Impact of High-Throughput Model Parameterization and Data Uncertainty on Thyroid-Based Toxicological Estimates for Pesticide Chemicals

Chemical-induced alteration of maternal thyroid hormone levels may increase the risk of adverse neurodevelop-mental outcomes in offspring. US federal risk assessments relyalmost exclusively on apical endpoints in animal models forderiving points of departure (PODs). New approach method-ologies (NAMs) such as high-throughput screening (HTS) andmechanistically informative in vitro human cell-based systems,combined with in vitro to in vivo extrapolation (IVIVE),supplement in vivo studies and provide an alternative approachto calculate/determine PODs. We examine how parameterizationof IVIVE models impacts the comparison between IVIVE-derivedequivalent administered doses (EADs) from thyroid-relevant invitro assays and the POD values that serve as the basis for riskassessments. Pesticide chemicals with thyroid-based in vitro bioactivity data from the US Tox21 HTS program were included (n=45). Depending on the model structure used for IVIVE analysis, up to 35 chemicals produced EAD values lower than the POD. Atotal of 10 chemicals produced EAD values higher than the POD regardless of the model structure. The relationship between IVIVE-derived EAD values and the in vivo-derived POD values is highly dependent on model parameterization. Here, we derive a range of potentially thyroid-relevant doses that incorporate uncertainty in modeling choices and in vitro assay data.