Hepatotoxicity assessment investigations on PFASs targeting L-FABP using binding affinity data and machine...

Area of Science:

• Environmental Chemistry
• Toxicology
• Computational Chemistry

Background:

• Per- and polyfluoroalkyl substances (PFASs) are persistent organic pollutants found globally.
• Assessing PFASs' safety, particularly their hepatotoxicity, is challenging.
• PFASs may accumulate in the liver and cause damage by interacting with liver fatty acid binding protein (L-FABP).

Purpose of the Study:

• To evaluate the binding affinity of various PFASs to L-FABP.
• To develop a predictive model for identifying potentially hazardous PFASs.
• To understand the molecular properties influencing PFASs-induced hepatotoxicity.

Main Methods:

• Computational analysis of two L-FABP binding sites.
• Development of a quantitative structure-activity relationship (QSAR) model using machine learning.
• Application of Bayesian Kernel Machine Regression (BKMR) to identify determinant molecular properties.

Main Results:

• L-FABP's inner site showed higher sensitivity to PFASs, with specific affinities for different PFAS chemical classes.
• A QSAR model was successfully developed for predicting PFAS hepatotoxicity.
• Molecular flexibility was identified as a critical factor in PFAS-induced hepatotoxicity, affecting binding affinity through individual and joint effects.

Conclusions:

• Understanding PFAS binding to L-FABP is crucial for assessing hepatotoxicity.
• The developed QSAR model offers an efficient method for predicting hazardous PFASs.
• Identifying molecular flexibility as a key determinant provides insights for risk assessment and developing safer alternatives.