Integrating time-resolved $nrf2$ gene-expression data into a full GUTS model as a proxy for toxicodynamic damage in zebrafish embryo

TL;DR

This paper presents a biologically anchored TKTD model integrating time-resolved $nrf2$ gene expression data to predict lethality in zebrafish embryos, advancing molecular-based risk assessment methods.

Contribution

It introduces a novel method for incorporating gene expression data into mechanistic models to improve toxicity predictions.

Findings

Successfully integrated gene expression data into TKTD models.

Demonstrated $nrf2$ as a predictor for zebrafish embryo lethality.

Provided a framework for molecular data-driven risk assessment.

Abstract

The immense production of the chemical industry requires an improved predictive risk assessment that can handle constantly evolving challenges while reducing the dependency of risk assessment on animal testing. Integrating 'omics data into mechanistic models offers a promising solution by linking cellular processes triggered after chemical exposure with observed effects in the organism. With the emerging availability of time-resolved RNA data, the goal of integrating gene expression data into mechanistic models can be approached. We propose a biologically anchored TKTD model, which describes key processes that link the gene expression level of the stress regulator $nrf2$ to detoxification and lethality by associating toxicodynamic damage with $nrf2$ expression. Fitting such a model to complex datasets consisting of multiple endpoints required the combination of methods from molecular biology, mechanistic dynamic systems modeling and Bayesian inference. In this study we successfully integrate time-resolved gene expression data into TKTD models, and thus provide a method for assessing the influence of molecular markers on survival. This novel method was used to test whether, $nrf2$, can be applied to predict lethality in zebrafish embryos. With the presented approach we outline a method to successively approach the goal of a predictive risk assessment based on molecular data.