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 neurodevelopmental outcomes in offspring. US federal risk assessments rely almost exclusively on apical endpoints in animal models for deriving points of departure (PODs). New approach methodologies (NAMs) such as high-throughput screening (HTS) and mechanistically informative in vitro human cell-based systems, combined with in vitro to in vivo extrapolation (IVIVE), supplement in vivo studies and provide an alternative approach to calculate/determine PODs. We examine how parameterization of IVIVE models impacts the comparison between IVIVE-derived equivalent administered doses (EADs) from thyroid-relevant in vitro assays and the POD values that serve as the basis for risk assessments. 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. A total 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.

Citation

Jeffrey M Carlson, Patricia A Janulewicz, Nicole C Kleinstreuer, Wendy Heiger-Bernays. Impact of High-Throughput Model Parameterization and Data Uncertainty on Thyroid-Based Toxicological Estimates for Pesticide Chemicals. Environmental science & technology. 2022 Apr 21;56(9):5620-5631

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PMID: 35446564

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