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Computational Prediction of Metabolic α-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment

[Image: see text] Recent withdrawal of several drugs from the market due to elevated levels of N-nitrosamine impurities underscores the need for computational approaches to assess the carcinogenicity risk of nitrosamines. However, current approaches are limited because robust animal carcinogenicity...

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Autor principal: Chakravarti, Suman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283024/
https://www.ncbi.nlm.nih.gov/pubmed/37267457
http://dx.doi.org/10.1021/acs.chemrestox.3c00083
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author Chakravarti, Suman
author_facet Chakravarti, Suman
author_sort Chakravarti, Suman
collection PubMed
description [Image: see text] Recent withdrawal of several drugs from the market due to elevated levels of N-nitrosamine impurities underscores the need for computational approaches to assess the carcinogenicity risk of nitrosamines. However, current approaches are limited because robust animal carcinogenicity data are only available for a few simple nitrosamines, which do not represent the structural diversity of the many possible nitrosamine drug substance related impurities (NDSRIs). In this paper, we present a novel method that uses data on CYP-mediated metabolic hydroxylation of CH(2) groups in non-nitrosamine xenobiotics to identify structural features that may also help in predicting the likelihood of metabolic α-carbon hydroxylation in N-nitrosamines. Our approach offers a new avenue for tapping into potentially large experimental data sets on xenobiotic metabolism to improve risk assessment of nitrosamines. As α-carbon hydroxylation is the crucial rate-limiting step in nitrosamine metabolic activation, identifying and quantifying the influence of various structural features on this step can provide valuable insights into their carcinogenic potential. This is especially important considering the scarce information available on factors that affect NDSRI metabolic activation. We have identified hundreds of structural features and calculated their impact on hydroxylation, a significant advancement compared to the limited findings from the small nitrosamine carcinogenicity data set. While relying solely on α-carbon hydroxylation prediction is insufficient for forecasting carcinogenic potency, the identified features can help in the selection of relevant structural analogues in read across studies and assist experts who, after considering other factors such as the reactivity of the resulting electrophilic diazonium species, can establish the acceptable intake (AI) limits for nitrosamine impurities.
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spelling pubmed-102830242023-06-22 Computational Prediction of Metabolic α-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment Chakravarti, Suman Chem Res Toxicol [Image: see text] Recent withdrawal of several drugs from the market due to elevated levels of N-nitrosamine impurities underscores the need for computational approaches to assess the carcinogenicity risk of nitrosamines. However, current approaches are limited because robust animal carcinogenicity data are only available for a few simple nitrosamines, which do not represent the structural diversity of the many possible nitrosamine drug substance related impurities (NDSRIs). In this paper, we present a novel method that uses data on CYP-mediated metabolic hydroxylation of CH(2) groups in non-nitrosamine xenobiotics to identify structural features that may also help in predicting the likelihood of metabolic α-carbon hydroxylation in N-nitrosamines. Our approach offers a new avenue for tapping into potentially large experimental data sets on xenobiotic metabolism to improve risk assessment of nitrosamines. As α-carbon hydroxylation is the crucial rate-limiting step in nitrosamine metabolic activation, identifying and quantifying the influence of various structural features on this step can provide valuable insights into their carcinogenic potential. This is especially important considering the scarce information available on factors that affect NDSRI metabolic activation. We have identified hundreds of structural features and calculated their impact on hydroxylation, a significant advancement compared to the limited findings from the small nitrosamine carcinogenicity data set. While relying solely on α-carbon hydroxylation prediction is insufficient for forecasting carcinogenic potency, the identified features can help in the selection of relevant structural analogues in read across studies and assist experts who, after considering other factors such as the reactivity of the resulting electrophilic diazonium species, can establish the acceptable intake (AI) limits for nitrosamine impurities. American Chemical Society 2023-06-02 /pmc/articles/PMC10283024/ /pubmed/37267457 http://dx.doi.org/10.1021/acs.chemrestox.3c00083 Text en © 2023 The Author. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Chakravarti, Suman
Computational Prediction of Metabolic α-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment
title Computational Prediction of Metabolic α-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment
title_full Computational Prediction of Metabolic α-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment
title_fullStr Computational Prediction of Metabolic α-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment
title_full_unstemmed Computational Prediction of Metabolic α-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment
title_short Computational Prediction of Metabolic α-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment
title_sort computational prediction of metabolic α-carbon hydroxylation potential of n-nitrosamines: overcoming data limitations for carcinogenicity assessment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283024/
https://www.ncbi.nlm.nih.gov/pubmed/37267457
http://dx.doi.org/10.1021/acs.chemrestox.3c00083
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