Understanding Quantitative Structure–property Relationships Uncertainty in Environmental Fate Modeling

Abstract

In cases in which experimental data on chemical-specific input parameters are lacking, chemical regulations allow the use of alternatives to testing, such as in silico predictions based on quantitative structure-property relationships (QSPRs). Such predictions are often given as point estimates; however, little is known about the extent to which uncertainties associated with QSPR predictions contribute to uncertainty in fate assessments. In the present study, QSPR-induced uncertainty in overall persistence (POV ) and long-range transport potential (LRTP) was studied by integrating QSPRs into probabilistic assessments of five polybrominated diphenyl ethers (PBDEs), using the multimedia fate model Simplebox. The uncertainty analysis considered QSPR predictions of the fate input parameters' melting point, water solubility, vapor pressure, organic carbon-water partition coefficient, hydroxyl radical degradation, biodegradation, and photolytic degradation. Uncertainty in POV and LRTP was dominated by the uncertainty in direct photolysis and the biodegradation half-life in water. However, the QSPRs developed specifically for PBDEs had a relatively low contribution to uncertainty. These findings suggest that the reliability of the ranking of PBDEs on the basis of POV and LRTP can be substantially improved by developing better QSPRs to estimate degradation properties. The present study demonstrates the use of uncertainty and sensitivity analyses in nontesting strategies and highlights the need for guidance when compounds fall outside the applicability domain of a QSPR.

Retrieval, Selection, and Evaluation of Chemical Property Data for Assessments of Chemical Emissions, Fate, Hazard, Exposure, and Risks

“…Preference should be given to QSARs/QSPRs that have been well-validated based on the OECD Guidance Document on the Validation of (Q)­SAR Models and provide detailed documentation of key information in compliance with the QSAR Model Reporting Format. Second, one must ensure that the QSAR/QSPR prediction matches exactly the property required by the chemical assessment, that is, the QSAR/QSPR is “fit-for-purpose” . For instance, since most QSARs/QSPRs do not consider chemical dissociation and stereochemistry, the predictions can only be used as values for the neutral form of a chemical without discrimination between stereoisomers.…”

“…Third, it is important to know whether a prediction falls within the applicability domain (AD) of a QSAR/QSPR . The AD describes the coverage of the training set, or the structural and property space, in which a QSAR/QSPR makes predictions by interpolation rather than extrapolation. , The determination of whether a chemical of interest falls within the AD of a QSAR/QSPR is based on the similarity between this chemical and the chemicals in the training set.…”

The QSPR-THESAURUS: The Online Platform of the CADASTER Project

“…The project dedicated substantial effort Figure 4: The Insubria graph is a variation of the Williams plot for prediction results on chemicals without experimental values toward its integration and the development of a server, which currently runs on a Windows virtual machine (28). SimpleBox requires a number of physicochemical properties as input, and models to predict these properties were made available on the QSPR-THESAURUS website according to our recent publication (32). Monte-Carlo simulations, which are based on the uncertainty of estimated properties, are run to produce a distribution of PEC values, and calculate percentiles to be used for risk assessment.…”

In silico environmental chemical science: properties and processes from statistical and computational modelling

“…12 Since then, there has been a growing number of reviews and databases of QSARs, [13][14][15][16][17] comparative analyses of QSAR accuracy, [18][19][20] and efforts to codify methods of calibration and validation. [21][22][23][24][25] Many QSARs have been incorporated into soware that facilitates their use for property prediction. 6 Currently, the two main examples of this are the estimation program interface (EPI Suite) by the U.S. Environmental Protection Agency (EPA) 26,27 and the QSAR Toolbox by the Organization for Economic Cooperation and Development (OECD), 28 but others are under development.…”