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Evaluating pharmacokinetic and pharmacodynamic interactions with computational models in supporting cumulative risk assessment.

Tan YM, Clewell H, Campbell J, Andersen M - Int J Environ Res Public Health (2011)

Bottom Line: In such cases, conducting quantitative cumulative risk assessment for chemicals present as a mixture is difficult.The uncertainties that arise from PK interactions can be addressed by developing physiologically based pharmacokinetic (PBPK) models to describe the disposition of chemical mixtures.Further, PK models can be developed to describe mechanisms of action and tissue responses.

View Article: PubMed Central - PubMed

Affiliation: National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, NC 27711, USA.

ABSTRACT
Simultaneous or sequential exposure to multiple chemicals may cause interactions in the pharmacokinetics (PK) and/or pharmacodynamics (PD) of the individual chemicals. Such interactions can cause modification of the internal or target dose/response of one chemical in the mixture by other chemical(s), resulting in a change in the toxicity from that predicted from the summation of the effects of the single chemicals using dose additivity. In such cases, conducting quantitative cumulative risk assessment for chemicals present as a mixture is difficult. The uncertainties that arise from PK interactions can be addressed by developing physiologically based pharmacokinetic (PBPK) models to describe the disposition of chemical mixtures. Further, PK models can be developed to describe mechanisms of action and tissue responses. In this article, PBPK/PD modeling efforts conducted to investigate chemical interactions at the PK and PD levels are reviewed to demonstrate the use of this predictive modeling framework in assessing health risks associated with exposures to complex chemical mixtures.

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Related in: MedlinePlus

The traditional empirical approach for examining dose additivity between chemicals A and B. Additive: Same response from the mixture is observed from the sum of A and B. Synergism: A greater response from the mixture is observed than expected from the sum of A and B. Inhibitory: A lower response from the mixture is observed than expected from the sum of A and B.
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f1-ijerph-08-01613: The traditional empirical approach for examining dose additivity between chemicals A and B. Additive: Same response from the mixture is observed from the sum of A and B. Synergism: A greater response from the mixture is observed than expected from the sum of A and B. Inhibitory: A lower response from the mixture is observed than expected from the sum of A and B.

Mentions: A challenge for estimating cumulative risks across multiple chemicals is determining whether the responses generated from exposures to multiple chemicals simultaneously are different from estimates based on the addition of individual responses. The hypothesis of dose additivity among chemicals in a mixture is typically evaluated through empiricism. If a greater response from the mixture is observed than expected from the sum of the individual chemicals, the mixture response is described as synergism or potentiation (Figure 1). If a lower mixture response is observed than expected, the mixture response is described as antagonism or inhibition (Figure 1). To better distinguish additivity and non-additivity, many efforts have concentrated on improving the analytical capability to more accurately measure responses or on developing statistical methods to properly test the hypothesis of dose additivity [1]. Even with the most advanced analytical and statistical tools, however, determining the cumulative risk from exposure to environmental chemicals is still a difficult challenge.


Evaluating pharmacokinetic and pharmacodynamic interactions with computational models in supporting cumulative risk assessment.

Tan YM, Clewell H, Campbell J, Andersen M - Int J Environ Res Public Health (2011)

The traditional empirical approach for examining dose additivity between chemicals A and B. Additive: Same response from the mixture is observed from the sum of A and B. Synergism: A greater response from the mixture is observed than expected from the sum of A and B. Inhibitory: A lower response from the mixture is observed than expected from the sum of A and B.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3108131&req=5

f1-ijerph-08-01613: The traditional empirical approach for examining dose additivity between chemicals A and B. Additive: Same response from the mixture is observed from the sum of A and B. Synergism: A greater response from the mixture is observed than expected from the sum of A and B. Inhibitory: A lower response from the mixture is observed than expected from the sum of A and B.
Mentions: A challenge for estimating cumulative risks across multiple chemicals is determining whether the responses generated from exposures to multiple chemicals simultaneously are different from estimates based on the addition of individual responses. The hypothesis of dose additivity among chemicals in a mixture is typically evaluated through empiricism. If a greater response from the mixture is observed than expected from the sum of the individual chemicals, the mixture response is described as synergism or potentiation (Figure 1). If a lower mixture response is observed than expected, the mixture response is described as antagonism or inhibition (Figure 1). To better distinguish additivity and non-additivity, many efforts have concentrated on improving the analytical capability to more accurately measure responses or on developing statistical methods to properly test the hypothesis of dose additivity [1]. Even with the most advanced analytical and statistical tools, however, determining the cumulative risk from exposure to environmental chemicals is still a difficult challenge.

Bottom Line: In such cases, conducting quantitative cumulative risk assessment for chemicals present as a mixture is difficult.The uncertainties that arise from PK interactions can be addressed by developing physiologically based pharmacokinetic (PBPK) models to describe the disposition of chemical mixtures.Further, PK models can be developed to describe mechanisms of action and tissue responses.

View Article: PubMed Central - PubMed

Affiliation: National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, NC 27711, USA.

ABSTRACT
Simultaneous or sequential exposure to multiple chemicals may cause interactions in the pharmacokinetics (PK) and/or pharmacodynamics (PD) of the individual chemicals. Such interactions can cause modification of the internal or target dose/response of one chemical in the mixture by other chemical(s), resulting in a change in the toxicity from that predicted from the summation of the effects of the single chemicals using dose additivity. In such cases, conducting quantitative cumulative risk assessment for chemicals present as a mixture is difficult. The uncertainties that arise from PK interactions can be addressed by developing physiologically based pharmacokinetic (PBPK) models to describe the disposition of chemical mixtures. Further, PK models can be developed to describe mechanisms of action and tissue responses. In this article, PBPK/PD modeling efforts conducted to investigate chemical interactions at the PK and PD levels are reviewed to demonstrate the use of this predictive modeling framework in assessing health risks associated with exposures to complex chemical mixtures.

Show MeSH
Related in: MedlinePlus