Prediction of cytotoxicity of polycyclic aromatic hydrocarbons from first principles
Taewoo Kim, Juyuan Zhen, Jung‐Hyun Lee, Shin Yeong Park, Changkeun Lee, Bong-Oh Kwon, Seongjin Hong, Hyeong‐Moo Shin, John Paul Giesy, Gap Soo Chang, Jong Seong Khim · The Science of The Total Environment · 2024
Ligand-specific binding interactions of xenobiotics with receptor proteins form the basis of cytotoxicity-based hazard assessment. Computational approaches enable predictive hazard assessment for a large number of chemicals in a high-throughput manner, minimizing the use of animal testing. However, in silico models for predicting mechanisms of toxic actions and potencies are difficult to develop because toxicity datasets or comprehensive understanding of the complicated kinetic process of ligand-receptor interactions are needed for model development. In this study, a directional reactive binding factor (DRBF) model based on first principles was used to predict cytotoxicity potencies of agonists of the aryl hydrocarbon receptor (AhR) for 16 different polycyclic aromatic hydrocarbons (PAHs). Molecular dynamics were simulated by accounting for the directional configuration factor toward receptor protein and the factor of binding to the Per-Arnt-Sim (PAS) domain. When comparing the experimental results of toxic potencies from in vitro bioassays with the predictions among two different in silico models, including quantitative structure-activity relationship (QSAR) and molecular docking models, the DRBF model exhibited the highest model performance (R 2 = 0.90 and p < 0.01). Our results showed that the DRBF model based on first principles and molecular and computational structural biology could serve as a novel framework to advance next generation hazard assessment for high-throughput screening of chemical substances. • We presented simple, accurate models to predict binding of ligands to AhR. • Development of a directional reactivity factor (DRF) as a prediction indicator. • A directional reactive binding factor by calculating a binding rate between ligands and AhR. • Greater accuracy than conventional toxicity prediction model (QSAR, docking model). • The model highlighted a more profound mechanism of chemical toxicity.