Reconstructing population exposures to environmental chemicals from biomarkers: challenges and opportunities

Abstract

A conceptual/computational framework for exposure reconstruction from biomarker data combined with auxiliary exposure-related data is presented, evaluated with example applications, and examined in the context of future needs and opportunities. This framework employs physiologically based toxicokinetic (PBTK) modeling in conjunction with numerical "inversion" techniques. To quantify the value of different types of exposure data "accompanying" biomarker data, a study was conducted focusing on reconstructing exposures to chlorpyrifos, from measurements of its metabolite levels in urine. The study employed biomarker data as well as supporting exposure-related information from the National Human Exposure Assessment Survey (NHEXAS), Maryland, while the MENTOR-3P system (Modeling ENvironment for TOtal Risk with Physiologically based Pharmacokinetic modeling for Populations) was used for PBTK modeling. Recently proposed, simple numerical reconstruction methods were applied in this study, in conjunction with PBTK models. Two types of reconstructions were studied using (a) just the available biomarker and supporting exposure data and (b) synthetic data developed via augmenting available observations. Reconstruction using only available data resulted in a wide range of variation in estimated exposures. Reconstruction using synthetic data facilitated evaluation of numerical inversion methods and characterization of the value of additional information, such as study-specific data that can be collected in conjunction with the biomarker data. Although the NHEXAS data set provides a significant amount of supporting exposure-related information, especially when compared to national studies such as the National Health and Nutrition Examination Survey (NHANES), this information is still not adequate for detailed reconstruction of exposures under several conditions, as demonstrated here. The analysis presented here provides a starting point for introducing improved designs for future biomonitoring studies, from the perspective of exposure reconstruction; identifies specific limitations in existing exposure reconstruction methods that can be applied to population biomarker data; and suggests potential approaches for addressing exposure reconstruction from such data.

Figure 1

A simplified schematic of a computational framework for exposure reconstruction showing major components and processes. Available exposure-related data can provide “prior estimates” of exposures, which in turn can be used in conjunction with biomarkers and PBTK modeling to obtain improved estimates of exposures and doses.

Figure 2

Impact of half-life on the relative contributions of different timescales of exposures to observed chemical biomarker levels. The example shown above is based on a one-compartment PK model (linear decay); the biomarker represents the level of chemical in the compartment.

Figure 3

Contribution of prior exposures to observed biomarker levels as a function of intake frequency, sampling time, and biochemical properties. The rows represent the time period of exposure (e.g. every 12 h, every 2 days, etc), the columns represent the time of sampling after the last exposure. For cases when sampling time is unknown, the mean values of the contributions are shown, assuming a uniformly random sampling time. The legend for the scales of gray is the same as in Figure 2.

Figure 4

Comparison of urinary TCPy biomarker levels predicted using exposure estimates of Pang et al. (2002) for the NHEXAS-MD population with the toxicokinetic model, and actual NHEXAS-MD biomarker measurements.

Figure 5

Comparison of predicted steady-state doses using creatinine-adjusted urinary TCPy biomarkers, and using liquid urinary concentration TCPy biomarkers: (a) cumulative distributions, and (b) scatter plot.

Figure 6

Cumulative distribution functions of dietary CPF uptakes for the NHEXAS-MD population estimated by the ECF and Bayesian methods, using three different dose assumptions. Results only show population for which the biomarkers were above the detection limit (which comprised 95% of the samples).

Figure 7

Probability density functions of dietary CPF uptakes for the NHEXAS-MD population estimated by the ECF and Bayesian methods, corresponding to results in Figure 6.

Figure 8

Cumulative distribution functions of dietary CPF uptakes for the synthetic population estimated by the ECF and Bayesian methods, using different dose assumptions. Results only show the synthetic individuals with detectible biomarker levels (which comprised 42% of the samples). “Apparent” CPF dose denotes the sum of the actual CPF and TCPy bolus doses (corrected for molecular weight), which may be misinterpreted as a CPF dose if TCPy exposure is neglected. “Base-case” results denote those results obtained using the random time–activity assumption with three potential CPF doses per day, 0–7 days per week.

Figure 9

Probability density functions of dietary CPF uptakes for the synthetic population estimated by the ECF and Bayesian methods, using different dose assumptions, corresponding to results in Figure 8.

Figure 10

Exposure reconstruction process using optimization-aided approach with the original PBTK model or fast equivalent operational models (FEOMs). The coupling with optimization techniques reduces the number of simulations significantly, and the use of FEOMs reduces the time required for each run.