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  1. Article: Structure-based QSAR Models to Predict Repeat Dose Toxicity Points of Departure.

    Pradeep, Prachi / Friedman, Katie Paul / Judson, Richard

    Computational toxicology (Amsterdam, Netherlands)

    2021  Volume 16, Issue November 2020

    Abstract: Human health risk assessment for environmental chemical exposure is limited by a vast majority of chemicals with little or no ... ...

    Abstract Human health risk assessment for environmental chemical exposure is limited by a vast majority of chemicals with little or no experimental
    Language English
    Publishing date 2021-05-20
    Publishing country Netherlands
    Document type Journal Article
    ISSN 2468-1113
    ISSN 2468-1113
    DOI 10.1016/j.comtox.2020.100139
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Ensemble QSAR Modeling to Predict Multispecies Fish Toxicity Lethal Concentrations and Points of Departure.

    Sheffield, Thomas Y / Judson, Richard S

    Environmental science & technology

    2019  Volume 53, Issue 21, Page(s) 12793–12802

    Abstract: QSAR modeling can be used to aid testing prioritization of the thousands of chemical substances for which no ecological toxicity data are available. We drew on the U.S. Environmental Protection Agency's ECOTOX database with additional data from ECHA to ... ...

    Abstract QSAR modeling can be used to aid testing prioritization of the thousands of chemical substances for which no ecological toxicity data are available. We drew on the U.S. Environmental Protection Agency's ECOTOX database with additional data from ECHA to build a large data set containing in vivo test data on fish for thousands of chemical substances. This was used to create QSAR models to predict two types of end points: acute LC
    MeSH term(s) Animals ; Fishes ; Lethal Dose 50 ; Quantitative Structure-Activity Relationship
    Language English
    Publishing date 2019-10-10
    Publishing country United States
    Document type Journal Article
    ISSN 1520-5851
    ISSN (online) 1520-5851
    DOI 10.1021/acs.est.9b03957
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  3. Article: Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing.

    Patlewicz, Grace / Richard, Ann M / Williams, Antony J / Judson, Richard S / Thomas, Russell S

    Computational toxicology (Amsterdam, Netherlands)

    2023  Volume 24

    Abstract: Per- and Polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals that are in widespread use and present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterised for their hazard profiles, the ... ...

    Abstract Per- and Polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals that are in widespread use and present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterised for their hazard profiles, the vast majority of PFAS have not been studied. The US Environmental Protection Agency (EPA) undertook a research project to screen ~150 PFAS through an array of different
    Language English
    Publishing date 2023-03-10
    Publishing country Netherlands
    Document type Journal Article
    ISSN 2468-1113
    ISSN 2468-1113
    DOI 10.1016/j.comtox.2022.100250
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  4. Article ; Online: Uncertainty quantification in ToxCast high throughput screening.

    Watt, Eric D / Judson, Richard S

    PloS one

    2018  Volume 13, Issue 7, Page(s) e0196963

    Abstract: High throughput screening (HTS) projects like the U.S. Environmental Protection Agency's ToxCast program are required to address the large and rapidly increasing number of chemicals for which we have little to no toxicity measurements. Concentration- ... ...

    Abstract High throughput screening (HTS) projects like the U.S. Environmental Protection Agency's ToxCast program are required to address the large and rapidly increasing number of chemicals for which we have little to no toxicity measurements. Concentration-response parameters such as potency and efficacy are extracted from HTS data using nonlinear regression, and models and analyses built from these parameters are used to predict in vivo and in vitro toxicity of thousands of chemicals. How these predictions are impacted by uncertainties that stem from parameter estimation and propagated through the models and analyses has not been well explored. While data size and complexity makes uncertainty quantification computationally expensive for HTS datasets, continued advancements in computational resources have allowed these computational challenges to be met. This study uses nonparametric bootstrap resampling to calculate uncertainties in concentration-response parameters from a variety of HTS assays. Using the ToxCast estrogen receptor model for bioactivity as a case study, we highlight how these uncertainties can be propagated through models to quantify the uncertainty in model outputs. Uncertainty quantification in model outputs is used to identify potential false positives and false negatives and to determine the distribution of model values around semi-arbitrary activity cutoffs, increasing confidence in model predictions. At the individual chemical-assay level, curves with high variability are flagged for manual inspection or retesting, focusing subject-matter-expert time on results that need further input. This work improves the confidence of predictions made using HTS data, increasing the ability to use this data in risk assessment.
    MeSH term(s) Computer Simulation ; Environmental Monitoring ; Environmental Pollutants/isolation & purification ; Environmental Pollutants/toxicity ; High-Throughput Screening Assays ; Humans ; Risk Assessment ; United States ; United States Environmental Protection Agency
    Chemical Substances Environmental Pollutants
    Language English
    Publishing date 2018-07-25
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0196963
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  5. Article: Exploring non-linear distance metrics in the structure-activity space: QSAR models for human estrogen receptor.

    Balabin, Ilya A / Judson, Richard S

    Journal of cheminformatics

    2018  Volume 10, Issue 1, Page(s) 47

    Abstract: Background: Quantitative structure-activity relationship (QSAR) models are important tools used in discovering new drug candidates and identifying potentially harmful environmental chemicals. These models often face two fundamental challenges: limited ... ...

    Abstract Background: Quantitative structure-activity relationship (QSAR) models are important tools used in discovering new drug candidates and identifying potentially harmful environmental chemicals. These models often face two fundamental challenges: limited amount of available biological activity data and noise or uncertainty in the activity data themselves. To address these challenges, we introduce and explore a QSAR model based on custom distance metrics in the structure-activity space.
    Methods: The model is built on top of the k-nearest neighbor model, incorporating non-linearity not only in the chemical structure space, but also in the biological activity space. The model is tuned and evaluated using activity data for human estrogen receptor from the US EPA ToxCast and Tox21 databases.
    Results: The model closely trails the CERAPP consensus model (built on top of 48 individual human estrogen receptor activity models) in agonist activity predictions and consistently outperforms the CERAPP consensus model in antagonist activity predictions.
    Discussion: We suggest that incorporating non-linear distance metrics may significantly improve QSAR model performance when the available biological activity data are limited.
    Language English
    Publishing date 2018-09-18
    Publishing country England
    Document type Journal Article
    ZDB-ID 2486539-4
    ISSN 1758-2946
    ISSN 1758-2946
    DOI 10.1186/s13321-018-0300-0
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  6. Article: Predicting molecular initiating events using chemical target annotations and gene expression.

    Bundy, Joseph L / Judson, Richard / Williams, Antony J / Grulke, Chris / Shah, Imran / Everett, Logan J

    BioData mining

    2022  Volume 15, Issue 1, Page(s) 7

    Abstract: Background: The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical ...

    Abstract Background: The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of chemical-protein interactions. Next, we trained binary classifiers on MCF7 human breast cancer cell line derived gene expression profiles and chemical-protein labels using six classification algorithms to identify optimal analysis parameters. To validate classifier accuracy, we used holdout data sets, training-excluded reference chemicals, and empirical significance testing of null models derived from permuted chemical-protein associations. To identify classifiers that have variable predicting performance across training data derived from different cellular contexts, we trained a separate set of binary classifiers on the PC3 human prostate cancer cell line.
    Results: We trained classifiers using expression data associated with chemical treatments linked to 51 molecular initiating events. This analysis identified and validated 9 high-performing classifiers with empirical p-values lower than 0.05 and internal accuracies ranging from 0.73 to 0.94 and holdout accuracies of 0.68 to 0.92. High-ranking predictions for training-excluded reference chemicals demonstrating that predictive accuracy extends beyond the set of chemicals used in classifier training. To explore differences in classifier performance as a function of training data cellular context, MCF7-trained classifier accuracies were compared to classifiers trained on the PC3 gene expression data for the same molecular initiating events.
    Conclusions: This methodology can offer insight in prioritizing candidate perturbagens of interest for targeted screens. This approach can also help guide the selection of relevant cellular contexts for screening classes of candidate perturbagens using cell line specific model performance.
    Language English
    Publishing date 2022-03-04
    Publishing country England
    Document type Journal Article
    ZDB-ID 2438773-3
    ISSN 1756-0381
    ISSN 1756-0381
    DOI 10.1186/s13040-022-00292-z
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  7. Article ; Online: Public databases supporting computational toxicology.

    Judson, Richard

    Journal of toxicology and environmental health. Part B, Critical reviews

    2010  Volume 13, Issue 2-4, Page(s) 218–231

    Abstract: A major goal of the emerging field of computational toxicology is the development of screening-level models that predict potential toxicity of chemicals from a combination of mechanistic in vitro assay data and chemical structure descriptors. In order to ...

    Abstract A major goal of the emerging field of computational toxicology is the development of screening-level models that predict potential toxicity of chemicals from a combination of mechanistic in vitro assay data and chemical structure descriptors. In order to build these models, researchers need quantitative in vitro and ideally in vivo data for large numbers of chemicals for common sets of assays and endpoints. A number of groups are compiling such data sets into publicly available web-based databases. This article (1) reviews some of the underlying challenges to the development of the databases, (2) describes key technologies used (relational databases, ontologies, and knowledgebases), and (3) summarizes several major database efforts that are widely used in the computational toxicology field.
    MeSH term(s) Animals ; Computational Biology/methods ; Computer Simulation ; Database Management Systems ; Databases, Factual ; Environmental Pollutants/analysis ; Environmental Pollutants/toxicity ; High-Throughput Screening Assays ; Humans ; Knowledge Bases ; Toxicology/methods
    Chemical Substances Environmental Pollutants
    Language English
    Publishing date 2010-02
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 1415246-0
    ISSN 1521-6950 ; 1093-7404
    ISSN (online) 1521-6950
    ISSN 1093-7404
    DOI 10.1080/10937404.2010.483937
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  8. Article ; Online: A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species.

    Dawson, Daniel E / Lau, Christopher / Pradeep, Prachi / Sayre, Risa R / Judson, Richard S / Tornero-Velez, Rogelio / Wambaugh, John F

    Toxics

    2023  Volume 11, Issue 2

    Abstract: Per- and polyfluoroalkyl substances (PFAS) are a diverse group of man-made chemicals that are commonly found in body tissues. The toxicokinetics of most PFAS are currently uncharacterized, but long half-lives ( ...

    Abstract Per- and polyfluoroalkyl substances (PFAS) are a diverse group of man-made chemicals that are commonly found in body tissues. The toxicokinetics of most PFAS are currently uncharacterized, but long half-lives (
    Language English
    Publishing date 2023-01-20
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2733883-6
    ISSN 2305-6304 ; 2305-6304
    ISSN (online) 2305-6304
    ISSN 2305-6304
    DOI 10.3390/toxics11020098
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  9. Article ; Online: Probabilistic Points of Departure and Reference Doses for Characterizing Human Noncancer and Developmental/Reproductive Effects for 10,145 Chemicals.

    Aurisano, Nicolò / Jolliet, Olivier / Chiu, Weihsueh A / Judson, Richard / Jang, Suji / Unnikrishnan, Aswani / Kosnik, Marissa B / Fantke, Peter

    Environmental health perspectives

    2023  Volume 131, Issue 3, Page(s) 37016

    Abstract: Background: Regulatory toxicity values used to assess and manage chemical risks rely on the determination of the point of departure (POD) for a critical effect, which results from a comprehensive and systematic assessment of available toxicity studies. ... ...

    Abstract Background: Regulatory toxicity values used to assess and manage chemical risks rely on the determination of the point of departure (POD) for a critical effect, which results from a comprehensive and systematic assessment of available toxicity studies. However, regulatory assessments are only available for a small fraction of chemicals.
    Objectives: Using
    Methods: We developed a curated data set restricted to effect levels, exposure routes, study designs, and species relevant for deriving toxicity values. Effect levels were adjusted to chronic human equivalent benchmark doses (
    Results: The
    Discussion: In providing surrogate PODs calibrated to regulatory values and deriving corresponding toxicity values, we have substantially expanded the coverage of chemicals from 744 to 8,023 for general noncancer effects, and from 41 to 6,697 for reproductive/developmental effects. These results can be used across various risk assessment and risk management contexts, from hazardous site and life cycle impact assessments to chemical prioritization and substitution. https://doi.org/10.1289/EHP11524.
    MeSH term(s) Humans ; Animals ; Reproduction ; Uncertainty ; Risk Assessment/methods
    Language English
    Publishing date 2023-03-29
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 195189-0
    ISSN 1552-9924 ; 0091-6765 ; 1078-0475
    ISSN (online) 1552-9924
    ISSN 0091-6765 ; 1078-0475
    DOI 10.1289/EHP11524
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  10. Article ; Online: Identification of potential endocrine disrupting chemicals using gene expression biomarkers.

    Corton, J Christopher / Kleinstreuer, Nicole C / Judson, Richard S

    Toxicology and applied pharmacology

    2019  Volume 380, Page(s) 114683

    Abstract: Recent technological advances have moved the field of toxicogenomics from reliance on microarray platforms to high-throughput transcriptomic (HTTr) technologies that measure global gene expression. Gene expression biomarkers are emerging as useful tools ... ...

    Abstract Recent technological advances have moved the field of toxicogenomics from reliance on microarray platforms to high-throughput transcriptomic (HTTr) technologies that measure global gene expression. Gene expression biomarkers are emerging as useful tools for interpreting gene expression profiles to identify perturbations of targets of xenobiotic chemicals including those that act as endocrine disrupting chemicals (EDCs). Gene expression biomarkers are lists of similarly-regulated genes identified in global gene expression comparisons of cells or tissues 1) exposed to known agonists or antagonists of the transcription factor (TF) and 2) after expression of the TF itself is knocked down/knocked out or overexpressed. Estrogen receptor α (ERα) and androgen receptor (AR) biomarkers have been shown to be very accurate at identifying both agonists (94-97%) and antagonists (93-98%) in microarray data derived from human breast or prostate cancer cell lines. Importantly, the biomarkers have been shown to accurately replicate the results of computational models that predict ERα or AR modulation using multiple ToxCast HT screening assays. An integrated screening strategy using sets of biomarkers that simultaneously predict various EDC targets in relevant cell lines should simplify chemical screening without sacrificing accuracy. The biomarker predictions can be put into the context of the adverse outcome pathway framework to help prioritize chemicals with the greatest risk of potential adverse outcomes in the endocrine systems of animals and people.
    MeSH term(s) Animals ; Biomarkers/analysis ; Endocrine Disruptors/toxicity ; Gene Expression ; Humans ; Receptors, Androgen/genetics ; Receptors, Estrogen/genetics
    Chemical Substances Biomarkers ; Endocrine Disruptors ; Receptors, Androgen ; Receptors, Estrogen
    Language English
    Publishing date 2019-07-17
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Review
    ZDB-ID 204477-8
    ISSN 1096-0333 ; 0041-008X
    ISSN (online) 1096-0333
    ISSN 0041-008X
    DOI 10.1016/j.taap.2019.114683
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