  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.
