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BACKGROUND: The field of toxicology has witnessed substantial advancements in recent years, particularly with the adoption of new approach methodologies (NAMs) to understand and predict chemical toxicity. Class-based methods such as clustering and classification are key to NAMs development and application, aiding the understanding of hazard and risk concerns associated with groups of chemicals without additional laboratory work. Advances in computational chemistry, data generation and availability, and machine learning algorithms represent important opportunities for continued improvement of these techniques to optimize their utility for specific regulatory and research purposes. However, due to their intricacy, deep understanding and careful selection are imperative to align the adequate methods with their intended applications. OBJECTIVES: This commentary aims to deepen the understanding of class-based approaches by elucidating the pivotal role of chemical similarity (structural and biological) in clustering and classification approaches (CCAs). It addresses the dichotomy between general end point-agnostic similarity, often entailing unsupervised analysis, and end point-specific similarity necessitating supervised learning. The goal is to highlight the nuances of these approaches, their applications, and common misuses. DISCUSSION: Understanding similarity is pivotal in toxicological research involving CCAs. The effectiveness of these approaches depends on the right definition and measure of similarity, which varies based on context and objectives of the study. This choice is influenced by how chemical structures are represented and the respective labels indicating biological activity, if applicable. The distinction between unsupervised clustering and supervised classification methods is vital, requiring the use of end point-agnostic vs. end point-specific similarity definition. Separate use or combination of these methods requires careful consideration to prevent bias and ensure relevance for the goal of the study. Unsupervised methods use end point-agnostic similarity measures to uncover general structural patterns and relationships, aiding hypothesis generation and facilitating exploration of datasets without the need for predefined labels or explicit guidance. Conversely, supervised techniques demand end point-specific similarity to group chemicals into predefined classes or to train classification models, allowing accurate predictions for new chemicals. Misuse can arise when unsupervised methods are applied to end point-specific contexts, like analog selection in read-across, leading to erroneous conclusions. This commentary provides insights into the significance of similarity and its role in supervised classification and unsupervised clustering approaches. https://doi.org/10.1289/EHP14001.
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Aprendizaje Automático , Análisis por Conglomerados , Aprendizaje Automático no Supervisado , Toxicología/métodos , AlgoritmosRESUMEN
In the real world, individuals are exposed to chemicals from sources that vary over space and time. However, traditional risk assessments based on in vivo animal studies typically use a chemical-by-chemical approach and apical disease endpoints. New approach methodologies (NAMs) in toxicology, such as in vitro high-throughput (HTS) assays generated in Tox21 and ToxCast, can more readily provide mechanistic chemical hazard information for chemicals with no existing data than in vivo methods. In this paper, we establish a workflow to assess the joint action of 41 modeled ambient chemical exposures in the air from the USA-wide National Air Toxics Assessment by integrating human exposures with hazard data from curated HTS (cHTS) assays to identify counties where exposure to the local chemical mixture may perturb a common biological target. We exemplify this proof-of-concept using CYP1A1 mRNA up-regulation. We first estimate internal exposure and then convert the inhaled concentration to a steady state plasma concentration using physiologically based toxicokinetic modeling parameterized with county-specific information on ages and body weights. We then use the estimated blood plasma concentration and the concentration-response curve from the in vitro cHTS assay to determine the chemical-specific effects of the mixture components. Three mixture modeling methods were used to estimate the joint effect from exposure to the chemical mixture on the activity levels, which were geospatially mapped. Finally, a Monte Carlo uncertainty analysis was performed to quantify the influence of each parameter on the combined effects. This workflow demonstrates how NAMs can be used to predict early-stage biological perturbations that can lead to adverse health outcomes that result from exposure to chemical mixtures. As a result, this work will advance mixture risk assessment and other early events in the effects of chemicals.
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Bioensayo , Exposición a Riesgos Ambientales , Humanos , Animales , Medición de Riesgo , Método de Montecarlo , Exposición a Riesgos Ambientales/análisisRESUMEN
Due to the increasing number of drugs and untested environmental compounds introduced into commercial use, there is recognition for a need to develop reliable and efficient screening methods to identify compounds that may adversely impact the nervous system. One process that has been implicated in neurodevelopment is neurite outgrowth; the disruption of which can result in adverse outcomes that persist later in life. Here, we developed a green fluorescent protein (GFP) labeled neurite outgrowth assay in a high-content, high-throughput format using induced pluripotent stem cell (iPSC) derived human spinal motor neurons and cortical glutamatergic neurons. The assay was optimized for use in a 1536-well plate format. Then, we used this assay to screen a set of 84 unique compounds that have previously been screened in other neurite outgrowth assays. This library consists of known developmental neurotoxicants, environmental compounds with unknown toxicity, and negative controls. Neurons were cultured for 40 h and then treated with compounds at 11 concentrations ranging from 1.56 nM to 92 µM for 24 and 48 h. Effects of compounds on neurite outgrowth were evaluated by quantifying total neurite length, number of segments, and maximum neurite length per cell. Among the 84 tested compounds, neurite outgrowth in cortical neurons and motor neurons were selectively inhibited by 36 and 31 compounds, respectively. Colchicine, rotenone, and methyl mercuric (II) chloride inhibited neurite outgrowth in both cortical and motor neurons. It is interesting to note that some compounds like parathion and bisphenol AF had inhibitory effects on neurite outgrowth specifically in the cortical neurons, while other compounds, such as 2,2',4,4'-tetrabromodiphenyl ether and caffeine, inhibited neurite outgrowth in motor neurons. The data gathered from these studies show that GFP-labeled iPSC-derived human neurons are a promising tool for identifying and prioritizing compounds with developmental neurotoxicity potential for further hazard characterization.
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Proteínas Fluorescentes Verdes/biosíntesis , Células Madre Pluripotentes Inducidas/efectos de los fármacos , Células-Madre Neurales/efectos de los fármacos , Proyección Neuronal/efectos de los fármacos , Neuronas/efectos de los fármacos , Pruebas de Toxicidad , Línea Celular , Genes Reporteros , Proteínas Fluorescentes Verdes/genética , Ensayos Analíticos de Alto Rendimiento , Humanos , Células Madre Pluripotentes Inducidas/metabolismo , Células Madre Pluripotentes Inducidas/patología , Células-Madre Neurales/metabolismo , Células-Madre Neurales/patología , Neuronas/metabolismo , Neuronas/patología , Medición de RiesgoRESUMEN
A 5-day in vivo rat model was evaluated as an approach to estimate chemical exposures that may pose minimal risk by comparing benchmark dose (BMD) values for transcriptional changes in the liver and kidney to BMD values for toxicological endpoints from traditional toxicity studies. Eighteen chemicals, most having been tested by the National Toxicology Program in 2-year bioassays, were evaluated. Some of these chemicals are potent hepatotoxicants (eg, DE71, PFOA, and furan) in rodents, some exhibit toxicity but have minimal hepatic effects (eg, acrylamide and α,ß-thujone), and some exhibit little overt toxicity (eg, ginseng and milk thistle extract) based on traditional toxicological evaluations. Male Sprague Dawley rats were exposed once daily for 5 consecutive days by oral gavage to 8-10 dose levels for each chemical. Liver and kidney were collected 24 h after the final exposure and total RNA was assayed using high-throughput transcriptomics (HTT) with the rat S1500+ platform. HTT data were analyzed using BMD Express 2 to determine transcriptional gene set BMD values. BMDS was used to determine BMD values for histopathological effects from chronic or subchronic toxicity studies. For many of the chemicals, the lowest transcriptional BMDs from the 5-day assays were within a factor of 5 of the lowest histopathological BMDs from the toxicity studies. These data suggest that using HTT in a 5-day in vivo rat model provides reasonable estimates of BMD values for traditional apical endpoints. This approach may be useful to prioritize chemicals for further testing while providing actionable data in a timely and cost-effective manner.
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Riñón/efectos de los fármacos , Hígado/efectos de los fármacos , Pruebas de Toxicidad/normas , Transcriptoma , Animales , Ensayos Analíticos de Alto Rendimiento , Masculino , Ratas , Ratas Sprague-DawleyRESUMEN
SUMMARY: A new version (version 2) of the genomic dose-response analysis software, BMDExpress, has been created. The software addresses the increasing use of transcriptomic dose-response data in toxicology, drug design, risk assessment and translational research. In this new version, we have implemented additional statistical filtering options (e.g. Williams' trend test), curve fitting models, Linux and Macintosh compatibility and support for additional transcriptomic platforms with up-to-date gene annotations. Furthermore, we have implemented extensive data visualizations, on-the-fly data filtering, and a batch-wise analysis workflow. We have also significantly re-engineered the code base to reflect contemporary software engineering practices and streamline future development. The first version of BMDExpress was developed in 2007 to meet an unmet demand for easy-to-use transcriptomic dose-response analysis software. Since its original release, however, transcriptomic platforms, technologies, pathway annotations and quantitative methods for data analysis have undergone a large change necessitating a significant re-development of BMDExpress. To that end, as of 2016, the National Toxicology Program assumed stewardship of BMDExpress. The result is a modernized and updated BMDExpress 2 that addresses the needs of the growing toxicogenomics user community. AVAILABILITY AND IMPLEMENTATION: BMDExpress 2 is available at https://github.com/auerbachs/BMDExpress-2/releases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Transcriptoma , Flujo de Trabajo , Genoma , Anotación de Secuencia Molecular , Programas InformáticosRESUMEN
BACKGROUND: The National Research Council's vision for toxicity testing in the 21st century anticipates that points of departure (PODs) for establishing human exposure guidelines in future risk assessments will increasingly be based on in vitro high-throughput screening (HTS) data. OBJECTIVES: The aim of this study was to compare different PODs for HTS data. Specifically, benchmark doses (BMDs) were compared to the signal-to-noise crossover dose (SNCD), which has been suggested as the lowest dose applicable as a POD. METHODS: Hill models were fit to > 10,000 in vitro concentration-response curves, obtained for > 1,400 chemicals tested as part of the U.S. Tox21 Phase I effort. BMDs and lower confidence limits on the BMDs (BMDLs) corresponding to extra effects (i.e., changes in response relative to the maximum response) of 5%, 10%, 20%, 30%, and 40% were estimated for > 8,000 curves, along with BMDs and BMDLs corresponding to additional effects (i.e., absolute changes in response) of 5%, 10%, 15%, 20%, and 25%. The SNCD, defined as the dose where the ratio between the additional effect and the difference between the upper and lower bounds of the two-sided 90% confidence interval on absolute effect was 1, 0.67, and 0.5, respectively, was also calculated and compared with the BMDLs. RESULTS: The BMDL40, BMDL25, and BMDL18, defined in terms of extra effect, corresponded to the SNCD1.0, SNCD0.67, and SNCD0.5, respectively, at the median. Similarly, the BMDL25, BMDL17, and BMDL13, defined in terms of additional effect, corresponded to the SNCD1.0, SNCD0.67, and SNCD0.5, respectively, at the median. CONCLUSIONS: The SNCD may serve as a reference level that guides the determination of standardized BMDs for risk assessment based on HTS concentration-response data. The SNCD may also have application as a POD for low-dose extrapolation.
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Exposición a Riesgos Ambientales/normas , Benchmarking , Relación Dosis-Respuesta a Droga , Exposición a Riesgos Ambientales/análisis , Exposición a Riesgos Ambientales/estadística & datos numéricos , Ensayos Analíticos de Alto Rendimiento , Humanos , Modelos Teóricos , Medición de Riesgo/métodos , Pruebas de ToxicidadRESUMEN
Using in vitro data in human cell lines, several research groups have investigated changes in gene expression in cellular systems following exposure to extremely low frequency (ELF) and radiofrequency (RF) electromagnetic fields (EMF). For ELF EMF, we obtained five studies with complete microarray data and three studies with only lists of significantly altered genes. Likewise, for RF EMF, we obtained 13 complete microarray datasets and 5 limited datasets. Plausible linkages between exposure to ELF and RF EMF and human diseases were identified using a three-step process: (a) linking genes associated with classes of human diseases to molecular pathways, (b) linking pathways to ELF and RF EMF microarray data, and (c) identifying associations between human disease and EMF exposures where the pathways are significantly similar. A total of 60 pathways were associated with human diseases, mostly focused on basic cellular functions like JAK-STAT signaling or metabolic functions like xenobiotic metabolism by cytochrome P450 enzymes. ELF EMF datasets were sporadically linked to human diseases, but no clear pattern emerged. Individual datasets showed some linkage to cancer, chemical dependency, metabolic disorders, and neurological disorders. RF EMF datasets were not strongly linked to any disorders but strongly linked to changes in several pathways. Based on these analyses, the most promising area for further research would be to focus on EMF and neurological function and disorders.
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Due to the increasing prevalence of neurological disorders and the large number of untested compounds in the environment, there is a need to develop reliable and efficient screening tools to identify environmental chemicals that could potentially affect neurological development. Herein, we report on a library of 80 compounds screened for their ability to inhibit neurite outgrowth, a process by which compounds may elicit developmental neurotoxicity, in a high-throughput, high-content assay using human neurons derived from induced pluripotent stem cells (iPSC). The library contains a diverse set of compounds including those that have been known to be associated with developmental neurotoxicity (DNT) and/or neurotoxicity (NT), environmental compounds with unknown neurotoxic potential (e.g., polycyclic aromatic hydrocarbons (PAHs) and flame retardants (FRs)), as well as compounds with no documented neurotoxic potential. Neurons were treated for 72h across a 6-point concentration range (â¼0.3-100µM) in 384-well plates. Effects on neurite outgrowth were assessed by quantifying total outgrowth, branches, and processes. We also assessed the number ofviable cells per well. Concentration-response profiles were evaluated using a Hill model to derive benchmark concentration (BMC) values. Assay performance was evaluated using positive and negative controls and test replicates. Compounds were ranked by activity and selectivity (i.e., specific effects on neurite outgrowth in the absence of concomitant cytotoxicity) and repeat studies were conducted to confirm selectivity. Among the 80 compounds tested, 38 compounds were active, of which 16 selectively inhibited neurite outgrowth. Of these 16 compounds, 12 were known to cause DNT/NT and the remaining 4 compounds included 3 PAHs and 1 FR. In independent repeat studies, 14/16 selective compounds were reproducibly active in the assay, of which only 6 were selective for inhibition of neurite outgrowth. These 6 compounds were previously shown in the literature to be neurotoxic. These studies shed light on the current status of human iPSCs in DNT/NT screening and their utility in identifying, ranking, and prioritizing compounds with DNT/NT potential for further in vivo testing.
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Células Madre Pluripotentes Inducidas/citología , Proyección Neuronal/efectos de los fármacos , Neuronas/efectos de los fármacos , Síndromes de Neurotoxicidad/diagnóstico , Neurotoxinas/toxicidad , Células Cultivadas , Relación Dosis-Respuesta a Droga , Humanos , Células Madre Pluripotentes Inducidas/efectos de los fármacos , Neuronas/citología , Síndromes de Neurotoxicidad/etiologíaRESUMEN
BACKGROUND: Increasing data on early biological changes from chemical exposures requires new interpretation tools to support decision-making. OBJECTIVES: To test the possibility of applying a quantitative approach using human data linking chemical exposures and upstream biological perturbations to overt downstream outcomes. METHODS: Using polychlorinated biphenyl (PCB) exposures and maternal thyroid hormone (TH) perturbations as a case study, we model three relationships: (1) prenatal PCB exposures and TH changes, using free T(4) (FT(4)); (2) prenatal TH and childhood neurodevelopmental outcomes; and (3) prenatal PCB exposures and childhood neurodevelopmental outcomes (IQ). We surveyed the epidemiological literature; extracted relevant quantitative data; and developed models for each relationship, applying meta-analysis where appropriate. RESULTS: For relationship 1, a meta-analysis of 3 studies gives a coefficient of -0.27 pg/mL FT(4) per ln(sum of PCBs) (95% confidence interval [CI] -0.82 to 0.27). For relationship 2, regression coefficients from three studies of maternal FT(4) levels and cognitive scores ranged between 0.99 IQ points/(pg/mL FT(4)) (95% CI -0.31 to 2.2) and 7.6 points/(pg/mL FT(4)) (95% CI 1.2 to 16.3). For relationship 3, a meta-analysis of five studies produces a coefficient of -1.98 IQ points (95% CI -4.46 to 0.50) per unit increase in ln(sum of PCBs). Combining relationships 1 and 2 yields an estimate of -2.0 to -0.27 points of IQ per unit increase in ln(sum of PCBs). CONCLUSIONS: Combining analysis of chemical exposures and early biological perturbations (PCBs and FT(4)) with analysis of early biological perturbations and downstream overt effects (FT(4) and IQ) yields estimates within the range of studies of exposures and overt effects (PCBs and IQ). This is an example approach using upstream biological perturbations for effect prediction.
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Desarrollo Infantil/efectos de los fármacos , Exposición a Riesgos Ambientales/efectos adversos , Inteligencia/efectos de los fármacos , Modelos Biológicos , Bifenilos Policlorados/toxicidad , Efectos Tardíos de la Exposición Prenatal/inducido químicamente , Hormonas Tiroideas/metabolismo , Carga Corporal (Radioterapia) , Niño , Femenino , Feto/metabolismo , Humanos , Lactante , Pruebas de Inteligencia , Masculino , Embarazo , Análisis de Regresión , Medición de RiesgoRESUMEN
There is a growing need for quantitative approaches to extrapolate relationships between chemical exposures and early biological perturbations from animals to humans given increasing use of biological assays to evaluate toxicity pathways. We have developed such an approach using polychlorinated biphenyls (PCBs) and thyroid hormone (TH) disruption as a case study. We reviewed and identified experimental animal literature from which we developed a low-dose, linear model of PCB body burdens and decrements in free thyroxine (FT(4)) and total thyroxine (TT(4)), accounting for 33 PCB congeners; extrapolated the dose-response from animals to humans; and compared the animal dose-response to the dose-response of PCB body burdens and TH changes from eleven human epidemiological studies. We estimated a range of potencies for PCB congeners (over 4 orders of magnitude), with the strongest for PCB 126. Our approach to developing toxic equivalency models produced relative potencies similar to the toxicity equivalency factors (TEFs) from the World Health Organization (WHO). We generally found that the dose-response extrapolated from the animal studies tends to under-predict the dose-response estimated from human epidemiological studies. A quantitative approach to evaluating the relationship between chemical exposures and TH perturbations, based on animal data can be used to assess human health consequences of thyroid toxicity and inform decision-making.
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Disruptores Endocrinos/toxicidad , Exposición a Riesgos Ambientales , Modelos Biológicos , Bifenilos Policlorados/toxicidad , Hormonas Tiroideas/sangre , Animales , Carga Corporal (Radioterapia) , Relación Dosis-Respuesta a Droga , Disruptores Endocrinos/sangre , Exposición a Riesgos Ambientales/efectos adversos , Exposición a Riesgos Ambientales/análisis , Monitoreo del Ambiente/métodos , Femenino , Humanos , Masculino , Bifenilos Policlorados/sangre , Embarazo , Ratas , Ratas Sprague-Dawley , Medición de RiesgoRESUMEN
The National Toxicology Program is developing a high-throughput screening (HTS) program to set testing priorities for compounds of interest, to identify mechanisms of action, and potentially to develop predictive models for human toxicity. This program will generate extensive data on the activity of large numbers of chemicals in a wide variety of biochemical- and cell-based assays. The first step in relating patterns of response among batteries of HTS assays to in vivo toxicity is to distinguish between positive and negative compounds in individual assays. Here, the authors report on a statistical approach developed to identify compounds positive or negative in an HTS cytotoxicity assay based on data collected from screening 1353 compounds for concentration-response effects in 9 human and 4 rodent cell types. In this approach, the authors develop methods to normalize the data (removing bias due to the location of the compound on the 1536-well plates used in the assay) and to analyze for concentration-response relationships. Various statistical tests for identifying significant concentration-response relationships and for addressing reproducibility are developed and presented.
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Relación Dosis-Respuesta a Droga , Ensayos Analíticos de Alto Rendimiento/métodos , Modelos Biológicos , Animales , Línea Celular , Humanos , Reproducibilidad de los ResultadosRESUMEN
A physiologically based pharmacokinetic model of the absorption, distribution, metabolism, and elimination of p,p'-dichlorodiphenylsulfone (DDS) in male and female rats and mice is presented. Data used in constructing the model come from single-dose intravenous administration of DDS to male Fischer 344 rats (10 mg/kg, with data taken up to 504 h after administration), from single-dose gavage administration to male rats (10, 100, or 1000 mg/kg, with data up to 72 h after administration), and from chronic feed studies in male and female rats and male and female B6C3F(1) mice (studies of duration from 2 weeks up to 18 months, with feed concentrations of DDS up to 300 ppm). The model uses diffusion-limited kinetic for the distribution of the parent compound. Because fewer data are available for the metabolites of DDS (at least five of which are known to exist in the data), the model groups the metabolites into one metabolic pathway and uses simpler flow-limited kinetics for the metabolites. The data show that the kinetics of DDS are nonlinear. Possible sources of nonlinearity considered in the model were nonlinear (Michaelis-Menten) metabolism, nonlinear absorption of DDS from the gut, and induction by DDS of its own metabolism. A model using Michaelis-Menten metabolism was not found to give a significantly better fit than one using first-order linear metabolism, but omitting either of the other nonlinear effects was found to give a significantly poorer fit to the data. Because the data from mice are limited compared to those from rats, there is more confidence in the model's description of DDS kinetics in rats than in its description of kinetics in mice.