Application of Modern Toxicology Approaches for Predicting Acute Toxicity for Chemical Defense (2015) / Chapter Skim
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5 Integration and Decision-Making for Predictive Toxicology
5 Integration and Decision-Making for Predictive Toxicology
Pages 80-95
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From page 80... ...
As described in Chapter 2, the goal at each tier of the prioritization strategy is to place chemicals in three categories: "high confidence of high toxicity," "high confidence of low toxicity," and "inadequate data." Box 5-1 presents a simplified illustration of the process to base decisions on the results of a single model for a single end point. As illustrated in this simple case, categorization depends on defining clear benchmarks that set the boundaries for "high" and "low" toxicity and on taking uncertainty or confidence in each individual prediction into account.
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From page 81... ...
Therefore, even within an "end point" domain, there might be a need to integrate multiple databases, assays, models, and tools to develop an "integrated" prediction for that end point. The committee notes that using various integrative approaches might also help to identify biological responses that can explain chemical-induced adverse reactions (Bauer-Mehren et al.
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From page 82... ...
: (1) Integrating potentially multiple databases, assays, models, and tools into an "integrated prediction," with its confidence interval, as to a chemical's toxicity potential for that end point.
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From page 83... ...
. Specific approaches are described in more detail below, particularly in relation to predicting acute toxicity; examples of applications to nontesting approaches (Chapter 3)
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From page 84... ...
They include methods that are based on results of significance testing, such as p values or z scores, and fixed and random effect models that use summary statistics, such as the mean and standard error derived from individual results (Hedges and Olkin 1985; Borenstein et al.
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From page 85... ...
. Given the need to categorize chemicals, intermediate approaches that are quantitative and incorporate expert judgment are likely to be most useful in predicting acute toxicity.
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From page 86... ...
in which categorization decisions involve integration of multiple, possibly conflicting criteria. For example, placing chemicals in the "high confidence of high toxicity" bin could necessitate synthesizing results when individual pieces of evidence serve as flags of high alert (such as solid evidence from a single assay that is deemed highly predictive of acute toxicity)
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From page 87... ...
. The red-framed profile is the reference chemical for "high confidence of high toxicity," and the green-framed profile is the reference chemical for "high confidence of low toxicity." Note that the confidence intervals extending from each reference chemical define the category thresholds (vertical dashed red and green lines)
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From page 88... ...
Specifically, as described in Chapter 3, nontesting approaches could be developed for initial or intermediate events along a mechanistic pathway. The predictions could then be inputs into models that predict acute toxicity on the basis of biological assay data on the intermediate events.
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From page 89... ...
and that acutely toxic chemicals might not require consideration of ADME to predict elicitation of debilitating effects. The following discussion provides a brief summary of the different components of ADME, tools available to predict the components, and probable effect of ADME on future attempts by DOD to predict acute toxicity.
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From page 90... ...
In Vitro to In Vivo Extrapolation Modeling to Inform Tissue Dosimetry and Dosimetric Potential Modeling approaches developed to inform dosimetry assessments use IVIVE. Measurements from in vitro assays and predictions from nontesting approaches can provide various model inputs (such as rate of absorption, metabolic activity, and tissue partitioning)
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From page 91... ...
This approach was illustrated in the simplified example in Box 5-1 and in Figure 5-1 with LD50 as the toxicity end point. Using reference chemicals of high and low toxicity via clustering approaches.
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From page 92... ...
, methods that pool datasets, methods that use datasets hierarchically, and methods that link models sequentially. Finding: There are several possible approaches to placing chemicals in categories of "high toxicity," "low toxicity," and "inadequate data," including quantitative thresholds based on reference chemicals (such as sarin)
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From page 93... ...
Finding: Toxicokinetic and ADME behavior can influence the prediction of a chemical's acute toxicity potential and resulting categorization, and this emphasizes the need to include such considerations into the tiered prioritization strategy. REFERENCES Andersen, M.E., H.J.
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From page 94... ...
2007. Reverse dosimetry: Interpreting trihalomethanes bio monitoring data using physiologically based pharmacokinetic modeling.
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From page 95... ...
2009. A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute tox icity of chemicals in rodents.
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