Abstract
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.
| Language | English |
|---|---|
| Pages | 1-13 |
| Number of pages | 13 |
| Journal | Computational Toxicology |
| Volume | 11 |
| DOIs | |
| Publication status | Published - 1 Aug 2019 |
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Keywords
- Bayesian networks
- Chronic kidney disease
- Potassium bromate
- Predictive toxicology
- Quantitative AOP
- Systems biology model
Cite this
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Application of three approaches for quantitative AOP development to renal toxicity. / Zgheib, Elias; Gao, Wang; Limonciel, Alice; Aladjov, Hristo; Yang, Huan; Tebby, Cleo; Gayraud, Ghislaine; Jennings, Paul; Sachana, Magdalini; Beltman, Joost B.; Bois, Frederic Y.
In: Computational Toxicology, Vol. 11, 01.08.2019, p. 1-13.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - Application of three approaches for quantitative AOP development to renal toxicity
AU - Zgheib, Elias
AU - Gao, Wang
AU - Limonciel, Alice
AU - Aladjov, Hristo
AU - Yang, Huan
AU - Tebby, Cleo
AU - Gayraud, Ghislaine
AU - Jennings, Paul
AU - Sachana, Magdalini
AU - Beltman, Joost B.
AU - Bois, Frederic Y.
PY - 2019/8/1
Y1 - 2019/8/1
N2 - 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.
AB - 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.
KW - Bayesian networks
KW - Chronic kidney disease
KW - Potassium bromate
KW - Predictive toxicology
KW - Quantitative AOP
KW - Systems biology model
UR - http://www.scopus.com/inward/record.url?scp=85061728465&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061728465&partnerID=8YFLogxK
U2 - 10.1016/j.comtox.2019.02.001
DO - 10.1016/j.comtox.2019.02.001
M3 - Article
VL - 11
SP - 1
EP - 13
JO - Computational Toxicology
T2 - Computational Toxicology
JF - Computational Toxicology
SN - 2468-1113
ER -