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Article ; Online: Early Drug-Induced Liver Injury Risk Screening: "Free," as Good as It Gets.

Martin, Matthew T / Koza-Taylor, Petra / Di, Li / Watt, Eric D / Keefer, Christopher / Smaltz, Daniel / Cook, Jon / Jackson, Jonathan P

Toxicological sciences : an official journal of the Society of Toxicology

2022 Volume 188, Issue 2, Page(s) 208–218

Abstract: For all the promise of and need for clinical drug-induced liver injury (DILI) risk screening systems, demonstrating the predictive value of these systems versus readily available physicochemical properties and inherent dosing information has not been ... ...

Abstract	For all the promise of and need for clinical drug-induced liver injury (DILI) risk screening systems, demonstrating the predictive value of these systems versus readily available physicochemical properties and inherent dosing information has not been thoroughly evaluated. Therefore, we utilized a systematic approach to evaluate the predictive value of in vitro safety assays including bile salt export pump transporter inhibition and cytotoxicity in HepG2 and transformed human liver epithelial along with physicochemical properties. We also evaluated the predictive value of in vitro ADME assays including hepatic partition coefficient (Kp) and its unbound counterpart because they provide insight on hepatic accumulation potential. The datasets comprised of 569 marketed drugs with FDA DILIrank annotation (most vs less/none), dose and physicochemical information, 384 drugs with Kp and plasma protein binding data, and 279 drugs with safety assay data. For each dataset and combination of input parameters, we developed random forest machine learning models and measured model performance using the receiver operator characteristic area under the curve (ROC AUC). The median ROC AUC across the various data and parameters sets ranged from 0.67 to 0.77 with little evidence of additive predictivity when including safety or ADME assay data. Subsequent machine learning models consistently demonstrated daily dose, fraction sp3 or ionization, and cLogP/D inputs produced the best, simplest model for predicting clinical DILI risk with an ROC AUC of 0.75. This systematic framework should be used for future assay predictive value assessments and highlights the need for continued improvements to clinical DILI risk annotation.
MeSH term(s)	Area Under Curve ; Biological Assay ; Chemical and Drug Induced Liver Injury/diagnosis ; Chemical and Drug Induced Liver Injury/etiology ; Humans
Language	English
Publishing date	2022-05-31
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1420885-4
ISSN	1096-0929 ; 1096-6080
ISSN (online)	1096-0929
ISSN	1096-6080
DOI	10.1093/toxsci/kfac054
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Article ; Online: ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses.

Watford, Sean / Ly Pham, Ly / Wignall, Jessica / Shin, Robert / Martin, Matthew T / Friedman, Katie Paul

Reproductive toxicology (Elmsford, N.Y.)

2019 Volume 89, Page(s) 145–158

Abstract: The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public ... ...

Abstract	The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling for nearly 28,000 datasets from nearly 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.
MeSH term(s)	Animals ; Computational Biology/methods ; Computational Biology/trends ; Databases, Factual/trends ; Dose-Response Relationship, Drug ; Hazardous Substances/chemistry ; Hazardous Substances/classification ; Hazardous Substances/toxicity ; Models, Biological ; Software ; Toxicology/methods ; Toxicology/trends ; United States ; United States Environmental Protection Agency
Chemical Substances	Hazardous Substances
Language	English
Publishing date	2019-07-21
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	639342-1
ISSN	1873-1708 ; 0890-6238
ISSN (online)	1873-1708
ISSN	0890-6238
DOI	10.1016/j.reprotox.2019.07.012
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Article ; Online: Retrospective mining of toxicology data to discover multispecies and chemical class effects: Anemia as a case study.

Judson, Richard S / Martin, Matthew T / Patlewicz, Grace / Wood, Charles E

Regulatory toxicology and pharmacology : RTP

2017 Volume 86, Page(s) 74–92

Abstract: Predictive toxicity models rely on large amounts of accurate in vivo data. Here, we analyze the quality of in vivo data from the U.S. EPA Toxicity Reference Database (ToxRefDB), using chemical-induced anemia as an example. Considerations include ... ...

Abstract	Predictive toxicity models rely on large amounts of accurate in vivo data. Here, we analyze the quality of in vivo data from the U.S. EPA Toxicity Reference Database (ToxRefDB), using chemical-induced anemia as an example. Considerations include variation in experimental conditions, changes in terminology over time, distinguishing negative from missing results, observer and diagnostic bias, and data transcription errors. Within ToxRefDB, we use hematological data on 658 chemicals tested in one or more of 1738 studies (subchronic rat or chronic rat, mouse, or dog). Anemia was reported most frequently in the rat subchronic studies, followed by chronic studies in dog, rat, and then mouse. Concordance between studies for a positive finding of anemia (same chemical, different laboratories) ranged from 90% (rat subchronic predicting rat chronic) to 40% (mouse chronic predicting rat chronic). Concordance increased with manual curation by 20% on average. We identified 49 chemicals that showed an anemia phenotype in at least two species. These included 14 aniline moiety-containing compounds that were further analyzed for their potential to be metabolically transformed into substituted anilines, which are known anemia-causing chemicals. This analysis should help inform future use of in vivo databases for model development.
Language	English
Publishing date	2017-06
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.2017.02.015
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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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Article: Profiling 58 compounds including cosmetic-relevant chemicals using ToxRefDB and ToxCast

Pham, Ly L / Truong, Lisa / Ouedraogo, Gladys / Loisel-Joubert, Sophie / Martin, Matthew T / Paul Friedman, Katie

Food and chemical toxicology. 2019 Oct., v. 132

2019

Abstract: Safety assessment for cosmetic-relevant chemicals (CRCs) in the European Union has been reshaped by restrictions on animal testing, and new approach methodologies (NAMs) for predicting toxicity are critical to ensure new cosmetic product safety. To ... ...

Abstract	Safety assessment for cosmetic-relevant chemicals (CRCs) in the European Union has been reshaped by restrictions on animal testing, and new approach methodologies (NAMs) for predicting toxicity are critical to ensure new cosmetic product safety. To demonstrate NAMs for safety assessment, we surveyed in vitro bioactivity and in vivo systemic toxicity data in the US Environmental Protection Agency's (EPA's) Toxicity Forecaster (ToxCast) and Toxicity Reference databases (ToxRefDB), respectively, for 58 chemicals identified as CRCs, including cosmetic ingredients as well as trace contaminants. CRCs were diverse in use types as suggested by broad chemical use categories. In terms of both target organ effects and study type, the median of the lowest effect level (LEL) doses in ToxRefDB for CRCs tended to be slightly higher than the median for the remaining 928 chemicals with study data in ToxRefDB, though the ranges of LELs were similar. For 17 of the 58 CRCs, high-throughput toxicokinetic data were used to calculate administered equivalent doses (AEDs) in mg/kg/day units for the in vitro bioactivity observed, and these AEDs served as conservative estimators of the systemic LELs observed in vivo. This work suggests that NAMs for bioactivity may inform a conservative point-of-departure estimate for diverse CRCs.
Keywords	European Union ; United States Environmental Protection Agency ; animals ; cosmetics ; databases ; ingredients ; prediction ; product safety ; safety assessment ; toxicity ; toxicology
Language	English
Dates of publication	2019-10
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	782617-5
ISSN	1873-6351 ; 0278-6915
ISSN (online)	1873-6351
ISSN	0278-6915
DOI	10.1016/j.fct.2019.110718
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Evaluation of food-relevant chemicals in the ToxCast high-throughput screening program.

Karmaus, Agnes L / Filer, Dayne L / Martin, Matthew T / Houck, Keith A

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association

2016 Volume 92, Page(s) 188–196

Abstract: Thousands of chemicals are directly added to or come in contact with food, many of which have undergone little to no toxicological evaluation. The landscape of the food-relevant chemical universe was evaluated using cheminformatics, and subsequently the ... ...

Abstract	Thousands of chemicals are directly added to or come in contact with food, many of which have undergone little to no toxicological evaluation. The landscape of the food-relevant chemical universe was evaluated using cheminformatics, and subsequently the bioactivity of food-relevant chemicals across the publicly available ToxCast highthroughput screening program was assessed. In total, 8659 food-relevant chemicals were compiled including direct food additives, food contact substances, and pesticides. Of these food-relevant chemicals, 4719 had curated structure definition files amenable to defining chemical fingerprints, which were used to cluster chemicals using a selforganizing map approach. Pesticides, and direct food additives clustered apart from one another with food contact substances generally in between, supporting that these categories not only reflect different uses but also distinct chemistries. Subsequently, 1530 food-relevant chemicals were identified in ToxCast comprising 616 direct food additives, 371 food contact substances, and 543 pesticides. Bioactivity across ToxCast was filtered for cytotoxicity to identify selective chemical effects. Initiating analyses from strictly chemical-based methodology or bioactivity/cytotoxicity-driven evaluation presents unbiased approaches for prioritizing chemicals. Although bioactivity in vitro is not necessarily predictive of adverse effects in vivo, these data provide insight into chemical properties and cellular targets through which foodrelevant chemicals elicit bioactivity.
MeSH term(s)	Cell Survival/drug effects ; Drug-Related Side Effects and Adverse Reactions ; Food Contamination/analysis ; High-Throughput Screening Assays/methods ; Humans ; Pharmaceutical Preparations/analysis ; Risk Assessment ; Toxicity Tests/methods ; United States ; United States Environmental Protection Agency
Chemical Substances	Pharmaceutical Preparations
Language	English
Publishing date	2016-06
Publishing country	England
Document type	Evaluation Studies ; Journal Article
ZDB-ID	782617-5
ISSN	1873-6351 ; 0278-6915
ISSN (online)	1873-6351
ISSN	0278-6915
DOI	10.1016/j.fct.2016.04.012
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Article: tcpl: the ToxCast pipeline for high-throughput screening data

Filer, Dayne L / Kothiya, Parth / Setzer, R. Woodrow / Judson, Richard S / Martin, Matthew T

Bioinformatics. 2017 Feb. 15, v. 33, no. 4

2017

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 ... ...

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: martin.matt@epa.gov
Keywords	algorithms ; bioinformatics ; computer software ; databases ; dose response ; drugs ; models ; screening ; toxicity
Language	English
Dates of publication	2017-0215
Size	p. 618-620.
Publishing place	Oxford University Press
Document type	Article
ZDB-ID	1468345-3
ISSN	1460-2059 ; 1367-4803
ISSN (online)	1460-2059
ISSN	1367-4803
DOI	10.1093/bioinformatics/btw680
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Profiling 58 compounds including cosmetic-relevant chemicals using ToxRefDB and ToxCast.

Pham, Ly L / Truong, Lisa / Ouedraogo, Gladys / Loisel-Joubert, Sophie / Martin, Matthew T / Paul Friedman, Katie

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association

2019 Volume 132, Page(s) 110718

Abstract	Safety assessment for cosmetic-relevant chemicals (CRCs) in the European Union has been reshaped by restrictions on animal testing, and new approach methodologies (NAMs) for predicting toxicity are critical to ensure new cosmetic product safety. To demonstrate NAMs for safety assessment, we surveyed in vitro bioactivity and in vivo systemic toxicity data in the US Environmental Protection Agency's (EPA's) Toxicity Forecaster (ToxCast) and Toxicity Reference databases (ToxRefDB), respectively, for 58 chemicals identified as CRCs, including cosmetic ingredients as well as trace contaminants. CRCs were diverse in use types as suggested by broad chemical use categories. In terms of both target organ effects and study type, the median of the lowest effect level (LEL) doses in ToxRefDB for CRCs tended to be slightly higher than the median for the remaining 928 chemicals with study data in ToxRefDB, though the ranges of LELs were similar. For 17 of the 58 CRCs, high-throughput toxicokinetic data were used to calculate administered equivalent doses (AEDs) in mg/kg/day units for the in vitro bioactivity observed, and these AEDs served as conservative estimators of the systemic LELs observed in vivo. This work suggests that NAMs for bioactivity may inform a conservative point-of-departure estimate for diverse CRCs.
MeSH term(s)	Animals ; Cosmetics/chemistry ; Databases, Chemical ; Humans ; Retrospective Studies ; United States ; United States Environmental Protection Agency
Chemical Substances	Cosmetics
Language	English
Publishing date	2019-07-26
Publishing country	England
Document type	Journal Article
ZDB-ID	782617-5
ISSN	1873-6351 ; 0278-6915
ISSN (online)	1873-6351
ISSN	0278-6915
DOI	10.1016/j.fct.2019.110718
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Article ; Online: Screening the ToxCast phase II libraries for alterations in network function using cortical neurons grown on multi-well microelectrode array (mwMEA) plates.

Strickland, Jenna D / Martin, Matthew T / Richard, Ann M / Houck, Keith A / Shafer, Timothy J

Archives of toxicology

2017 Volume 92, Issue 1, Page(s) 487–500

Abstract: Methods are needed for rapid screening of environmental compounds for neurotoxicity, particularly ones that assess function. To demonstrate the utility of microelectrode array (MEA)-based approaches as a rapid neurotoxicity screening tool, 1055 chemicals ...

Abstract	Methods are needed for rapid screening of environmental compounds for neurotoxicity, particularly ones that assess function. To demonstrate the utility of microelectrode array (MEA)-based approaches as a rapid neurotoxicity screening tool, 1055 chemicals from EPA's phase II ToxCast library were evaluated for effects on neural function and cell health. Primary cortical networks were grown on multi-well microelectrode array (mwMEA) plates. On day in vitro 13, baseline activity (40 min) was recorded prior to exposure to each compound (40 µM). Changes in spontaneous network activity [mean firing rate (MFR)] and cell viability (lactate dehydrogenase and CellTiter Blue) were assessed within the same well following compound exposure. Following exposure, 326 compounds altered (increased or decreased) normalized MFR beyond hit thresholds based on 2× the median absolute deviation of DMSO-treated wells. Pharmaceuticals, pesticides, fungicides, chemical intermediates, and herbicides accounted for 86% of the hits. Further, changes in MFR occurred in the absence of cytotoxicity, as only eight compounds decreased cell viability. ToxPrint chemotype analysis identified several structural domains (e.g., biphenyls and alkyl phenols) significantly enriched with MEA actives relative to the total test set. The top 5 enriched ToxPrint chemotypes were represented in 26% of the MEA hits, whereas the top 11 ToxPrints were represented in 34% of MEA hits. These results demonstrate that large-scale functional screening using neural networks on MEAs can fill a critical gap in assessment of neurotoxicity potential in ToxCast assay results. Further, a data-mining approach identified ToxPrint chemotypes enriched in the MEA-hit subset, which define initial structure-activity relationship inferences, establish potential mechanistic associations to other ToxCast assay endpoints, and provide working hypotheses for future studies.
MeSH term(s)	Action Potentials/drug effects ; Animals ; Cell Culture Techniques/instrumentation ; Cell Culture Techniques/methods ; Cerebral Cortex/cytology ; Data Mining ; Databases, Chemical ; Drug Evaluation, Preclinical/instrumentation ; Drug Evaluation, Preclinical/methods ; L-Lactate Dehydrogenase/metabolism ; Microelectrodes ; Nerve Net/drug effects ; Neurons/drug effects ; Neurons/physiology ; Neurotoxicity Syndromes/etiology ; Neurotoxicity Syndromes/pathology ; Rats, Long-Evans ; Toxicity Tests/instrumentation ; Toxicity Tests/methods
Chemical Substances	L-Lactate Dehydrogenase (EC 1.1.1.27)
Language	English
Publishing date	2017-08-02
Publishing country	Germany
Document type	Journal Article
ZDB-ID	124992-7
ISSN	1432-0738 ; 0340-5761
ISSN (online)	1432-0738
ISSN	0340-5761
DOI	10.1007/s00204-017-2035-5
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Article ; Online: tcpl: the ToxCast pipeline for high-throughput screening data.

Filer, Dayne L / Kothiya, Parth / Setzer, R Woodrow / Judson, Richard S / Martin, Matthew T

Bioinformatics (Oxford, England)

2017 Volume 33, Issue 4, Page(s) 618–620

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: martin.matt@epa.gov.
Language	English
Publishing date	2017-02-15
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btw680
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