Computational identification of structural factors affecting the mutagenic potential of aromatic amines: study design and experimental validation

Archives of Toxicology
Svetoslav H SlavovBeat J Brüschweiler

Abstract

A grid-based, alignment-independent 3D-SDAR (three-dimensional spectral data-activity relationship) approach based on simulated 13C and 15N NMR chemical shifts augmented with through-space interatomic distances was used to model the mutagenicity of 554 primary and 419 secondary aromatic amines. A robust modeling strategy supported by extensive validation including randomized training/hold-out test set pairs, validation sets, "blind" external test sets as well as experimental validation was applied to avoid over-parameterization and build Organization for Economic Cooperation and Development (OECD 2004) compliant models. Based on an experimental validation set of 23 chemicals tested in a two-strain Salmonella typhimurium Ames assay, 3D-SDAR was able to achieve performance comparable to 5-strain (Ames) predictions by Lhasa Limited's Derek and Sarah Nexus for the same set. Furthermore, mapping of the most frequently occurring bins on the primary and secondary aromatic amine structures allowed the identification of molecular features that were associated either positively or negatively with mutagenicity. Prominent structural features found to enhance the mutagenic potential included: nitrobenzene moieties, conjugated π-systems, nit...Continue Reading

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Citations

Feb 29, 2020·Archives of Toxicology·Svetoslav SlavovRichard D Beger
Apr 12, 2020·Environmental Monitoring and Assessment·Lin Jun ZhouZhi Ying Liu

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Methods Mentioned

BETA
NMR

Software Mentioned

SDAR
Leadscope Model Applier
Matlab
3D
ACDLabs
CODESSA
FSVO
Hyperchem
Toxmatch
XNMR

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