Per- and polyfluoroalkyl substances (PFASs) are a class of environmentally persistent industrial compounds that disrupt various metabolic pathways. Among the protein receptors to which PFASs bind, the human pregnane X receptor (hPXR) is found to be a host for a variety of long- and short-chain PFASs that lead to its overactivation. Overactivation of hPXR is linked to potential endocrine disruption, oxidative stress, hepatic steatosis, and adverse drug interactions. In this study, molecular dynamics (MD) is used to study the binding between hPXR and a number of PFAS compounds, including alternatives whose activity on hPXR has not been experimentally tested. This is the first-time MD is used to study the interactions between PFASs and hPXR, showing how relative binding free energies of PFASs relate to hPXR agonism. Binding free energy calculations, hydrogen bond analysis, per-residue decomposition calculations, and alanine scanning studies are done to provide further insight. Activities on hPXR for several short-chain and alternative PFAS compounds to long-chain PFASs that have yet to be reported will also be considered. These short-chain and alternative species include perfluorobutane sulfonic acid (PFBS), Gen-X (trade name for 2,3,3,3-tetrafluoro-2-heptafluoropropoxy propanoic acid), ADONA (trade name for 4,8-dioxa-3H-perfluorononanoic acid), and 6:2 fluorotelomer carboxylic acid (6:2 FTCA). The study shows key aspects of PFAS recognition on the hPXR, the link between PFAS binding to hPXR and the hPXR activity change observed upon the PFAS exposure, and the potential effects of alternative PFASs on hPXR activity.

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are a class of chemicals widely used in industrial applications due to their exceptional properties and stability. However, they do not readily degrade in the environment and are linked to contamination and adverse health effects in humans and wildlife. To find alternatives for the most commonly used PFAS molecules that maintain their desirable chemical properties but are not adverse to biological lifeforms, a novel approach based upon machine learning is utilized. The machine learning model is trained on an existing set of PFAS molecules to generate over 260,000 novel PFAS molecules, which we dub PFAS-AI-Gen. Using molecular descriptors with known relationships to toxicity and industrial suitability followed by molecular docking and molecular dynamics simulations, this set of molecules is screened. In this manner, increasingly complex calculations are performed only for candidate molecules that are most likely to yield the desired properties of low binding affinity toward two selected protein receptors, the human pregnane x receptor (hPXR) and peroxisome proliferator-activated receptor γ (PPAR-γ), and high industrial suitability, defined by critical micelle concentration (CMC). The selection criteria of low binding affinity and high industrial suitability are relative to the popular PFAS alternative GenX. hPXR and PPAR-γ are selected as they are PFAS targets and facilitate a variety of functions, such as drug metabolism and glucose regulation, respectively. Through this approach, 22 promising new PFAS substitutes that may warrant experimental investigation are identified. This integrated approach of molecular screening and toxicity estimation may be applicable to other chemical classes.