Variability in biological monitoring of organic solvent exposure. II. Application of a population physiological model

• Published in: British Journal of Industrial Medicine Vol. 46; no. 8; pp. 547 - 558
• Main Authors: Droz, P O, Wu, M M, Cumberland, W G
• Format: Journal Article
• Language: English
• Published: London BMJ Publishing Group Ltd 01.08.1989; British Medical Association; BMJ; BMJ Publishing Group LTD
• Subjects: Benzene - analysis; Biological and medical sciences; Blood; Body Constitution; Chemical and industrial products toxicology. Toxic occupational diseases; Environmental assessment; Environmental Exposure; Environmental Monitoring; Ethane - analogs & derivatives; Ethane - analysis; Humans; Hydrocarbons, Chlorinated - analysis; Kidney - physiology; Liver - physiology; Medical sciences; Metabolism; Metabolites; Models, Biological; Renal function; Solvents; Somatotypes; Styrene; Styrenes; Styrenes - analysis; Toluene - analysis; Toxicology; Trichloroethanes - blood; Trichloroethylene - urine; Urine; Workloads; Human; Organic solvent; Investigation method; Interindividual comparison; Biological marker; Compartment model; Simulation model; Occupational exposure; Statistical study; Biological monitoring
• ISSN: 0007-1072; 1351-0711; 1470-7926
• DOI: 10.1136/oem.46.8.547

A physiological population model is used to study the variability associated with the biological monitoring of solvent exposure. The model consists of a combination of a physiological compartmental model and statistical simulation technique. Variable components considered are: exposure concentration, physical workload, body build, liver function, and renal function. The model is applied to six solvents: trichloroethylene, tetrachloroethylene, methylchloroform, benzene, toluene, and styrene. Biological indicators and air monitoring are compared as predictors of exposure, both external and internal (uptake, brain concentration, reactive metabolite formation). It appears that the choice of the best indicator depends on the type of exposure which is to be predicted. The effects of the various factors, environmental, physiological, or metabolic, are quantified and discussed. It is shown that fluctuation in exposure plays a large part in the final variability of biological indicator results. Further improvements and applications of this population model are considered.