Pharmacokinetic Models for Lead

The relationship between air lead concentration and BLL is complex. Exposure to lead can occur through multiple pathways. BLL is the exposure metric most commonly described in association with health effects in humans, and lead exposure is typically assessed by using a pharmacokinetic model to relate air (or dietary) exposure concentratiorrs to BLLs. The committee s goal was not to review in depth the various dosimetry models available for lead but to explore how dosimetry models were used in the development of the OSHA generd industry lead standard and to evalirate the models and their assumptions (see Table 3-1). 

TABLE 3-1 Assumptions Used by Occupational Safety and Health Administration for Center for Policy Alternatives Model and Committee s Evaluation 

Particle size First 12.5 pg/m of airborne lead consisted of lead particles with an aerodynamic diameter of 1 pm remainder consisted of larger particles ( 1 pm) that would be deposited in upper respiratory tract. Size of lead aerosol can influence deposition and absorption of lead from respiratory tract and delivery to systemic bloodstream (Froines et al. 1986 Park and Paik 2002). For example, lead fumes are more easily absorbed from lungs and result in higher BLLs than inhalation of larger lead particles. Smaller lead particles also appear to be more soluble regardless of chemical form of dust (Spear et al. 1998). 

Deposition efficiency 37% of all lead particles 1 pm are deposited in veolar region. CPA model assiunes lliat veolar deposition of particles 1 pm does not occur. It is now generally accepted that some alveolar deposition occurs with particles of 1-10 pm (Froines et al. 1986 ACGIH 2012). 

Lung and gastrointestinal absorption Complete (100%) absorption occurs in alveolar region. In contrast, larger particles ( 1 pm) would be removed by mucociliary clearance and swallowed, and about 8% of lead would be absorbed in gastrointestinal tract. Bioavailability of lead is influenced by chemical speciation, age of exposed person, level of lead exposure, matrix, and nutritional status of person. 

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Beck, B.D., R.L. Mattuck, T.S. Bowers, J.T. Cohen, and E. O Flalierty. 2001. The development of a stochastic physiologically-based pharmacokinetic model for lead. Sci. Total Environ. 274(1-3) 15-19. 

Beck, B.D., Mattuck, R.L., Bowers, T.S., Muir, B., 2001. The development of a stochastic physiologically-based pharmacokinetic model for lead. Sci. Total Environ. 274, 15—19. Bergdahl, I.A., Schulz, A., Gerhardsson, L., Jensen, A., Skerfving, S., 1997. Lead concentrations in human plasma, urine and whole blood. Scand. J. Work Environ. Health 23, 359—363. Bert, J.L., van Dusen, L.J., Grace, J.R., 1989. A generalized model for the prediction of lead body burdens. Environ. Res. 48, 117—127. 

Absorbed lead is distributed in various tissue compartments. Several models of lead pharmacokinetics have been proposed to characterize such parameters as intercompartmental lead exchange rates, retention of lead in various pools, and relative rates of distribution among the tissue groups. See Section 2.3.5 for a discussion of the classical compartmental models and physiologically based pharmacokinetic models (PBPK) developed for lead risk assessments. 

While these models simulate the transfer of lead between many of the same physiological compartments, they use different methodologies to quantify lead exposure as well as the kinetics of lead transfer among the compartments. As described earlier, in contrast to PBPK models, classical pharmacokinetic models are calibrated to experimental data using transfer coefficients that may not have any physiological correlates. Examples of lead models that use PBPK and classical pharmacokinetic approaches are discussed in the following section, with a focus on the basis for model parameters, including age-specific blood flow rates and volumes for multiple body compartments, kinetic rate constants, tissue dosimetry,... 

To deal with this problem the EPA invested in the development and validation of a pharmacokinetic model that is capable of relating intake of lead to blood level. The model also allows the risk assessor to develop blood level estimates that integrate all sources of exposure. Using this model, it becomes possible to determine whether a specific source, such as our suspect water supply, is leading to exposures in excess of the target for all sources combined (this assumes that other sources do not contain levels of lead greater than normal, background... 

Kariv, I., Rourick, R.A., Kassel, D.B., and Chung, T.D.Y. Improvement of hit-to-lead optimization by integration of in vitro HTS experimental models for early determination of pharmacokinetic properties. Comb. Chem. High Throughput Screen. 2002, 5, 459-472. 

Overall, this study indicated that generic simulation of pharmacokinetics at the lead optimization stage could be useful to predict differences in pharmacokinetic parameters of threefold or more based upon minimal measured input data. Fine discrimination of pharmacokinetics (less than twofold) should not be expected due to the uncertainty in the input data at the early stages. It is also apparent that verification of simulations with in vivo data for a few compounds of each new compound class was required to allow an assessment of the error in prediction and to identify invalid model assumptions. 

Tardif et al. (1992, 1993 a, 1997) have developed a physiologically based toxicokinetic model for toluene in rats (and humans—see Section 4.1.1). They determined the conditions under which interaction between toluene and xylene(s) occurred during inhalation exposure, leading to increased blood concentrations of these solvents, and decreased levels of the hippurates in urine. Similar metabolic interactions have been observed for toluene and benzene in rats (Purcell et al., 1990) toluene inhibited benzene metabolism more effectively than the reverse. Tardif et al. (1997) also studied the exposure of rats (and humans) to mixtures of toluene, we/a-xylene and ethylbenzene, using their physiologically based pharmacokinetic model the mutual inhibition constants for their metabolism were used for simulation of the human situation. 

For the sake of simplicity, simple monophasic pharmacokinetics (one compartment and one half-life) was assumed in the above example and in many other examples in this report. In real life, most chemicals express biphasic or polyphasic pharmacokinetics (several compartments and several half-lives). Squeezing a polyphasic pharmacokinetic behavior into a one-compartment model by assuming a single half-life may lead to negligible errors for some chemicals and serious misinterpretation of biomarker concentrations for others. The same can be said about nonlinear processes, such as metabolic induction, inhibition, and saturation. A good way to check the accuracy of a simple pharmacokinetic model is to verify its performance by comparing with a physiologically based pharmacokinetic (PBPK) model that may encompass the mentioned factors. 

Parametric population methods also obtain estimates of the standard error of the coefficients, providing consistent significance tests for all proposed models. A hierarchy of successive joint runs, improving an objective criterion, leads to a final covariate model for the pharmacokinetic parameters. The latter step reduces the unexplained interindividual randomness in the parameters, achieving an extension of the deterministic component of the pharmacokinetic model at the expense of the random effects. Recently used individual empirical Bayes estimations exhibit more success in targeting a specific individual concentration after the same dose. 

Cruciani G, Crivori P, Carrupt PA, Testa B (2000) Molecular fields in quantitative structure-permeation relationships The VolSurf approach. Theochem 503 17-30 Cruciani G, Pastor M, Clementi S (2000) Handling information from 3D GRID maps for QSAR studies. In Gun-dertofte K, Jorgensen FS (eds) Molecular modelling and prediction of bioactivity, proceedings of the 12th European symposium on quantitative structure-activity relationships (QSAR 98). Plenum Press, New York, pp 73-81 Cruciani G, Pastor M, Guba W (2000) VolSurf A new tool for the pharmacokinetic optimization of lead compounds. Eur J Pharm Sd 11 S29-S39... 

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