Advancing probabilistic risk assessment by integrating human biomonitoring, new approach methods, and Bayesian modeling: A case study with the mycotoxin deoxynivalenol.

Deoxynivalenol (DON) is a mycotoxin frequently observed in cereals and cereal-based foods, with reported toxicological effects including reduced body weight, immunotoxicity and reproductive defects. The European Food Safety Authority used traditional risk assessment approaches to derive a deterministic Tolerable Daily Intake (TDI) of 1 µg/kg-day, however data from human biomarkers studies indicate widespread and variable exposure worldwide, necessitating more sophisticated and advanced methods to quantify population risk. The World Health Organization/International Programme on Chemical Safety (WHO/IPCS) has previously used DON as a case example in replacing the TDI with a probabilistic toxicity value, using default uncertainty and variability distributions to derive the Human Dose corresponding to an effect size M in the Ith percentile of the population (HDMI) for M = 5 % decrease in body weight and I = 1 %. In this study, we extend this case study by incorporating (1) Bayesian modeling approaches, (2) using both in vivo data and in vitro population new approach methods to replace default distributions for interspecies toxicokinetic (TK) differences and intraspecies TK and toxicodynamic (TD) variability, and (3) integrating biomonitoring data and probabilistic dose-response functions to characterize population risk distributions. We first derive an HDMI of 5.5 [1.4-24] µg/kg-day, also using TK modeling to converted the HDMI to Biomonitoring Equivalents, BEMI for comparison with biomonitoring data, with a blood BEMI of 0.53 [0.17-1.6] µg/L and a urinary excretion BEMI of 3.9 [1.0-16] µg/kg-day. We then illustrate how this integrative approach can advance quantitative risk characterization using two human biomonitoring datasets, estimating both the fraction of population with an effect size M ≥ 5 % as well as the distribution of effect sizes. Overall, we demonstrate that integration of Bayesian modeling, human biomonitoring data, and in vitro population-based TD data within the WHO/IPCS probabilistic framework yields more accurate, precise, and comprehensive risk characterization.

Systematic probabilistic risk assessment of pesticide residues in tea leaves.

Multiple pesticide residues are frequently present in tea leaves and while the majority of residues satisfy Taiwan's current health regulations, there are potential health effects from pesticide exposure that are of great concern for tea drinkers. We undertook a systematic probabilistic risk assessment of 59 pesticides in tea leaves from 1629 tea leaf samples obtained by Taiwan's Food and Drug Administration in two monitoring surveys in 2015. Bayesian statistics used a Markov Chain Monte Carlo approach to estimate posterior distributions of pesticide residues in tea leaves, lifetime average daily doses and hazard quotients (HQs) of evaluated pesticides. We classified 95th percentile values of HQs into three categories: 0 < HQ < 0.5, 0.5 ≤ HQ ≤ 1 and 1 < HQ. The 95th percentiles of HQs for triazophos (3.39), carbofuran (2.04) and endosulfan (1.80) exceeded 1 in the adult population; the HQ for 3-OH carbofuran was 0.97 and was less than 0.5 for the remaining 55 pesticides. The health risk posed by pesticide residues for tea drinkers is negligible, if triazophos, carbofuran, endosulfan, and 3-OH carbofuran residues satisfy regulatory standards. However, five legacy pesticides, DDT, methomyl, carbofuran, dicofol and endosulfan, were identified. To reduce uncertainties, this study combined Bayesian statistics with a mode of action approach for systematic risk assessment of co-exposure to multiple pesticide residues in tea leaf samples. Measuring pesticide transfer rates will improve the quality of future risk assessments concerning residues in tea leaves. Appropriate management of pesticides in Taiwanese tea farms and monitoring of pesticide residues in imported tea is warranted to protect Taiwan's tea drinkers.