Next-generation risk assessment (NGRA) frameworks use new approach methodologies (NAMs) to support regulatory decisions without animal testing. Although NAM-based approaches are well established for hazard and dose-response assessment, inter-individual variability is still typically addressed using default uncertainty factors for inter-individual variability. This study evaluated an NAM-based strategy to quantify chemical-specific variability using a human cell model. We hypothesized that integrating chemical-specific variability data into NGRA would yield more protective risk estimates. Using 131 human lymphoblastoid cell lines (LCLs) from four European and African subpopulations, we assessed differences in cytotoxic responses to 53 substances, including industrial chemicals, pharmaceuticals, pesticides, and consumer-use compounds. Concentration-response testing (0.3 nM to 300 μM) data were analyzed using Bayesian modeling to calculate points of departure per cell line. Of the substances tested, 18 exhibited cytotoxic effects, enabling the derivation of chemical-specific variability factors. These factors were designated as toxicodynamic variability factors at the 5th percentile (TDVF05) because of the limited metabolic capacity of lymphoblast cell lines. The median TDVF05 was 3.8 (range 1 to 46), largely consistent with default assumptions. A genome-wide association study (GWAS) identified genomic loci, primarily containing transporter and metabolism genes, associated with variability in cytotoxicity, suggesting mechanistic bases for inter-individual differences. Overall, this study shows that human LCLs are a practical high-throughput in vitro model for quantifying inter-individual variability, strengthening confidence in NGRA risk predictions and supporting hypothesis generation on chemical-specific genetic and mechanistic drivers of human variability. However, cell-based systems have limited coverage of adverse effects and require careful alignment with in vivo dosimetry.

An essential aspect of the EU's Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation is the European Chemicals Agency's (ECHA) evaluation of testing proposals submitted by registrants to address data gaps. Registrants may propose adaptations, such as read- across, to waive standard testing; however, it is widely believed that ECHA often finds justifications for read-across hypotheses inadequate. From 2008 to August 2023, 2,630 testing proposals were submitted to ECHA; of these, 1,538 had published decisions that were systematically evaluated in this study. Each document was manually reviewed and information extracted for further analyses, focusing on 17 assessment elements (AEs) from the Read-Across Assessment Framework (RAAF) and testing proposal evaluations (TPE). Each submission was classified as to the AEs relied upon by the registrants and by ECHA. Data was analyzed for patterns and associations. Adaptations were included in 23% (350) of proposals, with analogue (168) and group (136) read-across being most common. Of the 304 read-across hypotheses, 49% were accepted, with group read-across showing significantly higher odds of acceptance. Data analysis examined factors such as tonnage band (Annex), test guidelines, hypothesis AEs, and structural similarities of target and source substances. While decisions were often context-specific, several significant associations influencing acceptance emerged. Overall, this analysis provides a comprehensive overview of 15 years of experience with testing proposal-specific read-across adaptations by both registrants and ECHA. These data will inform future submissions as they identify most critical AEs to increase the odds of read-across acceptance.

The transition from traditional animal-based approaches and assessments to New Approach Methodologies (NAMs) marks a scientific revolution in regulatory toxicology, with the potential of enhancing human and environmental protection. However, implementing the effective use of NAMs in regulatory toxicology has proven to be challenging, and so far, efforts to facilitate this change frequently focus on singular technical, psychological or economic inhibitors. This article takes a system-thinking approach to these challenges, a holistic framework for describing interactive relationships between the components of a system of interest. In this case, the regulatory toxicology system. We do so by analysing and interpreting a very large qualitative data set of experts' observations, collected in a 3-day interactive workshop and three follow-up online workshops with a heterogeneous sample of experts representing major actors from the global regulatory toxicology system. We identified leverage points (where a small change within a system can have a disproportionately large effect) in the six core aspects-infrastructure, processes, culture, technology, goals, and actors-in the regulatory toxicology system to facilitate the effective use of NAMs. Identified systematic leverage points include the need for a functioning incentive structure for effectively discovering, developing, validating and using NAMs within academia, regulation, and industry; and measures that prevent or mitigate unwanted effects of using NAMs that acknowledge clashes between scientific, regulatory, political and social processes. The results serve as a basis for follow-up activities that reflect on the actual effectiveness of these levers and that develop measures for the regulatory toxicology system.

1,3-butadiene (BD) is a volatile organic pollutant. Upon inhalation, it is metabolically activated to reactive epoxides which alkylate genomic DNA and form potentially mutagenic monoadducts and DNA–DNA crosslinks including N7-(1-hydroxyl-3-buten-1-yl)guanine (EB-GII) and 1,4-bis-(guan-7-yl)-2,3-butanediol (bis-N7G-BD). While metabolic activation resulting in mutagenicity is a well-established mode of action for 1,3-butadiene, characterization of the extent of inter-individual variability in response to BD exposure is a gap in our knowledge. Previous studies showed that population-wide mouse models can be used to evaluate variability in 1,3-butadiene DNA adducts; therefore, we hypothesized that this approach can be used to also study variability in the formation and loss of BD DNA adducts across tissues and between sexes. To test this hypothesis, female and male mice from five genetically diverse Collaborative Cross (CC) strains were exposed to filtered air or 1,3-butadiene (600 ppm, 6 h/day, 5 days/week for 2 weeks) by inhalation. Some animals were kept for two additional weeks after exposure to study DNA adduct persistence. EB-GII and bis-N7G-BD adducts were quantified in liver, lungs and kidney using established isotope dilution ESI-MS/MS methods. We observed strain- and sex-specific effects on both the accumulation and loss of both DNA adducts, indicating that both factors play important roles in the mutagenicity of 1,3-butadiene. In addition, we quantified the intra-species variability for each adduct and found that for most tissues/adducts, variability values across strains were modest compared to default uncertainty factors.

Key characteristics (KCs) are properties of chemicals that are associated with different types of human health hazards. KCs are used for systematic reviews in support of hazard identification. Transcriptomic data are a rich source of mechanistic data and are frequently interpreted through "enriched" pathways/gene sets. Such analyses may be challenging to interpret in regulatory science because of redundancy among pathways, complex data analyses, and unclear relevance to hazard identification. We hypothesized that by cross-mapping pathways/gene sets and KCs, the interpretability of transcriptomic data can be improved. We summarized 72 published KCs across 7 hazard traits into 34 umbrella KC terms. Gene sets from Reactome and Kyoto Encyclopedia of Genes and Genomes (KEGG) were mapped to these, resulting in "KC gene sets." These sets exhibit minimal overlap and vary in the number of genes. Comparisons of the same KC gene sets mapped from Reactome and KEGG revealed low similarity, indicating complementarity. Performance of these KC gene sets was tested using publicly available transcriptomic datasets of chemicals with known organ-specific toxicity: benzene and 2,3,7,8-tetrachlorodibenzo-p-dioxin tested in mouse liver and drugs sunitinib and amoxicillin tested in human-induced pluripotent stem cell-derived cardiomyocytes. We found that KC terms related to the mechanisms affected by tested compounds were highly enriched, while the negative control (amoxicillin) showed limited enrichment with marginal significance. This study's impact is in presenting a computational approach based on KCs for the analysis of toxicogenomic data and facilitating transparent interpretation of these data in the process of chemical hazard identification.

MOTIVATION:HAP-SAMPLE2 extends the functionality of the original HAP-SAMPLE tool for simulating genotype-phenotype data, now with features to handle population admixture and rare variant analysis. It allows users to define parameters such as disease prevalence and allele effect sizes for both common and rare variant simulations. RESULTS:HAP-SAMPLE2 provides an efficient means for simulating complex datasets, suitable for large-scale projects like the 1000 Genomes Project. Its capabilities for population admixture allow users to create admixed populations or preserve substructures while introducing novel variation through artificial recombination. Additionally, the tool supports burden testing for rare variants using fixed and Madsen-Browning weighting schemes. AVAILABILITY AND IMPLEMENTATION:The software, along with a detailed vignette, is available on GitHub: https://github.com/M3dical/HAPSAMPLE2.

Chromatin accessibility quantitative trait locus (caQTL) studies have identified regulatory elements that underlie genetic effects on gene expression and metabolic traits. However, caQTL discovery has been limited by small sample sizes. Here, we map caQTLs in liver tissue from 138 human donors and identify caQTLs for 35,361 regulatory elements, including population-specific caQTLs driven by differences in allele frequency across populations. We identify 2126 genetic signals associated with multiple, presumably coordinately regulated elements. Coordinately regulated elements link distal elements to target genes and are more likely to be associated with gene expression compared with single-element caQTLs. We predict driver and response elements at coordinated loci and find that driver elements are enriched for transcription factor binding sites of key liver regulators. We identify colocalized caQTLs at 667 genome-wide association (GWAS) signals for metabolic and liver traits, and annotate these loci with predicted target genes and disrupted transcription factor binding sites. CaQTLs identify threefold more GWAS colocalizations than liver expression QTLs (eQTLs) in a larger sample size, suggesting that caQTLs can detect mechanisms missed by eQTLs. At a GWAS signal colocalized with a caQTL and an eQTL for TENM2, we validated regulatory activity for a variant within a predicted driver element that is coordinately regulated with 39 other elements. At another locus, we validate a predicted enhancer of RALGPS2 using CRISPR interference and demonstrate allelic effects on transcription for a haplotype within this enhancer. These results demonstrate the power of caQTLs to characterize regulatory mechanisms at GWAS loci.

Metabolic dysfunction-associated steatohepatitis (MASH), a severe form of fatty liver disease, is a leading cause of cirrhosis and lacks effective therapies. Understanding the molecular mediators of disease progression remains a critical gap. This study aimed to investigate the roles of molecular phenotypes as mediators of MASH disease features in a diet-induced mouse model. Data used for these analyses were from a previous study where male and female CC042 mice were fed either a control or high-fat, high-sucrose (HF/HS) diet for 20, 40, or 60 weeks. Associations and mediated relationships between molecular and metabolic phenotypes and histopathologic markers of liver injury, inflammation, and lipid accumulation were assessed using regression modeling and causal mediation analyses. We observed strong associations between the HF/HS diet and duration of treatment and liver pathology, with a limited effect of sex. Mediation analysis revealed that liver lipid phenotypes, particularly monounsaturated and polyunsaturated fatty acids, consistently mediated the effects of diet on liver disease scores. Tumor necrosis factor alpha and C-X-C motif chemokine ligand 10, despite being treatment-induced, showed modest evidence of mediation on MASH or specific liver disease outcomes. Serum insulin showed modest mediation of inflammation and osmium staining, while serum glucose and triglycerides were not significant mediators. These findings highlight evidence that liver lipid metabolism may act as a primary mediator of MASH progression in this mouse model. The study underscores the value of mediation analysis for improved characterization of metabolic pathways in disease pathogenesis and supports the use of serum lipids as accessible biomarkers for clinical risk stratification and therapeutic targeting in MASH.

Breastfeeding offers well-documented advantages but may inadvertently introduce lead (Pb) exposure to infants. Scarce data exist on the risks of Pb exposure for breastfed infants, and strategies for risk mitigation are needed, particularly considering the heightened susceptibility of children to adverse effects from Pb exposure. To investigate the potential influence of breastfeeding on blood Pb levels (BLL) in offspring, population variation in BLL between nonparous and parous mouse dams was quantified, as well as in dams exposed to low and high dose while breastfeeding, and their offspring. Female mice from 14 distinct collaborative cross (CC) mouse strains were mated with sires from different CC strains to produce 14 F1 hybrids. Subsequently, dams were administered either low- (100 ppm) or high- (1,000 ppm) dose Pb through ad libitum access drinking water starting the day of delivery for a duration of 4 wk, leading to the subsequent exposure of the offspring via lactation. Genetic background emerged as a predominant factor contributing to variation, with substantial interstrain variability observed in both CC dams and F1 hybrids exposed to low and high doses of Pb. Crucially, the degree of variation in BLL among CC dams exceeded the default variability estimate (geometric SD = 1.6) utilized in regulatory standard settings. These findings underscore the importance of integrating population variability in risk assessment. Ultimately, this study provides critical insights to guide public health decision-making processes concerning Pb exposure through breastfeeding and its potential implications for infants' health.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a spectrum of chronic pathologic conditions strongly associated with metabolic syndrome and affects approximately 38 % of the global population. Untreated MASLD may progress to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis, and cirrhosis and is currently recognized as one of the main risk factors for hepatocellular carcinoma (HCC). The molecular determinants of MASLD stratification are not clearly defined and require additional investigation. In this study, we used a dietary preclinical model of MASH-like liver injury induced by feeding male and female Collaborative Cross CC042/GeniUnc mice a high-fat and high-sucrose diet (HF/HS) for up to 60 weeks and analyzed the global hepatic transcriptomic alterations. Chronic feeding the HF/HS diet induced profound alterations in liver gene expression associated with the key toxicity pathways, including cell death, cell proliferation, inflammation, fibrosis, and hyperplasia. We identified a panel of 74 differentially expressed genes, the expression of which significantly correlated with total MASH pathology scores in the livers of both male and female mice. Using these genes, we developed a machine-learning model that accurately predicted the severity of MASH-like liver injury in several different animal models of MASH and demonstrated high accuracy for a smaller model with 37 genes. We also used this signature to analyze human gene expression data and show its translational relevance. The results of this study demonstrate that a panel of MASH-related genes can assist in the assessment of MASH-like liver injury, its monitoring, and in development of mechanism-based drugs against MASH.

New Approach Methods (NAMs) encompass a wide range of data types; it is increasingly common to have highly multi-dimensional data (e.g., cellular, molecular and gene expression effects) on the same chemicals. In addition, chemical structure descriptors (for mono-constituent substances) or fractional composition (for complex substances) inform similarity hypotheses for read-across. Still, the utility of these multi-dimensional datasets for decision-making is difficult to ascertain. To address this challenge, we hypothesized that correlation and mediation analyses methods can be used to uncover significant and interpretable relationships in complex NAMs datasets. We used previously published data on 141 petroleum UVCBs (substances of unknown or variable composition, complex reaction products and biological materials) that included (i) characterization of the polycyclic aromatic compound (PAC) content, (ii) 42 bioactivity measurements from 12 human cell types, and (iii) transcriptomic data from 6 cell types. We explored the relationships among data types and determined how these data can be used for bioactivity-based prioritization. We found that PAC content was highly informative for bioactivity prediction, while the addition of transcriptomic data provided modest improvements. We then applied the statistical procedure of mediation analysis to uncover relationships among transcriptomics, PAC, and bioactivity. The strongest relationships appeared to be nearly completely mediated, and phenotypes with high transcriptomic mediation tended to have high correlation with PAC content. This study shows how a mediation analysis approach can be used to uncover relationships in multi-dimensional NAMs datasets and provides further insights into strategies for hazard prioritization using a combination of transcriptomic and bioactivity data.

The relationships of human diversity with biomedical phenotypes are pervasive yet remain understudied, particularly in a single-cell genomics context. Here, we present the Asian Immune Diversity Atlas (AIDA), a multi-national single-cell RNA sequencing (scRNA-seq) healthy reference atlas of human immune cells. AIDA comprises 1,265,624 circulating immune cells from 619 donors, spanning 7 population groups across 5 Asian countries, and 6 controls. Though population groups are frequently compared at the continental level, we found that sub-continental diversity, age, and sex pervasively impacted cellular and molecular properties of immune cells. These included differential abundance of cell neighborhoods as well as cell populations and genes relevant to disease risk, pathogenesis, and diagnostics. We discovered functional genetic variants influencing cell-type-specific gene expression, which were under-represented in non-Asian populations, and helped contextualize disease-associated variants. AIDA enables analyses of multi-ancestry disease datasets and facilitates the development of precision medicine efforts in Asia and beyond.

Per- and polyfluoroalkyl substances (PFAS) are chemicals with important applications; they are persistent in the environment and may pose human health hazards. Regulatory agencies are considering restrictions and bans of PFAS; however, little data exists for informed decisions. Several prioritization strategies were proposed for evaluation of potential hazards of PFAS. Structure-based grouping could expedite the selection of PFAS for testing; still, the hypothesis that structure-effect relationships exist for PFAS requires confirmation. We tested 26 structurally diverse PFAS from 8 groups using human induced pluripotent stem cell-derived hepatocytes and cardiomyocytes, and tested concentration-response effects on cell function and gene expression. Few phenotypic effects were observed in hepatocytes, but negative chronotropy was observed in cardiomyocytes for 8 PFAS. Substance- and cell type-dependent transcriptomic changes were more prominent but lacked substantial group-specific effects. In hepatocytes, we found upregulation of stress-related and extracellular matrix organization pathways, and down-regulation of fat metabolism. In cardiomyocytes, contractility-related pathways were most affected. We derived phenotypic and transcriptomic points of departure and compared them to predicted PFAS exposures. Conservative estimates for bioactivity and exposure were used to derive a bioactivity-to-exposure ratio (BER) for each PFAS; 23 of 26 PFAS had BER > 1. Overall, these data suggest that structure-based PFAS grouping may not be sufficient to predict their biological effects. Testing of individual PFAS may be needed for scientifically-supported decision-making. Our proposed strategy of using two human cell types and considering phenotypic and transcriptomic effects, combined with dose-response analysis and calculation of BER, may be used for PFAS prioritization.

BACKGROUND:Expression quantitative trait locus (eQTL) analysis aims to detect the genetic variants that influence the expression of one or more genes. Gene-level eQTL testing forms a natural grouped-hypothesis testing strategy with clear biological importance. Methods to control family-wise error rate or false discovery rate for group testing have been proposed earlier, but may not be powerful or easily apply to eQTL data, for which certain structured alternatives may be defensible and may enable the researcher to avoid overly conservative approaches.RESULTS:In an empirical Bayesian setting, we propose a new method to control the false discovery rate (FDR) for grouped hypotheses. Here, each gene forms a group, with SNPs annotated to the gene corresponding to individual hypotheses. The heterogeneity of effect sizes in different groups is considered by the introduction of a random effects component. Our method, entitled Random Effects model and testing procedure for Group-level FDR control (REG-FDR), assumes a model for alternative hypotheses for the eQTL data and controls the FDR by adaptive thresholding. As a convenient alternate approach, we also propose Z-REG-FDR, an approximate version of REG-FDR, that uses only Z-statistics of association between genotype and expression for each gene-SNP pair. The performance of Z-REG-FDR is evaluated using both simulated and real data. Simulations demonstrate that Z-REG-FDR performs similarly to REG-FDR, but with much improved computational speed.CONCLUSION:Our results demonstrate that the Z-REG-FDR method performs favorably compared to other methods in terms of statistical power and control of FDR. It can be of great practical use for grouped hypothesis testing for eQTL analysis or similar problems in statistical genomics due to its fast computation and ability to be fit using only summary data.

Systematic review and evaluation of mechanistic evidence using the Key Characteristics approach was proposed by the International Agency for Research on Cancer (IARC) in 2012 and used by the IARC Monographs Working Groups since 2015. Key Characteristics are 10 features of agents known to cause cancer in humans. From 2015 to 2022, a total of 19 Monographs (73 agents combined) used Key Characteristics for cancer hazard classification. We hypothesized that a retrospective analysis of applications of the Key Characteristics approach to cancer hazard classification using heterogenous mechanistic data on diverse agents would be informative for systematic reviews in decision-making. We extracted information on the conclusions, data types, and the role mechanistic data played in the cancer hazard classification from each Monograph. Statistical analyses identified patterns in the use of Key Characteristics, as well as trends and correlations among Key Characteristics, data types, and ultimate decisions. Despite gaps in data for many agents and Key Characteristics, several significant results emerged. Mechanistic data from in vivo animal, in vitro animal, and in vitro human studies were most impactful in concluding that an agent could cause cancer via a Key Characteristic. To exclude the involvement of a Key Characteristic, data from large-scale systematic in vitro testing programs such as ToxCast, were most informative. Overall, increased availability of systemized data streams, such as human in vitro data, would provide the basis for more confident and informed conclusions about both positive and negative associations and inform expert judgments on cancer hazard.

Understanding the molecular mechanisms of complex traits is essential for developing targeted interventions. We analyzed liver expression quantitative-trait locus (eQTL) meta-analysis data on 1,183 participants to identify conditionally distinct signals. We found 9,013 eQTL signals for 6,564 genes; 23% of eGenes had two signals, and 6% had three or more signals. We then integrated the eQTL results with data from 29 cardiometabolic genome-wide association study (GWAS) traits and identified 1,582 GWAS-eQTL colocalizations for 747 eGenes. Non-primary eQTL signals accounted for 17% of all colocalizations. Isolating signals by conditional analysis prior to coloc resulted in 37% more colocalizations than using marginal eQTL and GWAS data, highlighting the importance of signal isolation. Isolating signals also led to stronger evidence of colocalization: among 343 eQTL-GWAS signal pairs in multi-signal regions, analyses that isolated the signals of interest resulted in higher posterior probability of colocalization for 41% of tests. Leveraging allelic heterogeneity, we predicted causal effects of gene expression on liver traits for four genes. To predict functional variants and regulatory elements, we colocalized eQTL with liver chromatin accessibility QTL (caQTL) and found 391 colocalizations, including 73 with non-primary eQTL signals and 60 eQTL signals that colocalized with both a caQTL and a GWAS signal. Finally, we used publicly available massively parallel reporter assays in HepG2 to highlight 14 eQTL signals that include at least one expression-modulating variant. This multi-faceted approach to unraveling the genetic underpinnings of liver-related traits could lead to therapeutic development.

BACKGROUND AND AIMS:It is not known why severe cystic fibrosis (CF) liver disease (CFLD) with portal hypertension occurs in only ~7% of people with CF. We aimed to identify genetic modifiers for severe CFLD to improve understanding of disease mechanisms. APPROACH AND RESULTS:Whole-genome sequencing was available in 4082 people with CF with pancreatic insufficiency (n = 516 with severe CFLD; n = 3566 without CFLD). We tested ~15.9 million single nucleotide polymorphisms (SNPs) for association with severe CFLD versus no-CFLD, using pre-modulator clinical phenotypes including (1) genetic variant ( SERPINA1 ; Z allele) previously associated with severe CFLD; (2) candidate SNPs (n = 205) associated with non-CF liver diseases; (3) genome-wide association study of common/rare SNPs; (4) transcriptome-wide association; and (5) gene-level and pathway analyses. The Z allele was significantly associated with severe CFLD ( p = 1.1 × 10 -4 ). No significant candidate SNPs were identified. A genome-wide association study identified genome-wide significant SNPs in 2 loci and 2 suggestive loci. These 4 loci contained genes [significant, PKD1 ( p = 8.05 × 10 -10 ) and FNBP1 ( p = 4.74 × 10 -9 ); suggestive, DUSP6 ( p = 1.51 × 10 -7 ) and ANKUB1 ( p = 4.69 × 10 -7 )] relevant to severe CFLD pathophysiology. The transcriptome-wide association identified 3 genes [ CXCR1 ( p = 1.01 × 10 -6 ) , AAMP ( p = 1.07 × 10 -6 ), and TRBV24 ( p = 1.23 × 10 -5 )] involved in hepatic inflammation and innate immunity. Gene-ranked analyses identified pathways enriched in genes linked to multiple liver pathologies. CONCLUSION:These results identify loci/genes associated with severe CFLD that point to disease mechanisms involving hepatic fibrosis, inflammation, innate immune function, vascular pathology, intracellular signaling, actin cytoskeleton and tight junction integrity and mechanisms of hepatic steatosis and insulin resistance. These discoveries will facilitate mechanistic studies and the development of therapeutics for severe CFLD.

Per- and poly-fluoroalkyl substances (PFAS) are emerging contaminants of concern because of their wide use, persistence, and potential to be hazardous to both humans and the environment. Several PFAS have been designated as substances of concern; however, most PFAS in commerce lack toxicology and exposure data to evaluate their potential hazards and risks. Cardiotoxicity has been identified as a likely human health concern, and cell-based assays are the most sensible approach for screening and prioritization of PFAS. Human-induced pluripotent stem cell (iPSC)-derived cardiomyocytes are a widely used method to test for cardiotoxicity, and recent studies showed that many PFAS affect these cells. Because iPSC-derived cardiomyocytes are available from different donors, they also can be used to quantify human variability in responses to PFAS. The primary objective of this study was to characterize potential human cardiotoxic hazard, risk, and inter-individual variability in responses to PFAS. A total of 56 PFAS from different subclasses were tested in concentration-response using human iPSC-derived cardiomyocytes from 16 donors without known heart disease. Kinetic calcium flux and high-content imaging were used to evaluate biologically-relevant phenotypes such as beat frequency, repolarization, and cytotoxicity. Of the tested PFAS, 46 showed concentration-response effects in at least one phenotype and donor; however, a wide range of sensitivities were observed across donors. Inter-individual variability in the effects could be quantified for 19 PFAS, and risk characterization could be performed for 20 PFAS based on available exposure information. For most tested PFAS, toxicodynamic variability was within a factor of 10 and the margins of exposure were above 100. This study identified PFAS that may pose cardiotoxicity risk and have high inter-individual variability. It also demonstrated the feasibility of using a population-based human in vitro method to quantify population variability and identify cardiotoxicity risks of emerging contaminants.

Per- and poly-fluoroalkyl substances (PFAS) are extensively used in commerce leading to their prevalence in the environment. Due to their chemical stability, PFAS are considered to be persistent and bioaccumulative; they are frequently detected in both the environment and humans. Because of this, PFAS as a class (composed of hundreds to thousands of chemicals) are contaminants of very high concern. Little information is available for the vast majority of PFAS, and regulatory agencies lack safety data to determine whether exposure limits or restrictions are needed. Cell-based assays are a pragmatic approach to inform decision-makers on potential health hazards; therefore, we hypothesized that a targeted battery of human in vitro assays can be used to determine whether there are structure-bioactivity relationships for PFAS, and to characterize potential risks by comparing bioactivity (points of departure) to exposure estimates. We tested 56 PFAS from 8 structure-based subclasses in concentration response (0.1-100 mu M) using six human cell types selected from target organs with suggested adverse effects of PFAS - human induced pluripotent stem cell (iPSC)-derived hepatocytes, neurons, and cardiomyocytes, primary human hepatocytes, endothelial and HepG2 cells. While many compounds were without effect; certain PFAS demonstrated cell-specific activity highlighting the necessity of using a compendium of in vitro models to identify potential hazards. No class-specific groupings were evident except for some chain lengthand structure-related trends. In addition, margins of exposure (MOE) were derived using empirical and predicted exposure data. Conservative MOE calculations showed that most tested PFAS had a MOE in the 1-100 range; similar to 20% of PFAS had MOE<1, providing tiered priorities for further studies. Overall, we show that a compendium of human cell-based models can be used to derive bioactivity estimates for a range of PFAS, enabling comparisons with human biomonitoring data. Furthermore, we emphasize that establishing structure-bioactivity relationships may be challenging for the tested PFAS.