Maël Conan’s PhD defense on “Constructing xenobiotic enriched maps of metabolism to predict the role of enzymes in DNA adduct formation” will take place on 23rd March 2021 14:30 (UTC+1).
The defense will be broadcasted live on youtube at: https://youtu.be/BJyDIzrgxF4
Committee:
- Cédric LHOUSSAINE , Professeur, Université de Lille (Président du jury)
- Karine Audouze, Maitresse de conférence, Université de Paris
- Fabien JOURDAN, Directeur de recherche, INRAE, Toulouse (Rapporteur)
- Sabine PERES, Maitresse de conférence, Université de Paris Saclay (Rapporteur)
- Sophie LANGOUËT, Directrice de recherche, INSERM, Rennes (Directrice de thèse)
- Anne SIEGEL, Directrice de recerche, CNRS, Rennes (Directrice de thèse)
Keywords: Metabolism prediction ; HAA ; DNA reactivity ; Bayesian Networks
Abstract:
The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food addi- tives, etc.). Among environmental contaminants of concern, heterocyclic aromatic amines (HAAs) are xenobiotics classified as possible or probable carcinogens (2A or 2B) by IARC, for which low information exists in humans. 30 AHAs have been identified to date, but the bioactivation pathways, metabolites and DNA adducts have been fully characterised in the human liver for only three of them (MeIQx, PhIP, AαC). We have developed a modelling approach to predict both metabolism (metabolites and reactions), DNA reactivity and the production probability of metabolite. Our approach is based on the construction of enriched metabolism maps. We bring together tools for predicting reactions and metabolites (SyGMa), pre- dicting metabolism sites (Way2Drug SOMP, Fame3), predicting DNA reactivity (XenoSite Reactivity V1) and calculating a production probability score based on the properties of Bayesian networks. This prediction pipeline was evaluated and validated using caffeine and then applied to six AHAs. Main results show that our approach allows us to predict the metabolism of xenobiotics and that the production probability score has different proper- ties that can lead to the filtration of the metabolism map or to the determination of the enzymatic pro- files associated with maximising the formation of DNA adducts. This predictive toxicology approach opens up prospects for estimating the genotoxicity of various environmental contaminants in normal or pathophysiological situations.
