TOXICITY TESTING IN THE 21ST CENTURY: A VISION AND A STRATEGY

By Daniel Krewski, Daniel Acosta, Melvin E. Andersen, H. Anderson, John C. Bailar, Kim Boekelheide, R. Brent, G. Charnley, V. Cheung, S. Green and 12 more. Journal of Toxicology and Environmental Health. Part B, Critical Reviews, 2010

2,006 citations recorded by the source corpus.

With the release of the landmark report Toxicity Testing in the 21st Century: A Vision and a Strategy, the U.S. National Academy of Sciences, in 2007, precipitated a major change in the way toxicity testing is conducted. It envisions increased efficiency in toxicity testing and decreased animal usage by transitioning from current expensive and lengthy in vivo testing with qualitative endpoints to in vitro toxicity pathway assays on human cells or cell lines using robotic high-throughput screening with mechanistic quantitative parameters. Risk assessment in the exposed human population would focus on avoiding significant perturbations in these toxicity pathways. Computational systems biology models would be implemented to determine the dose-response models of perturbations of pathway function. Extrapolation of in vitro results to in vivo human blood and tissue concentrations would be based on pharmacokinetic models for the given exposure condition. This practice would enhance human relevance of test results, and would cover several test agents, compared to traditional toxicological testing strategies. As all the tools that are necessary to implement the vision are currently available or in an advanced stage of development, the key prerequisites to achieving this paradigm shift are a commitment to change in the scientific community, which could be facilitated by a broad discussion of the vision, and obtaining necessary resources to enhance current knowledge of pathway perturbations and pathway assays in humans and to implement computational systems biology models. Implementation of these strategies would result in a new toxicity testing paradigm firmly based on human biology.

Subfields: Computational Theory and Mathematics, Health, Toxicology and Mutagenesis, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b757b7de6b7e5620387663422dc5aa812a64ef18

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Citations in the corpus

A Framework for Assessing Effects of the Food System (2015, background): “This area of research faces numerous challenges, but it may decrease cost in the future (Bialk et al., 2013; Firestone et al., 2010; Kavlock and Dix, 2010; Krewski et al., 2010).”
Caenorhabditis elegans: An Emerging Model in Biomedical and Environmental Toxicology (2008, methodology): “High-throughput chemical screening, in comparison, has been proposed as a quicker and less expensive method for toxicity testing (Gibb, 2008).”
Adverse outcome pathway (AOP) development I: strategies and principles. (2014, methodology): “As a conceptual framework, AOPs have promise as a tool to help support so called twenty-first century approaches to regulatory toxicology (Krewski et al., 2010).”
The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency. (2019, methodology): “As part of the CompTox strategic initiative, EPA, in collaboration with the National Toxicology Program (NTP), funded the formation of an expert committee by the U.S. National Academies of Science and National Research Council (NRC) to provide recommendations to advance the field in this area.”
Biology-inspired Microphysiological System Approaches to Solve the Prediction Dilemma of Substance Testing (2016, neutral): “The more immediate regulatory use of MPS comprises the OECD’s “adverse outcome pathways” initiative (OECD, 2013), integrated testing strategies, adverse biomarker identification and testing prioritization (e.g., similar to the Tox21-programme; Krewski et al., 2010).”
DeepTox: Toxicity Prediction using Deep Learning (2016, background): “The aim of the " Toxicity testing in the Twenty-first century " initiative is to develop more efficient and less timeconsuming approaches to predicting how chemicals affect human health (Andersen and Krewski, 2009; Krewski et al., 2010).”