Développement de seuils toxicologiques et prédiction de doses internes pour l’exposition professionnelle aux substances organiques à partir de leurs structures moléculaires

Occupational Exposure Limits (OELs) are valuable tools for workers’ health risk characterization and protection. However, in workplace, many substances do not have OELs. The main objective of this research project was to develop tools and predictive pharmacokinetic models, using molecular structure information, for occupational risk assessment and characterization of data-poor organic chemicals. Thus, three specific objectives were pursued as follows: i) derive occupational thresholds of toxicological concern (OTTCs) based on absorbed dose; ii) develop a quantitative property-property relationship (QPPR)-based human physiologically based pharmacokinetic (PBPK) modeling framework for high-throughput predictions of inhalation toxicokinetics, including the internal dose (ID), of organic chemicals; and finally, iii) derive ID-based OTTCs as well as quantitative models for predicting screening-level limit values for occupational exposure in data-poor situations. First, a dataset of 280 organic chemicals consisting of data on OEL as well as physicochemical and toxicologic parameters (i.e., partition coefficients (P), Cramer class of toxicity [low (class I), intermediate (class II) and high (class III)]) was compiled. Second, the dose absorbed in workers was predicted for each chemical in the dataset from consideration of the bioavailability (as per the QPPR-derived Pblood:air). Subsequently, distributional analyses of these absorbed doses were performed to identify the OTTCs. Third, for ID estimations (in ‘batch’ mode), a QPPR-based human PBPK modeling framework was parameterized with QPPR-derived values of Pblood:air, Ptissu:air, and hepatic clearance. Three ID metrics were identified for investigation: (i) the daily area under the venous blood concentration versus time curve (AUC24), (ii) the daily rate of the amount of parent chemical metabolized (RMET24) and (iii) the maximum venous blood concentration after an 8-h work shift. Using AUC24, the resulting model’s reliability was evaluated based on joint sensitivity and uncertainty analyses. Fourth, distributional analyses of the predicted ID were further performed to derive ID-based OTTCs; and simple linear regression analyses performed to study and quantify the relationship between the ID metrics and OEL. Based on the absorbed dose, the derived OTTCs were 0.15, 0.0085, and 0.006 mmol/day at the 10th percentile level for Cramer classes I-III respectively, while these values were 1.5, 0.09 and 0.03 mmol/day at the 25th percentile level. The reliability analysis indicated that the AUC24 values predicted with the proposed PBPK modeling framework were moderate to highly reliable for 55% of the chemicals, with 46% exhibiting highly reliable values. High and significant correlations were observed between the OEL and the ID metrics predicted with the modeling framework, specifically RMET24 (r2 = 0.81; n = 276). Based on RMET24, the proposed ID-based OTTCs were 5.61×10-2 and 9×10-4 mmol/day at the 10th percentile level for classes I and III, respectively, while they were 4.55×10-1 and 8.50×10-3 mmol/day at the 25th percentile level. Overall, for the first time, this research has developed thresholds of toxicological concern based on molecular structure of chemicals, to enable screening-level occupational risk assessment and prioritization in data poor situations.

Keyphrases: Occupational Exposure Limit, Threshold of Toxicological Concern, Physiologically based pharmacokinetic modeling, Quantitative property-property relationships, Occupational health risk assessment