Identification of Di-isononyl Phthalate Metabolites for Exposure Marker Discovery Using In Vitro/In Vivo Metabolism and Signal Mining Strategy with LC-MS Data

Di-isononyl phthalate esters (DINPs) are endocrine-disrupting chemicals and have replaced di(2-ethylhexyl) phthalate (DEHP) as the major plasticizer for poly(vinyl chloride) (PVC) products in recent years. Exposure marker discovery of DINPs is crucial, because of their high potential for human exposure and toxicity. Here, we propose an alternative approach for tracing signals derived from stable isotope-labeled precursors with varied labeling ratios to efficiently filter probable metabolite signals. The statistical process, which involves a signal mining algorithm with isotope tracing (SMAIT), has effectively filtered 13 probable DINP metabolite signals out of the 8867 peaks in the LC-MS data obtained from incubated stable isotope-labeled precursors with liver enzymes. Seven of the 13 probable metabolite signals were confirmed as DINP structure-related metabolites by preliminary MS/MS analyses. These 7 structure-related metabolite signals were validated as effective DINP exposure markers, using urine samples collected from DINP-administered rats without time-consuming comprehensive structure identification. We propose that the 7 identified possible DINP metabolite signals of m/z 279.1, 293.1, 305.1, 307.1, 321.1, 365.1, and 375.1 are potential markers for DINP exposure and should be investigated further. The integrated approach described here can efficiently, and systematically, filter probable metabolite signals from a complex LC-MS dataset for toxic exposure marker discovery. It is a relatively low-cost/rapid workflow for exposure marker discovery.