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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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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
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Evaluating potential refinements to existing Threshold of Toxicological Concern (TTC) values for environmentally-relevant compounds.

Nelms, Mark D / Pradeep, Prachi / Patlewicz, Grace

Regulatory toxicology and pharmacology : RTP

2019 Volume 109, Page(s) 104505

Abstract: The Toxic Substances Control Act (TSCA) mandates the US EPA perform risk-based prioritisation of chemicals in commerce and then, for high-priority substances, develop risk evaluations that integrate toxicity data with exposure information. One approach ... ...

Abstract	The Toxic Substances Control Act (TSCA) mandates the US EPA perform risk-based prioritisation of chemicals in commerce and then, for high-priority substances, develop risk evaluations that integrate toxicity data with exposure information. One approach being considered for data poor chemicals is the Threshold of Toxicological Concern (TTC). Here, TTC values derived using oral (sub)chronic No Observable (Adverse) Effect Level (NO(A)EL) data from the EPA's Toxicity Values database (ToxValDB) were compared with published TTC values from Munro et al. (1996). A total of 4554 chemicals with structures present in ToxValDB were assigned into their respective TTC categories using the Toxtree software tool, of which toxicity data was available for 1304 substances. The TTC values derived from ToxValDB were similar, but not identical to the Munro TTC values: Cramer I ((ToxValDB) 37.3 c. f. (Munro) 30 μg/kg-day), Cramer II (34.6 c. f. 9.1 μg/kg-day) and Cramer III (3.9 c. f. 1.5 μg/kg-day). Cramer III 5th percentile values were found to be statistically different. Chemical features of the two Cramer III datasets were evaluated to account for the differences. TTC values derived from this expanded dataset substantiated the original TTC values, reaffirming the utility of TTC as a promising tool in a risk-based prioritisation approach.
MeSH term(s)	Databases, Factual ; Hazardous Substances/standards ; Hazardous Substances/toxicity ; Humans ; No-Observed-Adverse-Effect Level ; Risk Assessment/standards ; Software ; Threshold Limit Values ; Toxicity Tests, Chronic/standards ; Toxicity Tests, Subchronic/standards ; Toxicology/legislation & jurisprudence ; Toxicology/standards ; United States ; United States Environmental Protection Agency/standards
Chemical Substances	Hazardous Substances
Language	English
Publishing date	2019-10-19
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	604672-1
ISSN	1096-0295 ; 0273-2300
ISSN (online)	1096-0295
ISSN	0273-2300
DOI	10.1016/j.yrtph.2019.104505
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Article: A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols.

Pradeep, Prachi / Mansouri, Kamel / Patlewicz, Grace / Judson, Richard

Computational toxicology (Amsterdam, Netherlands)

2018 Volume 4, Page(s) 22–30

Abstract: Read-across is an important data gap filling technique used within category and analog approaches for regulatory hazard identification and risk assessment. Although much technical guidance is available that describes how to develop category/analog ... ...

Abstract	Read-across is an important data gap filling technique used within category and analog approaches for regulatory hazard identification and risk assessment. Although much technical guidance is available that describes how to develop category/analog approaches, practical principles to evaluate and substantiate analog validity (suitability) are still lacking. This case study uses hindered phenols as an example chemical class to determine: (1) the capability of three structure fingerprint/descriptor methods (PubChem, ToxPrints and MoSS MCSS) to identify analogs for read-across to predict Estrogen Receptor (ER) binding activity and, (2) the utility of data confidence measures, physicochemical properties, and chemical R-group properties as filters to improve ER binding predictions. The training dataset comprised 462 hindered phenols and 257 non- hindered phenols. For each chemical of interest (target), source analogs were identified from two datasets (hindered and non-hindered phenols) that had been characterized by a fingerprint/descriptor method and by two cut-offs: (1) minimum similarity distance (range: 0.1 - 0.9) and, (2)
Language	English
Publishing date	2018-04-23
Publishing country	Netherlands
Document type	Journal Article
ISSN	2468-1113
ISSN	2468-1113
DOI	10.1016/j.comtox.2017.09.001
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Article: Using Chemical Structure Information to Develop Predictive Models for

Pradeep, Prachi / Patlewicz, Grace / Pearce, Robert / Wambaugh, John / Wetmore, Barbara / Judson, Richard

Computational toxicology (Amsterdam, Netherlands)

2020 Volume 16

Abstract: The toxicokinetic (TK) parameters fraction of the chemical unbound to plasma proteins and metabolic clearance are critical for relating exposure and internal dose when building in vitro-based risk assessment models. However, experimental toxicokinetic ... ...

Abstract	The toxicokinetic (TK) parameters fraction of the chemical unbound to plasma proteins and metabolic clearance are critical for relating exposure and internal dose when building in vitro-based risk assessment models. However, experimental toxicokinetic studies have only been carried out on limited chemicals of environmental interest (~1000 chemicals with TK data relative to tens of thousands of chemicals of interest). This work evaluated the utility of chemical structure information to predict TK parameters in silico; development of cluster-based read-across and quantitative structure-activity relationship models of fraction unbound or fub (regression) and intrinsic clearance or Cl
Language	English
Publishing date	2020-02-12
Publishing country	Netherlands
Document type	Journal Article
ISSN	2468-1113
ISSN	2468-1113
DOI	10.1016/j.comtox.2020.100136
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols

Pradeep, Prachi / Mansouri, Kamel / Patlewicz, Grace / Judson, Richard

Computational Toxicology. 2017,

2017

Abstract	Read-across is an important data gap filling technique used within category and analog approaches for regulatory hazard identification and risk assessment. Although much technical guidance is available that describes how to develop category/analog approaches, practical principles to evaluate and substantiate analog validity (suitability) are still lacking. This case study uses hindered phenols as an example chemical class to determine: (1) the capability of three structure fingerprint/descriptor methods (PubChem, ToxPrints and MoSS MCSS) to identify analogs for read-across to predict Estrogen Receptor (ER) binding activity and, (2) the utility of data confidence measures, physicochemical properties, and chemical R-group properties as filters to improve ER binding predictions. The training dataset comprised 462 hindered phenols and 257 non- hindered phenols. For each chemical of interest (target), source analogs were identified from two datasets (hindered and non-hindered phenols) that had been characterized by a fingerprint/descriptor method and by two cut-offs: (1) minimum similarity distance (range: 0.1 - 0.9) and, (2) N closest analogs (range: 1 - 10). Analogs were then filtered using: (1) physicochemical properties of the phenol (termed global filtering) and, (2) physicochemical properties of the R-groups neighboring the active hydroxyl group (termed local filtering). A read-across prediction was made for each target chemical on the basis of a majority vote of the N closest analogs. The results demonstrate that: (1) concordance in ER activity increases with structural similarity, regardless of the structure fingerprint/descriptor method, (2) increased data confidence significantly improves read-across predictions, and (3) filtering analogs using global and local properties can help identify more suitable analogs. This case study illustrates that the quality of the underlying experimental data and use of endpoint relevant chemical descriptors to evaluate source analogs are critical to achieving robust read-across predictions.
Keywords	Read-across ; Analog identification ; Analog evaluation ; Quantitative uncertainty analysis ; Estrogen receptor (ER) binding
Language	English
Publishing place	Elsevier B.V.
Document type	Article ; Online
Note	Pre-press version
ISSN	2468-1113
DOI	10.1016/j.comtox.2017.09.001
Database	NAL-Catalogue (AGRICOLA)

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Article: Integrating publicly available information to screen potential candidates for chemical prioritization under the Toxic Substances Control Act: A proof of concept case study using genotoxicity and carcinogenicity.

Patlewicz, Grace / Dean, Jeffry L / Gibbons, Catherine F / Judson, Richard S / Keshava, Nagalakshmi / Vegosen, Leora / Martin, Todd M / Pradeep, Prachi / Simha, Anita / Warren, Sarah H / Gwinn, Maureen R / DeMarini, David M

Computational toxicology (Amsterdam, Netherlands)

2022 Volume 20, Page(s) 1–100185

Abstract: The Toxic Substances Control Act (TSCA) became law in the U.S. in 1976 and was amended in 2016. The amended law requires the U.S. EPA to perform risk-based evaluations of existing chemicals. Here, we developed a tiered approach to screen potential ... ...

Abstract	The Toxic Substances Control Act (TSCA) became law in the U.S. in 1976 and was amended in 2016. The amended law requires the U.S. EPA to perform risk-based evaluations of existing chemicals. Here, we developed a tiered approach to screen potential candidates based on their genotoxicity and carcinogenicity information to inform the selection of candidate chemicals for prioritization under TSCA. The approach was underpinned by a large database of carcinogenicity and genotoxicity information that had been compiled from various public sources. Carcinogenicity data included weight-of-evidence human carcinogenicity evaluations and animal cancer data. Genotoxicity data included bacterial gene mutation data from the
Language	English
Publishing date	2022-01-24
Publishing country	Netherlands
Document type	Journal Article
ISSN	2468-1113
ISSN	2468-1113
DOI	10.1016/j.comtox.2021.100185
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Article ; Online: A Bayesian methodology for detecting targeted genes under two related experiments.

Bansal, Naveen K / Jiang, Hongmei / Pradeep, Prachi

Statistics in medicine

2015 Volume 34, Issue 25, Page(s) 3362–3375

Abstract: Many gene expression data are based on two experiments where the gene expressions of the targeted genes under both experiments are correlated. We consider problems in which objectives are to find genes that are simultaneously upregulated/downregulated ... ...

Abstract	Many gene expression data are based on two experiments where the gene expressions of the targeted genes under both experiments are correlated. We consider problems in which objectives are to find genes that are simultaneously upregulated/downregulated under both experiments. A Bayesian methodology is proposed based on directional multiple hypotheses testing. We propose a false discovery rate specific to the problem under consideration, and construct a Bayes rule satisfying a false discovery rate criterion. The proposed method is compared with a traditional rule through simulation studies. We apply our methodology to two real examples involving microRNAs; where in one example the targeted genes are simultaneously downregulated under both experiments, and in the other the targeted genes are downregulated in one experiment and upregulated in the other experiment. We also discuss how the proposed methodology can be extended to more than two experiments.
MeSH term(s)	Algorithms ; Bayes Theorem ; Computer Simulation ; Databases, Genetic ; Down-Regulation/genetics ; Gene Expression Profiling/methods ; Gene Expression Regulation ; Humans ; MicroRNAs/genetics ; Models, Statistical ; Up-Regulation/genetics
Chemical Substances	MicroRNAs
Language	English
Publishing date	2015-11-10
Publishing country	England
Document type	Journal Article
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.6555
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Zs.A 1756: Show issues

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Article ; Online: Integrating endocrine-related health effects into comparative human toxicity characterization.

Emara, Yasmine / Fantke, Peter / Judson, Richard / Chang, Xiaoqing / Pradeep, Prachi / Lehmann, Annekatrin / Siegert, Marc-William / Finkbeiner, Matthias

The Science of the total environment

2020 Volume 762, Page(s) 143874

Abstract: Endocrine-disrupting chemicals have the ability to interfere with and alter functions of the hormone system, leading to adverse effects on reproduction, growth and development. Despite growing concerns over their now ubiquitous presence in the ... ...

Abstract	Endocrine-disrupting chemicals have the ability to interfere with and alter functions of the hormone system, leading to adverse effects on reproduction, growth and development. Despite growing concerns over their now ubiquitous presence in the environment, endocrine-related human health effects remain largely outside of comparative human toxicity characterization frameworks as applied for example in life cycle impact assessments. In this paper, we propose a new methodological framework to consistently integrate endocrine-related health effects into comparative human toxicity characterization. We present two quantitative and operational approaches for extrapolating towards a common point of departure from both in vivo and dosimetry-adjusted in vitro endocrine-related effect data and deriving effect factors as well as corresponding characterization factors for endocrine-active/endocrine-disrupting chemicals. Following the proposed approaches, we calculated effect factors for 323 chemicals, reflecting their endocrine potency, and related characterization factors for 157 chemicals, expressing their relative endocrine-related human toxicity potential. Developed effect and characterization factors are ready for use in the context of chemical prioritization and substitution as well as life cycle impact assessment and other comparative assessment frameworks. Endocrine-related effect factors were found comparable to existing effect factors for cancer and non-cancer effects, indicating that (1) the chemicals' endocrine potency is not necessarily higher or lower than other effect potencies and (2) using dosimetry-adjusted effect data to derive effect factors does not consistently overestimate the effect of potential endocrine disruptors. Calculated characterization factors span over 8-11 orders of magnitude for different substances and emission compartments and are dominated by the range in endocrine potencies.
MeSH term(s)	Endocrine Disruptors/toxicity ; Endocrine System ; Humans ; Reproduction
Chemical Substances	Endocrine Disruptors
Language	English
Publishing date	2020-12-03
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2020.143874
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Zs.A 949: Show issues

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Article: Variability in

Ly Pham, Ly / Watford, Sean / Pradeep, Prachi / Martin, Matthew T / Thomas, Russell / Judson, Richard / Setzer, R Woodrow / Paul Friedman, Katie

Computational toxicology (Amsterdam, Netherlands)

2020 Volume 15, Issue August 2020, Page(s) 1–100126

Abstract: New approach methodologies (NAMs) for chemical hazard assessment are often evaluated via comparison to animal studies; however, variability in animal study data limits NAM accuracy. The US EPA Toxicity Reference Database (ToxRefDB) enables consideration ... ...

Abstract	New approach methodologies (NAMs) for chemical hazard assessment are often evaluated via comparison to animal studies; however, variability in animal study data limits NAM accuracy. The US EPA Toxicity Reference Database (ToxRefDB) enables consideration of variability in effect levels, including the lowest effect level (LEL) for a treatment-related effect and the lowest observable adverse effect level (LOAEL) defined by expert review, from subacute, subchronic, chronic, multi-generation reproductive, and developmental toxicity studies. The objectives of this work were to quantify the variance within systemic LEL and LOAEL values, defined as potency values for effects in adult or parental animals only, and to estimate the upper limit of NAM prediction accuracy. Multiple linear regression (MLR) and augmented cell means (ACM) models were used to quantify the total variance, and the fraction of variance in systemic LEL and LOAEL values explained by available study descriptors (e.g., administration route, study type). The MLR approach considered each study descriptor as an independent contributor to variance, whereas the ACM approach combined categorical descriptors into cells to define replicates. Using these approaches, total variance in systemic LEL and LOAEL values (in log
Language	English
Publishing date	2020-05-28
Publishing country	Netherlands
Document type	Journal Article
ISSN	2468-1113
ISSN	2468-1113
DOI	10.1016/j.comtox.2020.100126
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