Providing reliable environmental quality standards (EQS) is a challenging issue for environmental risk assessment (ERA). These EQS are derived from toxicity endpoints estimated from dose-response models to identify and characterize the environmental hazard of chemical compounds as those released by human activities. The classical toxicity endpoints are the x% effect/lethal concentrations at a specific time (i.e., EC/LC(x,t)), or the multiplication factors applied to environmental exposure profiles leading to x% of effect reduction at a specific time (i.e., MF(x,t)). However, classical dose-response models used to estimate the toxicity endpoints have some weaknesses such as their dependency on observation time-points which are likely to differ between species. Also, real exposure profiles are hardly ever constant over time, what makes impossible the use of classical dose-response models and compromises the derivation of MF(x,t), actually designed to tackle time-variable exposure profiles. 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, providing an explicit framework to analyse both time and concentration-dependent data sets, as well as a mechanistic derivation of EC/LC(x,t) and MF(x,t) whatever x and at any time of interest. In addition, the assessment of a risk is inherently built upon probability distributions, so that the next critical step for ERA is to characterize uncertainties of toxicity endpoints, and sequentially of EQS. The innovative approach investigated in our paper is the use of the Bayesian framework to deal with uncertainties raising in the calibration process and propagated all along the successive prediction steps until the LC(x,t) and MF(x,t) derivations. We also explored the mathematical properties of LC(x,t) and MF(x,t) as well as the impact of different experimental designs in order to provide some recommendations for a robust derivation of toxicity endpoints leading to reliable EQS.
Survival models, Dose-Response, GUTS, Lethal Concentration, Multiplication Factor, Margin of safety, Environmental Risk Assessment