Benchmark doses (BMD) extrapolated from in vitro cytotoxicity experiments in SH-SY5Y cells using the EFSA Bayesian BMD web app: The study case of imidacloprid

Abstract

Identifying possible chemical hazards is critical to establish toxicological risk assessment related to non-cancer health effects. The benchmark dose (BMD) is an estimate of the hazardous toxic level (dose or concentration) that produces a predetermined variation in the response rate of an adverse effect (exposure-related risk endpoint). Our goal was to assess how well Bayesian weighted averaging models enhances the imidacloprid-treated SH-SY5Y cells’ dose-toxicological response in the MTT cytotoxicity assay. Notably, the Gelman-Rubin statistics for our models were constantly between 0.9 and 1.0, and the effective sample size was greater than 150, which guaranteed practical sufficiency. We used a weighted average of posteriorly fitted models to estimate the final BMD = 26.40 and the lower confidence limit (BMDL) = 13.10. Including uncertainty factors (UF) in conjunction with MTT data into our risk analysis, we assessed the population’s imidacloprid exposure. The Point of Departure (PoD) at 5th percentil (8.14) indicated adverse effects. Moreover, a similar link was observed between the target human dose for minimal impact (HDMI) and the HD₅₀ (dose hazardous to 50 % of people). The determined Reference Dose (RfD) of 0.0003 µM suggested a high toxicity risk associated with imidacloprid exposure. Summarizing, dose–response evaluations were enhanced by Bayesian model averaging, highlighting the significance of probabilistic modeling and toxicological understanding.