From vision toward best practices: Evaluating

Area of Science:

• Toxicology
• Computational Biology
• Risk Assessment

Background:

• Increasing chemical production necessitates efficient risk assessment methods.
• Traditional animal testing is limited in human relevance and capacity.
• Novel approaches are needed to meet regulatory demands for toxicity data.

Purpose of the Study:

• To increase confidence in new approach methods (NAMs) for chemical risk assessment.
• To identify data gaps and limitations in current toxicity testing strategies.
• To demonstrate the utility of high-throughput transcriptomics (HTTr) for deriving practical endpoints.

Main Methods:

• Applied a uniform workflow to six gene expression datasets (117 chemicals, 3 cell types).
• Determined transcriptomic points of departure (tPODs) using benchmark concentration modeling.
• Translated in vitro tPODs to human-relevant administered equivalent doses (AEDs) using high-throughput toxicokinetics.

Main Results:

• In vitro tPODs yielded AEDs generally lower (more conservative) than traditional PODs, suggesting protection of human health.
• Longer exposure durations and 3D cell models decreased tPOD values, indicating higher chemical potency.
• Seven chemicals were flagged as outliers requiring further hazard assessment.

Conclusions:

• Findings support the use of tPODs derived from HTTr for regulatory risk assessment.
• Identified data gaps, including exposure duration and cell model type, require further investigation.
• Addressing these gaps will build confidence for adopting tPODs in risk assessment applications.