Use and validation of HT/HC assays to support 21st century toxicity evaluations
Open accessAbstract
Advances in high throughput and high content (HT/HC) methods such as those used in the fields of toxicogenomics, bioinformatics, and computational toxicology have the potential to improve both the efficiency and effectiveness of toxicity evaluations and risk assessments. However, prior to use, scientific confidence in these methods should be formally established. Traditional validation approaches that define relevance, reliability, sensitivity and specificity may not be readily applicable. HT/HC methods are not exact replacements for in vivo testing, and although run individually, these assays are likely to be used as a group or battery for decision making and use robotics, which may be unique in each laboratory setting. Building on the frameworks developed in the 2010 Institute of Medicine Report on Biomarkers and the OECD 2007 Report on (Q)SAR Validation, we present constructs that can be adapted to address the validation challenges of HT/HC methods. These are flexible, transparent, and require explicit specification of context and purpose of use such that scientific confidence (validation) can be defined to meet different regulatory applications. Using these constructs, we discuss how anchoring the assays and their prediction models to Adverse Outcome Pathways (AOPs) could facilitate the interpretation of results and support scientifically defensible fit-for-purpose applications.
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Mechanistic validation may provide a measure of test method performance even when a good reference dataset is not available.
“Such an approach may provide a measure of test method performance, even when a good reference dataset is not available (Leist et al., 2012; Patlewicz et al., 2013; Patterson et al., 2021; Tigges et al., 2021).”
From Recommendations on fit-for-purpose criteria to establish quality management for microphysiological systems and for monitoring their reproducibility (2024) · cites this paper as Patlewicz et al., 2013
Section 4 says mechanistic relevance provides a biological anchor for an assay, while analytical validation assesses its performance. Section 3.1 proposes assessing performance with a set of reference chemicals; it does not say that mechanistic validation measures performance when a good reference dataset is unavailable. Page numbers are not available in our copy. (LLM, conf=0.82)
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- Authors
- Patlewicz, G., Simon, T., Goyak, K., Phillips, R. D., Rowlands, J. C., Seidel, S. D., & Becker, R. A.
- Affiliations
- DuPont (United States) (US)
- ExxonMobil (United States) (US)
- Dow Chemical (United States) (US)
- American Chemistry Council (US)
- Corresponding author
- Patlewicz, Grace
- Venue
- Regulatory Toxicology and Pharmacology
- Publisher
- Elsevier BV
- Published
- 2013-03
- Volume
- 65
- Issue
- 2
- Pages
- 259-268
- ISSN
- 0273-2300 (ISSN-L)
- OpenAlex
- W1988231503
- Cited by
- 40
- References
- 38
- Citations by year
- 2025: 3 · 2024: 1 · 2022: 2 · 2021: 1 · 2020: 2 · 2019: 4 · 2018: 2 · 2017: 2 · 2016: 7 · 2015: 9 · 2014: 6 · 2013: 1
- FWCI
- 10.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Regulatory Toxicology and Pharmacology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Carcinogens and Genotoxicity Assessment
- Life Sciences › Immunology and Microbiology › Immunology › Immunotoxicology and immune responses
- Keywords
- high-throughput screening, high-content screening, assay validation, toxicity prediction, adverse outcome pathways, fit for purpose, computational toxicology, toxicogenomics, biomarkers, QSAR validation, risk assessment, scientific confidence
- MeSH terms
- Animals, Animal Testing Alternatives, Humans, Xenobiotics, Risk Assessment, Toxicity Tests, High-Throughput Screening Assays
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