Table 1.

Introduction to strengths and weaknesses of TKTD models

	Strengths	Weaknesses
TKTD models (applicable to GUTS, DEBtox and models for primary producers)	Make use of all available standard and non‐standard toxicity test data Make the link from the external concentration to the predicted effects over time Involve time‐independent parameters Enables extrapolation of effects from a set of tested exposure conditions to other, also time‐variable exposure profiles Applications with calibration only or also with validation data sets are available in the literature Different and variable environmental conditions can potentially be implemented to increase realism	Assume homogeneous mixing of toxic chemical within an organism Assume static biological status of an organism Usually based on a one‐compartment TK part Without turnkey dedicated tools, the fit of TKTD models requires some knowledge in statistics
GUTS	Use a standardised simple model formulation with strictly defined terminology Can be calibrated on raw data from standard toxicity testing of survival Allow for scanning large numbers of scenarios Application and validation data sets are available in the literature User‐friendly tools exist to either calibrate or simulate GUTS models	Need of measured internal concentrations to apply the full GUTS Duality of SD and IT death mechanisms
DEBtox	Provide a fully integrated mechanistic model of toxic effects within the DEB theory framework Provide a combined model for effects on growth and reproduction Allow for different formulations of the TKTD part depending on the mode of action of the toxicant Allows for predicting growth and reproduction under constant or time‐variable exposure profiles	Calibration requires combinations of time series for growth and reproduction. This could be experimentally demanding for growth Simultaneous calibration of all parameters may be difficult in some cases No user‐friendly dedicated tools are available to calibrate DEBtox models
Primary producers models	Non‐destructive high time‐resolution data can be obtained by measuring surface area for Lemna and shoot (and root) length for Myriophyllum Data obtained from microcosm studies can be used to validate model predictions Uptake of chemicals from the sediment by Myriophyllum can be incorporated	Standard tests are not adequate for calibration unless extended by a recovery period Assumes nutrient in excess (which might be valid for agricultural uplands). Flow‐through setups for algae tests are experimentally demanding and not standardised. Density dependent growth is missing for Myriophyllum. No growth validation under natural dissolved inorganic carbon (DIC) conditions for Myriophyllum is currently available. No laboratory → field extrapolation validation data for Myriophyllum (and more could be used for Lemna) are available. Root uptake by Myriophyllum is not considered, nor is transport between compartments explicitly described.