Integrating Time-Resolved nrf2 Gene-Expression Data into a Full GUTS Model as a Proxy for Toxicodynamic Damage in...

Area of Science:

• Environmental toxicology
• Computational toxicology
• Molecular biology

Background:

• Chemical industry growth necessitates advanced risk assessment methods.
• Reducing animal testing in risk assessment is a key challenge.
• Integrating omics data into mechanistic models offers a promising alternative.

Purpose of the Study:

• To develop a method for integrating time-resolved gene expression data into TKTD models.
• To link molecular markers, specifically Nrf2 expression, to organismal effects like lethality.
• To establish a predictive risk assessment framework based on molecular data.

Main Methods:

• Development of a biologically anchored TKTD model.
• Association of toxicodynamic damage with Nrf2 expression.
• Integration of time-resolved RNA data using Bayesian inference and dynamic systems modeling.
• Application of the model to zebrafish embryo data.

Main Results:

• Successful integration of time-resolved gene expression data into TKTD models.
• Demonstration of Nrf2 as a molecular marker influencing survival predictions.
• Validation of the method for predicting lethality in zebrafish embryos.

Conclusions:

• The presented approach enables predictive risk assessment using molecular data.
• This method reduces reliance on animal testing for chemical safety evaluation.
• The biologically anchored TKTD model provides a mechanistic link between gene expression and toxicity.