In Vitro to In Vivo Extrapolation for High Throughput Prioritization and Decision Making
Shannon M. Bell, Xiaoqing Chang, John F. Wambaugh, David G. Allen, Mike Bartels, Kim L.R. Brouwer, Warren M. Casey, Neepa Choksi, Stephen S. Ferguson, Grazyna Fraczkiewicz, Annie M. Jarabek, Alice Ke, Annie Lumen, Scott G. Lynn, Alicia Paini, Paul S. Price, Caroline Ring, Ted W. Simon, Nisha S. Sipes, Catherine S. Sprankle, Judy Strickland, John Troutman, Barbara A. Wetmore, Nicole C. Kleinstreuer.
Toxicology in Vitro (2018). DOI: https://doi.org/10.1016/j.tiv.2017.11.016 PMID: 29203341
In vitro chemical safety testing methods offer the potential for efficient and economical tools to provide relevant assessments of human health risk. To realize this potential, methods are needed to relate in vitro effects to in vivo responses, i.e., in vitro to in vivo extrapolation (IVIVE). Currently available IVIVE approaches need to be refined before they can be utilized for regulatory decision-making. To explore the capabilities and limitations of IVIVE within this context, the U.S. Environmental Protection Agency Office of Research and Development and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods co-organized a workshop and webinar series. Here, we integrate content from the webinars and workshop to discuss activities and resources that would promote inclusion of IVIVE in regulatory decision-making. We discuss properties of models that successfully generate predictions of in vivo doses from effective in vitro concentration, including the experimental systems that provide input parameters for these models, areas of success, and areas for improvement to reduce model uncertainty. Finally, we provide case studies on the uses of IVIVE in safety assessments, which highlight the respective differences, information requirements, and outcomes across various approaches when applied for decision-making.
Figure 1. Schematic overview of the EURL ECVAM strategy.
Schematic overview of the EURL ECVAM strategy for promoting the use of non-animal approaches in the assessment of toxicokinetics and systematic toxicity. IATA, integrated approaches for testing and assessment.
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Figure 2. Overview of in vitro to in vivo extrapolation.
Parameters describing ADME processes of the chemical through the system (i.e., hepatic clearance, protein binding) may be obtained via experimental measurements or in silico predictions. These parameters are used to develop a one compartment TK or a PBPK model that can be used to predict the population distribution of plasma concentration from any given daily dose. Reverse dosimetry predicts administered doses equivalent to in vitro active concentration, which can be compared to the in vivo measurements.
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Figure 3. Toxicokinetic information for ToxCast chemicals.
Histogram compares the number of chemicals in Phases I and II of EPA's ToxCast high throughput screening project having traditional toxicokinetics (TK) data versus those with high throughput TK data.
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Figure 4. Schematic of fit for purpose modeling.
Building from Fig. 2, there are multiple components to be considered to assess if a model is appropriate and will provide the level of confidence needed for the intended use.
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Figure 5. Putative AOP for uterotrophy.
Chemical binding to the estrogen receptor alpha (ERα) can lead to changes in gene expression and cell proliferation, both of which contribute to the adverse outcome of an increased uterine weight.
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Figure 6. The example uses the putative adverse outcome pathway.
The example uses the putative adverse outcome pathway for uterotrophy (Fig. 5) to illustrate how high throughput assays can be mapped to each key event of an adverse outcome pathway. NVS_NR_hER, cell free human estrogen receptor.
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Table 1. Assay systems to predict metabolic clearance.
Table cell contents represent a continuum of relevant characteristics, with “X” indicating low or minimal and “XXXX” indicating high or extensive. Use of each test system for a particular application should be considered within the context of the goals of the experiment.
- Table 1 (63 KB)