semiQ2 · Semiquantification based on ionization efficiency for human exposure leveraging ion mobility separation

Exposomics has revolutionized our understanding of how environmental factors influence diseases such as asthma, cancer, Alzheimer’s, Parkinson’s, and undiagnosed conditions. Exposure characterization enables epidemiologists to link biological pathways to disease outcomes. Contaminants of Emerging Concern (CECs), including industrial chemicals, pharmaceuticals, and persistent “forever chemicals,” are now commonly found in the environment and human samples. Detecting these compounds in biological matrices is not enough; quantification is essential to ensure their levels stay below thresholds associated with adverse health effects. Advances in liquid chromatography high-resolution mass spectrometry (LC-HRMS) have enhanced the targeted detection and quantification of CECs. However, this method depends on access to reference standards, which is impractical for non-target screening, where standards may be unavailable. Quantitative non-target screening (q-NTS) provides an alternative by offering concentration data for unknown compounds, enabling data interpretation beyond simple relative peak areas. We propose a new q-NTS approach optimized for human samples, using ion mobility separation to resolve co-elution issues. Employing LC-trapped ion mobility separation-HRMS (LC-TIMS-HRMS) and covering a broad range of CECs, our goal is to develop an LC-mobility resolved ionization efficiency (IE) model for both negative and positive electrospray ionization. These models will consider chemical structure, LC conditions (mobile phase and pH), adduct types, and signal areas from mobility versus intensity. TIMS is expected to reduce matrix complexity in biological samples, improving IE model accuracy and semiquantification. We will test the accuracy of these models using urine and blood samples from a prospective cohort of at least 60 participants (30 healthy and 30 patients). Risk assessment and prioritization of the detected CECs will utilize the generated semiquantitative data.