Article

Development of PK- and PBPK-based modeling tools for derivation of biomonitoring guidance values.

Toxicology and Environmental Research & Consulting, The Dow Chemical Company, Midland, MI, USA. Electronic address: .
Computer methods and programs in biomedicine (impact factor: 1.14). 06/2012; 108(2):773-88. DOI:10.1016/j.cmpb.2012.04.014 pp.773-88
Source: PubMed

ABSTRACT There are numerous programs ongoing to analyze environmental exposure of humans to xenobiotic chemicals via biomonitoring measurements (e.g.: EU ESBIO, COPHES; US CDC NHANES; Canadian Health Measures Survey). The goal of these projects is to determine relative trends in exposure to chemicals, across time and subpopulations. Due to the lack of data, there is often little information correlating biomarker concentrations with exposure levels and durations. As a result, it can be difficult to utilize biomonitoring data to evaluate if exposures adhere to or exceed hazard/exposure criteria such as the Derived No-Effect Level values under the EU REACH program, or Reference Dose/Concentration values of the US EPA. A tiered approach of simple, arithmetic pharmacokinetic (PK) models, as well as more standardized mean-value, physiologically-based (PBPK) models, have therefore been developed to estimate exposures from biomonitoring results. Both model types utilize a user-friendly Excel spreadsheet interface. QSPR estimations of chemical-specific parameters have been included, as well as accommodation of variations in urine production. Validation of each model's structure by simulations of published datasets and the impact of assumptions of major model parameters will be presented.

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Keywords

biomonitoring measurements
 
biomonitoring results
 
Canadian Health Measures Survey
 
chemical-specific parameters
 
Derived No-Effect Level values
 
estimate exposures
 
EU ESBIO
 
EU REACH program
 
exposure levels
 
exposures adhere
 
hazard/exposure criteria
 
information correlating biomarker concentrations
 
major model parameters
 
model types utilize
 
model's structure
 
QSPR estimations
 
relative trends
 
tiered approach
 
urine production
 
utilize biomonitoring data
 

M Bartels