METHODS
Study design and
population
This study protocol (NCT04188028) was approved by the Geneva Research
Ethics Committee and the Swiss Agency for Therapeutic Products
(Swissmedic). All participants provided written informed consent before
inclusion. Protocol conception and trial conduct were performed in
accordance with the Declaration of Helsinki ethical principles and the
Good Clinical Practice guidelines of the International Congress of
Harmonization.
Inclusion criteria were the following: age between 18-65 years, body
mass index between 18-27 kg m-2, CYP2D6 genotype
activity score (AS) = 0 or ≥ 1 (Gaedigk et al., 2008), reliable
contraception during the whole study, including a barrier method.
Exclusion criteria included: pregnancy/breastfeeding, any pathology,
drug or food affecting CYP activity, tobacco consumption (≥ 10
cigarettes/day), alcohol intake 2 days prior to session 1 and during
paroxetine intake, hepatic impairment, medical history of chronic
alcoholism or abuse of psychoactive drugs, regular use of psychotropic
substances, drug sensitivity, psychiatric disorders, and Beck Score ≥ 10
(question related to suicide >0).
Forty-three healthy volunteers participated in this study, which was
conducted in two sessions. Each session included the oral administration
of 5 mg dextromethorphan (DEM) (BEXIN syrup, Spirig Healthcare,
Egerkingen, Switzerland) to participants after an overnight fast and
urine collection for 4 hours following the administration of DEM for
CYP2D6 phenotyping. For metabolomic analyses, prior to DEM
administration, urine samples were also collected over a full 24-hour
period and venous blood samples were collected in tubes containing EDTA
(BD Vacutainer, Plymouth, UK) immediately before DEM ingestion.
Breakfast was served 1 hour after DEM intake. At session 2, the study
course was similar but participants were asked to take 20 mg (10 mg for
poor metabolizer (PM) subjects) of paroxetine (PAROXETIN‐MEPHA, Basel,
Switzerland), a time-dependant inhibitor, every morning for one week (7
doses in total) with the breakfast (Storelli et al., 2019). Participants
were specifically asked about the time at which paroxetine tablets were
taken and were asked to bring back empty blister packs to verify
compliance. For women participating in the study, a pregnancy test was
performed at inclusion and at each session prior to any medication
administration. Plasma was obtained through centrifugation at 2,750g for
10 min. All blood and urine samples were stored at −80°C until analysis.
Quantification of dextromethorphan and
dextrorphan
Subsequent to chemical hydrolysis and liquid-liquid extraction (Daali et
al., 2008), DEM and dextrorphan (DOR) were quantified in urine by liquid
chromatography-tandem mass spectrometry (Sciex, Darmstadt, Germany).
CYP2D6 phenotype was determined based on the urinary metabolic ratio
dextromethorphan to dextrorphan (UMRDEM/DOR) as follows:
PM phenotype (UMRDEM/DOR ≥ 0.3), intermediate
metabolizer (IM) phenotype (UMRDEM/DOR between
0.03-0.3), extensive metabolizer (EM) phenotype
(UMRDEM/DOR between 0.003-0.3) and ultrarapid
metabolizer (UM) phenotype (UMRDEM/DOR <
0.003) (Gaedigk et al., 2008).
CYP450 genotyping
Genomic DNA was extracted from whole blood (200 µl) using the QIAamp DNA
Blood Mini Kit (Qiagen, Hombrechtikon, Switzerland). Fourteen CYP2D6
allelic variants were screened using the TaqMan® OpenArray® PGx Panel
(Thermo Fisher Scientific, Waltham, USA) performed on the QuantStudio
12K Flex real‐time PCR system in compliance with the manufacturer’s
instructions. The following mutations were considered:
2850C>T, 4180G>C, 2549delA (*3),
100C>T (*4, *10), 1846G>A (*4A). 1707delT
(*6), 2935A>C (*7). 1758G>T (*8),
2613_2615delAGA(*9), 124G>A (*12), 1758G>A
(*14). 1023C>T (*17), 3183G>A (*29),
2988G>A (*41). Regarding CYP2C9 the following mutations
were measured: 430C>T, 3608C>T (*2),
1075A>C, 42614A>C (*3). CYP2C19*2
(681G>A, 19154G>A), CYP2C19*3
(636G>A, 17948G>A) and CYP2C19*17
(806C>T) were also determined, as well as CYP3A4*22
(15389C>T) and CYP3A5*3 (6986A>G).
CYP2D6 Taqman® Copy Number Assay (assay ID: Hs00010001_cn targeting
exon 9, Applied Biosystems, Foster City, USA) was performed on a 7900HT
Fast Real-Time PCR System (Applied Biosystems, Thermo Fisher Scientific,
CA, USA) instrument for the detection of gene deletion (*5 allele) and
duplication.
CYP2C9, CYP2C19, CYP2D6 and CYP3A activity scores were assigned using
previously developed scoring system (Gaedigk et al., 2008; Elens et al.,
2011; Karnes et al., 2020; Lima et al., 2020). Values of 0, 0.5, 1 and
1.5,were assigned to the non-functional, reduced function, fully
functional and gain-of-function alleles, respectively (Tay-Sontheimer et
al., 2014). In the case of CYP2D6, the values for alleles with two or
more gene copies are multiplied by the number of gene (Gaedigk et al.,
2018). Summing the values of the two alleles gives the AS of a genotype
(Gaedigk et al., 2018).
Untargeted metabolomics analysis by
LC-HRMS
300 µL of methanol/ethanol (50:50) containing hydrocodone-D6 and
phenobarbital-D5 100 ng/mL (internal standards for positive and negative
modes, respectively) were added to 100 µL of urine or plasma for protein
precipitation. Samples were centrifuged for 20 min at 16,000g. The
supernatant was then evaporated under a stream of nitrogen,
reconstituted in 100 µL of 10% methanol and 5 μL were injected into the
LC-MS system.
Non-targeted metabolomics analyses were carried out using a LC system
Ulimate 3000 coupled to a Q Exactive Plus system (Thermo Scientific
Fisher, Bremen, Germany).(Forchelet et al., 2018; Kowalczuk et al.,
2018) Separation was performed with a Kinetex® C18 column (50 × 2.1 mm,
2.6 µm) from Phenomenex (Brechbühler, Switzerland) with mobile phases
consisting of water (A) and methanol (B) both containing 0.1% formic
acid. The flow rate was fixed at 0.3 mL/min over 13 minutes. Gradient
program was set as follows: 2% B (0-0.3 minutes), 2-98% B (0.3-6
minutes), 98-100% B (6-9 minutes), 100-2% B (9-9.1 minutes), and 2% B
(9.1-13 minutes). Quality controls (i.e. pooled aliquots of all clinical
study samples) were included in the analytical sequence at regular
intervals. Data was acquired in a full scan mode in both positive and
negative polarities. The parameters were set as follows: the capillary
voltage at 3.2 kV and 2.5 kV in positive and negative mode,
respectively, sheath and auxiliary gas flow rate at 40 and 10
respectively, capillary temperature at 320 °C and S-lens RF level at
50.
Untargeted metabolomics Data and Statistical Analysis
The raw UPLC-HRMS files were converted to .mzXML format using MSConvert
(ProteoWizard 3.0, http://proteowizard.sourceforge.net/) and
pre-processed using the XCMS Online platform for features detection,
chromatogram alignment, isotope annotation and data visualization
(https://xcmsonline.scripps.edu).
All data transformation and statistical analyses were performed using
MetaboAnalyst (https://www.metaboanalyst.ca/). Data were
sum-normalized, Pareto-scaled and log-transformed. Subsequently,
features were filtered, and only those with a CV less than 20% in the
QC samples were selected. Isotopes were filtered out and finally (Kim et
al., 2018), ions of zero intensity in >20% of all
participants in both sessions were excluded.
Zero values were replaced by the half of the minimum value found for the
corresponding hit (Xia and Wishart, 2011). Principal Component Analysis
(PCA) was performed using QC samples to assess performance and stability
of the system. Data Volcano plots were generated in order to filter
metabolites that displayed both significant fold changes (≥ 1.5 or ≤
0.67) and statistical significance (FDR adjusted P -value
< 0.05) between the control and the inhibition session in
non-PM subjects (n = 37). The significant features obtained were then
filtered out according to genotype: fold changes of relative intensity
in the CYP2D6 EM-UM group (n = 37) compared to the PM group (n = 6) ≤
0.67 or ≥ 1.50 (P -value < 0.05). The data and
statistical analysis comply with recommendations of the British Journal
of Pharmacology on experimental design and analysis (Curtis et al.,
2018).
CYP2D6 biomarkers
identification
Metabolites molecular formulas were investigated further using MS
fragmentation and isotope pattern analysis with SIRIUS 4.0.1 (Dührkop et
al., 2019). The glucuronide metabolites were enzymatically deconjugated
prior to MS fragmentation using the
β-Glucuronidase /Arylsulfatase mixture from Roche
Diagnostics (Mannheim, Germany) in sodium acetate buffer (1 M, pH 5.0)
at 37°C (Schmidt et al., 2013). Main metabolomics databases: LIPID MAPS®
(https://www.lipidmaps.org/), METLIN (https://metlin.scripps.edu/) and
HMDB (http://www.hmdb.ca/) were then used to assist in the
identification of molecular structures of significant features on the
basis of the available experimental data (i.e. exact molecular weights,
molecular formulas, fragmentation patterns).
Production Reaction Monitoring analysis
To improve sensitivity, validate and refine results, a semi-quantitative
method using HRMS-based PRM was developed. The chromatographic
separation was performed using a LC system Vanquish coupled to a Q
Exactive Focus system (Thermo Scientific, Bremen, Germany). The
preparation of urine and plasma samples as well as the chromatographic
and mass spectrometry conditions were identical to those of the
metabolomics analyses, except that the extracts were concentrated twice
(reconstitution in 50 µL of 10% methanol). Hydrocodone-d6 at 15 ng/mL
was used as internal standard. The resolution was set at 17′500 for the
fragmentation experiments with an AGC target of 5e4 and a maximum IT of
100 ms. The NCE values for each compound were set individually and
urinary creatinine concentration was used for data normalization.
Production Reaction Monitoring Data and Statistical
analysis
Comparisons of two dependent and independent groups were performed using
paired and unpaired t test (two-tailed), respectively. Measures of
associations were established using Spearman’s rank correlation. The
statistical analyses were performed using GraphPad Prism 8.0.1 software
(San Diego, USA). A P value below 0.05 was considered
statistically significant. The data and statistical analysis comply with
recommendations of the British Journal of Pharmacology on experimental
design and analysis (Curtis et al., 2018).
Nomenclature of Targets and
Ligands
Key protein targets and ligands in this article are hyperlinked to
corresponding entries in http://www.guidetopharmacology.org, the common
portal for data from the IUPHAR/BPS Guide to PHARMACOLOGY (Harding et
al., 2018), and are permanently archived in the Concise Guide to
PHARMACOLOGY 2019/20 (Alexander et al., 2019).
RESULTS
Healthy subjects
A total of 43 healthy subjects were enrolled (Table 1 ).
Forty-two completed the study while one among the PM subjects only
attended the control session. The mean age was 24 (range 19-29) with a
slightly higher proportion of females (55.8 %, n = 24). Among women
participants, 45.8% used oral contraceptive pill (n = 11).
CYP2D6 genotype and
phenotype
Based on genotyping analyses, 6 participants were classified as PM
subjects (genetic‐predicted activity score (gAS) = 0), 33 as EM subjects
(1 ≤ gAS ≤ 2) and 4 as UM subjects (gAS > 2) (Gaedigk et
al., 2008; Crews et al., 2014). UMRDEM/DOR were measured
to establish CYP2D6 phenotype. As illustrated in Figure 1a ,
mean CYP2D6 activity was significantly reduced after paroxetine intake
compared to control session (P < 0.0001), demonstrating
an effective inhibition of CYP2D6 activity by paroxetine. In addition, a
significant Spearman’s rank correlation coefficient ofr s = 0.791 (P < 0.0001) was
found between the logarithm of UMRDEM/DOR and AS groups
(Figure 1b ).
Untargeted metabolomics
analysis
Using untargeted metabolomics assays, the goal of this project was to
identify biomarkers of CYP2D6 in urine and plasma reflecting the
activity of the enzyme. Figure 2 shows the flowchart of the
data analysis, from data extraction to statistical analysis. After the
filtering steps, 8926 and 5997 ions in plasma and urine, respectively,
were processed for statistical analysis, including sum-normalisation,
log-transformation and Pareto scaling. PCA scores plot revealed a tight
clustering of QC samples
(Supplementary Figure S1 )
indicating high experimental quality for both urine and plasma samples.
As seen in Table 2 , five endogenous metabolites were
significantly decreased in urine and/or plasma during the CYP2D6
inhibition phase relative to baseline with the following m/z in
positive mode: 220.1545, 416.3159, 432.3108, 444.3108 and 597.3382
(595.3236 in negative mode). The largest reduction was observed form/z 597.3382 (0.13-fold). In parallel, their intensity was
significantly lower in PM subjects than in EM-UM volunteers (fold
changes ≤ 0.67). These observations strongly imply that these five
features are metabolites produced via the CYP2D6 enzyme.
Structure identification
All MS/MS fragmentation patterns are shown in Figure 3 . Due to
the presence of a glucuronide moiety, the feature m/z 597.3382
was also enzymatically hydrolysed prior to MS/MS fragmentation,
resulting in m/z 421.3061 (-176.0321 Da).
MS/MS fragmentation of m/z 220.1543 was difficult to obtain
because several compounds with close mass (i.e. m/z 220.0966,
220.1329) co-elute. At 10 eV it is however possible to observe four
losses of water (-18.0109 Da): 202.1436, 184.1331, 166.1226 and
148.1120.
The compounds m/z 416.3159, 432.3108 and 444.3108 have identical
fragmentation patterns, with a major fragment at 98.0967. The skeletal
structure appears therefore similar for these three compounds, which
differ by one or two atoms: carbon loss between 444.3108 and 432.3108
(-12.0000 Da), oxygen loss between 432.3108 and 416.3159 (-15.9949 Da),
and carbon-oxygen loss between 444.3108 and 416.3159 (-27.9949 Da).
Regarding the feature m/z 444.3108, we observed the same MS/MS
fingerprint described by Tay-Sontheimer et al. (fragment ions atm/z: 98.0967, 370.2733, 206.1900, 56.0501, 55.0549, 150.1275 and
81.0703) (Tay-Sontheimer et al., 2014).
As described above, one of the major fragments of m/z 597.3382 is
421.3061, which corresponds to neutral loss of a glucuronide moiety.
Chromatograms before and after hydrolysis shown in Supplementary
Figure S2 confirm the presence of a glucuronide since the conjugated
peak (m/z 597.3382) decreases and the deconjugated peak
(m/z 421.3061) increases after hydrolysis.
Molecular formula for all compounds was obtained through fragmentation
and isotope pattern analysis using SIRIUS 4.0.1. Results are described
in Table 3. Fragmentation trees are presented inSupplementary Figure S3-S8 . Interestingly, all the features
contain one or two nitrogen atoms. Using exact mass, mass spectral
databases were employed for potential features annotation and
identification as shown in Table 3 . Results were, however,
inconclusive in HMDB (no results for the query masses). In LIPID MAPS,
only m/z 416.3159 showed a potential match: N-linoleyl dopamine.
Nonetheless, this lipid normally has a characteristic and predominant
fragment at 137, which was not observed, making this finding unlikely
(Thomas et al., 2009). In METLIN, the feature m/z 444.3108
matches a prostaglandin derivative: 17-phenyl trinor PGF2α diethyl
amide. However, MS/MS fragmentation patterns are not concordant.
Based on these findings and the physicochemical properties of these
compounds, in particular their high retention times and molecular
weight, it is likely that they belong to lipid species, including a
lipid glucuronide.
Relative quantification using a PRM
method
MS parameters were first optimized to enhance data quality using
parallel reaction monitoring (PRM) mode. In order to obtain the optimal
collision energy (CE) for each metabolite, the NCE were considered
individually using a step of 10. The results are presented inTable 4 . All analytes were detectable in the urine samples of
EM and UM subjects, with the exception of PM volunteers regardingm/z 432.3108 and 444.3108. Metabolites corresponding tom/z 220.1543 and 432.3108 were not measurable in any of the
plasma samples. Also, metabolites with m/z 416.3159 and 444.3108
were not detectable in plasma samples of PM participants.
The results confirmed the significant down-regulation observed after
paroxetine intake compared to the control session for all five hits in
both urine and plasma samples (Figure 4a and 5a ).
Likewise, the compounds were not detectable or significantly
down-regulated in PM subjects compared to EM-UM participants(Figure 4b and 5b ). Going even further, significant
correlations were observed between log(area) and
log(UMRDEM/DOR) (Figure 4c and 5c ).
Significant correlations between log(area) and CYP2D6 activity score
were also described, with UM subjects (gAS > 2) having the
highest mean intensities (Figure 4d and 5d ). AllP were < 0.05.
Mean relative intensity of m/z 220.1543, 416.3159 and 597.3382
was unchanged in PM subjects after paroxetine intake compared to
baseline, indicating that changes are solely due to CYP2D6 inhibition
(Figure 6) . Indeed, PM individuals do not express the enzyme
CYP2D6. Therefore, a down-regulation in PM subjects would indicate the
presence of false positives rather than changes due to CYP2D6
inhibition.
No significant correlation was observed with any CYP450 activity score
other than CYP2D6 (Supplementary Table S1).