QSAR and Classification Study on Prediction of Acute Oral Toxicity of N-Nitroso Compounds.

Rugang Zhong,Lijiao Zhao,Xin Cui,Tengjiao Fan,Guohui Sun

doi:10.3390/ijms19103015

Abstract

To better understand the mechanism of in vivo toxicity of N-nitroso compounds (NNCs), the toxicity data of 80 NNCs related to their rat acute oral toxicity data (50% lethal dose concentration, LD50) were used to establish quantitative structure-activity relationship (QSAR) and classification models. Quantum chemistry methods calculated descriptors and Dragon descriptors were combined to describe the molecular information of all compounds. Genetic algorithm (GA) and multiple linear regression (MLR) analyses were combined to develop QSAR models. Fingerprints and machine learning methods were used to establish classification models. The quality and predictive performance of all established models were evaluated by internal and external validation techniques. The best GA-MLR-based QSAR model containing eight molecular descriptors was obtained with Q2loo = 0.7533, R2 = 0.8071, Q2ext = 0.7041 and R2ext = 0.7195. The results derived from QSAR studies showed that the acute oral toxicity of NNCs mainly depends on three factors, namely, the polarizability, the ionization potential (IP) and the presence/absence and frequency of C–O bond. For classification studies, the best model was obtained using the MACCS keys fingerprint combined with artificial neural network (ANN) algorithm. The classification models suggested that several representative substructures, including nitrile, hetero N nonbasic, alkylchloride and amine-containing fragments are main contributors for the high toxicity of NNCs. Overall, the developed QSAR and classification models of the rat acute oral toxicity of NNCs showed satisfying predictive abilities. The results provide an insight into the understanding of the toxicity mechanism of NNCs in vivo, which might be used for a preliminary assessment of NNCs toxicity to mammals.

Highlights

N-nitroso compounds (NNCs) are an important class of potent toxicants that widely exist in the environment and diet [1]
We developed the quantitative structure-activity relationship (QSAR) and classification models of a large set of 80 NNCs with their rat acute oral toxicity
All QSAR models were established by Genetic algorithm (GA)-multiple linear regression (MLR) methods

Summary

Introduction

N-nitroso compounds (NNCs) are an important class of potent toxicants that widely exist in the environment and diet [1]. The carcinogenicity, mutagenicity and toxicity of NNCs and their metabolites have been evaluated in various experiments [2,3,4]. Among the 300 NNCs that have been tested for their carcinogenic potential, more than 90% were proven to be carcinogenic in a wide variety of animal species [5,6]. Human exposure to NNCs occurs mainly through food, tobacco products, drugs, car interiors, and cosmetics [7]. NNCs may be synthesized endogenously from precursors and nitrosating agents, mainly in the stomach, leading to the formation of potentially carcinogenic compounds [8,9,10,11]. Due to the potentially harmful effects of these compounds, it is necessary to study the mechanism of action of their biological effects, the structure-activity relationship (SAR)

Methods

Results

Conclusion