Population Distribution of Aldehyde Dehydrogenase-2 Genetic Polymorphism: Implications for Risk Assessment

Ginsberg, Gary; Smolenski, Susan; Hattis, Dale; Sonawane, Babasaheb

doi:10.1006/rtph.2002.1591

Cited by 25 publications

(12 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of MeIQx, increased susceptibility in children is assumed due to the mutagenic mode of action (USEPA 2005). In addition, certain genetic polymorphisms in metabolizing enzymes (e.g., in N-acetyltransferases) may enhance subgroup susceptibility (Ishibe et al 2002;Rovito et al 2005;Hein et al 2006) and therefore increase the overall distribution of variability within the population, as has been demonstrated for other polymorphic metabolizing enzymes (Ginsberg et al 2002). Indeed, a high degree of individual variability is found in MeIQx uptake and DNA adduct burden in human colon (Turteltaub et al 1997;Turesky et al 1998;Mauthe et al 1999).…”

Section: Discussionmentioning

confidence: 95%

Do 2-amino-3,8-dimethylimidazo[4,5-f] quinoxaline data support the conclusion of threshold carcinogenic effects?

Chen

Guyton

2007

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

The objectives of this paper are to (1) reexamine the data that were used to support the conclusion of a threshold effect for 2-amino-3,8-dimethylimidazo[4,5-f] quinoxaline (MeIQx)-induced initiation and carcinogenicity at low doses in the rat liver, and (2) discuss issues and uncertainties about assessing cancer risk at low doses. Our analysis is part of an effort to understand proper interpretation and modeling of data related to cancer mechanisms and is not an effort to develop a risk assessment for this compound. The data reanalysis presented herein shows that the low-dose initiation activity of MeIQx, which can be found in cooked meat, cannot be dismissed. It is argued that the threshold effect for carcinogenic agents cannot be determined by statistical non-significance alone; more relevant biological information is required. A biologically motivated procedure is proposed for data analyses. The concept and procedure that are appropriate for analyzing MeIQx data are equally applicable to other compounds with comparable data.

show abstract

Section: Discussionmentioning

confidence: 95%

Do 2-amino-3,8-dimethylimidazo[4,5-f] quinoxaline data support the conclusion of threshold carcinogenic effects?

Chen

Guyton

2007

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…Our experience with the ALDH2 polymorphism (Ginsberg et al, 2002) was that a high-risk trait in a small subpopulation (Asian-Americans) was too diluted to substantially contribute to variability when considering the total U.S. population, analyzed at the 95th percentile.…”

Section: Pbtk/monte Carlo Modeling Of Acrylamide/glycidamide Dosimetrmentioning

confidence: 99%

“…b Linearly extrapolated between pre-natal and post-natal levels in the Strange et al (1989) study (see Supplemental Table 4). c Inter-individual variability defined by the population distribution of GSTM1 for Caucasians as derived in Ginsberg, et al, 2002. This input distribution is bimodal due to the null genotype in 53% of subjects.…”

Section: Physiological Parametersmentioning

confidence: 99%

Approaches to Acrylamide Physiologically Based Toxicokinetic Modeling for Exploring Child–adult Dosimetry Differences

Walker

Hattis

Russ

et al. 2007

Journal of Toxicology and Environmental Health, Part A

Self Cite

View full text Add to dashboard Cite

Dietary exposure to acrylamide is common as a result of its formation during the cooking of carbohydrate foods. This leads to widespread human exposure in adults and children alike. Acrylamide is neurotoxic and is metabolized by cytochrome P-450 (CYP) 2E1 to a mutagenic epoxide, glycidamide. This article describes a modeling framework for assessing acrylamide and glycidamide dosimetry in rats and human adults and children. The challenges in building a physiologically based toxicokinetic (PBTK) model that is compatible with existing rat and human data are described, with an emphasis on calibration against the hemoglobin adduct database. This exploratory PBTK model was adapted to children by incorporating life-stage-specific parameters consistent with children's changing physiology and metabolic capacity for processes involved in acrylamide disposition in terms of CYP2E1, glutathione conjugation, and epoxide hydrolase. Monte Carlo analysis was used to simulate the distribution of internal doses to gain an initial understanding of the range of child/adult differences possible. This analysis suggests modest dosimetry differences between children and adults, with area-under-the-curve (AUC) doses for the 99th percentile child up to fivefold greater than the median adult for both acrylamide and glycidamide. Early life immaturities tended to exert a greater effect on acrylamide than glycidamide dosimetry because immaturities in CYP2E1 and glutathione counteract one another for glycidamide AUC, but both lead to greater acrylamide dose. The analysis points toward glutathione conjugation parameters as being particularly influential and uncertain in early life, making this a key area for future research.

show abstract

“…Often, such studies were used to develop the so‐called “categorical” IVF, i.e., factors attributed to the TK component of groups of substances sharing similar characteristics. These characteristics might be related to their pharmacological activity (e.g., Naumann et al ., ; Silvermann et al ., 1999; Skowronski and Abdel‐Rahman, ; Suh and Abdel‐Rahman, ) or their metabolic pathways (e.g., Dorne et al ., ; Ginsberg et al ., , , 2004; Lin and Lu, ). Of particular interest, Ginsberg et al .…”

Section: Introductionmentioning

confidence: 99%

Characterization of the human kinetic adjustment factor for the health risk assessment of environmental contaminants

Valcke

Krishnan

2013

J of Applied Toxicology

View full text Add to dashboard Cite

A default uncertainty factor of 3.16 (√10) is applied to account for interindividual variability in toxicokinetics when performing non-cancer risk assessments. Using relevant human data for specific chemicals, as WHO/IPCS suggests, it is possible to evaluate, and replace when appropriate, this default factor by quantifying chemical-specific adjustment factors for interindividual variability in toxicokinetics (also referred to as the human kinetic adjustment factor, HKAF). The HKAF has been determined based on the distributions of pharmacokinetic parameters (e.g., half-life, area under the curve, maximum blood concentration) in relevant populations. This article focuses on the current state of knowledge of the use of physiologically based algorithms and models in characterizing the HKAF for environmental contaminants. The recent modeling efforts on the computation of HKAF as a function of the characteristics of the population, chemical and its mode of action (dose metrics), as well as exposure scenario of relevance to the assessment are reviewed here. The results of these studies, taken together, suggest the HKAF varies as a function of the sensitive subpopulation and dose metrics of interest, exposure conditions considered (route, duration, and intensity), metabolic pathways involved and theoretical model underlying its computation. The HKAF seldom exceeded the default value of 3.16, except in very young children (i.e., <≈ 3 months) and when the parent compound is the toxic moiety. Overall, from a public health perspective, the current state of knowledge generally suggest that the default uncertainty factor is sufficient to account for human variability in non-cancer risk assessments of environmental contaminants.

show abstract

Population Distribution of Aldehyde Dehydrogenase-2 Genetic Polymorphism: Implications for Risk Assessment

Cited by 25 publications

References 46 publications

Do 2-amino-3,8-dimethylimidazo[4,5-f] quinoxaline data support the conclusion of threshold carcinogenic effects?

Do 2-amino-3,8-dimethylimidazo[4,5-f] quinoxaline data support the conclusion of threshold carcinogenic effects?

Approaches to Acrylamide Physiologically Based Toxicokinetic Modeling for Exploring Child–adult Dosimetry Differences

Characterization of the human kinetic adjustment factor for the health risk assessment of environmental contaminants

Contact Info

Product

Resources

About