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ID 18183 Poster Board 577Kidney is one of the major drug-elimination organs. The total renal excretion of a compound is the net result of glomerular filtration, tubular secretion, and reabsorption. Tubular secretion is a transporter-mediated process, which is often mediated by organic cation transporters (OCT/Oct) that facilitate the active secretion of several cationic substrates including drugs such as metformin as well as endogenous cations 1 . Endogenous biomarkers of OCTs can help in the early assessment of drug-drug interaction without the administration of exogenous OCT probe substrates 2 . We hypothesize that administration of cimetidine, an OCT/Oct inhibitor, will lead to increased plasma levels and decreased renal clearance (CL R ) of endogenous OCT/Oct substrates. Such substrates can potentially act as OCT/Oct biomarkers. We carried out a rat pharmacokinetic (PK) study where metformin (5 mg/kg, IV) was administered as an exogenous substrate of OCT (positive control) to four Sprague-Dawley rats with and without cimetidine (100mg/kg, IP) in a cross-over study design with one week washing period. Blood samples were collected from the tail vein at different time points, i.e, pre-dose, 0.17, 0.5, 1, 2, 4, 6, and 8 h, and urine samples were collected at 0-4 and 4-8 h intervals. Rat blood and urine samples were analyzed for metformin and cimetidine levels by a validated method using liquid chromatography with tandem mass spectrometry (LC-MS/MS) (Waters Xevo-TQ-XS MS; Waters, Milford, MA). Metformin area under the blood concentration-time curve (AUC (0-8h) ) was significantly increased by 3.2 folds when co-administered with cimetidine (p-value, 0.003). Similarly, metformin CL R(0-8h) was significantly decreased in cimetidine arm by 3.7 folds (p-value, 0.029). Further, to investigate the effect of cimetidine on endogenous metabolites, we carried out untargeted metabolomics for rat blood and urine samples using Easy Spray 1200 series nanoLC coupled Q-Exactive HF MS (Thermo Fisher Scientific, Waltham, MA). The rat blood samples were analyzed in three groups, i.e., pooled (0.5, 1, and 2h), 0.5 h, and 1 h samples, while rat urine samples were analyzed at 0-4 h interval samples. The generated data were analyzed by open-access XCMS Online software (xcmsonline.scripps.edu). Greater than 18,000 features were detected in the blood which were shortlisted using optimized selection criteria, i.e., fold differences (with versus without cimetidine) of 1.9-10 fold, p-value <0.05, reproducible retention time, and quality of chromatogram peak. Out of the 85 shortlisted hits, 52 were detected by METLIN software, and 30 were common in blood and urine. Among several potential compounds predicted by METLIN for each mass-to-charge ratio (m/z) value, only compounds containing nitrogen atoms with mass error (ppm) less than 4 were selected. Two significant hits (m/z, 134.06, and 233.09 corresponding to putative oxindole and robustine, respectively) were consistently found in pooled, 0.5 h, and 1h samples (Figure 1). Other putative meta...

ID 25135 Poster Board 182 Metabolite identification (MetID) studies provide critical information to elucidate the biotransformation pathways of a new chemical entity during the early stages of drug discovery and development. Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is the gold-standard technique for prediction of metabolite structures, however, the current MetID studies rely on manual data processing. The goal of our study was to analyze LC-HRMS data using a high-throughput metabolomics-based MetID workflow by utilizing an in-house theoretical metabolite predictor and open-access XCMS Online (xcmsonline.scripps.edu).In particular, we applied this approach to detect and identify biotransformation products of cimetidine, probenecid, and imatinib in blood and urine samples collected from three rat pharmacokinetic studies. Briefly, rats (n= 4 to 8) were dosed with placebo (control) or drugs [cimetidine (100 mg/kg, IP), probenecid (1000 mg/kg, IV), and imatinib (30 mg/kg, PO)], and the blood and urine samples were collected at various time points/intervals. The control and drug-treated samples were individually processed by protein precipitation followed by drying and reconstitution in LC-MS compatible solvents. The pooled blood (0.5, 1, and 2 h) and urine (0-4 h) samples of cimetidine and probenecid, and pooled blood (1, 2, and 4 h) samples of imatinib were individually analyzed using nanoLC coupled Thermo Q-Exactive HF mass spectrometer. Data-independent acquisition method was applied to allow detection of the low level metabolites. Cimetidine, probenecid, and imatinib treated samples and the corresponding controls were analyzed by XCMS Online software. Lists of all potential theoretical metabolites and their high-resolution masses were created for these three drugs using a novel in-house theoretical metabolite predictor. The output excel file was first processed to shortlist the potential metabolites using the following optimized criteria: a) metabolite elevated in the treatment group by fold difference of at least 2.5, b) accurate mass within ± 5 ppm of predicted theoretical mass, c) no detectable peak in any control samples and distinguishable peak in all treatment samples, and d) mass-defect shift and the relative retention time within a reasonable range of theoretically possible value. Then, the shortlisted data were searched against the theoretical metabolite list in a semi-automated fashion to identify drug metabolites (Figure 1). Cimetidine-treated rats showed 5 and 6 metabolite hits (3 common) in blood and urine, respectively, which included novel dihydroxyl, carboxyl, and N-acetylcysteine derivatives of cimetidine. Similarly, 6 and 14 potential metabolites (4 common) were detected in the probenecid-treated rat blood and urine, respectively. Of these, dihydroxyl, glycine and glucosylated derivatives of probenecid were detected for the first time. Five reported metabolites were detected and identified in imatinib-treated rat blood samples. Thus, a sensitive and semi-automated workflo...