Dose-response for assessing the cancer risk of inorganic arsenic in drinking water: the scientific basis for use of a threshold approach.

ABSTRACT

The biologic effects of inorganic arsenic predominantly involve reaction of the trivalent forms with sulfhydryl groups in critical proteins in target cells, potentially leading to various toxicologic events including cancer. This mode of action is a threshold process, requiring sufficient concentrations of trivalent arsenic to disrupt normal cellular function. Nevertheless, cancer risk assessments for inorganic arsenic have traditionally utilized various dose-response models that extrapolate risks from high doses assuming low-dose linearity without a threshold. We present here an approach for a cancer risk assessment for inorganic arsenic in drinking water that involves considerations of this threshold process. Extensive investigations in mode of action analysis, in vitro studies (>0.1 µM), and in animal studies (>2 mg/L in drinking water or 2 mg/kg of diet), collectively indicate a threshold basis for inorganic arsenic-related cancers. These studies support a threshold for the effects of arsenic in humans of 50-100 µg/L in drinking water (about 65 µg/L). We then evaluate the epidemiology of cancers of the urinary bladder, lung, and skin and non-cancer skin changes for consistency with this calculated value, focusing on studies involving low-level exposures to inorganic arsenic primarily in drinking water (approximately <150 µg/L). Based on the relevant epidemiological studies with individual-level data, a threshold level for inorganic arsenic in the drinking water for these cancers is estimated to be around 100 µg/L, with strong evidence that it is between 50 and 150 µg/L, consistent with the value calculated based on mechanistic, in vitro and in vivo investigations. This evaluation provides an alternative mode of action-based approach for assessing health-protective levels for oral arsenic exposure based on the collective in vitro, in vivo, and human evidence rather than the use of a linear low-dose extrapolation based on default assumptions and theories.