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

• Published in: International journal of molecular sciences Vol. 19; no. 10; p. 3015
• Main Authors: Fan, Tengjiao, Sun, Guohui, Zhao, Lijiao, Cui, Xin, Zhong, Rugang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 03.10.2018; MDPI AG
• Subjects: acute oral toxicity; Administration, Oral; Algorithms; Animals; classification; Molecular Structure; N-nitroso compounds; Nitroso Compounds - administration & dosage; Nitroso Compounds - chemistry; Nitroso Compounds - toxicity; QSAR; Quantitative Structure-Activity Relationship; Rats; Reproducibility of Results; toxicity mechanism; Toxicity Tests, Acute; N-nitroso compounds; classification; QSAR; toxicity mechanism; acute oral toxicity
• ISSN: 1422-0067; 1422-0067
• DOI: 10.3390/ijms19103015

To better understand the mechanism of in vivo toxicity of -nitroso compounds (NNCs), the toxicity data of 80 NNCs related to their rat acute oral toxicity data (50% lethal dose concentration, LD ) 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 Q² = 0.7533, R² = 0.8071, Q² = 0.7041 and R² = 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.