidated due to its complex chemical components and metabolic pathways. In this study, an ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry method, which was based on the characteristic ions from an established database of known triterpenes, was used to analyze the major metabolites in rats following the oral administration of Alismatis Rhizoma extracts (ARE). As a result, a total of 233 constituents, with 85 prototype compounds and 148 metabolites, were identified for the first time. Hydrogenation, oxidation, sulfate and glucuronidation conjugation were the major metabolic pathways for triterpenes in AR. In addition, the mutual in vivo transformation of known ARE triterpenes was discovered and confirmed for the first time. Those results provide comprehensive insights into the metabolism of AR in vivo, which will be useful for future studies on its pharmacodynamics and pharmacokinetics. Moreover, this established strategy may be useful in metabolic studies of similar compounds.

Alismatis rhizoma (AR) has been used as an herbal medicine in China for over a thousand years. Crude AR, salt-processed AR (SAR), and bran-processed AR (BAR) are recorded in the Pharmacopoeia of the People′s Republic of China. However, the differences of chemical composition between crude AR and its processing products remain limited. In this study, triterpenes were identified from crude AR, SAR, and BAR by ultra-high performance liquid chromatography coupled with quadrupole time-of-flight-mass spectrometer (UHPLC-QTOF-MS/MS). Subsequently, the differences of triterpenes between the crude AR and processed ARs were compared via a targeted metabolomics approach. Finally, a total of 114 triterpenes were identified, of which 83, 100, and 103 triterpenes were found in crude AR, SAR, and BAR, respectively. After salt-processing, there were 17 triterpenes newly generated, 7 triterpenes with trends of increasing, and 37 triterpenes decreased. Meanwhile, 56 triterpenes including 21 newly generated and 35 with significant increases were observed in BAR. This study could be benefit to investigate the processing mechanism of AR, as well as support their clinical applications.