[PFOA and PFOS Alternative PFAS Show Higher Binding Affinity for PPARα in Novel Machine Learning Study]
Per- and polyfluoroalkyl substances (PFAS) have long been used in products for their water-repellent and stain-resistant properties. However, the environmental and health risks associated with PFAS, particularly long-chain perfluoroalkyl acids such as PFOA and PFOS, have raised concerns globally. These substances are persistent, bioaccumulative, and toxic, leading to their regulation under the Stockholm Convention on Persistent Organic Pollutants.
One of the key toxicological aspects of PFAS, including PFOA and PFOS, is their ability to disrupt lipid metabolism through interaction with a receptor called PPARα. This receptor plays a crucial role in lipid metabolism, energy balance, and cell differentiation. When PFAS bind to PPARα, it disrupts signaling pathways and causes various adverse biological effects.
While the hazards of PFOA and PFOS are well-known, there is limited knowledge regarding the potential risks posed by thousands of alternative PFAS types, including the next-generation ones. To address this knowledge gap, researchers conducted a novel machine learning study to predict the binding affinity of PFAS to PPARα.
In this study, researchers utilized SMILES data for 6,798 PFAS from the U.S. EPA database. They employed the Molecular Operating Environment (MOE) to calculate 206 molecular descriptors and the binding affinity (known as S-score) of each PFAS to PPARα. Interestingly, the results showed that 4,089 PFAS exhibited lower S-scores compared to both PFOA and PFOS.
Through a systematic selection of essential molecular descriptors, the researchers developed a machine learning model. Remarkably, this model demonstrated good predictive performance using only three descriptors, achieving an R2 value of 0.72. The key molecular characteristics that influenced the binding affinity of PFAS to PPARα were found to be molecular size (b_single) and electrostatic properties (BCUT_PEOE_3 and PEOE_VSA_PPOS).
The study also shed light on the potential toxicity of alternative PFAS. While these substances are generally considered safer than their legacy counterparts, the researchers found that certain alternative PFAS with numerous carbon atoms and ether groups exhibited a higher binding affinity for PPARα than PFOA and PFOS. This finding raises concerns about the potential health effects associated with these alternative PFAS.
Moreover, the novel machine learning approach developed in this study surpasses traditional methods in terms of interpretability. It offers deeper insights into the molecular mechanisms underlying the toxicity of PFAS. This approach can be extended beyond PFAS-PPARα binding to study other ligand-receptor interactions and structure-property relationships.
Despite its success, the study has limitations. It primarily focused on the interaction of PFAS with PPARα, while PFAS may induce toxicity through other receptors as well. Additionally, it is important to note that a high binding score does not necessarily indicate toxicity. Therefore, experimental verification is crucial to determine the actual toxicity of PFAS.
Nevertheless, this novel approach provides a rapid and cost-effective screening method for PFAS, allowing for a preliminary understanding of their potential toxicity. It also serves as a valuable tool for guiding further in-depth experimental investigations.
In summary, a machine learning study has revealed the higher binding affinity of certain alternative PFAS for PPARα, a receptor involved in lipid metabolism. Although alternative PFAS are generally considered safer, the study highlights potential health concerns associated with specific alternative PFAS compounds. The novel machine learning approach provides valuable insights into the molecular mechanisms of PFAS toxicity and enables rapid screening for potential hazards. Further research and experimental verification are necessary to fully understand the risks posed by PFAS and guide appropriate risk mitigation strategies.
Source: [Ehime University website](https://www.ehime-u.ac.jp/en/)
