Novel Models for Accurate Estimation of Air-Blood Partitioning: Applications to Individual Compounds and Complex...

The air-blood partition coefficient (K) is extensively employed in human health risk assessment for chemical exposure. However, current K estimation approaches either require an extensive number of parameters or lack precision. In this study, we present two novel and parsimonious models to accurately estimate K values for individual neutral organic compounds, as well as their complex mixtures. The first model, termed the GC×GC model, was developed based on the retention times of nonpolar chemical analytes on comprehensive two-dimensional gas chromatography (GC×GC). This model is unique in its ability to estimate the K values for complex mixtures of nonpolar organic chemicals. The GC×GC model successfully accounted for the K variance (R² = 0.97) and demonstrated strong prediction power (RMSE = 0.31 log unit) for an independent set of nonpolar chemical analytes. Overall, the GC×GC model can be used to estimate K values for complex mixtures of neutral organic compounds. The second model, termed the partition model (PM), is based on two types of partition coefficients: octanol to water (K) and air to water (K). The PM was able to effectively account for the variability in K data (n = 344), yielding an R² value of 0.93 and root-mean-square error (RMSE) of 0.34 log unit. The predictive power and explanatory performance of the PM were found to be comparable to those of the parameter-intensive Abraham solvation models (ASMs). Additionally, the PM can be integrated into the software EPI Suite, which is widely used in chemical risk assessment for initial screening. The PM provides quick and reliable estimation of K compared to ASMs, while the GC×GC model is uniquely suited for estimating K values for complex mixtures of neutral organic compounds. In summary, our study introduces two novel and parsimonious models for the accurate estimation of K values for both individual compounds and complex mixtures.