The development of a stochastic physiologically-based pharmacokinetic model for lead.

This presentation describes the development of a prototype Monte Carlo module for the physiologically-based pharmacokinetic (PBPK) model for lead, created by Dr Ellen O'Flaherty. The module uses distributions for the following: exposure parameters (soil and dust concentrations, daily soil and ingestion rate, water lead concentration, water ingestion rate, air lead concentration, inhalation rate and dietary lead intake); absoption parameters; and key pharmacokinetic parameters (red blood binding capacity and half saturation concentration). Distributions can be specified as time-invariant or can change with age. Monte Carlo model predicted blood levels were calibrated to empirically measured blood lead levels for children living in Midvale, Utah (a milling/smelting community). The calibrated model was then evaluated using blood lead data from Palmerton, Pennsylvania (a town with a former smelter) and Sandy, Utah, (a town with a former smelter and slag piles). Our initial evaluation using distributions for exposure parameters showed that the model accurately predicted geometric (GM) blood lead levels of Palmerton and Sandy and slightly over predicted the GSD. Consideration of uncertainty in red blood cell parameters substantially inflated the GM. Future model development needs to address the correlation among parameters and the use of parameters for long-term exposure derived from short-term studies.

Recent trends in childhood blood lead levels.

Blood lead levels in children in the United States have declined through 1994, the date of the most recent National Health and Nutrition Examination Survey. In this investigation, the authors analyzed whether blood lead levels have changed since 1994 and quantified the magnitude of any change. The authors evaluated blood lead levels from 12 longitudinal data sets from 11 states and 1 city. Geometric mean blood lead levels declined between 4%/year and 14%/year in 8 of the data sets. No differences in decline rates were observed between data sets from states that had universal screening as a goal or that included repeat measures for an individual child and those data sets that did not. The authors' best estimate for these populations was a decline rate of 4-7%/year, which was comparable to the decline rate prior to 1994.

Blood lead slope factor models for adults: comparisons of observations and predictions.

Here we explore the appropriateness of various parameter values for the Bowers et al. model [Risk Anal 14:183-189, 1994] in the context of predicting the influence of site-related exposure to lead in soil on the blood lead (PbB) levels of women of childbearing age. We outline the parameters prescribed by Bowers et al. as well as those prescribed by the U.S. Environmental Protection Agency (U.S. EPA). Comparison of the PbB levels predicted by the Bowers et al. model to those predicted by the validated O'Flaherty pharmacokinetic model indicates that the Bowers et al. model performs favorably when parameter values prescribed here are used. Use of the U.S. EPA-prescribed parameters yields predicted PbB levels that substantially exceed the validated O'Flaherty model predictions. Finally, both the U.S. EPA-prescribed parameter values and the parameter values recommended herein are used to predict PbB levels among adults living in four Superfund communities. Comparison of predicted PbB levels for these communities indicates that the U.S. EPA parameters overstate the incremental influence of lead in soil on PbB levels. Differences between the parameter values prescribed here and the U.S. EPA-prescribed parameters yield substantially different cleanup criteria for lead in soil, although conservative parameter values may still be appropriate for screening purposes.

Issues in setting health-based cleanup levels for arsenic in soil.

Health risk assessments often do not take into account the unique aspects of evaluating exposures to arsenic in soil. For example, risks from ingestion of arsenic in soil are often based on toxicity factors derived from studies of arsenic (soluble arsenate or arsenite) in drinking water. However, the toxicity of arsenic in drinking water cannot be directly extrapolated to toxicity of soil arsenic because of differences in chemical form, bioavailability, and excretion kinetics. Because of the differences between soil arsenic and water arsenic, we conclude that risks from arsenic in soil are lower than what would be calculated using default toxicity values for arsenic in drinking water. Site-specific risk assessments for arsenic in soil can be improved by characterizing the form of arsenic in soil, by conducting animal feeding or in vitro bioavailability studies using site soils, and by conducting studies to evaluate the relationship between urinary arsenic and soil arsenic levels. Such data could be used to more accurately measure the contribution that soil arsenic makes to total intake of arsenic. Available data suggest that arsenic usually makes a small contribution to this total.

Assessing the relationship between environmental lead concentrations and adult blood lead levels.

This paper presents a model for predicting blood lead levels in adults who are exposed to elevated environmental levels of lead. The model assumes a baseline blood lead level based on average blood lead levels for adults described in two recent U.S. studies. The baseline blood level in adults arises primarily from exposure to lead in diet. Media-specific ingestion and absorption parameters are assessed for the adult population, and a biokinetic slope factor that relates uptake of lead into the body to blood lead levels is estimated. These parameters are applied to predict blood lead levels for adults exposed to a hypothetical site with elevated lead levels in soil, dust and air. Blood lead levels ranging from approximately 3-57 micrograms/dl are predicted, depending on the exposure scenarios and assumptions.

Use of the ouput of a lead risk assessment model to establish soil lead cleanup levels.

Three general methods to calculate soil contaminant cleanup levels are assessed: the truncated lognormal approach, Monte Carlo analysis, and the house-by-house approach. When these methods are used together with a lead risk assessment model, they yield estimated soil lead cleanup levels that may be required in an attempt to achieve specified target blood lead levels for a community. The truncated lognormal approach is exemplified by the Society for Environmental Geochemistry and Health (SEGH) model, Monte Carlo analysis is exemplified by the US EPA's LEAD Model, and the house-by-house approach is used with a structural equation model to calculate site-specific soil lead cleanup levels. The various cleanup methods can each be used with any type of lead risk assessment model. Although all examples given here are for lead, the cleanup methods can, in principle, be used as well with risk assessment models for other chemical contaminants to derive contaminant-specific soil cleanup levels.