Abstract：
Utilization of in vitro (cellular) techniques, like Cell Painting and transcriptomics, could provide powerful tools for agrochemical candidate sorting and selection in the discovery process. However, using these models generates challenges translating in vitro concentrations to the corresponding in vivo exposures. Physiologically based pharmacokinetic (PBPK) modeling provides a framework for quantitative in vitro to in vivo extrapolation (IVIVE). We tested whether in vivo (rat liver) transcriptomic and apical points of departure (PODs) could be accurately predicted from in vitro (rat hepatocyte or human HepaRG) transcriptomic PODs or HepaRG Cell Painting PODs using PBPK modeling. We compared two PBPK models, the ADMET predictor and the httk R package, and found httk to predict the in vivo PODs more accurately. Our findings suggest that a rat liver apical and transcriptomic POD can be estimated utilizing a combination of in vitro transcriptome-based PODs coupled with PBPK modeling for IVIVE. Thus, high content in vitro data can be translated with modest accuracy to in vivo models of ultimate regulatory importance to help select agrochemical analogs in early stage discovery program.
摘要：
利用体外(细胞)技术，像细胞绘画和转录组学，可以为发现过程中的农药候选物分类和选择提供强大的工具。然而,使用这些模型会产生挑战，将体外浓度转化为相应的体内暴露。基于生理的药代动力学（PBPK）建模提供了定量体外至体内外推（IVIVE）的框架。我们测试了是否可以从体外（大鼠肝细胞或人HepaRG）转录组POD或使用PBPK建模的HepaRG细胞绘画POD准确预测体内（大鼠肝脏）转录组和顶端出发点（POD）。我们比较了两种PBPK模型，ADMET预测器和httkR包，并发现httk可以更准确地预测体内POD。我们的发现表明，可以利用基于体外转录组的POD与IVIVIVE的PBPK建模相结合来估计大鼠肝顶端和转录组POD。因此，高含量的体外数据可以以适度的准确性转化为具有最终监管重要性的体内模型，以帮助在早期发现程序中选择农用化学类似物。
