RESUMEN
Amongst omics technologies, metabolomics should have particular value in regulatory toxicology as the measurement of the molecular phenotype is the closest to traditional apical endpoints, whilst offering mechanistic insights into the biological perturbations. Despite this, the application of untargeted metabolomics for point-of-departure (POD) derivation via benchmark concentration (BMC) modelling is still a relatively unexplored area. In this study, a high-throughput workflow was applied to derive PODs associated with a chemical exposure by measuring the intracellular metabolome of the HepaRG cell line following treatment with one of four chemicals (aflatoxin B1, benzo[a]pyrene, cyclosporin A, or rotenone), each at seven concentrations (aflatoxin B1, benzo[a]pyrene, cyclosporin A: from 0.2048 µM to 50 µM; rotenone: from 0.04096 to 10 µM) and five sampling time points (2, 6, 12, 24 and 48 h). The study explored three approaches to derive PODs using benchmark concentration modelling applied to single features in the metabolomics datasets or annotated metabolites or lipids: (1) the 1st rank-ordered unannotated feature, (2) the 1st rank-ordered putatively annotated feature (using a recently developed HepaRG-specific library of polar metabolites and lipids), and (3) 25th rank-ordered feature, demonstrating that for three out of four chemical datasets all of these approaches led to relatively consistent BMC values, varying less than tenfold across the methods. In addition, using the 1st rank-ordered unannotated feature it was possible to investigate temporal trends in the datasets, which were shown to be chemical specific. Furthermore, a possible integration of metabolomics-driven POD derivation with the liver steatosis adverse outcome pathway (AOP) was demonstrated. The study highlights that advances in technologies enable application of in vitro metabolomics at scale; however, greater confidence in metabolite identification is required to ensure PODs are mechanistically anchored.
Asunto(s)
Benchmarking , Benzo(a)pireno , Aflatoxina B1 , Ciclosporina , Rotenona , Metabolómica , Línea Celular , LípidosRESUMEN
Interpretation of untargeted metabolomics data from both in vivo and physiologically relevant in vitro model systems continues to be a significant challenge for toxicology research. Potency-based modeling of toxicological responses has served as a pillar of interpretive context and translation of testing data. In this study, we leverage the resolving power of concentration-response modeling through benchmark concentration (BMC) analysis to interpret untargeted metabolomics data from differentiated cultures of HepaRG cells exposed to a panel of reference compounds and integrate data in a potency-aligned framework with matched transcriptomic data. For this work, we characterized biological responses to classical human liver injury compounds and comparator compounds, known to not cause liver injury in humans, at 10 exposure concentrations in spent culture media by untargeted liquid chromatography-mass spectrometry analysis. The analyte features observed (with limited metabolites identified) were analyzed using BMC modeling to derive compound-induced points of departure. The results revealed liver injury compounds produced concentration-related increases in metabolomic response compared to those rarely associated with liver injury (ie, sucrose, potassium chloride). Moreover, the distributions of altered metabolomic features were largely comparable with those observed using high throughput transcriptomics, which were further extended to investigate the potential for in vitro observed biological responses to be observed in humans with exposures at therapeutic doses. These results demonstrate the utility of BMC modeling of untargeted metabolomics data as a sensitive and quantitative indicator of human liver injury potential.
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Benchmarking , Transcriptoma , Humanos , Hígado , Espectrometría de Masas , MetabolómicaRESUMEN
Asthma is a heterogeneous inflammatory disease characterized by increased airway hyper-responsiveness to external stimuli such as irritants. One may speculate that asthmatics are more sensitive to irritants in the air than healthy subjects, i.e. react at lower concentrations. We reviewed the scientific support for this speculation and investigated to what extent asthma is considered when setting exposure limits and guidance values. We found that the experimental studies comparing healthy and asthmatic subjects are often inconclusive. Still, the available studies are underused, by expert committees and industry alike. Data for a few irritants suggest that asthmatics are up to three-fold more sensitive than the healthy. The most abundant data were found for sulfur dioxide. Here, a benchmark concentration analysis suggests a nine-fold difference in sensitivity. Based on these data a default assessment factor of 10 is suggested when setting exposure limits and guidance values for irritants.
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Contaminantes Atmosféricos/normas , Asma , Irritantes/normas , Contaminantes Atmosféricos/toxicidad , Animales , Humanos , Irritantes/toxicidad , Concentración Máxima AdmisibleRESUMEN
BACKGROUND: Manganese is an essential nutrient, but in excess, can be a potent neurotoxicant. We previously reported findings from two cross-sectional studies on children, showing that higher concentrations of manganese in drinking water were associated with deficits in IQ scores. Despite the common occurrence of this neurotoxic metal, its concentration in drinking water is rarely regulated. OBJECTIVE: We aimed to apply a benchmark concentration analysis to estimate water manganese levels associated with pre-defined levels of cognitive impairment in children, i.e. drop of 1%, 2% and 5% in Performance IQ scores. METHODS: Data from two studies conducted in Canada were pooled resulting in a sample of 630 children (ages 5.9-13.7â¯years) with data on tap water manganese concentration and cognition, as well as confounders. We used the Bayesian Benchmark Dose Analysis System to compute weight-averaged median estimates for the benchmark concentration (BMC) of manganese in water and the lower bound of the credible interval (BMCL), based on seven different exposure-response models. RESULTS: The BMC for manganese in drinking water associated with a decrease of 1% Performance IQ score was 133⯵g/L (BMCL, 78⯵g/L); for a decrease of 2%, this concentration was 266⯵g/L (BMCL, 156⯵g/L) and for a decrease of 5% it was 676⯵g/L (BMCL, 406⯵g/L). In sex-stratified analyses, the manganese concentrations associated with a decrease of 1%, 2% and 5% Performance IQ in boys were 185, 375 and 935⯵g/L (BMCLs, 75, 153 and 386⯵g/L) and 78, 95, 192⯵g/L (BMCLs, 9, 21 and 74⯵g/L) for girls. CONCLUSION: Studies suggest that a maximum acceptable concentration for manganese in drinking water should be set to protect children, the most vulnerable population, from manganese neurotoxicity. The present risk analysis can guide decision-makers responsible for developing these standards.
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Agua Potable/análisis , Exposición a Riesgos Ambientales , Pruebas de Inteligencia/estadística & datos numéricos , Manganeso/análisis , Adolescente , Benchmarking , Niño , Preescolar , Cognición , Exposición a Riesgos Ambientales/análisis , Exposición a Riesgos Ambientales/estadística & datos numéricos , Femenino , Humanos , MasculinoRESUMEN
Prediction of human response to chemical exposures is a major challenge in both pharmaceutical and toxicological research. Transcriptomics has been a powerful tool to explore chemical-biological interactions, however, limited throughput, high-costs, and complexity of transcriptomic interpretations have yielded numerous studies lacking sufficient experimental context for predictive application. To address these challenges, we have utilized a novel high-throughput transcriptomics (HTT) platform, TempO-Seq, to apply the interpretive power of concentration-response modeling with exposures to 24 reference compounds in both differentiated and non-differentiated human HepaRG cell cultures. Our goals were to (1) explore transcriptomic characteristics distinguishing liver injury compounds, (2) assess impacts of differentiation state of HepaRG cells on baseline and compound-induced responses (eg, metabolically-activated), and (3) identify and resolve reference biological-response pathways through benchmark concentration (BMC) modeling. Study data revealed the predictive utility of this approach to identify human liver injury compounds by their respective BMCs in relation to human internal exposure plasma concentrations, and effectively distinguished drug analogs with varied associations of human liver injury (eg, withdrawn therapeutics trovafloxacin and troglitazone). Impacts of cellular differentiation state (proliferated vs differentiated) were revealed on baseline drug metabolizing enzyme expression, hepatic receptor signaling, and responsiveness to metabolically-activated toxicants (eg, cyclophosphamide, benzo(a)pyrene, and aflatoxin B1). Finally, concentration-response modeling enabled efficient identification and resolution of plausibly-relevant biological-response pathways through their respective pathway-level BMCs. Taken together, these findings revealed HTT paired with differentiated in vitro liver models as an effective tool to model, explore, and interpret toxicological and pharmacological interactions.