Reliable Prediction of the Octanol–Air Partition Ratio

• Published in: Environmental toxicology and chemistry Vol. 40; no. 11; pp. 3166 - 3180
• Main Authors: Baskaran, Sivani, Lei, Ying Duan, Wania, Frank
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.11.2021; John Wiley and Sons Inc
• Subjects: Aerosols; Aromatic compounds; blood; Chemical partition; ecotoxicology; Environmental partitioning; Environmental Toxicology; equations; Foliage; Free energy; Gibbs free energy; leaves; milk; Octanol; Octanols - chemistry; Organic chemicals; Organic Chemicals - chemistry; Organic chemistry; Organic contaminants; Organic matter; Partitioning coefficient; Partitioning ratio; Polymers; prediction; Predictions; Quantitative structure–activity relationships; Root-mean-square errors; solutes; Sorbents; Temperature; Toxicology; uncertainty; Water - chemistry; Quantitative structure-activity relationships; Organic contaminants; Partitioning coefficient; Environmental partitioning; Partitioning ratio
• ISSN: 0730-7268; 1552-8618; 1552-8618
• DOI: 10.1002/etc.5201

The octanol–air equilibrium partition ratio (KOA) is frequently used to describe the volatility of organic chemicals, whereby n‐octanol serves as a substitute for a variety of organic phases ranging from organic matter in atmospheric particles and soils, to biological tissues such as plant foliage, fat, blood, and milk, and to polymeric sorbents. Because measured KOA values exist for just over 500 compounds, most of which are nonpolar halogenated aromatics, there is a need for tools that can reliably predict this parameter for a wide range of organic molecules, ideally at different temperatures. The ability of five techniques, specifically polyparameter linear free energy relationships (ppLFERs) with either experimental or predicted solute descriptors, EPISuite's KOAWIN, COSMOtherm, and OPERA, to predict the KOA of organic substances, either at 25 °C or at any temperature, was assessed by comparison with all KOA values measured to date. In addition, three different ppLFER equations for KOA were evaluated, and a new modified equation is proposed. A technique's performance was quantified with the mean absolute error (MAE), the root mean square error (RMSE), and the estimated uncertainty of future predicted values, that is, the prediction interval. We also considered each model's applicability domain and accessibility. With an RMSE of 0.37 and a MAE of 0.23 for predictions of log KOA at 25 °C and RMSE of 0.32 and MAE of 0.21 for predictions made at any temperature, the ppLFER equation using experimental solute descriptors predicted the KOA the best. Even if solute descriptors must be predicted in the absence of experimental values, ppLFERs are the preferred method, also because they are easy to use and freely available. Environ Toxicol Chem 2021;40:3166–3180. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.