Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods.

Abstract

The partitioning of compounds between aqueous and other phases is important for predicting toxicity. Although thousands of octanol–water partition coefficients have been measured, these represent only a small fraction of the anthropogenic compounds present in the environment. The octanol phase is often taken to be a mimic of the inner parts of phospholipid membranes. However, the core of such membranes is typically more hydrophobic than octanol, and other partition coefficients with other compounds may give complementary information. Although a number of (cheap) empirical methods exist to compute octanol–water (log kOW) and hexadecane–water (log kHW) partition coefficients, it would be interesting to know whether physics-based models can predict these crucial values more accurately. Here, we have computed log kOW and log kHW for 133 compounds from seven different pollutant categories as well as a control group using the solvation model based on electronic density (SMD) protocol based on Hartree–Fock (HF) or density functional theory (DFT) and the COSMO-RS method. For comparison, XlogP3 (log kOW) values were retrieved from the PubChem database, and KowWin log kOW values were determined as well. For 24 of these compounds, log kOW was computed using potential of mean force (PMF) calculations based on classical molecular dynamics simulations. A comparison of the accuracy of the methods shows that COSMO-RS, KowWin, and XlogP3 all have a root-mean-square deviation (rmsd) from the experimental data of ≈0.4 log units, whereas the SMD protocol has an rmsd of 1.0 log units using HF and 0.9 using DFT. PMF calculations yield the poorest accuracy (rmsd = 1.1 log units). Thirty-six out of 133 calculations are for compounds without known log kOW, and for these, we provide what we consider a robust prediction, in the sense that there are few outliers, by averaging over the methods. The results supplied may be instrumental when developing new methods in computational ecotoxicity. The log kHW values are found to be strongly correlated to log kOW for most compounds.