The US Federal Tox21 Program: A strategic and operational plan for continued leadership
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The traditional approaches to toxicity testing have posed multiple challenges for evaluating the safety of commercial chemicals, pesticides, food additives/contaminants, and medical products.The challenges include number of chemicals that need to be tested, time and resource intensive nature of traditional toxicity tests, and unexpected adverse effects that occur in pharmaceutical clinical trials despite the extensive toxicological testing.Over a decade ago, the U.S. Environmental Protection Agency (EPA), National Toxicology Program (NTP), National Center for Advancing Translational Sciences (NCATS), and the Food and Drug Administration (FDA) formed a federal consortium for "Toxicology in the 21st Century" (Tox21) with a focus on developing and evaluating in vitro high-throughput screening (HTS) methods for hazard identification and providing mechanistic insights.The Tox21 consortium generated data on thousands of pharmaceuticals and datapoor chemicals, developed better understanding of the limits and applications of in vitro methods, and enabled incorporation of HTS data into regulatory decisions. To more broadly address the challenges in toxicology, Tox21 has developed a new strategic and operational plan that expands the focus of its research activities. The new focus areas include developing an expanded portfolio of alternative test systems, addressing technical limitations of in vitrotest systems, curating legacy in vivo toxicity testing data, establishing scientific confidence in the in vitrotest systems, and refining alternative methods for characterizing pharmacokinetics and in vitro assay disposition.The new Tox21 strategic and operational plan addresses key challenges to advance toxicology testing and will benefit both the organizations involved and the toxicology community.
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Animal experiments offer little guidance to human toxicity reactions due to inter-species differences and differential disease models.
“the results offer little guidance to human toxicity reactions, due to inter-species differences and differential disease models [6,7].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [9]
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- Authors
- Thomas, R.
- Affiliations
- Environmental Protection Agency (US)
- Corresponding author
- Thomas, Russell
- ORCID
- Thomas, Russell
- Venue
- ALTEX
- Publisher
- ALTEX Edition
- Published
- 2018
- Volume
- 35
- Issue
- 2
- Pages
- 163-168
- ISSN
- 0946-7785 (ISSN-L)
- 1868-596X
- PMC
- PMC6664816
- OpenAlex
- W2790282224
- Cited by
- 229
- References
- 29
- Citations by year
- 2026: 30 · 2025: 29 · 2024: 27 · 2023: 29 · 2022: 36 · 2021: 16 · 2020: 28 · 2019: 27 · 2018: 7
- FWCI
- 28.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- ALTEX (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- Topics
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Life Sciences › Immunology and Microbiology › Immunology › Immunotoxicology and immune responses
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Carcinogens and Genotoxicity Assessment
- Keywords
- Tox21, high-throughput screening, in vitro toxicity testing, alternative test methods, hazard identification, chemical safety assessment, regulatory toxicology, pharmacokinetics, data curation, pharmaceuticals, data-poor chemicals
- MeSH terms
- Animals, Animal Testing Alternatives, Cooperative Behavior, Humans, Leadership, Pharmacokinetics, United States, United States Environmental Protection Agency, United States Food and Drug Administration, Toxicity Tests, High-Throughput Screening Assays, In Vitro Techniques
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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