Étude de l’impact de la charge de travail sur
les indicateurs biologiques d’exposition de l’acétone et du styrène par la modélisation toxicocinétique
Workload has been recognized as a major determinant for the pulmonary absorption and the kinetics of industrial solvents, which are volatile compounds largely used in the workplace. This study was undertaken to characterize the relationship between different levels of workload and the biological levels of acetone and styrene or its metabolites used as biological exposure indices (BEIs). Physiologically based pharmacokinetic models were adapted and validated in order to simulate a typical week long occupational exposure (8h/day, 5 days) to acetone and styrene alone or in co-exposure. Simulations were conducted at the current threshold limit values of 500 ppm and 20 ppm, respectively, and under workload levels corresponding to rest (12,5 W), 25 W and 50 W. The predicted values were compared to the current reference value of the BEIs. The end-of-shift level of acetone in urine for a workload of 50 W showed a 3,5-fold increase compared to the value at rest (28 mg/L), whereas the level of styrene in venous blood and its metabolites in urine showed about 3,0-fold increases compared to rest (0,17 mg/L and 144 mg/g creatinine, respectively). Simulations showed that a combined exposure of 20 ppm of styrene with 200 ppm of acetone at 50 W lead to an increase of styrene in blood of 10% of the corresponding level without acetone, while the level of metabolites in urine was slightly decreased. The simulated values for both acetone and styrene showed that workers performing heavy tasks (>25 W) are more likely to present higher internal levels which exceed the current BEIs reference values and may lead to health effects.The models described well the impact of workload on internal exposure and highlighted that workload needs to be taken into account while determining reference values for biological monitoring of acetone and styrene.
Industrial solvents Physical activity Biological monitoring PBTK model