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Human Exposure and Atmospheric Sciences


PROcEED User Information

Given the growing number of population-based biomonitoring surveys, such as Centers for Disease Control and Prevention’s (CDC) National Health and Nutrition Examination Survey (NHANES), there is an escalated interest in converting biomarker measurements to exposure concentrations for supporting risk assessment and risk management. Here, ‘biomarker measurements’ refer to measurements of chemicals or their metabolites in human tissues or specimens. The conversion is in essence an inverse problem that involves two steps: (1) Formulating a model that relates an exposure concentration to a biomarker concentration by describing the pharmacokinetics (forward dosimetry); and (2) Solving for the plausible exposure concentrations that are consistent with the observed biomarker concentrations (reverse dosimetry). At the population level, a probabilistic reverse dosimetry approach takes into account the variability and/or uncertainty in exposure factors (e.g., frequency and duration of exposure, time of biomarker sample collection) and pharmacokinetics (i.e., absorption, distribution, metabolism and excretion of a chemical in the body) to convert a distribution of “measured” biomarker concentrations to a distribution of “unmeasured” exposure concentrations.

What is PROcEED?
Probabilistic Reverse dOsimetry Estimating Exposure Distribution (PROcEED) is a Web-based application that is used to perform probabilistic reverse dosimetry calculations for estimating a distribution of exposure concentrations likely to have produced biomarker concentrations measured in a population. PROcEED transforms the distribution of measured biomarker concentrations to a potential distribution of unmeasured exposure concentrations using one of two approaches: (1) the Discretized Bayesian Approach, or (2) the Exposure Conversion Factor Approach. The difference between the two approaches is that the second approach is only suitable in the case where the exposure-biomarker relationship is linear.

EPA scientists have developed PROcEED to support the agency’s Chemical Safety for Sustainability (CSS) Research Program. One of the key outcomes the CSS program seeks to achieve is “Biomarkers of exposure are developed that enable the reconstruction of conditions that led to the observed results or relate to the health outcome.” By reconstructing potential exposure concentrations from measured values of biomarkers of exposure, PROcEED allows the user to (1) better utilize biomarker data to assess exposures; and (2) compare the estimated distribution of exposure concentrations with an exposure guidance value to assess health risks.

PROcEED is not a pharmacokinetic modeling tool or Monte Carlo simulation software. The reliability of the potential distribution of exposure concentrations estimated by PRoCEED depends on the quality of the biomarker measurements, the user’s prior knowledge regarding exposure concentrations, the predictive ability of the pharmacokinetic model used to predict biomarker concentrations at given exposure concentrations, and the accuracy of probabilistic distributions used in the Monte Carlo simulations. Users are assumed to be familiar with the concepts and terminology of pharmacokinetic modeling, tissue dosimetry, the Monte Carlo method, and Bayesian statistics.

There is no need for a license and the program is free of charge. The software uses a web-based graphical user interface enabling users to upload the necessary input files and display the results of reverse dosimetry calculations.

How does PROcEED work?
PROcEED requires, as inputs, (1) predicted biomarker concentrations from a Monte Carlo simulation of a pharmacokinetic model and (2) a vector of measured biomarker concentrations. PROcEED accepts a variety of text-based file formats outlined in the help documentation and described through the web-interface. PROcEED then performs reverse dosimetry calculations using either the Descretized Bayesian Approach or the Exposure Conversion Factor Approach to provide both graphical probability distributions as well as exportable results.

Published References

  • Georgopoulos P.G., Sasso A.F., Isukapalli S.S., Lioy P.J., Vallero D.A., Okino M., Reiter L. 2009. Reconstructing population exposures to environmental chemicals from biomarkers: challenges and opportunities. J Expo Sci Environ Epidemiol,19: 149-171.
  • Tan Y.-M., Liao K.H., Conolly R.B., Blount B.C., Mason A.M., Clewell H.J. 2006. Use of a physiologically based pharmacokinetic model to identify exposures consistent with human biomonitoring data for chloroform. J Toxicol Environ Health, Part A 69: 1727-1756.
  • Tan Y.-M., Liao K.H., Clewell H.J. 2007. Reverse dosimetry: interpreting trihalomethanes biomonitoring data using physiologically based pharmacokinetic modeling. J Expo Sci Environ Epidemiol, 17: 591-603.

Software and web requirements

  • Installed software as a server-side application on a tomcat webserver
  • Access software via Firefox or Internet Explorer (other browsers have not be tested)

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