Citation:

Beal, M., M. Gagne, S. Kulkarni, G. Patlewicz, R. Thomas, AND T. Barton-Maclaren. Implementing in vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories. ALTEX. Society ALTEX Edition, Kuesnacht, Switzerland, 39(1):123-139, (2022). https://doi.org/10.14573/altex.2106171

Impact/Purpose:

Methods for identifying priorities for chemical risk assessment and risk management serve a critical role in chemicals management systems globally (OECD, 2019). In most jurisdictions, prioritization schemes select from existing inventories of chemicals known to be in commerce for that region. Each chemical inventory is unique to the country or regulatory agency, but there is acknowledgement of the presence of overlapping interests and priorities internationally. A common challenge for prioritization efforts, and risk assessment in general, is the lack of exposure or toxicity data available to inform risk. Consequently, chemicals have been traditionally prioritized for assessment based on data sufficiency rather than inherent toxicity and potential risk. Thus, there is a need to leverage emerging technologies for the development of more innovative and modern approaches, capable of addressing both hazard and exposure data gaps, to make prioritization schemes more pragmatic, efficient, transparent, and proactive.

Description:

Internationally, there are thousands of existing and newly introduced chemicals in commerce, highlighting the ongoing importance of innovative approaches to identify emerging chemicals of concern. For many chemicals, there is a paucity of hazard and exposure data. Thus, there is a crucial need for efficient and robust approaches to address data gaps and support risk-based prioritization. Several studies have demonstrated the utility of in vitro bioactivity data from the ToxCast program in deriving points of departure (PODs). ToxCast contains data for nearly 1,400 endpoints per chemical, and the bioactivity concentrations, indicative of potential adverse outcomes, can be converted to human-equivalent PODs using high-throughput toxicokinetics (HTTK) modeling. However, data gaps need to be addressed for broader application: the limited chemical space of HTTK and quantitative high-throughput screening data. Here we explore the applicability of in silico models to address these data needs. Specifically, we used ADMET predictor for HTTK predictions and a generalized read-across approach to predict ToxCast bioactivity potency. We applied these models to profile 5,801 chemicals on Canada’s Domestic Substances List (DSL). To evaluate the approach’s performance, bioactivity PODs were compared with in vivo results from the EPA Toxicity Values database for 1,042 DSL chemicals. Comparisons demonstrated that the bioac­tivity PODs, based on ToxCast data or read-across, were conservative for 95% of the chemicals. Comparing bioactivity PODs to human exposure estimates supports the identification of chemicals of potential interest for further work. The bioac­tivity workflow shows promise as a powerful screening tool to support effective triaging of chemical inventories.

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