Quantitative Estimates of Inter-individual Variability for New Approach Methodologies-Based Systemic Safety Toolbox Using a Population-Based Human in Vitro Model

Next-Generation Risk Assessment (NGRA) frameworks use New Approach Methodologies (NAMs) to support regulatory decisions without animal testing. While NAM-based approaches are well established for hazard and dose-response assessment, inter-individual variability is still typically addressed using default uncertainty factors for inter-individual variability. This study evaluated a NAM-based strategy to quantify chemical-specific variability using a human cell model. We hypothesized that integrating chemical-specific variability data into NGRA would yield more protective risk estimates. Using 131 human lymphoblastoid cell lines (LCLs) from four European and African subpopulations, we assessed differences in cytotoxic responses to 53 substances, including industrial chemicals, pharmaceuticals, pesticides, and consumer-use compounds. Concentration-response testing (0.3 nM-300 μM) data were analyzed using Bayesian modeling to calculate points of departure per cell line. Of the substances tested, 18 exhibited cytotoxic effects, enabling the derivation of chemical-specific variability factors. These factors were designated as toxicodynamic variability factors at the 5th percentile (TDVF05) because of the limited metabolic capacity of lymphoblast cell lines. The median TDVF05 was 3.8 (range 1-46), largely consistent with default assumptions. A genome-wide association study (GWAS) identified genomic loci, primarily containing transporter and metabolism genes, associated with variability in cytotoxicity, suggesting mechanistic bases for inter-individual differences. Overall, this study shows that human LCLs are a practical high-throughput in vitro model for quantifying inter-individual variability, strengthening confidence in NGRA risk predictions and supporting hypothesis generation on chemical-specific genetic and mechanistic drivers of human variability. However, cell-based systems have limited coverage of adverse effects and require careful alignment with in vivo dosimetry.