Quantifying uncertainty in predicted chemical partition ratios required for chemical assessments

Abstract

Three Quantitative Structure Property Relationship (QSPR) software packages, IFSQSAR, OPERA, and EPI Suite are compared and assessed for prediction accuracy, applicability domain (AD) and uncertainty of the predictions. A database of experimental physical–chemical (PC) properties is compiled, merged, and filtered, and the QSPRs are assessed with datasets of octanol–water (K_OW), octanol–air (K_OA), and air–water (K_AW) partition ratios. Upper and lower limits on PC property predictions are proposed based on theory, data, and applications of the properties in hazard screening and risk assessment. Validations of the uncertainty metrics of the QSPR packages are done for the PC properties using experimental data external to all training datasets. The IFSQSAR 95% prediction interval (PI95) calculated from root mean squared error of prediction (RMSEP) captures 90% of the external data, while OPERA and EPI Suite require a factor increase of at least 4 and 2 respectively for their PI95 to capture a similar 90% of the external experimental data. The assessment of QSPR consensus predictions identified future research and experimental testing to improve the predictive models for data-poor chemicals such as polyfluorinated or per-fluorinated alkyl substances (PFAS), ionizable chemicals, and chemicals with complex and multifunctional structures.