A toxicokinetic-toxicodynamic modelling workflow assessing the quality of input mortality data.

Toxicokinetic-toxicodynamic (TKTD) models simulate organismal uptake and elimination of a substance (TK) and its effects on the organism (TD). The Reduced General Unified Threshold model of Survival (GUTS-RED) is a TKTD modelling framework that is well established for aquatic risk assessment to simulate effects on survival. TKTD models are applied in three steps: parameterization based on experimental data (calibration), comparing predictions to independent data (validation) and prediction of endpoints under environmental scenarios. Despite a clear understanding of GUTS-RED predictions' sensitivity to the model parameters, the influence of the input data on the quality of GUTS-RED calibration and validation is not systematically explored. We analyzed performance of GUTS-RED calibration and validation based on a unique, comprehensive dataset, covering different types of substances, exposure patterns and aquatic animal species taxa that are regularly used for risk assessment of plant protection products. We developed a software code to automatically calibrate and validate GUTS-RED against survival measurements from 59 toxicity tests and calculate selected model evaluation metrics. To assess whether specific survival data sets were better suited for calibration or validation, we applied a design where all possible combinations of studies for the same species-substance combination are used for calibration and validation. We found that uncertainty of calibrated parameters was lower when the full range of effects (i.e. from high survival to high mortality) was covered by input data. Increasing the number of toxicity studies used for calibration, further decreased parameter uncertainty. Including data from both acute and chronic studies as well as studies under pulsed and constant exposure in model calibrations improved model predictions on different types of validation data. Using our results we derive a workflow, including recommendations for the sequence of modelling steps from the selection of input data to a final judgement on the suitability of GUTS-RED for the dataset.