Application of three approaches for quantitative AOP development to renal toxicity

Elias Zgheib, Wang Gao, Alice Limonciel, Hristo Aladjov, Huan Yang, Cleo Tebby, Ghislaine Gayraud, Paul Jennings, Magdalini Sachana, Joost B. Beltman, Frederic Y. Bois*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


While hazard assessment of chemicals can make direct use of descriptive adverse outcome pathways (AOPs), risk assessment requires quantitative relationships from exposure to effect timing and magnitude. To seamlessly integrate the data generated by alternative methods or in vivo testing, quantitative AOPs (qAOPs) providing dose-time-response predictions are more valuable than qualitative AOPs. Here, we compare three approaches to qAOP building: empirical dose-response modeling, Bayesian network (BN) calibration, and systems biology (SB) modeling. These methods were applied to the quantification of a simplified oxidative stress induced chronic kidney disease AOP, on the basis of in vitro data obtained on RPTEC/TERT1 cells exposed to potassium bromate. Effectopedia was used to store the experimental data and the developed models in a unified representation so they can be compared and further analyzed. We argue that despite the fact that dose-response models give adequate fits to the data they should be accompanied by mechanistic SB modeling to gain a proper perspective on the quantification. BNs can be both more precise than dose-response models and simpler than SB models, but more experience with their usage is needed.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalComputational Toxicology
Publication statusPublished - 1 Aug 2019


This work was supported by the EU-ToxRisk project (An Integrated European “Flagship” Program Driving Mechanism-Based Toxicity Testing and Risk Assessment for the 21st Century) funded by the European Commission under the Horizon 2020 programme (Grant Agreement No. 681002 ). AL was also partly funded by the 2015 Long Range Initiative Innovative Science Award of the European Chemical Industry Council .

FundersFunder number
European Chemical Industry Council
Horizon 2020 Framework Programme
European Commission681002


    • Bayesian networks
    • Chronic kidney disease
    • Potassium bromate
    • Predictive toxicology
    • Quantitative AOP
    • Systems biology model


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