er- and polyfluoroalkyl substances (PFAS) are a diverse class of synthetic organofluoride chemicals that are used widely in commercial and industrial applications. [Recently, these GEITP pages had a long email chain of discussion about these “forever chemicals”.] Owing to the strength of their carbon-fluorine bonds, PFAS and their terminal breakdown products are resistant to enzymatic and thermal degradation, and thus accumulate in the environment. The persistence, mobility, and worldwide use of PFAS — since the 1950s — have resulted in the ubiquitous detection of these chemicals in many environmental matrices, and in humans, plants, and wildlife.
As discussed in these GEITP pages, the toxicity of some PFAS — targets several organ systems (hepatic, renal, immune, endocrine, etc.) and has caused escalating public and regulatory concern regarding the adverse health impacts resulting from exposure. Although PFAS comprise a large heterogenous class of thousands of chemicals, research has predominantly focused on a small subset of perfluoroalkyl acid congeners, whereas the fundamental physiochemical properties of several thousand PFAS remain undefined.
Due to major physiochemical differences between the highly fluorinated PFAS and other persistent organic pollutants, which tend to be lipophilic, data and computational models describing the transport and fate of other organic pollutants do not adequately provide us with accurate data about PFAS toxicokinetics. As amphiphilic compounds [chemical compounds possessing both hydrophilic (water-loving, polar) and lipophilic (fat-loving, nonpolar) groups], many PFAS are bound by proteins and accumulate in highly perfused protein-rich tissue compartments (e.g., blood, liver, and kidneys). Protein–PFAS interactions are thus a critical variable for computationally determining transport, bioaccumulation, and half-life of PFAS.
Human serum albumin (HSA) is the primary transport protein in human blood for many endogenous and exogenous ligands, including many PFAS. Increased HSA–PFAS binding affinities are associated with increased circulatory half-life, delayed elimination, and decreased plasma-free fraction of PFAS in the body. Whereas an increased HSA–PFAS binding affinity leads to increased bioaccumulative potential of the PFAS, however, the unbound or free fraction of PFAS in the plasma is the more bioavailable fraction — which is often associated with greater likelihood of tissue transport, reactivity, and toxicity. Authors [see attached pdf file] believe that accurate measures of albumin–PFAS binding might help improve the accuracy of pharmacologically-based pharmacokinetic (PBPK) models, and other computational models aimed at elucidating PFAS toxicokinetics, which remain poorly understood.
Yet, experimentally-determined HAS-binding affinity estimates exist for only a small number of PFAS congeners. Although several advanced computational approaches hold great potential for predicting PFAS–protein interactions, as well as PFAS bioactivity and toxicity, the efficacy of these models is limited by this lack of basic physiochemical data. One contributing factor for the paucity of PFAS-binding data is that several of the most common approaches for measuring albumin–PFAS binding — including equilibrium dialysis and isothermal titration calorimetry — are expensive, time-consuming, and/or require large volumes of protein, making them insufficient for large-scale determinations of binding affinities for thousands of PFAS congeners.
To address the need for directly comparable data describing albumin binding of PFAS, these authors [see attached; the senior author Scott Belcher is a GEITP-er, so if anyone has questions, fire away — directly at him ] — developed an in vitro differential scanning fluorimetry (DSF) assay to determine relative binding affinities of HSA to structurally informative sets of different PFAS. [The DSF assay measures concentration-dependent changes in the melting temperature (TM) of HSA with increasing concentrations of a PFAS, and those data are then used to estimate binding affinity (Ka) values.] Using this DSF assay, authors have determined relative PFAS-binding affinities in serum albumin from humans, as well as from laboratory and livestock animals. However, due to the vast number of PFAS in production and use (with a least 14,000 PFAS chemical structures currently identified), there is a critical need for high-throughput methodologies to facilitate the rapid evaluation of protein–PFAS interactions. This is where the AI subset, machine-learning, steps in. Machine-learning approaches are uniquely positioned to address these data gaps, because machine-learning is a powerful, artificial intelligence (AI)-based tool used to automate analytical model building, and to allow for robust pattern identification in complex datasets.
To address the lack of experimental binding data for thousands of PFAS congeners, authors developed a stepwise, high-throughput approach in which [i] DSF is used to calculate HSA-binding affinities for diverse PFAS, [ii] DSF binding data is then used to train and optimize machine-learning algorithms to build: [iii] optimized classification models to predict binary binding (yes/no) of PFAS, and [iv] optimized regression models to predict HSA–PFAS binding affinities.
In this study [see attached], authors first used DSF to determine HSA-binding affinities for a curated binding affinity dataset of 65 PFAS from 20 chemical classes. DSF binding-affinity data for 3 fatty acids — representing non-fluorinated structural analogs of some perfluoroalkyl carboxylic acids (PFCAs), were also included in the training data for machine-learning models (these fatty acids were included to further assess the impact of fluorination on HSA-binding of PFAS). Authors trained 7 machine-learning models to make binary binding predictions (yes/no binding), and 9 regression-based models were trained to make quantitative binding-affinity (Ka) predictions. The optimally performing binary classifier and quantitative regression models were ultimately used to generate binding predictions for hundreds of PFAS in the EPAPFASINV chemical inventory list, allowing the direct comparison of HSA binding across hundreds of diverse PFAS congeners.
The top-performing classifier model, logistic regression, had an AUROC (area under the receiver operating characteristic curve) statistic of 0.936. The top-performing regression model, support vector regression, had an R2 of 0.854. These top-performing models were then used to predict HSA–PFAS binding for chemicals in EPA’s Computational Toxicology Chemicals Dashboard (EPAPFASINV) list of 430 PFAS. These data — which developed new in vitro and in silico methodologies — represent a high-throughput framework for predicting protein–PFAS binding based on empirical data, and generate directly comparable binding data of potential use in predictive modeling of PFAS bioaccumulation and other toxicokinetic endpoints. ☹
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Toxicol Sci Jan 2025; 203: 67–78