Assessment of chemical screening outcomes based on different partitioning property estimation methods.

Screening is widely used to prioritize chemicals according to their potential environmental hazard, as expressed in the attributes of persistence, bioaccumulation (B), toxicity and long range transport potential (LRTP). Many screening approaches for B and LRTP rely on the categorization of chemicals based on a comparison of their equilibrium partition coefficients between octanol and water (K(OW)), air and water (K(AW)) and octanol and air (K(OA)) with a threshold value. As experimental values of the properties are mostly unavailable for the large number of chemicals being screened, the use of quantitative structure-property relationships (QSPRs) and other computational chemistry methods becomes indispensable. Predictions by different methods often deviate considerably, and flawed predictions may lead to false positive/negative categorizations. We predicted the partitioning properties of 529 chemicals, culled from previous prioritization efforts, using the four prediction methods EPI Suite, SPARC, COSMOtherm, and ABSOLV. The four sets of predictions were used to screen the chemicals against various LRTP and B criteria. Screening results based on the four methods were consistent for only approximately 70% of the chemicals. To further assess whether the means of estimating environmental phase partitioning has an impact, a subset of 110 chemicals was screened for elevated arctic contamination potential based on single-parameter and poly-parameter linear free energy relationships respectively. Different categorizations were observed for 5 out of 110 chemicals. Screening and categorization methods that rely on a decision whether a chemical's predicted property falls on either side of a threshold are likely to lead to a significant number of false positive/negative outcomes. We therefore suggest that screening should rather be based on numerical hazard or risk estimates that acknowledge and explicitly take into account the uncertainties of predicted properties.