Biokinetic models application

Guiknette, R. A., and Mewhinney, J. A. (1989). A Biokinetic Model of Inhaled Cm Compounds in Dogs Application to Human Exposure Data. Health Physics 57(1) 187-197. 

Leggett, R.W., Eckerman, K.F., and McGinn, C.W. (2012). Controlling intake of uranium in the workplace Applications of biokinetic modeling and occupational monitoring data, ORNL/TM-2012/14. Oak Ridge, TN Oak Ridge National Laboratory. 

Application of biokinetic models to actual exposure populations first requires steps to determine the relative reliability of the particular model. Two critical steps are sensitivity analysis and model evaluation. The first judges the assumptions within the model and magnitude of the predicted response. The latter assesses the degree of concordance between simulation and observation. 

A related qualifier is the extent to which some particular model for some site- or scenario-specific use is flexible for multiple uses by the regulator or risk manager. For example, biokinetic models such as EPA s lEUBK model for children (U.S. EPA, 1994a,b,c) or the O Flaherty PB-PK model for children and adults (O Flaherty, 1995, 1998) theoretically permit assessment of the results of altered land uses, altered population demographics, or impact of lead remedial actions when projecting into future years for body lead burdens indexed by PbB. Such applications should not require recalibration with empirical data for every use. 

Current biokinetic models differ in their flexibility for use in other than stable, (near) steady-state Pb exposures of human risk populations. The lEUBK is only operative in cases where Pb exposures are stable and long term. By contrast, the O Haherty PB-PK model is constructed to reflect acute or episodic Pb exposures. Current biokinetic models also differ in the age ranges of exposed populations to be modeled. The lEUBK model is for children up to 84 months old. The PB-PK models are basically hfetime in their application. 

Finley BL, Monnot AD, Gaffney SH, Paustenbach DJ. Dose-response relationships for blood cobalt concentrations and health effects a review of the literature and application of a biokinetic model. J Toxicol Environ Health B Crit Rev 2012 15(8) 493-523. 

It does not contain a probabilistic modeling component that simulates variability therefore, it is not used to predict PbB probability distributions in exposed populations. Accordingly, the current version will not predict the probability that children exposed to lead in environmental media will have PbB concentrations exceeding a health-based level of concern (e.g., 10 pg/dL). Efforts are currently underway to explore applications of stochastic modeling methodologies to investigate variability in both exposure and biokinetic variables that will yield estimates of distributions of lead concentrations in blood, bone, and other tissues. 

One can, however, attempt to sidestep some of the above constraints to acquiring measurement data in epidemiological smdies through exposure simulations, if Pb input measurements are available. This would be the case with biokinetic or metabolic models. This would also be the case in some situations where ad hoc or statistical empirical models derived from a modeled relationship for a particular site and set of environmental Pb site parameters were applied to other sites very similarly simated. The relative flexibility of the ad hoc or setting-specific empirical models may or may not be less widely applicable, i.e., more problematic, than various metabolic models. The relative merits of these model forms emerge through comparisons contrasting measured to simulated or predicted data outputs. 

The lEUBK model employs a log-normal Pb input distribution and computational approach using calculations of geometric means from an assumption of intakes and biokinetics based on median values, and employs a GSD. The model presents a default GSD for a typical array of exposed children and also offers user-selected GSD choices. The choice of GSD is critical in such log-normal relationships as Pb exposures in human populations, since in applications for risk assessment the higher the GSD for a given typical child or typical children in an exposed population, the higher the fraction of individuals in the upper tail of the distribution and vice versa. 

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