Complex Mixtures Methods for In Vivo Toxicity Testing (1988) / Chapter Skim
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5. Interpretation and Modeling of Toxicity-Test Results
5. Interpretation and Modeling of Toxicity-Test Results
Pages 99-124
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From page 99... ...
First, they provide some guidance in developing an overall strategy to test the toxicity of mixtures economically. Second, they offer directions for experimental design so that maximal information can be extracted from the results of studies that cannot address directly every possible combination, dose, and regimen of exposures.
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From page 100... ...
Different measures of response can lead to different conclusions concerning departure from additivity. For nonlinear models, such as log-logistic, log-linear, logprobit, and multistage models, "interaction" can be defined as a departure from additivity for a transformation of the response variable.
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From page 101... ...
For example, where there is sufficient confidence in a model for regulatory use, it might not be necessary to conduct experiments at joint doses; reliance on experimental results with separate components might be sufficient. Models contribute to the estimation ofthe toxicity of complex mixtures.
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From page 102... ...
The third section provides experimental design criteria and guidelines that arise from the previous considerations regarding dose-response models. Finally, the fourth section offers recommendat~ons for research that could generate better methods of dose-response modeling and interpretation.
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From page 103... ...
Some workplace exposures might also lead to high risk for some populations. Appendix E gives an example of a mixture with a much greater than additive effect in a specific multistage model; in fact, the example assumes one of the largest synergistic effects that could be practically estimated with a two-dose bioassay design with 50 animals in each of four treatment groups (control for each agent; control agent X, treatment agent Y; control agent Y
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From page 104... ...
Appendix E presents a multistage model for a two-material mixture. Estimation of the values of parameters for this conceptually simple model can require considerable data including toxicity data on combinations of the two mixture components at several dose combinations.
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From page 105... ...
Current pharmacokinetic models attempt to describe the fate of a single substance and ignore possible complexities introduced by a mixture. Such complexities could distort the predicted concentration and composition of a mixture at the target sites where toxic effects are initiated.
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From page 106... ...
If a mixture were tested at a high dose, the estimated toxicity would include the contribution of interactions; that is, the estimated toxicity ofthe mixture would be the sum of the toxicities of the individual components plus the interaction terms (which could be negative if there were antagonism or inactivation of some components)
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From page 107... ...
Fractional factorial designs are particularly useful. They permit estimation of the individual effects of each factor, and they provide information on the most important interactions among factors.
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From page 108... ...
They thereby provide useful information on the formation and distribution of the reactive metabolites responsible for the induction of toxic effects in individual tissues. These models can be used to describe the relationship between the dose administered in toxicity tests and the dose delivered to the target tissue.
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From page 109... ...
However, some interactions can be assumed to occur only among a few components; fractional factorial designs may be used to test for interactions with a relatively small number of test combinations. In developing pharmacokinetic models for toxicity assessment of mixtures consideration should be given to the following questions: · Which constituents (or transformation products derived therefrom)
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From page 110... ...
EXPERIMENTAL DESIGN Experimental design that seeks the optimal allocation of resources is an important part of the testing of potentially toxic materials. The primary goals of such tests are the identification of toxic agents, the establishment of doseresponse relationships, and the estimation of risks at environmental levels of exposure.
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From page 111... ...
1) cells provide for an evaluation of the TABLE 5-2 A Factorial Experimental Design for Binary Mixtures Involving Two Chemicals C1 and C2 c2 d2o d21 ..
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From page 112... ...
In general, they demonstrated that, for the case of carcinogens that were expected to increase background rates by a factor of 2 - when operating singly at the doses administered, the optimal allocation of subjects needed to determine whether the combination of the two materials yields an interactive effect is roughly as follows: Controls (0,01: Each material alone (1,0 and 0,11: Two materials in combination (1,11: 0.12 0.25 0.38 These proportions will vary with background response rate, the toxicities of the two materials, and the magnitude of any presence of interactive effects. In the examples considered by Wahrendorf et al.
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From page 113... ...
These include the acute-mortality studies with rats exposed to CO and CO2 described in Appendix G Experimental designs for investigating response surfaces have been investigated in detail for use in nontoxicologic applications (Cornell, 19811.
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From page 114... ...
. + Xq = 1 represents all possible treatment combinations available for use in the experimental design.
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From page 115... ...
DESIGNS FOR PREDICTING LOW-DOSE RISKS Optimal designs for low-dose extrapolation with single chemicals require more information at low doses, because it is desirable to have more information at or near the doses about which we wish to make inferences and because the biologic responses at low doses might be different from those at high doses. This implies that more doses are needed at the low end of the dose-response curve, and greater weight will be placed on these doses, with respect to the number of animals to be assigned there.
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From page 116... ...
To explore this point further, optimal designs for extrapolating from an exTABLE 5-3 Optimal Experimental Designs Based on a Logistic Model for Low-Dose Extrapolation Response Probabilities Doses OptimalAllocationb pOo pal = plO pll ~ = ~ Coo Co!
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From page 117... ...
DESIGNS FOR INITIATION-PROMOTION STUDIES Considerable evidence suggests that chemical carcinogenesis sometimes involves an initial irreversible change in target cells (initiation) that is followed by a partially reversible developmental phase (promotion)
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From page 118... ...
Such hybrid designs will necessarily involve departures from optimal designs for carcinogen identification or dose-response curve development. In an attempt to identify a nearly optimal design that will perform reasonably well for purposes of both screening and low-dose extrapolation, the committee evaluated the efficiency of different four-point designs.
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From page 119... ...
This is an appealing procedure for materials to which the range of human exposure is rather narrow, so that exposures outside this range are rare enough to be uninteresting in a practical sense. When the number of chemicals in combination is over two, fractional factorial designs should be considered.
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From page 120... ...
Establishing doseresponse curves can provide some insight into whether a mixture is carcinogenic, the mechanism of such carcinogenicity, and how the mixture can be expected to act at low doses. To predict whether interactions will occur and how a mixture might behave in environmental exposures, short-term bioassays—such as cell transformation, mutation, and proliferation might help to determine the potential mechanisms of action of the mixture or its constituents and the shape of the doseresponse curve (see Chapter 3~.
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From page 121... ...
Experimental design and generation of data for generalized linear models need more attention. Some candidate methods were discussed earlier; they need to be explored further.
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From page 122... ...
More work is needed to extend some of the results with binary mixtures to more complex mixtures. Because of toxicity problems that might arise with joint high-dose exposures, perhaps such combinations should be eliminated or de-emphasized in experimental designs involving complex mixtures.
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From page 123... ...
1983. Experimental design, pp.
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From page 124... ...
1986. Optimal experimental designs for low dose extrapolation.
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