Adverse outcome pathways (AOPs) provide a framework to organize and weigh the evidence linking a toxicant’s initial interactions with molecules in the cell to adverse outcomes of regulatory concern. AOPs are constructed in modules that include key events (KEs) and key event relationships (KERs). Quantitative understanding of the KERs is critical for the development of predictive toxicological models. The objective of this project was to investigate the ability to define the quantitative associations of the KERs upstream, and contained in, an existing AOP (#296): “Oxidative DNA Damage Leading to Chromosomal Aberrations and Mutations”. The data supporting quantitative associations between these KERs was gathered through literature review and experimental methods. I first used systematic literature review tools to develop and apply a pragmatic and transparent method to search the literature for AOP evidence. A broad search, covering all of the KERs of interest, was initially conducted. This search, which retrieved more than 230 thousand articles, demonstrates the data-rich nature of the AOP. An artificial intelligence informed prioritization of the top 100 articles were then examined in detail. This approach identified 39 articles containing qualitative empirical support for the AOP, but limited quantitative evidence of the KERs. A second search was conducted to address the need for quantitative evidence as well as the lack of evidence for the KER between and increase in reactive oxygen species (ROS) and oxidative DNA damage. The second search retrieved 12 articles that could be used to define a quantitative relationship between cellular ROS and oxidative DNA damage. To begin to address gaps in quantitative understanding, I then conducted experiments in the laboratory to measure oxidative DNA damage, DNA strand breaks, chromosomal aberrations, and mutations in TK6 cells after exposure to a range of concentrations of 4-Nitroquinoline 1-oxide (4NQO: a prototype ROS producing agent). An increase in both oxidative DNA damage and DNA strand breaks was observed after 2, 4, and 6 h exposures with the high throughput comet assay (CometChip). An increase in the incidence of micronuclei was observed after a 24 h exposure to a low concentration of 4NQO, as measured with the flow cytometry micronucleus assay, while high cytotoxicity was found at higher concentrations. Lastly an increase in mutation frequency was observed with Duplex Sequencing, an error-corrected sequencing technology. Additionally, an increase in the proportion of C>A transversions was observed, consistent with the expected mutations following oxidative DNA lesions. Overall, my work contributes to the quantitative understanding of AOP #296 and this project serves as a key example of AOP-informed study design, highlighting notable challenges in characterizing quantitative relationships.

Avec les nouvelles avancées en biologie et toxicologie, on constate de plus en plus la complexité des mécanismes et le grand nombre de voies de toxicité. Les concepts de ‘biologie systémique’ (SB) et de ‘voies des effets indésirables’ (adverse outcome pathway, AOP) pourraient être des outils appropriés pour l’étude de la toxicologie à ces niveaux de complexité élevés. Le point central du travail de cette thèse est le développement d’un modèle de SB du rôle de la voie de signalisation Nrf2 dans le contrôle du stress oxydant. Pour la calibration de ce modèle avec des données expérimentales (exposition des cellules rénales RPTEC/TERT1 à différentes doses de bromate de potassium), plusieurs cycles de proposition/vérification d’hypothèses ont progressivement contribué à l’ajout de nouvelles réactions. Ces nouvelles hypothèses (par exemple : action directe du bromate de potassium sur le DCF, atténuation de la fluorescence du DCF avec le temps, etc.) devraient être confirmées par de futures expérimentations. Ce modèle de SB a été ensuite utilisé pour la quantification d’un AOP de l’insuffisance rénale chronique et comparé à deux autres approches: l’utilisation de modèles statistiques empiriques et celle d’un réseau Bayésien dynamique. Les calibrations des paramètres ont été effectuées par chaînes de Markov simulées MCMC avec le logiciel GNU MCSim avec une quantification des incertitudes associées aux prédictions. Même si la mise au point du modèle SB a été une tâche complexe, la compréhension de la biologie qu’offre ce modèle n’est pas accessible aux deux autres approches. Nous avons aussi évalué les interactions entre Nrf2 et deux autres voies de toxicité, AhR et ATF4, dans le cadre d’une analyse utilisant des données de toxico-génomique provenant de trois projets différents. Les résultats de cette dernière analyse suggèrent d’ajouter au modèle SB de Nrf2 la co-activation par AhR de plusieurs gènes (par exemple, HMOX1, SRXN1 et GCLM) ainsi que d’associer (au moins partiellement) à ce modèle la voie ATF4. Malgré leur complexité, les modèles SB constituent un investissement intéressant pour le développement de la toxicologie prédictive. / New understanding of biology shows more and more that the mechanisms that underlie toxicity are complex and involve multiple biological processes and pathways. Adverse outcome pathways (AOPs) and systems biology (SB) can be appropriate tools for studying toxicology at this level of complexity. This PhD thesis focuses on the elaboration of a SB model of the role of the Nrf2 pathway in the control of oxidative stress. The model’s calibration with experimental data (obtained with RPTEC/TERT1 renal cells exposed to various doses of potassium bromate) comprised several rounds of hypotheses stating/verification, through which new reactions were progressively added to the model. Some of these new hypotheses (e.g., direct action of potassium bromate on DCF, bleaching of DCF with time, etc.) could be confirmed by future experiments. Considered in a wider framework, this SB model was then evaluated and compared to two other computational models (i.e., an empirical dose-response statistical model and a dynamic Bayesian model) for the quantification of a ‘chronic kidney disease’ AOP. All parameter calibrations were done by MCMC simulations with the GNU MCSim software with a quantification of uncertainties associated with predictions. Even though the SB model was indeed complex to conceive, it offers insight in biology that the other approaches could not afford. In addition, using multiple toxicogenomic databases; interactions and cross-talks of the Nrf2 pathway with two other toxicity pathways (i.e., AhR and ATF4) were examined. The results of this last analysis suggest adding new AhR contribution to the control of some of the Nrf2 genes in our SB model (e.g., HMOX1, SRXN1 and GCLM), and integrating in it description of the ATF4 pathway (partially at least). Despites their complexity, precise SB models are precious investments for future developments in predictive toxicology.