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A Bayesian population PBPK model for multiroute chloroform exposure

Journal of Exposure Science & Environmental Epidemiology volume 20, pages 326–341 (2010)Cite this article

Abstract

A Bayesian hierarchical model was developed to estimate the parameters in a physiologically based pharmacokinetic (PBPK) model for chloroform using prior information and biomarker data from different exposure pathways. In particular, the model provides a quantitative description of the changes in physiological parameters associated with hot-water bath and showering scenarios. Through Bayesian inference, uncertainties in the PBPK parameters were reduced from the prior distributions. Prediction of biomarker data with the calibrated PBPK model was improved by the calibration. The posterior results indicate that blood flow rates varied under two different exposure scenarios, with a two-fold increase of the skin's blood flow rate predicted in the hot-bath scenario. This result highlights the importance of considering scenario-specific parameters in PBPK modeling. To demonstrate the application of a probability approach in toxicological assessment, results from the posterior distributions from this calibrated model were used to predict target tissue dose based on the rate of chloroform metabolized in liver. This study demonstrates the use of the Bayesian approach to optimize PBPK model parameters for typical household exposure scenarios.

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Acknowledgements

This work was supported by the Center for Exposure and Risk Modeling (CERM - EPAR827033) and the Environmental Bioinformatics and Computational Toxicology Center (GAD R 832721-010). Additional support has been provided by the NIEHS Center for Environmental Health Sciences at EOHSI (Grant No. P01 ES11256-01). We acknowledge contributions of Dr. Amit Roy, Dr. Clifford Weisel, Linda Everett, Pamela Shade, Alan Sasso, and numerous EOHSI collaborators. We thank Dr. Harvey Clewell for his insightful and important comments on the applications of PBPK models in risk assessment. The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of the funding agencies.

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Author notes

  1. Yuching Yang

    Present address: a2. Present address: The Hamner Institutes for Health Sciences, Center for Human Health Assessment, 6 Davis Drive, Research Triangle Park, NC 27709, USA,

  2. Xu Xu

    Present address: b3. Present address: Global Clinical Pharmacokinetics & Clinical Pharmacology, Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Raritan, NJ 08869, USA,

Authors and Affiliations

  1. Exposure Science Division, Environmental and Occupational Health Sciences Institute (EOHSI), Joint Institute of UMDNJ—Robert Wood Johnson Medical School and Rutgers University, 170 Frelinghuysen Road, Piscataway, 08854, NJ, USA

    Yuching Yang & Xu Xu

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Correspondence to Yuching Yang.

Appendices

APPENDIX A

The rate of change of the amount of chemical uptake in viable skin compartment in the DP skin model is described as:

The mass flux at interface between stratum corneum and viable skin is calculated as:

The rate of change of the concentration in Nth layer of stratum corneum is calculated using the central difference formula:

And, the stratum corneum diffusivity can be calculated as:

Dsc=effective stratum corneum diffusivity [cm2/min]

Perm=stratum corneum permeability [cm/min]

Lsc=stratum corneum thickness [cm]

Psc,water=partition coefficient between stratum corneum and water [unitless]

Csc,n=chemical concentration in the nth layer of stratum corneum

N=number of layer defined in the stratum corneum

Jsc=dermal mass flux

APPENDIX B

The sensitivity coefficient were calculated as follows:

Sensitivity coefficient (Clewell et al., 2003):

where

A is the exhaled conc with 1% increased parameter value,

B is the exhaled conc at the starting parameter value,

C is the parameter value after 1% increase, and

D is the original parameter value.

APPENDIX C

Age, gender, and bodyweight-dependent scaling functions for selected PBPK model parameters.

Equations used to calculate the cardiac output (Qcardiac) and inhalation rate (QPulmonary)

where

VO2=oxygen consumption (ml/kg/min)

AVOdiff=difference in volume of oxygen between arterial and venous blood (40–60 ml/l for adult, a uniform distribution between 40 and 60 was used in the model)

MET=metabolic equivalent of tasks (unitless)

BM=body mass, assumed to be same as body weight (BW)

F=conversion factor (3.5) from oxygen consumption to MET (ml/kg/min/MET)

where BMR=basal metabolic rate (e.g., 1.16 MJ/day for 30 year old, 70 kg male)

H=oxygen update factor, the volume of oxygen consumed in the production of 1 MJ energy expended (e.g., 0.21 m3/MJ for 30 year old, 70 kg male)

VQ=ventilatory equivalent (unitless), the ratio of minute volume to oxygen uptake (i.e., 27.5 for 30 year old, 70 kg male)

Scaling functions used to calculate tissue volumes

Body surface area (SA) [cm2]

Male, age .ge. 18: SA=252 × BW

Female, age .ge. 14: SA=288 × (1−0.00201 × (age−14) × BW

Fat tissue mass (assume the fat density=0.93)

Male, age .ge. 18: Volumefat=(0.21+0.000307 × (age−18)) × BW2/100/0.93

Female, age .ge. 14: Volumefat=0.003732 × (1+0.0055 × (age−14) × BW2)/0.93

Other tissues (assume the density=1)

Skin tissue mass

Male, age .ge. 15: Volumeskin=0.0367 × BW

Female, age .ge. 15: Volumeskin=0.044 × BW

Kidney: Volumekidney=8.38 × BW0.85 × 0.001

Liver: Volumeliver=92 × BW0.7 × 0.001

Rapidly perfused:

Slowly perfused:

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Yang, Y., Xu, X. & Georgopoulos, P. A Bayesian population PBPK model for multiroute chloroform exposure. J Expo Sci Environ Epidemiol 20, 326–341 (2010). https://doi.org/10.1038/jes.2009.29

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