Li Yubo, Ju Liang, Hou Zhiguo, Deng Haoyue, Zhang Zhenzhu, Wang Lei, Yang Zhen, Yin Jia, Zhang Yanjun
†Tianjin State Key Laboratory of Modern Chinese Medicine and ‡School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, 312 Anshan West Road, Tianjin 300193, China.
J Proteome Res. 2015 Jun 5;14(6):2437-45. doi: 10.1021/pr501116c. Epub 2015 May 18.
Drug-induced cardiotoxicity seriously affects human health and drug development. However, many conventional detection indicators of cardiotoxicity exhibit significant changes only after the occurrence of severe heart injuries. Therefore, we investigated more sensitive and reliable indicators for the evaluation and prediction of cardiotoxicity. We created rat cardiotoxicity models in which the toxicity was caused by doxorubicin (20 mg/kg), isoproterenol (5 mg/kg), and 5-fluorouracil (125 mg/kg). We collected data from rat plasma samples based on metabolomics using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry. Following multivariate statistical and integration analyses, we selected 39 biomarker ions of cardiotoxicity that predict cardiotoxicity earlier than biochemical analysis and histopathological assessment. Because drugs with different toxicities may cause similar metabolic changes compared with other noncardiotoxic models (hepatotoxic and nephrotoxic models), we obtained 10 highly specific biomarkers of cardiotoxicity. We subsequently used a support vector machine (SVM) to develop a predictive model to verify and optimize the exclusive biomarkers. l-Carnitine, 19-hydroxydeoxycorticosterone, LPC (14:0), and LPC (20:2) exhibited the strongest specificities. The prediction rate of the SVM model is as high as 90.0%. This research provides a better understanding of drug-induced cardiotoxicity in drug safety evaluations and secondary development and demonstrates novel ideas for verification and optimization of biomarkers via metabolomics.
药物性心脏毒性严重影响人类健康和药物研发。然而，许多传统的心脏毒性检测指标只有在严重心脏损伤发生后才会出现显著变化。因此，我们研究了更敏感、可靠的指标用于心脏毒性的评估和预测。我们建立了大鼠心脏毒性模型，其毒性由阿霉素（20毫克/千克）、异丙肾上腺素（5毫克/千克）和5-氟尿嘧啶（125毫克/千克）引起。我们基于代谢组学，使用超高效液相色谱四极杆飞行时间质谱从大鼠血浆样本中收集数据。经过多变量统计和整合分析，我们筛选出39个心脏毒性生物标志物离子，这些标志物比生化分析和组织病理学评估能更早地预测心脏毒性。由于与其他非心脏毒性模型（肝毒性和肾毒性模型）相比，具有不同毒性的药物可能会引起相似的代谢变化，我们获得了10个高度特异性的心脏毒性生物标志物。随后，我们使用支持向量机（SVM）建立预测模型来验证和优化这些独特的生物标志物。左旋肉碱、19-羟基脱氧皮质酮、LPC（14:0）和LPC（20:2）表现出最强的特异性。SVM模型的预测率高达90.0%。本研究为药物安全性评价和二次研发中更好地理解药物性心脏毒性提供了依据，并展示了通过代谢组学验证和优化生物标志物的新思路。
