Quantum ChemicalEvaluation and QSAR Modeling of <i>N</i>‑NitrosamineCarcinogenicity

<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.

Publication Details

Journal
Figshare
Published
2025-02-07
DOI
https://doi.org/10.1021/acs.chemrestox.4c00476.s001
Primary Topic
Computational Drug Discovery Methods
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article
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Quantum ChemicalEvaluation and QSAR Modeling of N‑NitrosamineCarcinogenicity

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Computational Drug Discovery Methods
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Quantum ChemicalEvaluation and QSAR Modeling of N‑NitrosamineCarcinogenicity

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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.

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Computational Drug Discovery Methods
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