Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants. Some individual PFAS are linked to adverse health effects, but little data is available to evaluate PFAS mixtures. We tested the biological effects of 20 defined PFAS mixtures (containing 4-56 PFAS) across eight human cell types, including iPSC-derived cardiomyocytes, neurons, and hepatocytes; primary hepatocytes; and HepG2s, endothelial, and renal proximal tubule epithelial cell lines. Mixtures were designed to reflect realistic exposures based on drinking/surface water data, regulatory limits, biomonitoring results, and prior in vitro studies. Cells were exposed to five 10-fold dilutions spanning environmentally relevant concentrations, and cell-specific viability and functional endpoints were measured. Concentration-response was modeled to derive mixture-specific points of departure (PODs). Mixtures based on prior in vitro bioactivity and highly contaminated water samples were the most potent. HepG2s, hepatocytes, and iPSC-derived neurons were the most sensitive cells. Experimental mixture PODs were compared with predictions from concentration addition (CA) models based on individual PFAS data. CA showed low accuracy in distinguishing active from inactive mixtures and underestimated potency (∼300-fold). Overall, our findings demonstrate that direct testing of PFAS mixtures provides more health-protective estimates than component-based models and supports a tiered testing strategy prioritizing sensitive human liver cell models.

Although PFAS exposure is widespread in the general population, concern is heightened for individuals with unique occupational exposure scenarios. Accordingly, the PROject for Military Exposures and Toxin History Evaluation in US service members (PROMETHEUS) study is evaluating whether serum chemical exposure profiles correlate with cancer incidence in large cohorts (typically hundreds of samples per analysis) among military service members who may experience distinct occupational and environmental exposures. Here we describe analytical workflow development and results from a pilot subset (n = 36) of human serum samples using an integrated targeted and suspect-screening LC-IMS-MS platform. Serum (50 µL) was extracted by acid-assisted protein precipitation with isotopically labeled internal standards, concentrated, and analyzed by LC coupled to an Agilent 6560 IM-QTOF. Targeted PFAS quantitation was performed using matrix-matched calibration curves and was benchmarked against NIST SRM 1957 to assess method accuracy. Across the samples, the targeted panel captured predominantly legacy PFAS as anticipated noting their prevalence in prior studies (e.g., PFOS, PFOA, 8 : 2 FTS, N-MeFOSAA, etc.). Ultra-short-chain PFAS presented class-specific analytical challenges; trifluoromethanesulfonic acid (TFMS) was observed, whereas trifluoroacetic acid (TFA) eluted near the void volume and exhibited pronounced clustering in the ion mobility dimension, precluding reliable quantitation. In parallel, CCS-based mobility filtering supported suspect screening against an exposomic library (∼1100 entries) to expand detectable chemical space beyond targeted PFAS. Suspect screening yielded 49 non-PFAS candidates meeting accurate mass and CCS agreement criteria, and correlation analysis recapitulated expected co-exposure groupings among legacy PFCAs/PFSAs and structurally related suspect analytes. Collectively, these results establish a scalable, CCS-informed LC-IM-MS workflow for integrated targeted PFAS quantitation and exposomic suspect screening, enabling higher-powered association testing in the full set of PROMETHEUS samples and other large-scale human biomonitoring studies.

Cardiovascular disease is the leading cause of death globally, yet cardiovascular risks from environmental pollutants remain under-recognized and are not considered a stand-alone hazard trait. Growing evidence shows that industrial and environmental chemicals, including air pollutants, metals, solvents, pesticides and other chemicals, may be hazardous to human cardiovascular health. Indeed, many human and animal studies demonstrate that environmental chemicals can act through oxidative stress, inflammation, and endothelial dysfunction, potentially causing hypertension, arrhythmias, and other cardiovascular conditions. This commentary reviews the state of the science for recognizing chemical cardiotoxicity under the Organisation for Economic Co-operation and Development (OECD) Working Party on Hazard Assessment project [ENV/CBC/HA(2024)13]. New approach methodologies (NAMs) provide organizing principles for assembling mechanism-based data on cardiovascular toxicants and evaluating potential cardiovascular hazards. Case studies demonstrate how broad toxicity testing programs like ToxCast and cardiovascular-specific NAMs can assess hazards and risks for environmental chemical cardiovascular toxicity. The pharmaceutical industry's approach and strategies for de-risking cardiovascular toxicities serve as an example of regulatory applicability of a tiered context-of-use-focused approach consisting of in silico, in vitro and targeted animal and human studies. Overall, a framework for integrating clinical, animal, and NAMs data to support decisions regarding potential chemical cardiovascular toxicity is proposed.

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.

New approach methodologies (NAMs) are increasingly recognized as essential tools for modernizing regulatory science, yet their adoption in food safety risk assessment is not well characterized. To address this gap, we conducted a landscape analysis of NAMs use across the food industry. An anonymous survey was conducted between February and April 2026 with responses from seventeen international companies. Fourteen respondents reported using NAMs in the previous five years. Conventional cell-based assays, in silico toxicodynamic models, threshold of toxicological concern frameworks, and physiologically based pharmacokinetic (PBPK) modeling represented the most adopted approaches. More advanced platforms such as organ-on-chip systems were not routinely used. NAMs were predominantly applied to internal screening and prioritization, with ten companies reporting either experience with or interest in regulatory submissions incorporating NAMs-derived data. Novel food ingredients was the primary application area. Despite the interest in NAMs, significant barriers persist to their use in regulatory submissions, including challenges in data interpretation, limited availability of formally validated or qualified methods, high cost, and lack of specialized expertise to generate and interpret data. Regulatory acceptance emerged as the dominant factor influencing both internal adoption and submission decisions. Respondents indicated strong willingness to engage regulators through voluntary data-sharing mechanisms under appropriate "safe-space" conditions. These findings indicate that NAMs use in the food sector is established, but remains constrained and that progress toward broader regulatory integration will depend on fit-for-purpose guidance, clearly defined contexts of use, and improved frameworks for evidence integration.

Animal-based toxicity testing is limited in throughput and mechanistic characterization, underscoring the need for cell-based approaches. Incorporating molecular readouts, such as lipidomics, can enhance the translational relevance of cell-based assays. Because untargeted lipidomic analyses are time-consuming and can require large sample volumes, a targeted workflow was used in this study to characterize drug-induced liver injury-related lipid responses in primary hepatocytes from multiple species. Multiple reaction monitoring was applied in a targeted lipidomics workflow by coupling liquid-chromatography with triple quadrupole mass spectrometry (LC-MS/MS) for 148 lipid targets across 3 categories and 11 classes. We used primary hepatocytes from human, rat, monkey, and dog cultured in 96-well plates and exposed to chlorpromazine (1-30 μM), bosentan (1-200 μM), and fialuridine (1-100 μM) from culture day 4 to 8. Chlorpromazine induced the most pronounced lipid alterations in human, monkey, and rat hepatocytes, while responses in dog hepatocytes were minimal, with human cells showing a dose-dependent trend up to 10 μM. Bosentan induced dose-dependent lipid changes across species, strongest in human hepatocytes, whereas fialuridine caused limited lipid alterations. Overall, lipidomic effects closely aligned with conventional toxicity markers, reflecting compound and species-specific hepatotoxic sensitivity. The targeted lipidomics workflow provided mechanism-informed phenotyping of chemical-induced lipid responses in cultured hepatocytes. Moreover, it provided a framework for mechanistic evaluation, supported cross-species comparison, and complemented in vitro hepatotoxicity assessments.

Read-across is an expert-driven new approach methodology (NAM) used to fill gaps in chemical toxicity data. While qualitative read-across is widely used, quantitative read-across (qRAx) for deriving points of departure (PODs) has received limited attention. We compared the applicability domain, consistency, and conservatism of PODs derived from qRAx, in vitro data, and in silico predictions. Specifically, we first identified 41 substances evaluated by the U.S. EPA's Provisional Peer-Reviewed Toxicity Value (PPRTV) program for qRAx-derived oral chronic PODs. For these same substances, we generated PODs using: (i) in vitro-to-in vivo extrapolation from ToxCast bioactivity; (ii) database-calibrated in silico assessment from ToxValDB; and (iii) three quantitative structure-activity relationship (QSAR) models. Success rates for generating PODs varied considerably: qRAx 83% (34/41), ToxCast 22% (9/41), ToxValDB 66% (27/41), and QSAR 46-100% (19-41/41). qRAx yielded the most conservative PODs in the largest number of cases (44-54%). Combining multiple NAMs including at least one of ToxValDB or a QSAR model results in coverage exceeding 90% and including both in a tiered approach produces PODs that are on average within one order of magnitude of qRAx-derived values. We conclude that well-calibrated in silico methods can rapidly derive PODs with defined uncertainty, supporting time-sensitive health risk decisions.

The cosmetic safety evaluation has undergone a paradigm shift driven by animal testing bans now implemented across countries representing one-third of the global population and nearly half of worldwide cosmetics sales. The legislative mandates have accelerated the development and adoption of Next Generation Risk Assessment (NGRA), an innovative framework that proposes making of context-specific safety decisions using exposure-driven, mechanism-based approaches based on non-animal test methods. Significant progress has been achieved in replacing animal tests for specific endpoints including skin sensitization, irritation, and phototoxicity through validated New Approach Methodologies (NAMs). However, major gaps remain for one-for-one replacement of systemic toxicity, reproductive and developmental toxicity, and carcinogenicity animal tests. Recent advances demonstrate that NGRA can successfully address many of these complex endpoints through integrated approaches combining physiologically-based kinetic (PBK) modeling, high-throughput transcriptomics and in vitro phenotyping to derive Bioactivity-Exposure Ratios (BERs) for risk characterization. Numerous case studies across diverse chemical types, including coumarin, benzophenone-4, caffeine, and benzyl salicylate, have demonstrated NGRA's utility for making protective safety decisions without animal data. These examples highlight critical considerations that need to be addressed in NGRA, including metabolite assessment, dose metric selection, and quantitative uncertainty characterization. While like-for-like replacement of all animal tests remains unachievable, NGRA provides a scientifically robust pathway for protective assessments where an absence of systemic effects is the primary goal, particularly for cosmetic ingredients. The challenge now lies in harmonizing methodologies, building regulatory confidence in these approaches, and evolving institutional structures to enable routine NGRA implementation across global regulatory regimes.

Cardiotoxicity remains a major cause of drug attrition and post-market withdrawal, yet the vast majority of environmental chemicals to which humans may be exposed remain uncharacterized for cardiotoxicity risk. Human induced pluripotent stem cell (hiPSC)-based testing has been proposed to address this gap. Here we present an unsupervised deep learning framework for multi-donor cardiotoxicity screening using high-throughput calcium transient recordings from hiPSC-derived cardiomyocytes (hiPSC-CMs). We used data from a library of 1,029 compounds that were tested in hiPSC-CM from five donors in concentration-response. An autoencoder trained exclusively on baseline signals quantified chemical-induced functional perturbations through reconstruction error, bypassing the need for labeled training data while capturing the full spectrum of calcium handling disruptions. Using this framework, we generated effect levels. Aggregation of donor-specific scores revealed substantial inter-individual variability in potential cardiotoxicity, underscoring the value of this approach for population-level risk prediction. We found that microbiocides, dyes, and pesticides to have potential concern, characterized by high toxicity scores and low inter-donor variability. This framework establishes a scalable, human-relevant, and genetically diverse platform for cardiotoxicity surveillance across both pharmacological and environmental chemical spaces, with direct implications for drug and chemical safety evaluation and prioritization for additional studies.

The cosmetic safety evaluation has undergone a paradigm shift driven by animal testing bans now implemented across countries representing one-third of the global population and nearly half of worldwide cosmetics sales. The legislative mandates have accelerated the development and adoption of next generation risk assessment (NGRA), an innovative framework that proposes the making of context-specific safety decisions using exposure-driven, mechanism-based approaches based on non-animal test methods. Significant progress has been achieved in replacing animal tests for specific endpoints, including skin sensitization, irritation, and phototoxicity through validated new approach methodologies (NAMs). However, major gaps remain for the one-for-one replacement of systemic toxicity, reproductive and developmental toxicity, and carcinogenicity animal tests. Recent advances demonstrate that NGRA can successfully address many of these complex endpoints through integrated approaches combining physiologically based kinetic (PBK) modeling, high-throughput transcriptomics and in vitro phenotyping to derive bioactivity-exposure ratios (BERs) for risk characterization. Numerous case studies across diverse chemical types, including coumarin, benzophenone-4, caffeine, and benzyl salicylate, have demonstrated NGRA's utility for making protective safety decisions without animal data. These examples highlight critical considerations that need to be addressed in NGRA, including metabolite assessment, dose metric selection, and quantitative uncertainty characterization. While a like-for-like replacement of all animal tests remains unachievable, NGRA provides a scientifically robust pathway for protective assessments where the absence of systemic effects is the primary goal, particularly for cosmetic ingredients. The challenge now lies in harmonizing methodologies, building regulatory confidence in these approaches, and evolving institutional structures to enable routine NGRA implementation across global regulatory regimes.

Drug-induced liver injury (DILI) remains a major challenge in drug development, highlighting the need for reliable in vitro tools to assess hepatotoxicity and improve translation from animal to human studies. Models using hepatocytes from preclinical species are also needed to evaluate species-specific toxicity. In this study, we evaluated the basal function and DILI sensitivity of primary hepatocytes from human, monkey, rat, and dog cultured for up to 14 days in static monolayers, static spheroids, and spheroids cultured in a microfluidics-based microphysiological system (MPS). Hepatocyte function and injury responses were assessed using albumin, urea, and liver enzymes. Cells were exposed to species-specific DILI compounds chlorpromazine (CPZ), bosentan (BOS), and fialuridine (FIAU). Across platforms, human and monkey hepatocytes exhibited greater functional stability and sensitivity to DILI compounds than rat and dog hepatocytes. CPZ and BOS induced cytotoxicity primarily in human and monkey hepatocytes, while FIAU produced species-dependent effects consistent with known in vivo outcomes. The microfluidics-based MPS exhibited modestly improved hepatocyte spheroid function relative to static models, although limited MPS throughput constrained our ability for testing drugs beyond FIAU. Overall, these results demonstrate that integrating multi-species hepatocyte spheroids across static and microfluidic platforms enables comparative DILI assessment and supports improved preclinical-to-clinical translation.

Microphysiological systems (MPS) have been extensively developed in the past decade and are now used in mechanistic studies, as well as in drug and chemical toxicity testing. The utility of MPS for studies of a broad range of molecules is becoming ever more important. Many MPS models utilize polydimethylsiloxane (PDMS) as the material of choice. Despite its advantages, including biocompatibility, optical transparency, and gas permeability, PDMS exhibits significant molecular adsorption due to its hydrophobic surface properties, which is a well-known phenomenon. Although some MPS can be made from low-adsorbance materials, not all models can easily transition away from PDMS. Here, we investigated the potential of Parylene-C, a widely used coating for medical devices, for surface modification of a PDMS-based feto-maternal interface (FMi) MPS device. The impact of this coating on molecular adsorption was tested with five different chemicals (four drugs and one environmental pollutant) using a two-chambered microchannel-interconnected MPS device. We showed that the Parylene-C coating did not obstruct the microchannels, allowed chemical diffusion between chambers, did not compromise cell viability, and minimized molecular adsorption. The beneficial effect of Parylene-C coating was most prominent for highly adsorbed drugs (lipophilic)─celicoxib and tamoxifen─while the low-adsorbed compounds (amphiphilic or hydrophilic) like aspirin, sofosbuvir, and perfluorooctanoic acid were unaffected. Importantly, by limiting molecular adsorption, we were able to demonstrate chemical effects in the FMi MPS device even when using highly PDMS-adsorbed compounds. Collectively, our results demonstrate an easily adoptable strategy to increase the toxicological utility of PDMS-made MPS devices by modifying surface properties and adsorption behavior through the use of a Parylene-C coating.

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.

Contemporary approaches for developing interventions and assessing pre-clinical cardiovascular risk frequently utilize animal and in vitro models. However, these models currently lack normal species-specific reference ranges similar to what exists for humans. The genetically diverse Collaborative Cross (CC) population that models human genetic heterogenetiety was characterized to develop mouse-specific cardiac reference ranges for Mus muscuslus , the most commonly used pre-clincial model. Heart function was analyzed in males and females from 58 CC strains and C57BL/6J using high-frequency ultrasound under both conscious and anesthetized conditions, as well as conscious electrocardiography to develop two standard deviation-based reference ranges. The sources and magnitude of measurement variability were identified, and inter-laboratory comparisons determined to quantify phenotypic robustness and heritability. Strain was the largest source of variability, while laboratory where data were collected was also significant but sex was not. Additionally, strains were identified that have characteristics of disease-associated phenotypes in cardiac function and electrophysiology similar to human cases including dilated cardiomyopathy, systolic cardiomyopathy, cancer therapy-related cardiac dysfunction, and long QT. These new models allow a more natural, and therefore more translatable progession to a cardiac disease state, supporting development of strain-specific models for cardiac pathologies, ultimately allowing more accurate diagnoses and informative safety assessments in humans.

BACKGROUND:Contemporary approaches for developing interventions and assessing pre-clinical cardiovascular risk frequently utilize animal and in vitro models. However, these models currently lack normal species-specific reference ranges similar to what exists for humans. The genetically diverse Collaborative Cross (CC) population that models human genetic heterogenetiety was characterized to develop mouse-specific cardiac reference ranges for Mus musculus, the most commonly used pre-clincial model. METHODS:Heart function was analyzed in males and females from 58 CC strains and C57BL/6J using high-frequency ultrasound under both conscious and anesthetized conditions, as well as conscious electrocardiography to develop two standard deviation-based reference ranges. The sources and magnitude of measurement variability were identified, and inter-laboratory comparisons determined to quantify phenotypic robustness and heritability. RESULTS:Strain was the largest source of variability, while laboratory where data were collected was also significant but sex was not. Additionally, strains were identified that have characteristics of disease-associated phenotypes in cardiac function and electrophysiology similar to human cases including dilated cardiomyopathy, cancer therapy-related cardiac dysfunction, and long QT. CONCLUSIONS:These new models allow a more natural, and therefore more translatable progression to a cardiac disease state, supporting development of strain-specific models for cardiac pathologies, ultimately allowing more accurate diagnoses and informative human relevant assessments.

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.

Accurate in vitro models of intestinal permeability are essential for predicting oral drug absorption. Standard models like Caco-2 cells have well-known limitations, including lack of segment-specific physiology, but are widely used. Emerging models such as organoid-derived monolayers and microphysiological systems (MPS) offer enhanced physiological relevance but require comparative validation. We performed a head-to-head evaluation of Caco-2 cells, human jejunal (J2) and duodenal (D109) enteroid-derived cells, and EpiIntestinalTM tissues cultured on either static Transwell and flow-based MPS platforms. We assessed tissue morphology, barrier function (TEER, dextran leakage), and permeability of three model small molecules (caffeine, propranolol, and indomethacin), integrating the data into a physiologically based gut absorption model (PECAT) to predict human oral bioavailability. J2 and D109 cells demonstrated more physiologically relevant morphology and higher TEER than Caco-2 cells, while the EpiIntestinalTM model exhibited thicker and more uneven tissue structures with lower TEER and higher passive permeability. MPS cultures offered modest improvements in epithelial architecture but introduced greater variability, especially with enteroid-derived cells. Predictions of human fraction absorbed (Fabs) were most accurate when using static Caco-2 data with segment-specific corrections based on enteroid-derived values, highlighting the utility of combining traditional and advanced in vitro gut models to optimize predictive performance for Fabs. While MPS and enteroid-based systems provide physiological advantages, standard static models remain robust and predictive when used with in silico modeling. Our findings support the need for further refinement of enteroid-MPS integration and advocate for standardized benchmarking across gut model systems to improve translational relevance in drug development and regulatory reviews.

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.

As our health is affected by the xenobiotic chemicals we are exposed to, it is important to rapidly assess these molecules both in the environment and our bodies. Targeted analytical methods coupling either gas or liquid chromatography with mass spectrometry (GC-MS or LC-MS) are commonly utilized in current exposure assessments. While these methods are accepted as the gold standard for exposure analyses, they often require multiple sample preparation steps and more than 30 minutes per sample. This throughput limitation is a critical gap for exposure assessments and has resulted in an evolving interest in using ion mobility spectrometry and MS (IMS-MS) for non-targeted studies. IMS-MS is a unique technique due to its rapid analytical capabilities (millisecond scanning) and detection of a wide range of chemicals based on unique collision cross section (CCS) and mass-to-charge (m/z) values. To increase the availability of IMS-MS information for exposure studies, here we utilized drift tube IMS-MS to evaluate 4,685 xenobiotic chemical standards from the Environmental Protection Agency Toxicity Forecaster (ToxCast) program including pesticides, industrial chemicals, pharmaceuticals, consumer products, and per- and polyfluoroalkyl substances (PFAS). In the analyses, 3,993 [M+H]+, [M+Na]+, [M-H]- and [M+]+ ion types were observed with high confidence and reproducibility (≤1% error intra-laboratory and ≤2% inter-laboratory) from 2,140 unique chemicals. These values were then assembled into an openly available multidimensional database and uploaded to PubChem to enable rapid IMS-MS suspect screening for a wide range of environmental contaminants, faster response time in environmental exposure assessments, and assessments of xenobiotic-disease connections.