Gao Yiqiao, Li Wei, Chen Jiaqing, Wang Xu, Lv Yingtong, Huang Yin, Zhang Zunjian, Xu Fengguo
Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), State Key Laboratory of Natural Medicine, China Pharmaceutical University, Nanjing 210009, China.
Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing 210009, China.
Acta Pharm Sin B. 2019 Jan;9(1):157-166. doi: 10.1016/j.apsb.2018.09.006. Epub 2018 Sep 12.
Pharmacometabolomics has been already successfully used in toxicity prediction for one specific adverse effect. However in clinical practice, two or more different toxicities are always accompanied with each other, which puts forward new challenges for pharmacometabolomics. Gastrointestinal toxicity and myelosuppression are two major adverse effects induced by Irinotecan (CPT-11), and often show large individual differences. In the current study, a pharmacometabolomic study was performed to screen the exclusive biomarkers in predose serums which could predict late-onset diarrhea and myelosuppression of CPT-11 simultaneously. The severity and sensitivity differences in gastrointestinal toxicity and myelosuppression were judged by delayed-onset diarrhea symptoms, histopathology examination, relative cytokines and blood cell counts. Mass spectrometry-based non-targeted and targeted metabolomics were conducted in sequence to dissect metabolite signatures in predose serums. Eventually, two groups of metabolites were screened out as predictors for individual differences in late-onset diarrhea and myelosuppression using binary logistic regression, respectively. This result was compared with existing predictors and validated by another independent external validation set. Our study indicates the prediction of toxicity could be possible upon predose metabolic profile. Pharmacometabolomics can be a potentially useful tool for complicating toxicity prediction. Our findings also provide a new insight into CPT-11 precision medicine.
药物代谢组学已成功应用于针对一种特定不良反应的毒性预测。然而,在临床实践中,两种或更多种不同的毒性总是相伴出现,这给药物代谢组学提出了新的挑战。胃肠道毒性和骨髓抑制是伊立替康(CPT - 11)引起的两种主要不良反应,且常表现出较大的个体差异。在本研究中,开展了一项药物代谢组学研究,以筛选给药前血清中的特异性生物标志物,这些标志物能够同时预测CPT - 11的迟发性腹泻和骨髓抑制。通过迟发性腹泻症状、组织病理学检查、相关细胞因子和血细胞计数来判断胃肠道毒性和骨髓抑制的严重程度及敏感性差异。依次进行基于质谱的非靶向和靶向代谢组学分析,以剖析给药前血清中的代谢物特征。最终,分别使用二元逻辑回归筛选出两组代谢物作为迟发性腹泻和骨髓抑制个体差异的预测指标。将该结果与现有的预测指标进行比较,并通过另一个独立的外部验证集进行验证。我们的研究表明,根据给药前的代谢谱预测毒性是可行的。药物代谢组学可能是一种用于复杂毒性预测的有用工具。我们的研究结果也为CPT - 11精准医学提供了新的见解。