Impact/Purpose:

1) Identify potential pathways of exposure for chemicals of interest based regulatory assumptions, product use labels and existing field study data.

2)Develop chemical or class specific PBPK/PD models suitable for biomarker analysis

3)Generate PBPK/PD model output to include simulation-based distributions of projected biomarkers from exposure time-histories.

4)Test biomarker data from human exposure field studies against the in silicoderived prior distributions of exposure to produce ?posterior? distributions.

5)Derive PK and PD dose metrics and calculate cumulative risks.

Description:

Congress, the public, the National Academy of Sciences (NAS) and other expert panels have urged EPA to rationally address aggregate exposure and cumulative risk. Of special concern are estimates of cumulative risk resulting from aggregate exposure of sensitive sub-populations (infants and children and the elderly) to complex mixtures of chemicals. This research effort focuses on enhancing ORD's core research activities to meet some of the most critical research needs arising from a broader definition of cumulative risk -- by improving aggregate exposure assessment through the use of Physiologically-based Pharmacokinetic (PBPK) and Pharmacodynamic (PD) models. PBPK/PD models are computational tools that can be used to test risk based assumptions and data-based exposure algorithms prior to the collection of biological data. Chemical specific PBPK/PD models have been developed within the Exposure Related Dose Estimating Model (ERDEM) platform to test exposure time-histories of regulatory interest to EPA. These exposure time-histories might involve multiple exposures along defined pathways (dermal, oral, and inhalation) in time and space as determined by occupational and incidental human activity profiles. Absorption, distribution, metabolism and excretion (ADME components of PBPK modeling) of the parent chemical and metabolites are tracked and mass balance is accounted with each iteration of the model. Biomarkers of exposure are evaluated as the dose metrics based on the physiological and biochemical processes assigned to each in silico test species. Distributions of exposure can then be generated within factors (groups) and subjects for selected biomarkers. These "prior" distributions can then be tested against incoming data. This task will focus on building PBPK/PD simulation-based distributions of projected biomarkers, most likely urinary metabolites, to establish presumptive scenario-based prior distributions of exposure. Biomarker data from human exposure field studies shall be tested against the in silico derived prior distributions of exposure to produce "posterior" distributions. These posterior biomarker distributions will be used to generate PK and PD dose metrics in order to reconstruct dose and calculate cumulative risk, e.g., Reference Dose (RfD) or Margin of Exposure (MOE).

Record Details:

Record Type:PROJECT

Start Date:10/01/2005

Projected Completion Date:10/01/2007

OMB Category:Other

Record ID: 137228

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Project Information:

Progress :This is a new task. This task was modified from FY2004 task 15566 and 3906.

Relevance :PBPK/PD models hold great promise for simplifying cumulative risk assessment. These models may be anthropomorphically viewed as being in silico biological (e.g., human) subjects that can be tested repeatedly under most any test conditions. Exposure time-histories that can vary greatly from regulatory restrictions to every day life may be imposed on the models. The time-histories and the PBPK/PD models can be adjusted as better data becomes available to provide a continuous quality assurance (QA) trail. However, it must be recognized that PBPK/PD models require quality data to meaningfully adjust model parameters. Laboratory animal data and human biomarker data (primarily urinary metabolite data) have been successfully used to augment model parameters for chemical specific PBPK/PD model applications (Knaak et al., 2004; Dary et al., 2004 and Okino et al., 2004). One must bear in mind, however, that human biomarker data are difficult to interpret because of the ubiquitous nature of a biomarker (many stressors may result in the same biomarker) and because of the incompleteness of a sampling schedule. Moreover, it may be difficult to relate the biomarker back to the source and magnitude (dose) of exposure because of the inadequacy of exposure pathway and scenario information.

The Center for Disease Control and Prevention (CDC) has obtained data on a variety of biomarkers in the blood and urine of the US population. EPA has been called on to interpret the CDC data for human exposure and health. This task is intended to utilize biomarker data as a means to test the applicability of the Exposure Related Dose Estimating Model (ERDEM) platform for characterizing aggregate exposure and cumulative risk under long term goals (LTG) 2 (Aggregate exposures) and 3 (cumulative risk) of strategic goal 4 (healthy Communities and Ecosytems) of the ORD strategic multi-year plan (MYP) in support of FQPA. Advances in PBPK/PD modeling are expected to support the August 2006 reassessment of pesticide tolerances under annual performance goal (APG) 42 of the SMYP. PBPK/PD models are expected to provide OPPTS with a rational link between regulatory based exposure factors, biomarker measurement data, and relevant dose metrics to calculate meaningful risk factors (e.g., RfD and MOE) accross the exposure-to-dose-to-effects continuum in support of FQPA by FY 2008.

Clients :OPPTS, OPP, OSWER, OAR, OW, OEI, OCHP, ORD, NERL, NCEA, NHEERL, Scientific Community

Project IDs:

ID Code :20711

Project type :OMIS