Drees Annika, Nassiri Vahid, Tabernilla Andrés, Serroyen Jan, Gustin Emmanuel, Dos Santos Rodrigues Bruna, Moss Darren Michael, De Smedt Ann, Vinken Mathieu, Van Goethem Freddy, Sanz-Serrano Julen
Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Belgium.
Open Analytics, Belgium.
Toxicology. 2025 Jun;514:154119. doi: 10.1016/j.tox.2025.154119. Epub 2025 Mar 17.
Cholestatic drug-induced liver injury (cDILI) is a frequent reason for drug failure and withdrawal during premarketing and postmarketing stages of drug development. Strategies for reliable detection of cDILI in early drug development are therefore urgently needed. The drug-induced cholestasis index (DICI) concept was previously introduced as a tool for assessing the cholestatic potential of drug candidates. DICI is calculated as the ratio between the viability values obtained in drug-treated liver cells in the presence and absence of bile acids. The present in vitro study was set up to investigate the applicability of DICI in a novel high-throughput and large sample setting. Furthermore, the improvement of the predictivity of the DICI by introduction of advanced modeling was explored. Fifty-eight well-documented drugs were selected and categorized as drugs inducing cDILI, non-cholestatic DILI (ncDILI), and not inducing DILI (non-DILI). Cultures of human hepatoma HepaRG cells in 3D spheroid configuration were exposed to 9 half-log concentrations of each drug for 1, 3 and 7 days in the absence or presence of a concentrated mixture of human bile acids. The highest concentration of each drug was based on solubility and the maximum concentrations in human plasma (total Cmax). DICI values were computed for all drugs and time points. In addition, the area under the curve ratio and the occurrence of a trend in the cytotoxicity profiles were included as modeling descriptors. As such, 3 time-related scenarios were considered upon modeling, while categories were modeled on a nominal or an ordinal scale. Applying DICI with a cut-off value of 0.8 resulted in a high sensitivity for the cDILI class, but in turn, a low sensitivity for the non- DILI class. From the 28 predictive models generated, the best performing models integrated all descriptors and the ordinal scale for either the 7-day time point from a 3-time-point model or the 3-day time point. While these models were unable to accurately identify ncDILI drugs, the 7-day time point identified 84 % of the cDILI drugs and the 3-day time point correctly identified 94 % of non-DILI drugs. Based on the obtained results, it can be concluded that the reported DICI modeling provides an optimized approach that could be applied in an integrated DILI testing strategy.
胆汁淤积性药物性肝损伤（cDILI）是药物在上市前和上市后研发阶段失败及撤市的常见原因。因此，迫切需要在药物研发早期可靠检测cDILI的策略。药物诱导胆汁淤积指数（DICI）概念此前被引入作为评估候选药物胆汁淤积潜力的工具。DICI计算为在有和无胆汁酸存在的情况下药物处理的肝细胞中获得的活力值之比。本体外研究旨在调查DICI在新型高通量和大样本设置中的适用性。此外，还探索了通过引入先进建模来提高DICI预测性的方法。选择了58种有充分文献记载的药物，并将其分类为诱导cDILI的药物、非胆汁淤积性药物性肝损伤（ncDILI）药物和不诱导药物性肝损伤（非DILI）的药物。将人肝癌HepaRG细胞以3D球体构型培养，在不存在或存在人胆汁酸浓缩混合物的情况下，将每种药物的9个半对数浓度暴露1、3和7天。每种药物的最高浓度基于溶解度和人血浆中的最大浓度（总Cmax）。计算所有药物和时间点的DICI值。此外，曲线下面积比和细胞毒性谱中趋势的出现作为建模描述符。因此，在建模时考虑了3种与时间相关的情况，而类别则按名义或有序尺度建模。应用截断值为0.8的DICI对cDILI类别具有较高的敏感性，但对非DILI类别则具有较低的敏感性。从生成的28个预测模型中，性能最佳的模型整合了所有描述符以及来自3时间点模型的7天时间点或3天时间点的有序尺度。虽然这些模型无法准确识别ncDILI药物，但7天时间点识别出了84%的cDILI药物，3天时间点正确识别出了94%的非DILI药物。根据获得的结果，可以得出结论，所报道的DICI建模提供了一种可应用于综合药物性肝损伤测试策略的优化方法。
