Potential impact of organophosphate esters on thyroid eye disease based on machine learning and molecular docking

Abstract

Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers in daily commodities and building materials. Some OPEs, acting as agonists of the thyroid-stimulating hormone receptor (TSHR), may contribute to the development of thyroid eye disease (TED). This study analyzes the serum and urine of patients and control groups, using machine learning and molecular docking to investigate the potential impact of OPEs on TED. Results indicate significantly higher concentrations of OPEs and di-OPEs of TED patients compared to controls (Mann-Whitney U test, p < 0.05). Aryl OPEs exhibit the strongest binding affinity with TSHR. We developed a predictive model for OPE-TSHR affinity to explore the impact of OPE structural features on TSHR activity and effectively capture the complex relationships between changes in OPE side chains and their effects on TSHR. Predictions from the USEPA's database indicate that 28 % of 1011 OPEs have a tendency to bind with TSHR. Furthermore, a high-accuracy classification model successfully identified key substructures associated with high affinity for TSHR. This study not only enhances our understanding of the complex relationship between the structural diversity of OPEs and their thyroid impact but also offers molecular design insights to prevent releasing OPEs with high thyroid harm potential into the environment.

Keywords: Machine learning; Molecular docking; OPEs; TSHR; Thyroid eye disease.