How robust are transcriptomics-based points of departure when translated to in vivo doses by kinetic-modeling? A case study with the herbicide pendimethalin using HepG2 and HepaRG cells

Risk assessment of agrochemicals has traditionally relied on animal studies to derive points of departure (PODs) for the establishment of regulatory guidance values. More recently, new approach methodologies (NAMs) have been explored as a means to estimate protective exposure levels without generating new animal data. In the present case study, we evaluated whether a transcriptomics-based NAM workflow combined with physiologically-based kinetic (PBK) modeling can provide biologically plausible estimates of systemic toxicity thresholds for pendimethalin (PDM), an agrochemical with extensive toxicological and kinetic reference data. We compared two in vitro liver models and transcriptomic approaches: targeted TempO-Seq in HepG2 cells and untargeted RNA sequencing in HepaRG cells. After 48-hour exposure to PDM in concentration ranges of 0.07-202 µM (HepaRG) and 0.8-56 µM (HepG2), gene expression changes were analyzed using benchmark-dose modeling in BMDExpress 3.0. Transcriptomic PODs (tPODs) were derived using gene- and distribution-based approaches and subsequently translated to external doses by in vitro-to-in vivo extrapolation (IVIVE) employing three rat PBK models. The resulting PODs were compared with regulatory in vivo PODs reported for PDM. In addition, a human population-based PBK model was used to derive an oral equivalent dose (OED) from the most sensitive transcriptomic POD. Across transcriptomic platforms, tPOD derivation methods, and PBK models, the resulting IVIVE-derived PODs generally fell within the range of regulatory in vivo PODs reported for PDM. The most sensitive prediction yielded a POD of 6.0 mg/kg bw/day for the rat, while human population-based modeling resulted in a 5 ^th -percentile OED of 0.068 mg/kg bw/day, close to currently established regulatory guidance values. Variability introduced by the toxicokinetic models has a comparable impact as the variability that is associated with the different transcriptomic analysis approaches. Across transcriptomic platforms, tPOD derivation methods, and PBK models, the resulting IVIVE-derived PODs generally fell within the range of regulatory in vivo PODs reported for PDM. The most sensitive prediction yielded a POD of 6.0 mg/kg bw/day for the rat, while human population-based modeling resulted in a 5 ^th -percentile OED of 0.068 mg/kg bw/day, close to currently established regulatory guidance values. Notably, the variability introduced by the choice of toxicokinetic model was of a similar magnitude as the variability associated with transcriptomic POD derivation, indicating that uncertainty in IVIVE contributes at least as much to the final prediction as uncertainty in transcriptomic data analysis. Overall, this study supports the feasibility of combining in vitro transcriptomics with IVIVE to estimate systemic toxicity thresholds for an agrochemical compound. The results encourage further evaluation of transcriptomics-based NAM workflows and next-generation risk assessment of agrochemicals. Assessment of a broader range of agrochemicals, together with continued refinement of toxicokinetic modeling and in vitro dosimetry, will be required to establish their predictive performance and future regulatory applicability.