目的考察前期发现的肾毒性小分子代谢标志物在中药毒性评价中的适用性。方法以已知具有肾毒性的5种中药雷公藤、马钱子、广防己、大黄和苍耳子提取液ig给予大鼠建立肾脏损伤模型,收集给药1和7 d后的血样,应用超高效液相色谱-质谱联用（UPLC/Q-TOF-MS）检测5种肾毒性生物标志物胸苷、溶血磷脂酰胆碱LPC（16：1）、LPC（18：4）、LPC（20：5）和LPC（22：5）水平,建立支持向量机（SVM）预测模型对其毒性进行判断；全自动生化仪测定血清中Cr和BUN的水平；各组大鼠于取血后处死,迅速取肾脏进行HE染色,光学显微镜下观察病理表现。结果对照组没有表现出毒性。5种中药在给药1 d后,生化检测没有发现肾脏损伤,肾毒性支持向量机预测模型发现异常；在给药7天后,SVM的预测结果与生化和病理检测结果一致,均出现肾毒性。结论代谢组学技术结合支持向量机模型可将肾毒性小分子代谢物更加灵敏、快速、准确地用于中药肾脏毒性评价,对于临床药源性肾损伤的防治具有重要意义。

Objective To evaluate the applicability of small molecular markers of nephrotoxicity that in prediction of drug toxicity. Method Extracts of five kinds of traditional Chinese medicines (Tripterygium wilfordii,Strychni semen,Aristolochia fangchi,Rhei Radix et Rhizoma,and Xanthium sibiricum) that had known as nephrotoxicity were ig given to rats to establish renal injury models, and the blood samples were collected after administration for 1 and 7 d.Then blood samples were analyzed by UPLC/Q-TOF-MS for five kinds of small molecule biomarkers——thymidine,lyso-phosphatidylcholine (LPC 16:1),LPC (18:4),LPC (20:5),and LPC (22:5).The support vector machine (SVM) prediction model was established to determine the toxicity.The levels of Cr and BUN in serum were determined by automatic biochemical analyzer.The rats in each group were sacrificed after blood collection,and the kidneys were taken for HE staining.Result No toxicity was observed in the control group,and the biochemical test results showed no renal injury after mentioned five kinds Chinese herbs were given for 1 d,while SVM model of nephrotoxicity had been found abnormal.After administration for 7 d,the results of SVM model show renal toxicity,which were consistent with biochemical and pathological examination.Conclusion Metabonomics combined with the earlier established SVM model enabled prediction of drug nephrotoxicity more sensitively,quickly and accurately,and it is of great significance for the discovery of drug toxicity as well as the prevention and treatment of drug-induced renal injuries in clinic.

国家科技支撑计划课题（2011BAI07B08）；国家自然科学基金资助项目（81573835）