A Toxicokinetic-Toxicodynamic Modeling Workflow Assessing the Quality of Input Mortality Data.

Area of Science:

• Environmental toxicology
• Ecotoxicology
• Risk assessment modeling

Background:

• Toxicokinetic-toxicodynamic (TKTD) models are crucial for aquatic risk assessment, simulating chemical uptake, elimination, and organismal effects.
• The Reduced General Unified Threshold model of Survival (GUTS-RED) is a widely used TKTD framework for predicting survival effects.
• Systematic exploration of input data's influence on GUTS-RED calibration and validation is lacking.

Purpose of the Study:

• To systematically analyze how input data characteristics affect the calibration and validation performance of the GUTS-RED model.
• To assess the impact of data quantity, quality, and diversity on GUTS-RED parameter uncertainty and predictive accuracy.
• To develop recommendations for data selection and workflow optimization in GUTS-RED applications.

Main Methods:

• Utilized a comprehensive dataset covering various substances, exposure patterns, and aquatic species for GUTS-RED analysis.
• Developed automated software to calibrate and validate GUTS-RED against 59 toxicity test survival measurements.
• Employed a cross-validation design, systematically varying calibration and validation datasets for species-substance combinations.

Main Results:

• Parameter uncertainty in GUTS-RED calibration decreased when input data covered the full spectrum of effects (high survival to high mortality).
• Increasing the number of toxicity studies for calibration reduced parameter uncertainty.
• Incorporating data from acute, chronic, pulsed, and constant exposure studies enhanced GUTS-RED's predictive performance on validation datasets.

Conclusions:

• The quality and diversity of input data significantly influence GUTS-RED calibration and validation outcomes.
• Recommendations for data selection and a workflow are provided to enhance the reliability of GUTS-RED in aquatic risk assessment.
• Optimizing input data selection is key to improving the suitability and predictive power of TKTD models like GUTS-RED.