In silico prediction of Heterocyclic Aromatic Amines metabolism susceptible to form DNA adducts in humans

Delannée, Victorien; Langouët, Sophie; Siegel, Anne; Théret, Nathalie

doi:10.1016/j.toxlet.2018.10.011

Cited by 13 publications

(20 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the state of our knowledge, this recent method was only applied to the metabolism of Terbinafine (TBF), permitting to detect a new pathway that can explain the formation of TBF-A from TBF [35]. Another strategy, detailed in [36] uses SOM predictions to filter metabolic maps by removing predicted reactions which are not supported by an accurate SOM prediction. In this study, the SOM-filter threshold is determined by using a training set of analog chemicals to the chemicals of interest.…”

Section: Prediction Of Metabolitesmentioning

confidence: 99%

“…Contribution To get further in the prediction of formation of DNA adducts by HAA, we introduce a new method which combines the concept of filtered metabolic map introduced in [36] and the concept of ranked pathways introduced in [35]. Instead of filtering metabolic maps according to individual reaction SOM scores, we introduce a production probability score which describes the probability for a metabolite to be produced according to one or several chains of reactions weighted by SOM scores.…”

Section: Prediction Of Metabolitesmentioning

confidence: 99%

“…For each HAAs, we estimated the number of metabolites reactive to DNA by assuming that each metabolite with a XenoSite Reactivity score greater than 0.85 is reactive to DNA, following the criteria introduced in [36]. The four HAAs, 4-CH2OH-8-MeIQx, 7,8-DiMeIQx, 7,9-DiMeIgQx and 4,7,8-TriMeIQx have both the largest map and the greatest amount of metabolites reactive to DNA (91, 80, 81 and 81).…”

Section: Definition and Construction Of Enriched Metabolic Mapsmentioning

confidence: 99%

“…To determine if a metabolite is considered as reactive to DNA, we apply a threshold on the SOR scores if at least one SOR score of an atom of the metabolite is retained the metabolite is considered as reactive to DNA. We use the same threshold as in [36] where XenoSite Reactivity where a threshold of 0.85 was learned according to metabolites known to be reactive to DNA.…”

Section: Prediction Of Sormentioning

confidence: 99%

“…The description of HAAs by SMILE formula were provided in [36]. The SMILES formula of caffeine was extracted from PubChem: https://pubchem.ncbi.nlm.nih.gov/.…”

Section: Fundingmentioning

confidence: 99%

See 4 more Smart Citations

Constructing Xenobiotic Maps of Metabolism to Predict the Role of Enzymes in DNA Adduct Formation

Conan¹,

Théret²,

Langouët³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Background : The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified as possible or probable carcinogens (2A or 2B) by IARC for which low information exist in humans. While HAA is a family of more than thirty identified chemicals, the metabolism activation and DNA adduct formation have been fully characterized in human liver for few of them (MeIQx, PhIP, AalphaC). Results: We developed a modeling approach in order to predict all the possible metabolite derivatives of a xenobiotic. Our approach relies on the construction of an enriched and annotated map of derivative metabolites from an input metabolite. The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAAs. The method was applied to determine enzyme profiles associated with the maximization of DNA adducts formation derived from each HAA. These profiles could be very different depending on the chemicals allowing to classify HAAs which have been grouped by their associated profiles. Conclusions: Overall, such a predictive toxicological model based on a in silico systems biology approach open perspectives to estimate genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination open the perspective to predict various xenobiotics derived metabolites susceptible to bind DNA adducts in both normal and physiopathological situations.

show abstract

Section: Prediction Of Metabolitesmentioning

confidence: 99%

Section: Prediction Of Metabolitesmentioning

confidence: 99%

Section: Definition and Construction Of Enriched Metabolic Mapsmentioning

confidence: 99%

Section: Prediction Of Sormentioning

confidence: 99%

“…The description of HAAs by SMILE formula were provided in [36]. The SMILES formula of caffeine was extracted from PubChem: https://pubchem.ncbi.nlm.nih.gov/.…”

Section: Fundingmentioning

confidence: 99%

See 3 more Smart Citations

Constructing Xenobiotic Maps of Metabolism to Predict the Role of Enzymes in DNA Adduct Formation

Conan¹,

Théret²,

Langouët³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Unlocking The Mysteries of DNA Adducts with Artificial Intelligence

Arora,

Satija,

Mittal

et al. 2023

ChemBioChem

View full text Add to dashboard Cite

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade‐off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e.g., mutations) or non‐genetic (e.g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)‐based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI‐based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts’ identification, prediction of biochemical routes for adduct formation, adducts’ half‐life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI‐based approaches in DNA adduct biology.

show abstract

New insight into the effect of hydroxyl substituted flavonoids on the cytotoxicity of 2‐amino‐3‐methylimidazo[4,5‐f]quinoline

Deng

Cao

et al. 2022

Food Frontiers

View full text Add to dashboard Cite

The aim of the present work was to evaluate different structures of flavonoids interfering with the cytotoxicity of heterocyclic amines (HCA). Eight flavonoids with similar structure including myricetin, rutin, fisetin, quercetin, myricetrin, quercetrin, kaempferol, and galangin were coincubated with 2-amino-4-methylimidazo[4,5f]quinolone (IQ) respectively to determine the cytotoxicity. The results revealed that IQ at low doses showed no cytotoxicity; however, in the presence of myricetin and rutin, the cytotoxicity significantly elevated. It seems that C-3′, C-4′, C-5′, and C-5 hydroxyl substituents on the flavonoid skeleton of B ring play important role in the increasing cytotoxicity induced by IQ in HepG2 cells. Interestingly, the data obtained from the present study revealed an unexpected adverse effect of the supposed beneficial food ingredient myricetin, which appears to be able to increase the cytotoxicity of the procarcinogen HCA.

show abstract

In silico prediction of Heterocyclic Aromatic Amines metabolism susceptible to form DNA adducts in humans

Cited by 13 publications

References 42 publications

Constructing Xenobiotic Maps of Metabolism to Predict the Role of Enzymes in DNA Adduct Formation

Constructing Xenobiotic Maps of Metabolism to Predict the Role of Enzymes in DNA Adduct Formation

Unlocking The Mysteries of DNA Adducts with Artificial Intelligence

New insight into the effect of hydroxyl substituted flavonoids on the cytotoxicity of 2‐amino‐3‐methylimidazo[4,5‐f]quinoline

Contact Info

Product

Resources

About