Detection of an Endogenous Urinary Biomarker Associated With CYP2D6 Activity Using Global Metabolomics

Jessica Tay-Sontheimer; Laura M Shireman; Richard P Beyer; Taurence Senn; Daniela Witten; Robin E Pearce; Andrea Gaedigk; Cletus L Gana Fomban; Justin D Lutz; Nina Isoherranen; Kenneth E Thummel; Oliver Fiehn; J Steven Leeder; Yvonne S Lin


Pharmacogenomics. 2014;15(16):1947-1962. 

In This Article

Patients & Methods


Healthy pediatric subjects (n = 189) between 6 and 15 years of age on the date of study enrollment were recruited at Children's Mercy Hospitals and Clinics (CMH), Kansas City, MO, USA for a longitudinal study of CYP2D6 activity. Results from the first study visit are presented. Following an overnight fast, subjects received a single oral 0.5 mg/kg dose of DM (Robitussin® Pediatric). Urine was collected predose (spot sample) and throughout the next 4 h following DM administration (timed sample). Samples were stored at -80°C until analysis. Subjects were randomized into training and validation sets based on their CYP2D6 phenotype (PM or non-PM) so there was equal representation between the two sets.

Studies were approved by the Institutional Review Boards at CMH and the University of Washington (UW), Seattle, WA, USA. The study population was slightly skewed towards males due to an additional study aim to recruit children diagnosed with attention-deficit hyperactivity disorder, an aim not explored here. Exclusion criteria included: current therapy with medications metabolized by or known to inhibit CYP2D6 (although atomoxetine was permitted for the attention-deficit hyperactivity disorder component of the overall study); inability or unwillingness to fast 4 h prior to the study session; existence of diagnoses that may influence absorption and gastric emptying, such as reflux, inflammatory bowel disease or Crohn's disease; a demonstrated adverse reaction to previous DM exposure; impaired hepatic or renal activity or physical examination as determined by pediatrician subinvestigator's discretion; pregnancy; body mass index (BMI) <5th or >95th percentile. Subjects were given a complete medical examination including assessment of Tanner stage and blood samples were taken for liver function testing and DNA testing at the screening visit.

Ten healthy adults (five females and five males) were enrolled at UW in a study to evaluate the complex DDI of fluoxetine on CYP2D6, CYP3A4 and CYP2C19 activities. A detailed description of the study design, demographics and results has been reported elsewhere.[25] All subjects included in the study were genotypic CYP2D6 extensive metabolizers. Each subject was given an oral probe drug cocktail, which included 30 mg DM, at baseline (day 1) and during a 2-week multiple-dose fluoxetine treatment (day 16). Urine was collected 0–12 and 12–24 h on days 1–4 and 16–19. Secondary use of the samples in this study was approved by the UW Human Subjects Review Board.

Genotype Analysis & Assignment of Activity Score of Pediatric Subjects

CYP2D6 genotype analysis of greater than 20 allelic variants was performed at CMH. Genomic DNA was isolated from whole blood with a QIAamp DNA Blood Mini kit (Qiagen, CA, USA). Genotype analysis was performed using long-range (XL) PCR coupled with commercially available TaqMan (Life Technologies, CA, USA) and restriction fragment length polymorphism assays. The following allelic variants were assessed: CYP2D6*2, *3, *4, *5 (gene deletion), *6, *7, *9, *10, *11, *12, *13 (2D7/6 hybrid genes), *15, *17, *29, *31, *35, *36, *41, *42, *45 or *46 and *59. Analysis included XL-PCR-based detection of gene deletions, duplications/multiplications and CYP2D7/6 hybrid gene arrangements. Gene duplications were characterized for their allelic variation (*1xN, *2xN, *4xN, etc.) and gene copy number assessed at four gene loci by quantitative multiplex PCR. Additional XL-PCR testing in conjunction with DNA sequencing was applied to resolve complex cases or cases with ambiguous results.

Genotyping procedures and assignment of CYP2D6 activity scores have been described previously.[21,33–36] Nonfunctional, reduced function, fully functional and gain-of-function CYP2D6 alleles were given values of 0, 0.5, 1 and 1.5, respectively; alleles with duplications received double the value assigned to the single counterparts. The CYP2D6 activity score was obtained by summing the two alleles for each individual.

Analysis of DM & Metabolites

Pediatric urine samples were analyzed for DM and its metabolites at CMH.[37] Briefly, urine samples were adjusted to a pH of 4.5–5.0 and subsequently incubated with β-glucuronidase at 37°C overnight prior to analysis. Concentrations of DM and total deconjugated dextrorphan (DX) were determined using reverse-phase HPLC with fluorescence detection. The urinary DM metabolic ratio was calculated as the molar ratio of DM/DX. Based on the DM metabolic ratio (DM/DX), subjects were assigned to PM (DM/DX ≥ 0.3) or non-PM phenotypes (DM/DX < 0.3) as described previously.[21,32] Analytical details for the urinary analysis of DM and its metabolites in the adult study are described by Sager et al..[25]

Determination of Creatinine Concentrations

Creatinine was quantified on an Agilent 1100 series HPLC in line with an Agilent 1050 series UV detector set to monitor absorbance at 234 nm. Urine was diluted 100-fold with 10 mM potassium phosphate buffer, pH 6.5 and 10 µl were injected onto a Waters Symmetry C18 5 µm, 4.6 × 1.5 mm, 300 Å analytical column using 10 mM potassium phosphate buffer, pH 6.5, with 0.1% acetonitrile for mobile phase A and 100% acetonitrile for mobile phase B. The flow rate was 0.5 ml/min with a linear gradient consisting of 0% B until 5 min, a linear increase to 100% B from 5.0 min until 5.5 min, 100% B until 6.5 min, a linear decrease to 0% B from 6.5 to 7 min and re-equilibration with 0% B for 8 min.

Global Metabolomics Analysis of Pediatric Training set by LC-QTOF

Samples were prepared for metabolomics analysis by adding 800 μl of ice-cold acetonitrile to 200 μl of urine to precipitate proteins. Following centrifugation (20,000 × G for 10 min, 4°C), the supernatant was evaporated under nitrogen gas. The resulting residue was reconstituted in 40 μl of methanol followed by 40 μl of 0.4% (v/v) acetic acid.

Global metabolomic analyses of samples were performed using an Agilent (Santa Clara, CA, USA) 1200 HPLC coupled to an Agilent 6520 QTOF mass spectrometer. Samples (2-µl injection) were separated chromatographically using a 3.5 μm, 2.1 × 30 mm Agilent Zorbax SB-C8 guard column and a 1.8 μm, 2.1 × 50 mm Agilent Zorbax SB-Aq analytical column heated to 60°C. The flow rate was 0.6 ml/min, and the mobile phase consisted of A: 0.2% acetic acid in water and B: 0.2% acetic acid in methanol utilizing the following gradient profile: 2% B at 0 min, 2–98% B in 13 min, 98% B until 19 min followed by re-equilibration for 6.5 min. The source temperature was maintained at 350°C with a nitrogen gas flow rate of 12 l/min and a capillary voltage of 3500 V. Scans were obtained between m/z 100 and 1000 at an acquisition rate of 3 spectra/s. Data were collected in centroid mode using positive and negative electrospray ionization (ESI+ and ESI-, respectively).

Global Metabolomics Data Processing & Statistical Analyses

For the pediatric training set, raw LC quadrupole TOF (LC-QTOF) mass spectral data from each ionization mode were aligned using the Bioconductor R-package XCMS.[38,39] Raw data files were exported to mzData format using MassHunter Qualitative Analysis (Agilent, B.05.00) with a minimum peak height of 1000 counts. Feature detection was performed using the Bioconductor R-package XCMS.[38,39] Peak picking was performed using the centWave algorithm, requiring within-peak m/z deviations of less than 15 ppm, five consecutive scans above 500 counts and peak widths between 4 and 12 s. The default peak integration method was used, and the peaks were fit to a Gaussian shape with a Mexican-hat wavelet for integration. The peaks were grouped using the 'density' method with a mass accuracy requirement of 0.007 m/z and a peak width at half height of 4 s (before retention time correction) or 2 s (after retention time correction). A retention time correction was performed using the method 'obiwarp,' and peaks were recursively filled. Subsequently, ion signals were normalized to the sum of all mass feature abundances in that sample as a surrogate marker for urine concentration, and finally log transformed.

Statistical analyses were performed on each spot and timed dataset and ionization mode independently. Log-transformed DM/DX was regressed on each ion with no additional covariates. To account for possible dependencies among siblings and to avoid assuming homoscedasticity, we used a generalized estimating equation approach implemented in the R package 'gee'.[40] We adjusted the p-values for multiple-hypothesis testing using the Benjamini–Hochberg method as implemented in the R stats package's p.adjust function. This methodology addresses the multiple hypothesis testing problem without using excessively conservative approaches to control the family-wise error, such as a Bonferroni correction. An adjusted p-value <0.01 was considered to be significant.

LC-QTOF Spectral Fragmentation

Ions of interest were analyzed as described for global metabolomics except that the quadrupole selected the precursor ion and the TOF mass analyzer scanned product ions. Selected precursor ions (1.3 amu isolation width, 0.5 min allowable retention time shift) were fragmented at fixed collision energies of 10, 20, 40 and 80 V in ESI+ mode. The MS/MS scan rate was 4 spectra/s.

Database Queries

For significant ions, we attempted to determine identities by querying major metabolomics databases using the accurate mass (within 20 ppm), and MS/MS fragmentation spectra or retention time, when available. Databases queried included the Scripps METLIN Metabolite Database,[41,42] the Human Metabolome Database[43] (version 3.6[44]) and in-house databases.

Relative Quantification of M1 by LC-QqQ

MRM MS was performed on an Agilent 1290 Infinity HPLC coupled to an Agilent 6460 Triple Quadrupole (QqQ) to quantify the relative abundance of M1 in urine samples. A similar method as described for LC-QTOF was employed but with a shortened total run time of 6 min and a fixed collision energy of 40 V in ESI+ mode. We injected 5 µl and up to 15 µl for pediatric non-PM and PM samples, respectively. Two non-PM samples from the pediatric timed urine set were not analyzed due to insufficient sample volume. Peak areas for the mass transition of m/z 444.3→98.1 were divided by creatinine concentration (mM) to account for urine concentration differences. A value of 5, approximately the value of noise, was assigned to missing values. For the adult samples, creatinine normalized signals (m/z 444.3→98.1) for 0–12 and 12–24 h urine samples from days 3–4 (without fluoxetine) and days 18–19 (with fluoxetine) were summed to obtain a composite 0–24 h value.

General Statistical Analyses

GraphPad Prism (version 5.04, GraphPad Software, CA, USA) was used for general statistical analyses. Univariate regression was used to compare log(creatinine/M1) and log(DM/DX). Univariate regression was also used to model log M1 given creatinine, age, or urinary pH; and creatinine given age or urinary pH. A two-tailed t-test was used to determine whether log(DM/DX), log M1 or creatinine differed between genders. One-way analysis of variance (ANOVA) was used to determine the relationship between log(DM/DX) and Tanner stage or activity score, and to determine the relationship between log(creatinine/M1) and activity score. Spearman rank correlation was used to determine the relationship between log(DM/DX) and log(creatinine/M1) in adult subjects. A p-value <0.05 was considered to be significant.