Applications for estimation of in vivo toxicity point of departure for discovery stage molecule predictive safety assessment

Utilization of in vitro (cellular) techniques, like Cell Painting and transcriptomics-based methods could provide powerful tools for risk assessment and regulatory decision-making. However, using these models generates challenges translating in vitro concentrations to corresponding in vivo internal exposures. We tested whether in vivo (rat liver) transcriptional and apical points of departure (PODs) could be accurately predicted from in vitro (rat hepatocyte or human HepaRG) transcriptional PODs or HepaRG Cell Painting PODs using PBPK modeling. We compared two PBPK models, ADMET predictor and the httk R package, and found httk to predict the rat hepatocyte no observed transcriptional effect level (NOTEL)-derived PODs more accurately. Our findings suggest that a rat liver apical and transcriptome POD can be estimated utilizing a combination of in vitro transcriptome-based PODs coupled with PBPK modeling for IVIVE.体外（细胞）技术的应用——诸如细胞绘画（Cell Painting）与基于转录组学（transcriptomics）的方法——可为风险评估与监管决策提供强有力的技术工具。然而，应用此类模型时，存在将体外浓度换算为对应体内内暴露水平的技术挑战。本研究借助生理药代动力学建模（PBPK modeling），旨在探究能否通过体外（大鼠肝细胞或人源HepaRG细胞）的转录与顶点起始点（points of departure, PODs），或是HepaRG细胞绘画PODs，精准预测体内（大鼠肝脏）的转录与顶点起始点PODs。本研究对比了两款PBPK模型：ADMET预测器（ADMET predictor）与httk R包（httk R package），结果显示httk R包对基于大鼠肝细胞转录未观察到效应水平（no observed transcriptional effect level, NOTEL）得到的PODs预测精度更高。本研究结果表明，结合基于体外转录组学的PODs与用于体外-体内外推（IVIVE）的PBPK建模，即可估算大鼠肝脏的顶点起始点与转录组PODs。