Identification of Emerging Persistent and Mobile Substances by HILIC Driven EDA

Substances with a certain combination of intrinsic properties pose a risk to drinking water sources. The combination of the two persistent (P) and mobile (M) properties of substances increases the chances of substances passing natural barriers, such as river banks, and artificial barriers in water treatment plants. Consequently, the presence of persistent and mobile (PM) substances in drinking water sources is currently unavoidable and may pose a risk to human health. Non-target screening based on reversed-phase (RP) liquid chromatography is being used more frequently to identify emerging compounds. However, unconventional chromatographic approaches should also be considered to analyse PM substances due to their high polarity. Furthermore, identification priority should be given to PM substances that pose a risk to human health, which can be facilitated by the use of bioassays. This study shows the application of an effect-directed analysis (EDA) method for the analysis of surface and well water samples from the Rhine River. The sample preparation approach used enables a significant preconcentration of a wide range of PM compounds (multi-layered solid phase extraction). The extracts of the samples were separated using two orthogonal separation techniques - RP and hydrophilic interaction liquid chromatography (HILIC) and analyzed by HRMS. The same extracts were fractionated using high-resolution fractionation collector (FractioMate) to guide chemical identification to the biologically relevant fractions, which greatly reduces the complexity of the identification approach. The prioritization of features for the identification was based on a partly automated transthyretin-binding assay that measures the competitive binding of chemicals to thyroid hormone distributor protein transthyretin (TTR). TTR-binding assay for the RP and HILIC fractionated samples showed active fractions in both separation modes, which facilitated the prioritization of features with human health relevance