Showing papers by "Richard S. Judson published in 2016"
TL;DR: The ToxCast chemical library is demonstrated to provide comprehensive coverage of the knowledge domains and target inventories of potential interest to EPA, and the varied representations and approaches presented here define local chemistry domains potentially worthy of further investigation.
Abstract: The U.S. Environmental Protection Agency’s (EPA) ToxCast program is testing a large library of Agency-relevant chemicals using in vitro high-throughput screening (HTS) approaches to support the development of improved toxicity prediction models. Launched in 2007, Phase I of the program screened 310 chemicals, mostly pesticides, across hundreds of ToxCast assay end points. In Phase II, the ToxCast library was expanded to 1878 chemicals, culminating in the public release of screening data at the end of 2013. Subsequent expansion in Phase III has resulted in more than 3800 chemicals actively undergoing ToxCast screening, 96% of which are also being screened in the multi-Agency Tox21 project. The chemical library unpinning these efforts plays a central role in defining the scope and potential application of ToxCast HTS results. The history of the phased construction of EPA’s ToxCast library is reviewed, followed by a survey of the library contents from several different vantage points. CAS Registry Numbers ar...
455 citations
Research Triangle Park1, Umeå University2, Mario Negri Institute for Pharmacological Research3, University of North Carolina at Chapel Hill4, University of Strasbourg5, National Institutes of Health6, United States Environmental Protection Agency7, University of Bari8, Technical University of Denmark9, University of Milano-Bicocca10, Food and Drug Administration11, Lockheed Martin Corporation12, Research Institute for Fragrance Materials13, University of Milan14, United States Department of Agriculture15
TL;DR: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches and the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing.
Abstract: BACKGROUND: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most ...
252 citations
TL;DR: The tcpl R package and its associated MySQL database provide a generalized platform for efficiently storing, normalizing and dose‐response modeling of large high‐throughput and high‐content chemical screening data.
Abstract: Motivation Large high-throughput screening (HTS) efforts are widely used in drug development and chemical toxicity screening Wide use and integration of these data can benefit from an efficient, transparent and reproducible data pipeline Summary: The tcpl R package and its associated MySQL database provide a generalized platform for efficiently storing, normalizing and dose-response modeling of large high-throughput and high-content chemical screening data The novel dose-response modeling algorithm has been tested against millions of diverse dose-response series, and robustly fits data with outliers and cytotoxicity-related signal loss Availability and implementation tcpl is freely available on the Comprehensive R Archive Network under the GPL-2 license Contact martinmatt@epagov
161 citations
TL;DR: Responses of 1060 chemicals including pharmaceuticals, natural products, pesticidals, consumer, and industrial chemicals across a battery of in vitro assay endpoints from 7 high-throughput assay technology platforms were analyzed to suggest that activity can be broadly divided into specific biomolecular interactions against one or more targets at concentrations below which overt cytotoxicity-associated activity is observed.
156 citations
TL;DR: A novel suspect screening methodology to prioritize chemicals of interest for subsequent targeted analysis is demonstrated that relies on strategic integration of available public resources and should be considered in future non-targeted and suspect screening assessments of environmental and biological media.
137 citations
109 citations
TL;DR: This effort represents the most extensive TPO inhibition screening campaign to date and illustrates a tiered screening approach that focuses resources, maximizes assay throughput, and reduces animal use.
84 citations
TL;DR: The development of an automated KNIME workflow to curate and correct errors in the structure and identity of chemicals using the publicly available PHYSPROP physicochemical properties and environmental fate datasets is described.
Abstract: The increasing availability of large collections of chemical structures and associated experimental data provides an opportunity to build robust QSAR models for applications in different fields. On...
81 citations
United States Environmental Protection Agency1, Research Triangle Park2, National Institutes of Health3, Brown University4, Engineer Research and Development Center5, Texas A&M University6, University of Cambridge7, Clark University8, European Chemicals Agency9, University of Ottawa10, National Center for Toxicological Research11, Harvard University12, Environmental Defense Fund13, National Institute for Occupational Safety and Health14, University of California, Berkeley15, University of California, San Francisco16, Health Canada17
TL;DR: The NexGen of Risk Assessment effort has advanced the ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure–response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects.
Abstract: Background:The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and ...
81 citations
TL;DR: An algorithmic, automated approach to evaluate the utility of using in vitro bioactivity data ("bioactivity descriptors", from EPA's ToxCast program) in conjunction with chemical descriptor information to derive local validity domains to facilitate read-across for up to ten in-vivo repeated dose toxicity study types.
66 citations
TL;DR: The results of this screening-level analysis suggest that the spectrum of environmental chemicals to consider in research related to diabetes and obesity is much broader than indicated by research papers and reviews published in the peer-reviewed literature.
Abstract: Background:Diabetes and obesity are major threats to public health in the United States and abroad. Understanding the role that chemicals in our environment play in the development of these conditi...
TL;DR: It is demonstrated that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells.
TL;DR: This proposed in silico approach is an inexpensive and rapid strategy for the detection of chemicals with estrogenic metabolites and may reduce potential false negative results from in vitro assays.
Abstract: The US Environmental Protection Agency’s (EPA) Endocrine Disruptor Screening Program (EDSP) is using in vitro data generated from ToxCast/Tox21 high-throughput screening assays to assess the endocrine activity of environmental chemicals. Considering that in vitro assays may have limited metabolic capacity, inactive chemicals that are biotransformed into metabolites with endocrine bioactivity may be missed for further screening and testing. Therefore, there is a value in developing novel approaches to account for metabolism and endocrine activity of both parent chemicals and their associated metabolites. We used commercially available software to predict metabolites of 50 parent compounds, out of which 38 chemicals are known to have estrogenic metabolites, and 12 compounds and their metabolites are negative for estrogenic activity. Three ER QSAR models were used to determine potential estrogen bioactivity of the parent compounds and predicted metabolites, the outputs of the models were averaged, and the ch...
TL;DR: The accuracy of QSAR models are demonstrated to predict the biological activity of chemicals specifically for each one of the studied assays, including the bovine nonselective dopamine receptor (bDR_NS) GPCR assay.
Abstract: The US EPA's ToxCast program is screening thousands of chemicals of environmental interest in hundreds of in vitro high-throughput screening (HTS) assays. One goal is to prioritize chemicals for more detailed analyses based on activity in assays that target molecular initiating events (MIEs) of adverse outcome pathways (AOPs). However, the chemical space of interest for environmental exposure is much wider than ToxCast's chemical library. In silico methods such as quantitative structure-activity relationships (QSARs) are proven and cost-effective approaches to predict biological activity for untested chemicals. However, empirical data is needed to build and validate QSARs. ToxCast has developed datasets for about 2000 chemicals ideal for training and testing QSAR models. The overall goal of the present work was to develop QSAR models to fill the data gaps in larger environmental chemical lists. The specific aim of the current work was to build QSAR models for 18 G-protein-coupled receptor (GPCR) assays, part of the aminergic family. Two QSAR modeling strategies were adopted: classification models were developed to separate chemicals into active/non-active classes, and then regression models were built to predict the potency values of the bioassays for the active chemicals. Multiple software programs were used to calculate constitutional, topological, and substructural molecular descriptors from two-dimensional (2D) chemical structures. Model-fitting methods included PLSDA (partial least square discriminant analysis), SVMs (support vector machines), kNNs (k-nearest neighbors), and PLSs (partial least squares). Genetic algorithms (GAs) were applied as a variable selection technique to select the most predictive molecular descriptors for each assay. N-fold cross-validation (CV) coupled with multi-criteria decision-making fitting criteria was used to evaluate the models. Finally, the models were applied to make predictions within the established chemical space limits. The most accurate model was for the bovine nonselective dopamine receptor (bDR_NS) GPCR assay, for which the classification balanced accuracy reached 0.96 in fitting and 0.95 in fivefold CV, with only two latent variables. These results demonstrate the accuracy of QSAR models to predict the biological activity of chemicals specifically for each one of the studied assays.
TL;DR: A predictive model for cytotoxicity based on U.S. EPA Toxicity ForeCaster data tested up to 100 μM is built and probabilistic design rules are provided to help synthetic chemists minimize the chance that a newly synthesized chemical will be cytotoxic.
TL;DR: A probabilistic design diagram was developed to guide chemical design with the twin goals of minimizing NRF2 antioxidant pathway activity and cytotoxicity, initiating a simultaneous design strategy against two toxicity pathways of interest in molecular design research.
TL;DR: The computational malformation index provides an objective manner for rapid phenotypic brightfield assessment of individual larva in a developmental zebrafish assay.
Abstract: One of the rate-limiting procedures in a developmental zebrafish screen is the morphological assessment of each larva. Most researchers opt for a time-consuming, structured visual assessment by trained human observer(s). The present studies were designed to develop a more objective, accurate and rapid method for screening zebrafish for dysmorphology. Instead of the very detailed human assessment, we have developed the computational malformation index, which combines the use of high-content imaging with a very brief human visual assessment. Each larva was quickly assessed by a human observer (basic visual assessment), killed, fixed and assessed for dysmorphology with the Zebratox V4 BioApplication using the Cellomics® ArrayScan® V(TI) high-content image analysis platform. The basic visual assessment adds in-life parameters, and the high-content analysis assesses each individual larva for various features (total area, width, spine length, head-tail length, length-width ratio, perimeter-area ratio). In developing the computational malformation index, a training set of hundreds of embryos treated with hundreds of chemicals were visually assessed using the basic or detailed method. In the second phase, we assessed both the stability of these high-content measurements and its performance using a test set of zebrafish treated with a dose range of two reference chemicals (trans-retinoic acid or cadmium). We found the measures were stable for at least 1 week and comparison of these automated measures to detailed visual inspection of the larvae showed excellent congruence. Our computational malformation index provides an objective manner for rapid phenotypic brightfield assessment of individual larva in a developmental zebrafish assay. Copyright © 2016 John Wiley & Sons, Ltd.
TL;DR: This paper focuses on three such institutions and the tools they offer to the public: the National Library of Medicine and its Toxicology and Environmental Health Information Program, the United States Environmental Protection Agency (EPA), and the Organisation for Economic Co-operation and Development (OECD).
TL;DR: This analysis did not reveal a genetic signature consistent with processes previously shown to be involved in toluene-induced narcosis in mammals, but the list of the human orthologs included Gene Ontology terms associated with signaling, nervous system development and embryonic morphogenesis may provide insight into potential new pathways that could mediate the narcotic effects of toLUene.
Journal Article•
01 Jan 2016
TL;DR: The crossand external validation indicate a robust model with high predictive performance for TPO inhibition and predictions from this screening can be used in a tiered approach to prioritize potential thyroid disrupting chemical substances for further evaluation.
Abstract: REACH substances DTU Orbit (07/12/2018) Development of a QSAR Model for Thyroperoxidase Inhibition and Screening of 72,526 REACH substances Thyroid hormones (THs) are involved in multiple biological processes and are critical modulators of fetal development. Even moderate changes in maternal or fetal TH levels can produce irreversible neurological deficits in children, such as lower IQ. The enzyme thyroperoxidase (TPO) plays a key role in the synthesis of THs, and inhibition of TPO by xenobiotics results in decreased TH synthesis. Recently, a high-throughput screening assay for TPO inhibition (AUR-TPO) was developed and used to test the ToxCast Phase I and II chemicals. In the present study, we used the results from AUR-TPO to develop a Quantitative Structure Activity Relationship (QSAR) model for TPO inhibition. The training set consisted of 898 discrete organic chemicals: 134 inhibitors and 764 non-inhibitors. A five times two-fold cross-validation of the model was performed, yielding a balanced accuracy of 78.7%. More recently, an additional ~800 chemicals were tested in the AUR-TPO assay. These data were used for a blinded external validation of the QSAR model, demonstrating a balanced accuracy of 85.7%. Overall, the crossand external validation indicate a robust model with high predictive performance. Next, we used the QSAR model to predict 72,526 REACH pre registered substances. The model could predict 49.5% (35,925) of the substances in its applicability domain and of these, 8,863 (24.7%) were predicted to be TPO inhibitors. Predictions from this screening can be used in a tiered approach to prioritize potential thyroid disrupting chemical substances for further evaluation.