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

Research output: Contribution to JournalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalComputational Toxicology
Volume11
DOIs
Publication statusPublished - 1 Aug 2019

Fingerprint

Systems Biology
Toxicity
Kidney
Chronic Renal Insufficiency
Calibration
Oxidative stress
Oxidative Stress
Bayesian networks
Risk assessment
Potassium
Hazards
Testing

Keywords

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

Cite this

Zgheib, E., Gao, W., Limonciel, A., Aladjov, H., Yang, H., Tebby, C., ... Bois, F. Y. (2019). Application of three approaches for quantitative AOP development to renal toxicity. Computational Toxicology, 11, 1-13. https://doi.org/10.1016/j.comtox.2019.02.001
Zgheib, Elias ; Gao, Wang ; Limonciel, Alice ; Aladjov, Hristo ; Yang, Huan ; Tebby, Cleo ; Gayraud, Ghislaine ; Jennings, Paul ; Sachana, Magdalini ; Beltman, Joost B. ; Bois, Frederic Y. / Application of three approaches for quantitative AOP development to renal toxicity. In: Computational Toxicology. 2019 ; Vol. 11. pp. 1-13.
@article{4921e5bfba4a4fc1a8a18263892cd713,
title = "Application of three approaches for quantitative AOP development to renal toxicity",
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.",
keywords = "Bayesian networks, Chronic kidney disease, Potassium bromate, Predictive toxicology, Quantitative AOP, Systems biology model",
author = "Elias Zgheib and Wang Gao and Alice Limonciel and Hristo Aladjov and Huan Yang and Cleo Tebby and Ghislaine Gayraud and Paul Jennings and Magdalini Sachana and Beltman, {Joost B.} and Bois, {Frederic Y.}",
year = "2019",
month = "8",
day = "1",
doi = "10.1016/j.comtox.2019.02.001",
language = "English",
volume = "11",
pages = "1--13",
journal = "Computational Toxicology",
issn = "2468-1113",
publisher = "Elsevier",

}

Zgheib, E, Gao, W, Limonciel, A, Aladjov, H, Yang, H, Tebby, C, Gayraud, G, Jennings, P, Sachana, M, Beltman, JB & Bois, FY 2019, 'Application of three approaches for quantitative AOP development to renal toxicity' Computational Toxicology, vol. 11, pp. 1-13. https://doi.org/10.1016/j.comtox.2019.02.001

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 JournalArticleAcademicpeer-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

JF - Computational Toxicology

SN - 2468-1113

ER -