Glyphosate is an herbicide found worldwide in glyphosate-based formulations (GBFs). Although glyphosate appears to have a low toxicity profile for humans and mammals, conflicting reports exist regarding the risk for cancer in humans. US-EPA and European regulatory agencies have described glyphosate as unlikely to pose a carcinogenic hazard to humans. However, the International Agency for Research on Cancer (IARC) classified glyphosate as "probably carcinogenic to humans (Group 2A)," citing "mechanistic data provide strong evidence for genotoxicity and oxidative stress." Given these discrepancies, the Division of Translational Toxicology at NIEHS designed an experimental strategy to expand mechanistic evidence and address critical gaps within existing literature (e.g. mechanistic evaluations of glyphosate alongside GBFs, inclusion of context-defining positive controls). Cell morphology, viability, H2O2, and γH2AX formation were assayed in human keratinocytes (HaCaT), previously cited by IARC, and human hepatocytes (HepaRG) to derive benchmark concentrations and fold-change response metrics. Our findings revealed glyphosate alone was weakly and inconsistently bioactive for oxidative stress and DNA damage when compared with positive controls. In contrast, most of the 13 GBFs evaluated were more clearly bioactive with no apparent correlation to varied glyphosate concentrations. Hierarchical clustering of biological responses revealed some bioactive GBFs to cluster near well-characterized positive controls for oxidative stress, whereas 4 GBFs clustered more similarly to negative controls and glyphosate. Collectively, this study provides a robust dataset with context-defining results that advance our understanding of the hazard potential of GBFs while revealing that glyphosate is likely not a primary driver of oxidative stress from GBF exposures.

PFASs are widely present and persistent in the environment, and exposure can occur via multiple pathways. Human and animal PFAS exposures have been associated with alterations in thyroid hormones, hepatotoxicity, and other adverse effects. This study evaluated the subchronic toxicities of four specific PFASs in 90-day oral rat studies. Studies were conducted in male and female Sprague–Dawley rats exposed to PFASs in corn oil via oral gavage. The PFASs studied were 1H,1H,9H-perfluorononyl acrylate (PFNAC), 1H,1H,2H,2H-perfluorohexyl iodide (PFHI), methyl heptafluoropropyl ketone (MHFPK), and 2-chloro-2,3,3,3-tetrafluoropropanoic acid (CTFPA). High doses were 10 mg/kg-day (male) and 30 mg/kg-day (female) for PFNAC, 200 mg/kg-day for PFHI, 300 mg/kg-day for MHFPK, and 30 (male) and 100 mg/kg-day (female) for CTFPA. The four lower doses for each PFAS were spaced at two- or threefold dose increments. The most consistent effect was dose-dependent increases in the relative and absolute liver weights for PFNAC, PFHI, and CTFPA but not for MHFPK. Increased liver weights were correlated with findings of hepatocellular hypertrophy. Increased kidney weights for PFNAC and PFHI were correlated with increased incidence of minimal tubule epithelial hypertrophy (PFNAC) or increased incidence and severity of chronic progressive nephropathy and hyaline droplet accumulation (PFHI). There were no compound-related effects on morbidity and mortality or overt signs of toxicity.

Hexabromocyclododecane (HBCD) is a brominated flame retardant, that is added, but not chemically bonded, to consumer products. HBCD is sold as a commercial-grade HBCD mixture containing three major stereoisomers: alpha (α), beta (β), and gamma (γ), with relative amounts of 12

In this article, we provide a proof of concept evaluating the utility of the U.S. Tox21 high-throughput screening approach to assess the hazard of chemical mixtures using 2 estrogen receptor (ER) assays. A subset of chemicals identified in Phase I of the Tox21 program as active in the ER agonist assay were used to design mixtures for testing in Phase II. Individual chemicals and mixtures were evaluated in 2 cell-based ER alpha (ERα) activation assays: One incorporating a transfected ligand-binding domain in an ERα β-lactamase reporter cell line (ER-bla) and the full-length endogenous receptor in the MCF7 cell line with a luciferase reporter gene (ER-luc). Concentration-response data from individual chemicals were used to predict the joint effect based on mixtures modeling methods and were compared with observed mixtures data to assess model fit. The models tended to overpredict mixture responses in the ER-bla assay, whereas predictions were closer to observed responses in the ER-luc assay, indicating that a full-length endogenous ER is a preferred model for high-throughput mixture analysis. Lessons learned from this research include the importance of analyzing the individual chemicals used for predictions and the mixtures in the same experimental paradigm to minimize variation, developing methods for imputing missing values from incomplete concentration-response curves, and establishing criteria to determine when inactive chemicals should be omitted from mixture predictions.

High-resolution accurate mass non-targeted analysis (NTA) is a useful discovery tool for metabolite characterization of in vivo dosing studies since it enables detection of both predicted and unexpected biotransformation products. We used NTA to investigate biotransformation of perfluorohexanesulfonamide (PFHxSA) in plasma and liver from male and female Sprague Dawley rats after a 5-day repeat exposure study. PFHxSA is an emerging per- and polyfluoroalkyl substance (PFAS) with unknown toxicity and a potentially reactive headgroup. NTA revealed the presence of predicted in vivo biotransformation products (BP) such as perfluorohexane sulfonic acid (PFHxS) and perfluorohexanesulfinic acid (PFHxSi). PFHxSi also has unknown toxicity and has not, to our knowledge, been previously reported as a PFHxSA BP in mammals. Multiple perfluoroalkyl ether sulfonamides, associated BPs, and novel PFAS were also detected in rat plasma and liver. We observed sex-specific distributions of the dosed compound and BPs, suggesting different toxicokinetics and biological responses. The presence of a complex mixture of predicted and unexpected PFAS in plasma and liver not only mimics the complexity of environmental exposure but also highlights the need for toxicity testing with mixtures and a more complete assessment of dosing solution purity.

High throughput toxicokinetic (HTTK) methods address chemical risk assessment data gaps but require chemical-specific values that can be obtained by in vitro measurements or in silico models. In this study, seven quantitative structure property relationship (QSPR) models were used to estimate intrinsic hepatic clearance (Clint), fraction of chemical unbound in plasma (fup), and/or TK elimination half-life (t½). Performance of the QSPR models was evaluated using literature time-course in vivo TK data, mainly from rats. Simulations of the in vivo data were made with a high throughput physiologically based TK (HT-PBTK) model using the different QSPR model predictions as inputs. We estimate that using rat in vivo data to evaluate QSPR models trained on human in vitro measured data might inflate error estimates by as much as root mean squared log10 error (RMSLE) 0.8. A sensitivity analysis showed that Clint and fup parameters inform predictions of area under the curve (AUC) and steady-state concentration (Css). We estimate that AUC can be predicted by HTTK with RMSLE 0.9 using in vitro measurements and 0.6-0.8 using QSPR model values. We anticipate that, for some novel compounds, QSPRs for HTTK input parameters will give predictions of TK similar to those based on in vitro measurements.

Perfluorohexanesulfonamide (PFHxSA) is used as a replacement for legacy PFAS. Non-targeted analysis identified perfluorohexane sulfonic acid (PFHxS) as the primary metabolite of PFHxSA in plasma and liver in a short-term (5-day) repeat dose study with male and female Sprague Dawley rats (Crl:CD(SD)). This evaluation sought to quantitate PFHxSA and PFHxS concentrations by targeted liquid chromatography/mass spectrometry (LC/MS/MS) to further evaluate metabolism and dosimetry following in vivo PFHxSA exposure. In males, quantified plasma and liver PFHxS concentrations were higher than those of its parent, PFHxSA. PFHxS was detected in female plasma and liver at on average 5.3- and 2.9-fold lower, respectively, than PFHxSA. In both sexes, plasma and liver PFHxSA dose concentrations decreased with increasing doses, suggesting hepatic enzyme induction. Liver-to-plasma partitioning favored plasma across all doses in both sexes. In vitro–in vivo extrapolation (IVIVE) suggests higher steady-state plasma concentrations in humans vs. rats for PFHxSA and PFHxS. The in vivo concentrations aligned reasonably (i.e., within 6- to 12.1-fold) with the IVIVE-derived rat plasma estimates. Identifying when PFAS co-exposures may result due to metabolic biotransformation of the parent PFAS to a stable and potentially bioactive metabolite is important to better inform the interpretation of in vivo and in vitro findings.

Toxicokinetic modeling describes the absorption, distribution, metabolism, and elimination of chemicals by the body. Chemical-specific in vivo toxicokinetic data is often unavailable for the thousands of chemicals in commerce. However, predictions from generalized toxicokinetic models allow for extrapolation from in vitro toxicological data, obtained via new approach methods (NAMs), to predict in vivo human health outcomes and provide key information on chemicals for public health risk assessment. The httk R package provides an open-source software tool containing a suite of generalized toxicokinetic models covering various exposure scenarios, a library of chemical-specific data from peer-reviewed high-throughput toxicokinetic (HTTK) studies, and other utility functions to parameterize and evaluate toxicokinetic models. Generalized HTTK models in httk use the open-source language MCSim to describe the compartmental and physiologically based toxicokinetics (PBTK). New HTTK models may be integrated into httk with a model description code file (C script generated via MCSim) and a model documentation file (R script). httk provides a series of functionalities such as model parameterization, in vivo-derived data for evaluating model predictions, unit conversion, Monte Carlo simulations for uncertainty propagation and biological variability, and other model utilities. Here, we describe in detail how to add new HTTK models into the httk package to leverage its pre-existing data and functionality. As a demonstration, we describe the integration of a gas inhalation PBTK model. The intention of httk is to provide a transparent, open-source tool for toxicokinetics, bioinformatics, and public health risk assessment that makes use of publicly available data on more than one thousand chemicals.

Some PFASs are immunotoxic in rodent models and associated with diminished vaccine response in exposed humans. This study assessed the immunotoxicity of four PFASs via the T cell-dependent IgM antibody response (TDAR) to sheep red blood cells (SRBCs) in adult male rats following 28-day oral repeat dosing. The PFASs included 1H,1H,9H-perfluorononyl acrylate (PFNAC), 1H,1H,2H,2H-perfluorohexyl iodide (PFHI), 2-chlorotetrafluoropropionic acid (CTFPA), and 3,3,4,4,5,5,5-heptafluoropentan-2-one (MHFPK), administered in corn oil. The positive control was cyclophosphamide (CPS). Rats were dosed with vehicle or PFAS from Days 0 to 27. On Day 22, an immunogenic dose of SRBCs was administered intravenously. Positive control animals were administered CPS by intraperitoneal injection from Days 22-27. On Day 28, the animals were euthanized; blood, thymus, and spleen samples were collected and weighed. Serum IgM was quantified by enzyme-linked immunosorbent assay. Body weights were unaffected in PFAS-treated rats, except for 3 and 10 mg/kg/day PFNAC-treated rats on Days 24, 27, and 28. Relative spleen and thymus weights and serum IgM levels were not affected by the PFASs at the doses tested, whereas CPS-treated animals had significant decreases in these parameters. The rat TDAR, as assessed by the anti-SRBC IgM response, was not affected by these four PFAS test agents following a 28-day oral exposure.

The US Environmental Protection Agency (US EPA) and other regulatory agencies routinely assess whether certain chemical exposures might result in harmful health effects. Traditional human health assessments rely upon expert judgment of dose-effect linkages observed in animal toxicology or human studies. Because both collection of toxicology data and synthesis of information might take multiple years to complete, there are relatively few available assessments for decision-making. Identifying methods that yield significant time and resource efficiencies to the process will have scalable public health benefits. To address the need, US EPA developed the database-calibrated assessment process (DCAP) to generate oral, non-cancer human health toxicity values that builds on previously published approaches and guidance. The approach uses the US EPA Toxicity Values Database (ToxValDB) that contains dose-response summary values (DRSVs) from in vivo toxicity studies. The DRSVs are converted to an oral, chronic, human equivalent dose using a series of standard conversion factors. A point-of-departure (POD) is then calculated across a distribution of studies for a given chemical using a calibration percentile that is benchmarked to critical effect PODs from published human health assessments. Traditional and process-specific uncertainties are incorporated to derive a calibrated toxicity value (CTV), defined as an estimate of a daily oral dose to the human population that is likely to be without appreciable risk of adverse non-cancer health effects over a lifetime. This review presents the rationale and methods for the approach, resulting in reporting of 1001 CTVs for chemicals that currently lack a human health assessment.

Accurate prediction of human renal clearance is essential for evaluating drug pharmacokinetics and environmental chemical risks, yet current methods often neglect rate-determining active transporter-mediated mechanisms. This study aimed to expand and validate a unified in vitro-in silico workflow for predicting renal clearance of both pharmaceuticals and per- and polyfluoroalkyl substances (PFAS) with varied elimination half-life ranges. We hypothesized that robust predictions of human renal clearance across diverse chemical classes can be achieved by combining human proximal tubule cell-based permeability/uptake assays with computational models of renal physiology. Human RPTEC/TERT1 cells and their OAT1-overexpressing variant were cultured in 96-well plates and Transwells to measure uptake, directional transport, and intracellular accumulation of 36 chemicals (28 PFAS, 7 drugs, 1 cosmetic ingredient). Time-course concentration data were used for either twocompartment (96-well) or three-compartment (Transwell) kinetic models. Permeability parameters were integrated into a physiologically-based kidney model for in vitro-to-in vivo extrapolation (IVIVE). A follow-up validation study with PFAS used independent experiments to derive similar predictions. Transwell-based threecompartment modeling yielded the most accurate absolute renal clearance predictions for rapidly eliminated drugs. For slowly cleared PFAS, simpler 96-well two-compartment modeling provided high correlation with observed human clearance, accurately distinguishing low-, medium- and high-clearance compounds; model predictions were consistently human health-protective. The PFAS validation study confirmed reproducibility of the approach. The proposed workflow is a conservative, scalable, mechanistically-informed and empiricallybenchmarked approach for predicting renal clearance in humans. Transwell assays best support drug clearance estimation, whereas high-throughput 96-well formats enable reliable relative clearance ranking for PFAS, supporting both pharmaceutical development and environmental chemical risk assessment.

Per- and polyfluoroalkyl substances (PFAS) comprise a large class of human-made chemicals that are in widespread use and present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterized for their hazard profiles, the vast majority of PFAS have not been extensively studied. A comprehensive evaluation to characterize the hazard profiles of the thousands of available PFAS would require extensive resources in terms of cost, number of animals and time. An alternative and more efficient approach is to develop a structural chemical categorization approach to prioritize which PFAS or categories of PFAS should be subject to additional study. To that end, the U.S. Environmental Protection Agency (EPA), in collaboration with the National Institute of Environmental Health Sciences (NIEHS) Division of Translational Toxicology (DTT), initiated a research project in 2018 to screen approximately 150 PFAS through a battery of alternative model organisms, in vitro cell and biochemical assays, and in vitro toxico kinetic (TK) assays in order to inform chemical category and read-across approaches. The aim of this review summarizes the experimental testing undertaken, how data were processed, what insights were derived from a category perspective and how these might potentially inform subsequent tiered testing.

Toxicokinetic (TK) assays and in vitro–in vivo extrapolation (IVIVE) models are New Approach Methods (NAMs) used to translate in vitro points of departure to exposure estimates required to reach equivalent blood concentrations. Per- and polyfluoroalkyl substances (PFAS) are a large chemical class with wide-ranging industrial applications for which only limited toxicity data are available for human health evaluation. To address the lack of TK data, a pooled primary human hepatocyte suspension model was used with targeted liquid chromatography–mass spectrometry to investigate substrate depletion for 54 PFAS. A median value of 4.52 μL/(min x million cells) was observed across those that showed significant clearance, with 35 displaying no substrate depletion. Bayesian modeling propagated uncertainty around clearance values for use in IVIVE models. Structural evaluations showed the fluorotelomer carboxylic acids were the only PFAS carboxylates showing appreciable clearance, and per- and polyfluorosulfonamides were more readily metabolized than other PFAS sulfonates. Biotransformation product prediction, using the chemical transformation simulator, suggested hydrolysis of PFAS sulfonamides to more stable sulfonic acids, which is an important consideration for exposure modeling. This effort greatly expands the PFAS in vitro toxicokinetic dataset, enabling refined TK modeling, in silico tool development, and NAM-based human health evaluations across this important set of emerging contaminants.

In October 2022, the World Health Organization (WHO) convened an expert panel in Lisbon, Portugal in which the 2005 WHO TEFs for chlorinated dioxin-like compounds were reevaluated. In contrast to earlier panels that employed expert judgement and consensus-based assignment of TEF values, the present effort employed an update to the 2006 REP database, a consensus-based weighting scheme, a Bayesian dose response modeling and meta-analysis to derive "Best-Estimate" TEFs. The updated database contains almost double the number of datasets from the earlier version and includes metadata that informs the weighting scheme. The Bayesian analysis of this dataset results in an unbiased quantitative assessment of the congener-specific potencies with uncertainty estimates. The "Best-Estimate" TEF derived from the model was used to assign 2022 WHO-TEFs for almost all congeners and these values were not rounded to half-logs as was done previously. The exception was for the mono-ortho PCBs, for which the panel agreed to retain their 2005 WHO-TEFs due to limited and heterogenous data available for these compounds. Applying these new TEFs to a limited set of dioxin-like chemical concentrations measured in human milk and seafood indicates that the total toxic equivalents will tend to be lower than when using the 2005 TEFs.

Toxicokinetics describes the absorption, distribution, metabolism, and elimination of chemicals by the body. Predictions from toxicokinetic models provide key information for chemical risk assessment. Traditionally, these predictions extrapolate from experimental animal species data (for example, in rats) to humans. More recently, toxicokinetics has been used for extrapolation from in vitro new approach methods (NAMs) for toxicology to in vivo. Chemical-specific in vivo toxicokinetic data are often unavailable for the thousands of chemicals in commerce. Therefore, large amounts of in vitro data measuring chemical-specific toxicokinetics have been collected. These data enable high-throughput toxicokinetic or HTTK modeling. The httk R package provides a library of chemical-specific data from peer-reviewed HTTK studies. httk further provides a suite of tools for parameterizing and evaluating toxicokinetic models. httk uses the open-source language MCSim to describe models for compartmental and physiologically based toxicokinetics (PBTK), MCSim can convert the model descriptions into a high-speed C code script. New models are integrated into httk using the open-source package development functionality in R, a model documentation file (R script), and the HTTK model description code file (C script). In addition to HTTK models, httk provides a series of functionalities such as unit conversion, model parameterization, Monte Carlo simulations for uncertainty propagation and biological variability, in vivo-derived data for evaluating model predictions, and other model utility functions. Here, we describe in detail how to add new HTTK models to httk and take advantage of the pre-existing data and functionality in the package. As a demonstration, we describe the integration of the gas inhalation PBTK model into httk. Modern modeling approaches, as exemplified by httk, allow for clear communication, reproducibility, and public scrutiny. The intention of httk is to provide a transparent, open-source tool for toxicokinetics, bioinformatics, and public health risk assessment. ### Competing Interest Statement The authors have declared no competing interest.

Per- and polyfluoroalkyl substances (PFAS) are widely used, and their fluorinated state contributes to unique uses and stability but also long half-lives in the environment and humans. PFAS have been shown to be toxic, leading to immunosuppression, cancer, and other adverse health outcomes. Only a small fraction of the PFAS in commerce have been evaluated for toxicity using in vivo tests, which leads to a need to prioritize which compounds to examine further. Here, we demonstrate a prioritization approach that combines human biomonitoring data (blood concentrations) with bioactivity data (concentrations at which bioactivity is observed in vitro) for 31 PFAS. The in vitro data are taken from a battery of cell-based assays, mostly run on human cells. The result is a Bioactive Concentration to Blood Concentration Ratio (BCBCR), similar to a margin of exposure (MoE). Chemicals with low BCBCR values could then be prioritized for further risk assessment. Using this method, two of the PFAS, PFOA (Perfluorooctanoic Acid) and PFOS (Perfluorooctane Sulfonic Acid), have BCBCR values < 1 for some populations. An additional 9 PFAS have BCBCR values < 100 for some populations. This study shows a promising approach to screening level risk assessments of compounds such as PFAS that are long-lived in humans and other species.

Current methods for cancer risk assessment are resource-intensive and not feasible for most of the thousands of untested chemicals. In earlier studies, we developed a new approach methodology (NAM) to identify liver tumorigens using gene expression biomarkers and associated tumorigenic activation levels (TALs) after short-term exposures in rats. The biomarkers are used to predict the six most common rodent liver cancer molecular initiating events. In the present study, we wished to confirm that our approach could be used to identify liver tumorigens at only one time point/dose and if the approach could be applied to (targeted) RNA-Seq analyses. Male rats were exposed for 4 days by daily gavage to 15 chemicals at doses with known chronic outcomes and liver transcript profiles were generated using Affymetrix arrays. Our approach had 75% or 85% predictive accuracy using TALs derived from the TG-GATES or DrugMatrix studies, respectively. In a dataset generated from the livers of male rats exposed to 16 chemicals at up to 10 doses for 5 days, we found that our NAM coupled with targeted RNA-Seq (TempO-Seq) could be used to identify tumorigenic chemicals with predictive accuracies of up to 91%. Overall, these results demonstrate that our NAM can be applied to both microarray and (targeted) RNA-Seq data generated from short-term rat exposures to identify chemicals, their doses, and mode of action that would induce liver tumors, one of the most common endpoints in rodent bioassays.

The World Health Organization (WHO) assesses potential health risks of dioxin-like compounds using Toxic Equivalency Factors (TEFs). This study systematically updated the relative potency (REP) database underlying the 2005 WHO TEFs and applied advanced methods for quantitative integration of study quality and dose-response. Data obtained from fifty-one publications more than doubled the size of the previous REP database (∼1300 datasets). REP quality and relevance for these data was assessed via application of a consensus-based weighting framework. Using Bayesian dose-response modeling, available data were modeled to produce standardized dose/concentration-response Hill curves. Study quality and REP data were synthesized via Bayesian meta-analysis to integrate dose/concentration-response data, author-calculated REPs and benchmark ratios. The output is a prediction of the most likely relationship between each congener and its reference as model-predicted TEF uncertainty distributions, or the 'best estimate TEF' (BE-TEF). The resulting weighted BE-TEFs were similar to the 2005 TEFs, though provide more information to inform selection of TEF values as well as to provide risk assessors and managers with information needed to quantitatively characterize uncertainty around TEF values. Collectively, these efforts produce an updated REP database and an objective, reproducible approach to support development of TEF values based on all available data.

In 2005, the World Health Organization (WHO) re-evaluated Toxic Equivalency factors (TEFs) developed for dioxin-like compounds believed to act through the Ah receptor based on an updated database of relative estimated potency (REP)(REP2004 database). This re-evalution identified the need to develop a consistent approach for dose-response modeling. Further, the WHO Panel discussed the significant heterogeneity of experimental datasets and dataset quality underlying the REPs in the database. There is a critical need to develop a quantitative, and quality weighted approach to characterize the TEF for each congener. To address this, a multi-tiered approach that combines Bayesian dose-response fitting and meta-regression with a machine learning model to predict REPS' quality categorizations was developed to predict the most likely relationship between each congener and its reference and derive model-predicted TEF uncertainty distributions. As a proof of concept, this ‘Best-Estimate TEF workflow’ was applied to the REP2004 database to derive TEF point-estimates and characterizations of uncertainty for all congeners. Model-TEFs were similar to the 2005 WHO TEFs, with the data-poor congeners having larger levels of uncertainty. This transparent and reproducible computational workflow incorporates WHO expert panel recommendations and represents a substantial improvement in the TEF methodology.

Few studies are available on the environmental and toxicological effects of perfluoroalkyl ether carboxylic acids (PFECAs), such as GenX, which are replacing legacy PFAS in manufacturing processes. To collect initial data on the toxicity and toxicokinetics of a longer-chain PFECA, male and female Sprague Dawley rats were exposed to perfluoro-(2,5,8-trimethyl-3,6,9-trioxadodecanoic) acid (HFPO-TeA) by oral gavage for five days over multiple dose levels (0.3–335.2 mg/kg/day). Clinically, we observed mortality at doses >17 mg/kg/day and body weight changes at doses ≤17 mg/kg/day. For the 17 mg/kg/day dose level, T3 and T4 thyroid hormone concentrations were significantly decreased (p < 0.05) from controls and HFPO-TeA plasma concentrations were significantly different between sexes. Non-targeted analysis of plasma and in vitro hepatocyte assay extractions revealed the presence of another GenX oligomer, perfluoro-(2,5-dimethyl-3,6-dioxanonanoic) acid (HFPO-TA). In vitro to in vivo extrapolation (IVIVE) parameterized with in vitro toxicokinetic data predicted steady-state blood concentrations that were within seven-fold of those observed in the in vivo study, demonstrating reasonable predictivity. The evidence of thyroid hormone dysregulation, sex-based differences in clinical results and dosimetry, and IVIVE predictions presented here suggest that the replacement PFECA HFPO-TeA induces a complex and toxic exposure response in rodents.