An accurate-mass-based spectral-averaging isotope-pattern-filtering algorithm for extraction of drug metabolites possessing a distinct isotope pattern from LC-MS data.

Detection and identification (ID) of all drug metabolites following liquid chromatography (LC)/mass spectrometry (MS) analysis of complex biological matrixes are not trivial. To facilitate detection of drug-derived materials that possess highly diagnostic isotopic patterns (e.g., chlorine- and bromine-containing compounds), we report an accurate-mass-based spectral-averaging isotope-pattern-filtering (AMSA-IPF) algorithm developed in the computational language R. The AMSA-IPF algorithm offers three significant improvements over the traditional isotope filtering method often provided by instrument vendors. First, spectral averaging is performed before the IPF to reduce scan-to-scan variability of ion intensities. Second, the IPF process is strictly based on accurate mass typically obtained on high resolution mass spectrometers. The designated isotopic ion-pairs (e.g., M + 2:M or M + 1:M, where M is the molecular ion and M + 1 and M + 2 are the isotopic ions) must fall into the predefined accurate mass tolerance window (e.g., 5 ppm) and at the same time satisfy the predefined relative abundance criteria. Third, both M + 1:M and M + 2:M ion pairs are inspected in the filtering process. The inclusion of M + 1:M ion pair enhanced the specificity of this algorithm by removing background ions that form M:M + 2 ion pairs within predefined isotope ratios by coincidence. The algorithm demonstrated excellent effectiveness in detecting drug-related ions in in vivo samples (plasma, bile, urine and feces) obtained from rats orally dosed with 14C-loratadine. The ion chromatograms of the filtered LC-MS data files showed near perfect qualitative correlation with the corresponding radioprofiles. AMSA-IPF will be another great tool to facilitate detection and ID of drug metabolites in complex LC-MS data without the help of radiolabels. The AMSA-IPF algorithm is applicable to not only compounds containing distinct natural isotopes (such as Cl and Br) but also compounds that contain synthetically incorporated isotopes (13C, 15N, etc) generating a distinct isotope pattern. The ability to detect and identify metabolites from nonradiolabeled studies will be extremely beneficial to achieve compliance with FDA's most recent guidance on metabolites in safety testing (MIST).