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Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

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A Bayesian population PBPK model for multiroute chloroform exposure.

Yuching Yang¹, Xu Xu, Panos G Georgopoulos

¹Exposure Science Division, Environmental and Occupational Health Sciences Institute, Joint Institute of UMDNJ-Robert Wood Johnson Medical School and Rutgers University, Piscataway, NJ 08854, USA. YYang@TheHamner.org

Journal of Exposure Science & Environmental Epidemiology

|May 28, 2009

Summary

This study optimized a physiologically based pharmacokinetic (PBPK) model for chloroform using Bayesian methods. The enhanced model accurately predicts biomarker data and reveals scenario-specific physiological changes during household exposures.

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Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

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High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents (HPHC)

High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents (HPHC)

Published on: May 10, 2016

Area of Science:

Environmental Health
Toxicology
Computational Biology

Background:

Physiologically based pharmacokinetic (PBPK) models are crucial for understanding chemical exposure and risk.
Accurate parameter estimation is essential for reliable PBPK model predictions, especially for household chemicals like chloroform.
Bayesian methods offer a robust framework for integrating prior knowledge and data to refine model parameters.

Purpose of the Study:

To develop and apply a Bayesian hierarchical model for estimating parameters in a PBPK model of chloroform.
To quantitatively describe physiological parameter changes in specific exposure scenarios, such as hot-water bathing and showering.
To improve the prediction accuracy of PBPK models using biomarker data and Bayesian inference.

Main Methods:

Developed a Bayesian hierarchical model to estimate PBPK parameters for chloroform.
Integrated prior information with biomarker data from various exposure pathways.
Utilized Bayesian inference to reduce parameter uncertainty and calibrate the PBPK model.
Applied the calibrated model to predict target tissue dose based on metabolic rates.

Main Results:

The Bayesian approach successfully reduced uncertainty in PBPK model parameters.
Model calibration significantly improved the prediction of biomarker data.
A two-fold increase in skin blood flow rate was predicted for the hot-bath scenario, highlighting scenario-specific parameter importance.
The study demonstrated the utility of probabilistic methods in toxicological risk assessment.

Conclusions:

Bayesian inference is effective for optimizing PBPK model parameters, particularly for household exposure scenarios.
Scenario-specific physiological parameters are critical for accurate PBPK modeling and risk assessment.
This approach enhances the reliability of toxicological assessments by providing better dose predictions.