An approach for the quantitative consideration of genetic polymorphism data in chemical risk assessment: examples with warfarin and parathion.

In recent years, a great deal of research has been conducted to identify genetic polymorphisms. One focus has been to characterize variability in metabolic enzyme systems that could impact internal doses of pharmaceuticals or environmental pollutants. Methods are needed for using this metabolic information to estimate the resulting variability in tissue doses associated with chemical exposure. We demonstrate here the use of physiologically based pharmacokinetic (PBPK) modeling in combination with Monte Carlo analysis to incorporate information on polymorphisms into the analysis of toxicokinetic variability. Warfarin and parathion were used as case studies to demonstrate this approach. Our results suggest that polymorphisms in the PON1 gene, that give rise to allelic variants of paraoxonase, which is involved in the metabolism of paraoxon (a metabolite of parathion), make only a minor contribution to the overall variability in paraoxon tissue dose, while polymorphisms in the CYP2C9 gene, which gives rise to allelic variants of the major metabolic enzyme for warfarin, account for a significant portion of the overall variability in (S)-warfarin tissue dose. These analyses were used to estimate chemical-specific adjustment factors (CSAFs) for the human variability in toxicokinetics for both parathion and warfarin. Implications of alternatives in the calculation of CSAFs are explored. Key decision points for applying the PBPK-Monte Carlo approach to evaluate toxicokinetic variability for other chemicals are also discussed.

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