A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods
Journal
Food and Chemical Toxicology
Journal Volume
160
Date Issued
2022
Author(s)
Abstract
Carcinogenicity is one of the most critical endpoints for the risk assessment of food contact chemicals (FCCs). However, the carcinogenicity of FCCs remains insufficiently investigated. To fill the data gap, the application of standard experimental methods for identifying chemicals of carcinogenic concerns from a large set of FCCs is impractical due to their resource-intensive nature. In contrast, computational methods provide an efficient way to quickly screen chemicals with carcinogenic potential for subsequent experimental validation. Since every model was developed based on a limited number of training samples, the use of single models for carcinogenicity assessment may not cover the complex mechanisms of carcinogenesis. This study proposed a novel machine learning-based weight-of-evidence (WoE) model for prioritizing chemical carcinogenesis. The WoE model can nonlinearly integrate complementary computational methods of structural alerts, quantitative structure-activity relationship models and in silico toxicogenomics models into a WoE-score. Compared to the best single method, the WoE model gained 8% and 19.7% improvement in the area under the receiver operating characteristic curve (AUC) value and chemical coverage, respectively. The prioritization of 1623 FCCs concludes 44 chemicals of high carcinogenic concern. The machine learning-based WoE approach provides a fast and comprehensive way for prioritizing chemicals of carcinogenic concern. ? 2022 Elsevier Ltd
Subjects
Food contact chemical
Machine learning
Quantitative structure-activity relationship
Structural alert
Toxicogenomics
Weight-of-evidence
article
carcinogenicity
chemical carcinogenesis
computer model
controlled study
machine learning
quantitative structure activity relation
receiver operating characteristic
toxicogenomics
Type
journal article