In silico toxicology protocols
Open accessAbstract
The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.
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In silico models for genotoxicity and drug regulation
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Self-citationExploring current read-across applications and needs among selected U.S. Federal Agencies
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Self-citationThe predictivity of QSARs for toxicity: Recommendations for improving model performance
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Towards developing novel and sustainable molecular light-to-heat converters
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Filling Data Gaps by Read‐across: A Mini Review on its Application, Developments and Challenges
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Making in silico predictive models for toxicology FAIR
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Going All In: A Strategic Investment in In Silico Toxicology
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Cheminformatics in MS-based environmental exposomics: Current achievements and future directions
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Evaluating confidence in toxicity assessments based on experimental data and in silico predictions
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Self-citationPrinciples and procedures for assessment of acute toxicity incorporating in silico methods
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Self-citationTechnology Readiness Level Roadmap for Developing Innovative Herbal Medicinal Products
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Nonclinical safety evaluation of a novel ionizable lipid for mRNA delivery
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In silico approaches for prediction of genotoxic and carcinogenic potential of cosmetic ingredients
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Toxicological testing of syringaresinol and enterolignans
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Interactions between T-2 toxin and its metabolites in HepG2 cells and in silico approach
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In Silico Assessment of Acute Oral Toxicity for Mixtures
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Antimicrobial activity and toxicity of glass ionomer cement containing an essential oil
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In silico environmental risk assessment improves efficiency for pesticide safety management
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Criteria for assessing the reliability of toxicity predictions: I. TIMES Ames mutagenicity model
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- Authors
- Myatt, G. J., Ahlberg, E., Akahori, Y., Allen, D., Amberg, A., Anger, L. T., Aptula, A., Auerbach, S., Beilke, L., Bellion, P., Benigni, R., Bercu, J., Booth, E. D., Bower, D., Brigo, A., Burden, N., Cammerer, Z., Cronin, M. T. D., Cross, K. P., Custer, L., Dettwiler, M., Dobo, K., Ford, K. A., Fortin, M. C., Gad-McDonald, S. E., Gellatly, N., Gervais, V., Glover, K. P., Glowienke, S., Van Gompel, J., Gutsell, S., Hardy, B., Harvey, J. S., Hillegass, J., Honma, M., Hsieh, J.-H., Hsu, C.-W., Hughes, K., Johnson, C., Jolly, R., Jones, D., Kemper, R., Kenyon, M. O., Kim, M. T., Kruhlak, N. L., Kulkarni, S. A., Kümmerer, K., Leavitt, P., Majer, B., Masten, S., Miller, S., Moser, J., Mumtaz, M., Muster, W., Neilson, L., Oprea, T. I., Patlewicz, G., Paulino, A., Lo Piparo, E., Powley, M., Quigley, D. P., Reddy, M. V., Richarz, A.-N., Ruiz, P., Schilter, B., Serafimova, R., Simpson, W., Stavitskaya, L., Stidl, R., Suarez-Rodriguez, D., Szabo, D. T., Teasdale, A., Trejo-Martin, A., Valentin, J.-P., Vuorinen, A., Wall, B. A., Watts, P., White, A. T., Wichard, J., Witt, K. L., Woolley, A., Woolley, D., Zwickl, C., & Hasselgren, C.
- Affiliations
- LeadScope (United States) (US)
- AstraZeneca (Sweden) (SE)
- Chemicals Evaluation and Research Institute (JP)
- Integrated Laboratory Systems, Inc. (US)
- Sanofi (Germany) (DE)
- Unilever (United Kingdom) (GB)
- National Institutes of Health (US)
- DSM (Switzerland) (CH)
- Gilead Sciences (United States) (US)
- Syngenta (United Kingdom) (GB)
- F. Hoffmann-La Roche AG (CH)
- National Centre for the Replacement Refinement and Reduction of Animals in Research (GB)
- Janssen Research & Development (United States) (US)
- Liverpool John Moores University (GB)
- Bristol-Myers Squibb (United States) (US)
- Pfizer (United States) (US)
- Rutgers, The State University of New Jersey (US)
- Defense Threat Reduction Agency (US)
- Novartis (Switzerland) (CH)
- GlaxoSmithKline (United Kingdom) (GB)
- National Institute of Health Sciences (JP)
- United States Food and Drug Administration (US)
- Leuphana University of Lüneburg (DE)
- Eli Lilly (United States) (US)
- Vertex Pharmaceuticals (United States) (US)
- Takeda (United States) (US)
- United States Department of Homeland Security (US)
- Agency for Toxic Substances and Disease Registry (US)
- British American Tobacco (United Kingdom) (GB)
- University of New Mexico (US)
- Environmental Protection Agency (US)
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- European Commission (BE)
- European Food Safety Authority (IT)
- AstraZeneca (United Kingdom) (GB)
- UCB Pharma (Belgium) (BE)
- Colgate-Palmolive (United States) (US)
- Bibra International (GB)
- Corresponding author
- Myatt, Glenn J.
- ORCID
- Ahlberg, Ernst
- Allen, David
- Amberg, Alexander
- Aptula, Aynur
- Auerbach, Scott
- Beilke, Lisa
- Bellion, Phillip
- Benigni, Romualdo
- Bercu, Joel
- Bower, Dave
- Brigo, Alessandro
- Burden, Natalie
- Cronin, Mark T.D.
- Cross, Kevin P.
- Dobo, Krista
- Ford, Kevin A.
- Glowienke, Susanne
- Gutsell, Steve
- Hardy, Barry
- Harvey, James S.
- Hillegass, Jedd
- Honma, Masamitsu
- Johnson, Candice
- Jolly, Robert
- Kim, Marlene T.
- Kruhlak, Naomi L.
- Kulkarni, Sunil A.
- Kümmerer, Klaus
- Masten, Scott
- Miller, Scott
- Mumtaz, Moiz
- Muster, Wolfgang
- Oprea, Tudor I.
- Patlewicz, Grace
- Richarz, Andrea-Nicole
- Ruiz, Patricia
- Stavitskaya, Lidiya
- Szabo, David T.
- Teasdale, Andrew
- White, Angela T.
- Wichard, Joerg
- Witt, Kristine L.
- Woolley, Adam
- Woolley, David
- Venue
- Regulatory Toxicology and Pharmacology
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 96
- Pages
- 1-17
- ISSN
- 0273-2300 (ISSN-L)
- PMC
- PMC6026539
- OpenAlex
- W2799297366
- Cited by
- 240
- References
- 60
- Citations by year
- 2026: 21 · 2025: 23 · 2024: 36 · 2023: 37 · 2022: 24 · 2021: 35 · 2020: 36 · 2019: 21 · 2018: 7
- FWCI
- 18.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Regulatory Toxicology and Pharmacology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Multilingual Matters (Channel View Publications) (publishedVersion, cc-by)
- Liverpool John Moores University (acceptedVersion)
- PubMed Central (acceptedVersion)
- Joint Research Centre (European Commission) (submittedVersion)
- Funders
- National Institute of Environmental Health Sciences
- National Institutes of Health (R43ES026909)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Carcinogens and Genotoxicity Assessment
- Keywords
- computational toxicology, standardized protocols, toxicity prediction, genotoxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity, hazard identification, forecast reliability, confidence level, regulatory acceptance, reproducibility
- MeSH terms
- Animals, Computer Simulation, Humans, Toxicology, Toxicity Tests
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