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How Can Biologically-Based Modeling of Arsenic Kinetics and Dynamics Inform the Risk Assessment Process? -- ETD
Citation:
KENYON, E. M., W. T. Klimecki, H. A. EL-MASRI, R. CONOLLY, H. J. CLEWELL, AND B. D. Beck. How Can Biologically-Based Modeling of Arsenic Kinetics and Dynamics Inform the Risk Assessment Process? -- ETD. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, 232(3):359-368, (2008).
Impact/Purpose:
For a chemical presenting as many unique challenges as arsenic, organizing information from diverse sources in a quantitative pharmacokinetic and pharmacodynamic modeling framework has many advantages. The logistics of this type of undertaking as well as its advantages and limitations were explored in a series of presentations during a recent workshop at the 2007 Annual Meeting of the Society of Toxicology. These presentations covered qualitative and quantitative aspects of variability in arsenic metabolism, development of a human pharmacokinetic model to describe the target tissue dosimetry of arsenic, how computational modeling can be applied to evaluate multiple potential modes of action, how genomic data fits into a computational modeling framework, and finally, how quantitative modeling might be applied in an arsenic risk analysis.
Description:
Quantitative biologically-based models describing key events in the continuum from arsenic exposure to the development of adverse health effects provide a framework to integrate information obtained across diverse research areas. For example, genetic polymorphisms in arsenic metabolizing enzymes can lead to differences in target tissue dosimetry for key metabolites causative in toxic and carcinogenic response. This type of variation can be quantitatively incorporated into pharmacokinetic (PK) models and used together with population based modeling approaches to evaluate the impact of genetic variation in methylation capacity on dose of key metabolites to target tissue. The PK model is an essential bridge to the pharmacodynamic (PD) models. A particular benefit of PD modeling for arsenic is that alternative models can be constructed for multiple proposed modes of action for arsenicals. Genomics data will prove useful for identifying the key pathways involved in particular responses and aid in determining other types of data needed for quantitative modeling. These models, when linked with PK models, can be used to better understand and explain dose- and time-response behaviors. This in turn assists in prioritizing modes of action with respect to their risk assessment relevance and future research. This type of integrated modeling approach can form the basis for a highly informative mode-of-action directed risk assessment for inorganic arsenic (IAs). This paper will address both practical and theoretical aspects of integrating PK and PD data in a modeling framework, including practical barriers to its application.
