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A Reexamination of the PPAR-α Activation Mode of Action as a Basis for Assessing Human Cancer Risks of Environmental Contaminants

Kathryn Z. Guyton, Weihsueh A. Chiu, Thomas F. Bateson, Jennifer Jinot, Cheryl Siegel Scott, Rebecca C. Brown and Jane C. Caldwell
Environmental Health Perspectives
Vol. 117, No. 11 (Nov., 2009), pp. 1664-1672
Stable URL: http://www.jstor.org/stable/40382449
Page Count: 9
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Abstract

Background: Diverseenvironmcntal contaminants, including the plasticizerdi(2-ethylhexyl)phthalate (DEHP), are hepatocarcinogenic peroxisome proliferators in rodents. Peroxisome prolifcrator-activated receptor-α (PPAR-α) activation and its sequelae have been proposed to constitute a mode of action (MOA) for hepatocarcinogenesis by such agents as a sole causative factor. Further, based on a hypothesized lower sensitivity of humans to this MOA, prior reviews have concluded that rodent hepatocarcinogenesis by PPAR-U agonists is irrelevant to human carcinogenic risk. Data Synthesis: Herein, we review recent studies that experimentally challenge the PPAR-α activation MOA hypothesis, providing evidence that DEHP is hepatocarcinogenic in PPAR-α.-null mice and that the MOA but not hepatocarcinogenesis is evoked by PPAR-α activation in a transgenic mouse model. We further examine whether relative potency for PPAR-α activation or other steps in the MOA correlates with tumorigenic potency. In addition, for most PPAR-α agonists of environmental concern, available data are insufficient to characterize relative human sensitivity to this rodent MOA or to induction of hepatocarcinogenesis. Conclusions: Our review and analyses raise questions about the hypothesized PPAR-α activation MOA as a sole explanation for rodent hepatocarcinogenesis by PPAR-α agonists and therefore its utility as a primary basis for assessing human carcinogenic risk from the diverse compounds that activate PPAR-α. Ihese findings have broad implications for how MOA hypotheses are developed, tested, and applied in human health risk assessment. We discuss alternatives to the current approaches to these key aspects of mechanistic data evaluation.

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