Evaluation of an extraction method for a mixture of endocrine disrupters in sediment using chemical and in vitro biological analyses

Aquatic sediments are contaminated by a wide diversity of organic pollutants such as endocrine-disrupting chemicals (EDCs) which encompass a broad range of chemical classes having natural and anthropogenic origins. The use of in vitro bioassays is now widely accepted as an alternative method for their detection in complex samples. However, based on the diversity of EDC chemical properties, their common extraction is difficult and comprehensive validation of extraction methods for a bioanalysis purpose is still weakly documented. In this study, we compared the performance of several organic solvents, i.e., acetone, methanol, dichloromethane, heptane, dichloromethane/acetone (50:50, v/v), dichloromethane/methanol (50:50, v/v), heptane/acetone (50:50, v/v), and heptane/methanol (50:50, v/v), to extract a diversity of active chemicals from a spiked sediment matrix using pressurized liquid extraction. For this purpose, we defined a mixture of 12 EDCs with a wide range of polarity (2?<?log Kow?<?8) (i.e., estrone, 17β-estradiol, bisphenol A, o,p′DDT, 4-tert-octylphenol, fenofibrate, triphenyl phosphate, clotrimazole, PCB-126, 2,3,7,8 TCDD, benzok]fluoranthene, and dibenzoa,h]anthracene). Working concentrations of each individual compound in the mixture were determined as equipotent concentrations on the basis of the concentration-addition (CA) model applied to in vitro estrogenic, dioxin-like, and pregnane X receptor (PXR)-like activities. Extraction efficiencies based on both chemical and biological analyses were assessed in triplicate in artificial blank sediment spiked with this mixture and in natural sediment contaminated by native EDCs. In both spiked and natural sediment, MeOH/DCM yields the best recovery while heptane was the least efficient solvent. Our study provided the validation of a sediment extraction methodology for EDC bioanalysis purposes, which can be used for comprehensive environmental contamination characterization.