On the translation of uncertainty from toxicokinetic to toxicodynamic models – The TCDD example

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When estimating human health risks from exposure to TCDD using toxicokinetic and toxicodynamic models, it is important to understand how model choice and assumptions necessary for modeling add to the uncertainty of risk estimates. Several toxicokinetic models have been proposed for the risk assessment of dioxins, in particular the elimination kinetics in humans has been a matter of constant debate. For a long time, a simple linear elimination kinetics has been common choice. Thus, it was used for the statistical analysis of the largest occupationally exposed cohort, the German Boehringer cohort. We challenge this assumption by considering, amongst others, a nonlinear modified Michaelis-Menten-type elimination kinetics, the so-called Carrier kinetics. Using the area under the lipid TCDD concentration time curve as dose metrics, we model the time to cancer-related death using the Cox proportional hazards model as toxicodynamic model. This risk assessment set-up was simulated in order to quantify uncertainty of both the dose (TCDD body burden) and the risk estimates, depending on the use of the kinetic model, variations of carcinogenic effect of TCDD and variations of latency period (lag time). If past exposure is estimated assuming a linear elimination kinetics although a Carrier kinetics actually holds, then high exposures in reality will be underestimated through statistical analysis and low exposures will be overestimated, respectively. This bias will carry over on the estimated individual concentration-time curves and the therefrom derived TCDD dose metric values. Using biased dose values when estimating a dose-response relationship will finally lead to biased risk estimates. The extent of bias and the decrease of precision are quantified in selected scenarios through this simulation approach. Our findings are in concordance with recent results in the field of dioxin risk assessment. They also reinforce the general demand for the scheduled uncertainty assessments in risk analyses.