A novel application of a competitive binding model in dioxin risk assessment.

The EPA-recommended toxicity equivalence factor (TEF) approach to estimating the lifetime incremental cancer risks for dioxins does not address (a) differences in the severity of toxicity according to the composition of chemical mixture and (b) potentials for modification of tissue-level doses of congeners in mixtures and consequently the cancer risk estimates. Our earlier efforts to model the binding of congeners to the Ah receptor in the low-dose range and to develop quantitative estimates for the formation of fractions of Ah receptor-congener complexes resulted in the definition of a unique parameter, defined as competitive binding ratio (CBR), to adjust tissue-level doses for mixture exposure. We made an effort to incorporate CBR values in the dose-response analysis and risk characterization of congeners in two distinct exposure scenarios. The modified approach to estimating tissue-level doses of congeners in mixtures by the use of a competitive binding model indicated that (a) the Ah receptor affinity is an important criterion in the determination of tissue-level dose of congeners, (b) the TEF doses calculated by using the model algorithms modified the tissue-level doses for congeners in mixture exposures, and (c) the combined lifetime incremental cancer risks for all congeners were generally lower when model algorithms were used in the dose-response analysis. However, the percentage contribution of toxic congeners was significantly higher when model algorithms were used. The percentage contribution of higher congeners with low toxicity was considerably reduced when model algorithms were used.(ABSTRACT TRUNCATED AT 250 WORDS)

Logistic regression of inhalation toxicities of perchloroethylene--application in noncancer risk assessment.

Unlike the impressive advancement of cancer risk assessment, the "cutoff approach" based on hazard quotient in noncancer risk assessments recommended by the EPA has crucial deficiencies. Several alternative approaches have been suggested in the literature to modify the noncancer risk characterization based on reference doses. Recent studies have indicated that the effects of perchloroethylene (PERC) on the central nervous system (CNS) is a much more sensitive noncancer endpoint than cancer which is currently the basis for deriving its public health criteria and standards. Studies indicate that 20 ppm of inhaled PERC concentration elicited adverse effects on the CNS in experimental animals and humans. However, the existing EPA oral reference dose (RfD), a noncancer toxicity parameter for PERC (0.01 mg/kg/day), is based on the induction of hepatotoxicity and increased body weight gain induced by PERC in rats. An attempt was made in this paper to examine whether logistic regression of dose-response data could be applied to assess the noncancer risks. In order to perform logistic regression the inhalation toxicity data of PERC were classified according to the severity of toxicity paradigm used in toxicity analysis. Based on the sensitive noncancer endpoints identified from severity classification, a logistic regression analysis of the data was performed and its potential applicability in noncancer risk characterization was described for workers exposure to PERC in dry-cleaning operations.