From blood to body tissues: a dynamic framework for estimating volatile organic compound exposure using Kalman filtering and physiological models

Accurate quantification of volatile organic compound (VOC) concentrations in target tissues is critical for robust exposure assessment and toxicological risk analysis. Conventional methods that rely on blood measurements and partition behaviors often fail to consider the transient nature of real-time exposure. Physiologically-based pharmacokinetic (PBPK) models advance predictive capabilities by simulating absorption, distribution, metabolism, and excretion (ADME) processes. However, their accuracy is limited by measurement errors and parameter uncertainties. This study combines the Kalman Filter (KF) with a linear PBPK model (KF-PBPK) to dynamically refine VOC tissue concentration estimates and support real-time exposure assessment using blood measurements. The Kalman Filter is an algorithm that continuously updates model predictions based on new measurements. It filters out noise and improves the accuracy of estimates. The application of the KF-Expectation Maximization (KF-EM) approach to human m-xylene exposure data improved the signal-to-noise ratio (SNR) from 13.9 dB to 17.4 dB. The KF-PBPK scheme effectively captured the multi-compartment kinetics of VOC distribution across several compartments. Filtered estimates closely matched the experimental data, demonstrating the framework's effectiveness in modeling and predicting human VOC exposure. This research suggests that the KF-PBPK is a reliable tool for improving VOC exposure assessments, with potential implications for environmental pollution monitoring, risk assessment and regulatory decision-making.