Quantitative estimates of inter-individual variability for new approach methodologies-based systemic safety toolbox...

Area of Science:

• Toxicology
• Genetics
• Risk Assessment

Background:

• Next-Generation Risk Assessment (NGRA) utilizes New Approach Methodologies (NAMs) to reduce animal testing in regulatory decisions.
• Current NAMs often rely on default factors for inter-individual variability, potentially limiting risk assessment precision.
• Quantifying chemical-specific variability is crucial for refining NGRA and enhancing risk protection.

Purpose of the Study:

• To evaluate a NAM-based strategy for quantifying chemical-specific inter-individual variability using human cell models.
• To integrate this chemical-specific variability data into NGRA for more protective risk estimates.
• To identify genetic and mechanistic drivers of cytotoxicity variability in human populations.

Main Methods:

• Utilized 131 human lymphoblastoid cell lines (LCLs) from diverse subpopulations for cytotoxicity testing.
• Assessed responses to 53 diverse chemical substances across a wide concentration range.
• Employed Bayesian modeling to derive chemical-specific variability factors (TDVF05) and conducted genome-wide association studies (GWAS).

Main Results:

• Identified 18 substances with cytotoxic effects, enabling derivation of chemical-specific variability factors (median TDVF05 = 3.8, range 1-46).
• GWAS revealed genomic loci, including transporter and metabolism genes, associated with cytotoxicity variability.
• Demonstrated LCLs as a practical high-throughput in vitro model for assessing inter-individual variability.

Conclusions:

• Human LCLs provide a viable in vitro method for quantifying inter-individual variability in chemical response.
• Integrating chemical-specific variability data strengthens NGRA confidence and supports mechanistic hypothesis generation.
• Cell-based models offer practical advantages but require careful consideration of metabolic limitations and in vivo dosimetry alignment.