Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations
Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations
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DOI：
10.1007/s00204-019-02492-9
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发表时间：
2019-06-01
影响因子：
6.1
通讯作者：
Hengstler, Jan G.
中科院分区：
文献类型：
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作者：
Albrecht, Wiebke;Kappenberg, Franziska;Hengstler, Jan G.
药物性肝损伤（DILI）不能通过动物模型准确预测。此外，目前可用的体外方法无法估计肝毒性剂量或确定每日可接受摄入量（ADI）。为了克服这一局限性，建立了一种体外/计算机模拟方法，预测与口服剂量和血液浓度相关的人DILI风险。如果已知供试化合物的最大血药浓度（C-max），则该方法可用于估计DILI风险。此外，即使没有关于血液浓度的信息，也可以估计化合物的ADI。为了系统地优化体外系统，引入了两个新的测试性能指标，毒性分离指数（TSI），其量化了测试区分肝毒性和非肝毒性化合物的程度，以及毒性估计指数（TEI），其测量了体内肝毒性血液浓度的估计程度。基于TSI和TEI，针对28种化合物的训练集优化了体外测试性能，证明（1）细胞毒性在体外首次变得明显的浓度（EC 10）产生比更高毒性阈值（EC 50）更好的度量;（2）化合物孵育48小时优于24小时，孵育7天后TSI没有进一步改善;（3）通过将基因表达添加到测试组合中，度量得到适度改善;（4）药代动力学参数的评价表明，总血液化合物浓度和基于95%人群的C-max百分位数最适合估计人体毒性。以EC 10和C-max为变量，采用基于支持向量机的分类器，对肝毒性预测的交叉验证敏感性、特异性和准确性分别为100%、88%和93%。培养基中的浓度允许外推至与肝毒性特定概率相关的体内血液浓度，并通过反向建模获得相应的经口给药剂量。将该体外/计算机模拟方法应用于大鼠肝毒性胡薄荷酮，得到的ADI与先前基于动物实验确定的值相似。总之，拟定方法将供试化合物的口服剂量和血液浓度与肝毒性概率联系起来。
Drug-induced liver injury (DILI) cannot be accurately predicted by animal models. In addition, currently available in vitro methods do not allow for the estimation of hepatotoxic doses or the determination of an acceptable daily intake (ADI). To overcome this limitation, an in vitro/in silico method was established that predicts the risk of human DILI in relation to oral doses and blood concentrations. This method can be used to estimate DILI risk if the maximal blood concentration (C-max) of the test compound is known. Moreover, an ADI can be estimated even for compounds without information on blood concentrations. To systematically optimize the in vitro system, two novel test performance metrics were introduced, the toxicity separation index (TSI) which quantifies how well a test differentiates between hepatotoxic and non-hepatotoxic compounds, and the toxicity estimation index (TEI) which measures how well hepatotoxic blood concentrations in vivo can be estimated. In vitro test performance was optimized for a training set of 28 compounds, based on TSI and TEI, demonstrating that (1) concentrations where cytotoxicity first becomes evident in vitro (EC10) yielded better metrics than higher toxicity thresholds (EC50); (2) compound incubation for 48h was better than 24h, with no further improvement of TSI after 7days incubation; (3) metrics were moderately improved by adding gene expression to the test battery; (4) evaluation of pharmacokinetic parameters demonstrated that total blood compound concentrations and the 95%-population-based percentile of C-max were best suited to estimate human toxicity. With a support vector machine-based classifier, using EC10 and C-max as variables, the cross-validated sensitivity, specificity and accuracy for hepatotoxicity prediction were 100, 88 and 93%, respectively. Concentrations in the culture medium allowed extrapolation to blood concentrations in vivo that are associated with a specific probability of hepatotoxicity and the corresponding oral doses were obtained by reverse modeling. Application of this in vitro/in silico method to the rat hepatotoxicant pulegone resulted in an ADI that was similar to values previously established based on animal experiments. In conclusion, the proposed method links oral doses and blood concentrations of test compounds to the probability of hepatotoxicity.