(61f) Detection of Low-Level Tuberculosis Biomarkers in Patient Breath Utilizing Extracted Ion Chromatograms in GCMS Analysis

Two volatile organic compounds (VOCs), namely methyl-nicotinate (MN) and methyl-p-anisate (MPA), have been uniquely identified as VOC indicators of active tuberculosis disease in adults(2). Typical GCMS analysis using total ion chromatograms (TICs) has proven to be inconsistent in identifying the presence of these biomarkers due to their trace concentrations in patient populations in Uganda. As a result, a method employing extracted ion chromatograms (EICs) in reference to compound standards was developed which was shown to confirm the presence in MN and MPA patient breath samples.

Patient breath is collected in Tedlar bags and loaded onto Tenax sample collection tubes in Uganda. The samples are then analyzed using gas chromatography mass spectroscopy (GCMS) using a method tailored to MN and MPA peak detection at the University of Utah. During GCMS analysis, compounds are desorbed from the sample tubes onto a focusing trap, separated using a polar column selected for effective separation of MN and MPA, and subsequently ionized in the mass spectrometer. Focusing trap and oven temperature ramp rates have been optimized for detection of these low level biomarkers. Compounds ionize in the mass spectrometer in consistent abundance profiles and the constitutive ions exit the column, or elute, at identical times. By extracting these relative abundance ion profiles of each target compound, biomarkers can be identified in a sample. These ion profiles, or EICs, contain signature relative abundances of each constitutive ion, meaning that the relative amounts of each ion, or peak ratio, remains consistent for each biomarker. In addition to patient samples, daily standards of known concentrations of MN and MPA are tested using an identical GCMS method. These standards provide reference elution times for MN and MPA, as elution times for compounds drift gradually over time due to column age and atmospheric variables. The standards also provide tolerance limits on peak ratios and widths. These tolerance limits aid in deconvoluting the complex chromatograms seen in patient breath samples with their high concentration of volatile organic compounds. The EIC data obtained from both the standards and patient samples are then analyzed using an algorithm that automatically averages the most recent standards for both compounds and extracts elution times, ion peak ratios and peak widths on the standards and applies these values to patient data.

The results obtained from the standards inform GCMS patient data analysis by giving limits on biomarker elution times and ion peak ratios. With these tolerance values in place, the algorithm evaluates the signal-to-noise ratios found in the patient samples and makes an informed call on biomarker presence or absence. The presentation will describe the GCMS, EIC extraction and algorithm methods used and results obtained.