Quantum Chemical Evaluation and QSAR Modeling of <i>N</i>-Nitrosamine Carcinogenicity

<i>N</i>-Nitrosamine compounds in pharmaceuticals are a major concern due to their carcinogenic potential. However, not all nitrosamines are strong carcinogens, and understanding the structure-activity relationships of this compound group is a major challenge. The determination of the acceptable intake limits for this compound group is determined by applying either a simple carcinogenic potency categorization approach (CPCA) or read-across analysis from simple nitrosamines where experimental data exist. However, the emergence of structurally complex nitrosamines makes quantitative models desirable. Here, we present a two-step modeling approach based on a linear discriminant analysis of a set of quantum mechanical and classical descriptors followed by a 3D-QSAR PLS regression model to predict the logTD<sub>50</sub> of nitrosamine compounds.

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Publication Details

Journal
Chemical Research in Toxicology
Published
2025-02-06
DOI
https://doi.org/10.1021/acs.chemrestox.4c00476
Citations
8
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
12.03
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article

Quantum Chemical Evaluation and QSAR Modeling of N-Nitrosamine Carcinogenicity

Sebastian Schieferdecker, Esther Vock
8 citations
Chemical Research in Toxicology
Computational Drug Discovery Methods
12.03
article

Quantum Chemical Evaluation and QSAR Modeling of N-Nitrosamine Carcinogenicity

Sebastian Schieferdecker, Esther Vock
article en
8 citations

Abstract

<i>N</i>-Nitrosamine compounds in pharmaceuticals are a major concern due to their carcinogenic potential. However, not all nitrosamines are strong carcinogens, and understanding the structure-activity relationships of this compound group is a major challenge. The determination of the acceptable intake limits for this compound group is determined by applying either a simple carcinogenic potency categorization approach (CPCA) or read-across analysis from simple nitrosamines where experimental data exist. However, the emergence of structurally complex nitrosamines makes quantitative models desirable. Here, we present a two-step modeling approach based on a linear discriminant analysis of a set of quantum mechanical and classical descriptors followed by a 3D-QSAR PLS regression model to predict the logTD<sub>50</sub> of nitrosamine compounds.

Chemical Research in ToxicologyVol. 38(2)
Boehringer Ingelheim (Germany) (DE)
Zero hunger
Openalex Percentile: Top 2%
Computational Drug Discovery Methods
12.03
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