Identification of a metabolic biomarker panel in rats for prediction of acute and idiosyncratic hepatotoxicity.
Identification of a metabolic biomarker panel in rats for prediction of acute and idiosyncratic hepatotoxicity.
复制标题
鉴定大鼠代谢生物标志物组，用于预测急性和特异质肝毒性。
DOI：
10.1016/j.csbj.2014.08.001
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发表时间：
2014-07
影响因子：
6
通讯作者：
Beger, Richard
中科院分区：
文献类型：
--
作者：
Sun, Jinchun;Slavov, Svetoslav;Schnackenberg, Laura K;Ando, Yosuke;Greenhaw, James;Yang, Xi;Salminen, William;Mendrick, Donna L;Beger, Richard
据估计，10%的急性肝衰竭是由于“特异质肝毒性”。无法用肝毒性的经典临床前标志物鉴定此类化合物，因此需要发现基于机制的肝毒性生物标志物组。本研究包括7种化合物：2种明显肝毒性物质（对乙酰氨基酚和四氯化碳）、2种特异质肝毒性物质（非氨酯和丹曲林）和3种非肝毒性物质（美洛昔康、青霉素和二甲双胍）。雄性Sprague-Dawley大鼠经口管饲单剂量的媒介物、低剂量或高剂量的化合物。在给药后6 h和24 h，采集血液用于代谢组学和临床化学分析，同时采集器官用于组织病理学分析。对先前肝毒性研究中的41种代谢物进行了半定量，并用于建立模型以预测肝毒性。所选代谢物参与各种途径，已注意到这些途径与肝毒性的潜在机制有关。基于所有41种代谢物或6种（6 h）、7种（24 h）和20种（6 h和24 h）代谢物的较小子集的PLS模型产生的模型对于保持测试集的准确度至少为97.4%，对于训练集的准确度至少为100%。当应用于外部测试集时，PLS模型预测，在6小时和24小时用特异质肝毒物处理的9只大鼠中有1只暴露于肝毒性化学物质。总之，生物标志物组可以提供沿着其他终点数据（例如，转录组学和蛋白质组学）可以在临床环境中诊断急性和特异质肝毒性。
It has been estimated that 10% of acute liver failure is due to “idiosyncratic hepatotoxicity”. The inability to identify such compounds with classical preclinical markers of hepatotoxicity has driven the need to discover a mechanism-based biomarker panel for hepatotoxicity. Seven compounds were included in this study: two overt hepatotoxicants (acetaminophen and carbon tetrachloride), two idiosyncratic hepatotoxicants (felbamate and dantrolene), and three non-hepatotoxicants (meloxicam, penicillin and metformin). Male Sprague–Dawley rats were orally gavaged with a single dose of vehicle, low dose or high dose of the compounds. At 6 h and 24 h post-dosing, blood was collected for metabolomics and clinical chemistry analyses, while organs were collected for histopathology analysis. Forty-one metabolites from previous hepatotoxicity studies were semi-quantified and were used to build models to predict hepatotoxicity. The selected metabolites were involved in various pathways, which have been noted to be linked to the underlying mechanisms of hepatotoxicity. PLS models based on all 41 metabolite or smaller subsets of 6 (6 h), 7 (24 h) and 20 (6 h and 24 h) metabolites resulted in models with an accuracy of at least 97.4% for the hold-out test set and 100% for training sets. When applied to the external test sets, the PLS models predicted that 1 of 9 rats at both 6 h and 24 h treated with idiosyncratic liver toxicants was exposed to a hepatotoxic chemical. In conclusion, the biomarker panel might provide information that along with other endpoint data (e.g., transcriptomics and proteomics) may diagnose acute and idiosyncratic hepatotoxicity in a clinical setting.