Recommendations to address uncertainties in environmental risk assessment using toxicokinetics-toxicodynamics models

Abstract

Cite as:Baudrot V and Charles S. (2018). Recommendations to address uncertainties in environmental risk assessment using toxicokinetics-toxicodynamics models. bioRxiv 356469, ver. 3 peer-reviewed and recommended by PCI Ecol. ABSTRACTProviding reliable environmental quality standards (EQSs) is a challenging issue in environmental risk assessment (ERA). These EQSs are derived from toxicity endpoints estimated from dose-response models to identify and characterize the environmental hazard of chemical compounds such as those released by human activities. These toxicity endpoints include the classical x% effect/lethal concentrations at a specific time t (EC/LC(x, t)) and the new multiplication factors applied to environmental exposure profiles leading to x% effect reduction at a specific time t (M F (x, t), or denoted LP (x, t) by the EFSA). However, classical dose-response models used to estimate toxicity endpoints have some weaknesses, such as their dependency on observation time points, which are likely to differ between species (e.g., experiment duration). Furthermore, real-world exposure profiles are rarely constant over time, which makes the use of classical dose-response models difficult and compromises the derivation of M F (x, t). When dealing with survival or immobility toxicity test data, these issues can be overcome with the use of the general unified threshold model of survival (GUTS), a toxicokinetics-toxicodynamics (TKTD) model that provides an explicit framework to analyse both time-and concentration-dependent data sets as well as obtain a mechanistic derivation of EC/LC(x, t) and M F (x, t) regardless of x and at any time t of interest. In addition, the assessment of a risk is inherently built upon probability distributions, such that the next critical step for ERA is to characterize the uncertainties of toxicity endpoints and, consequently, those of EQSs. With this perspective, we investigated the use of a Bayesian framework to obtain the uncertainties from the calibration process and to propagate them to model predictions, including LC(x, t) and M F (x, t) derivations. We also explored the mathematical properties of LC(x, t) and M F (x, t) as well as the impact of different experimental designs to provide some recommendations for a robust derivation of toxicity endpoints leading to reliable EQSs: avoid computing LC(x, t) and M F (x, t) for extreme x values (0 or 100%), where uncertainty is maximal; compute M F (x, t) after a long period of time to take depuration time into account and test survival under few correlated and uncorrelated pulses of the contaminant in terms of depuration.

Taking full advantage of modelling to better assess environmental risk due to xenobiotics—the all-in-one facility MOSAIC

“…As detailed in Baudrot & Charles (2019), the lethal concentration can be obtained from a GUTS model as a continuous function of both the chosen percentage x and the exposure duration t according to model parameter estimates.…”

‘rbioacc’: an ‘R’-package to analyze toxicokinetic data

“…To mimic realistic environmental conditions, most often fluctuating than constant ( e.g ., when collecting field data), it may need to predict the internal concentration under time-variable exposure profiles (Ashauer & Brown, 2013; Baudrot & Charles, 2019). Such situations can easily be explored with ‘rbioacc’.…”

Taking full advantage of modelling to better assess environmental risk due to xenobiotics

“…As detailed in [33], the lethal concentration can be obtained from a GUTS model as a continuous function of both the chosen percentage x and the exposure duration t according to model parameter estimates. Hence, the calculation of any LC (x,t) can be associated to its uncertainty, by propagating the uncertainty associated to model parameter estimates as accessible from the joint posterior distribution after performing Bayesian inference.…”