Sheng Wei, Sun Runbin, Zhang Ran, Xu Peng, Wang Youmei, Xu Hui, Aa Jiye, Wang Guangji, Xie Yuan
China Pharmaceutical University Nanjing Drum Tower Hospital, Nanjing 210000, China.
Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China.
Metabolites. 2022 Dec 10;12(12):1250. doi: 10.3390/metabo12121250.
Methamphetamine (METH) abuse has become a global public health and safety problem. More information is needed to identify the time of drug abuse. In this study, methamphetamine was administered to male C57BL/6J mice with increasing doses from 5 to 30 mg kg (once a day, i.p.) for 20 days. Serum and urine samples were collected for metabolomics studies using gas chromatography-mass spectrometry (GC-MS). Six machine learning models were used to infer the time of drug abuse and the best model was selected to predict administration time preliminarily. The metabolic changes caused by methamphetamine were explored. As results, the metabolic patterns of methamphetamine exposure mice were quite different from the control group and changed over time. Specifically, serum metabolomics showed enhanced amino acid metabolism and increased fatty acid consumption, while urine metabolomics showed slowed metabolism of the tricarboxylic acid (TCA) cycle, increased organic acid excretion, and abnormal purine metabolism. Phenylalanine in serum and glutamine in urine increased, while palmitic acid, 5-HT, and monopalmitin in serum and gamma-aminobutyric acid in urine decreased significantly. Among the six machine learning models, the random forest model was the best to predict the exposure time (serum: MAE = 1.482, RMSE = 1.69, R squared = 0.981; urine: MAE = 2.369, RMSE = 1.926, R squared = 0.946). The potential biomarker set containing four metabolites in the serum (palmitic acid, 5-hydroxytryptamine, monopalmitin, and phenylalanine) facilitated the identification of methamphetamine exposure. The random forest model helped predict the methamphetamine exposure time based on these potential biomarkers.
甲基苯丙胺（METH）滥用已成为一个全球性的公共卫生与安全问题。需要更多信息来确定药物滥用的时间。在本研究中，以5至30毫克/千克的递增剂量（每天一次，腹腔注射）给雄性C57BL/6J小鼠注射甲基苯丙胺，持续20天。收集血清和尿液样本，使用气相色谱 - 质谱联用仪（GC - MS）进行代谢组学研究。使用六种机器学习模型来推断药物滥用时间，并选择最佳模型初步预测给药时间。探讨了甲基苯丙胺引起的代谢变化。结果显示，甲基苯丙胺暴露小鼠的代谢模式与对照组有很大不同，且随时间变化。具体而言，血清代谢组学显示氨基酸代谢增强、脂肪酸消耗增加，而尿液代谢组学显示三羧酸（TCA）循环代谢减缓、有机酸排泄增加以及嘌呤代谢异常。血清中的苯丙氨酸和尿液中的谷氨酰胺增加，而血清中的棕榈酸、5 - 羟色胺和单棕榈酸甘油酯以及尿液中的γ - 氨基丁酸显著减少。在六种机器学习模型中，随机森林模型在预测暴露时间方面表现最佳（血清：平均绝对误差 = 1.482，均方根误差 = 1.69，决定系数 = 0.981；尿液：平均绝对误差 = 2.369，均方根误差 = 1.926，决定系数 = 0.946）。血清中包含四种代谢物（棕榈酸、5 - 羟色胺、单棕榈酸甘油酯和苯丙氨酸）的潜在生物标志物集有助于识别甲基苯丙胺暴露情况。随机森林模型基于这些潜在生物标志物有助于预测甲基苯丙胺暴露时间。
