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. Overall design: RNAseq transcriptomics profiling on Rat primary hepatocytes and Human HepaRG after treatments with a set of agrochemicals, with known developmental toxicity endpoints, at multiple doses

体外（in vitro）细胞技术，例如细胞绘画（Cell Painting）及基于转录组学的分析方法，可为风险评估与监管决策提供强有力的研究工具。然而，应用此类模型时，存在将体外暴露浓度转化为对应体内（in vivo）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），验证了能否基于体外（大鼠肝细胞或人源HepaRG细胞）的转录组学基准点（point of departure, POD）或HepaRG细胞绘画基准点，精准预测体内（大鼠肝脏）的转录组学与顶端终点基准点（PODs）。本研究对比了两款PBPK模型：ADMET预测器与httk R软件包，结果显示httk软件包可更精准地预测由大鼠肝细胞未观察到转录影响水平（no observed transcriptional effect level, NOTEL）推导得到的基准点。研究结果表明，结合基于体外转录组学的基准点与用于体外-体内外推（in vitro-in vivo extrapolation, IVIVE）的PBPK建模，可估算得到大鼠肝脏的顶端终点与转录组学基准点。实验整体设计：采用一系列具备明确发育毒性终点的农用化学品，以多个剂量处理大鼠原代肝细胞与人源HepaRG细胞后，开展RNA测序转录组学分析。