Keywords
pharmacogenetics - phenoconversion - pharmacokinetics - antidepressants - antipsychotics
Introduction
For pharmacogenetic (PGx) considerations in psychopharmacological treatment, clinical
recommendations are available for patients treated with tricyclic antidepressants
and selective serotonin reuptake inhibitors, which specify how to adjust dosages
according to the CYP2D6 and CYP2C19 phenotypes of the patient [1]
[2]
[3]
[4]. Currently, the genotype-inferred phenotypes
are primarily considered [2]
[4]. Concomitant drugs that inhibit cytochrome
P450 (CYP) enzyme activity or induce their expression can cause phenoconversion (PC)
effects. PC, therefore, leads to a discordance between the genotype-derived
phenotype and the clinically observed phenotype (functional enzyme status) [5]
[6]
[7]
[8]
[9]
[10]. In our case, for example,
bupropion, or fluoxetine (CYP2D6), and fluvoxamine, or fluoxetine (CYP2C19) [6]
[7] are
potential pertubators of relevant CYP enzymes. Experimental methods to measure PC
in
patients (for example, using the “Geneva Micrococktail” [11]) are not suitable for clinical routine;
therefore, a method that does not interfere with the complex therapy of vulnerable
psychiatric patients would be desirable. To address this, a calculator tool for
CYP2D6 was established to integrate standardized assessments of PC in clinical
practice [5]
[7]. The activity score of CYP2D6 is multiplied by a factor corresponding
to the inhibitory properties of the comedication
(strong/moderate/weak). The adjusted activity score is then assigned
to the adjusted phenotype [5]. As patients are
routinely treated with multiple drugs in clinical practice, PC is common among
psychiatric inpatients [12]. Considering the
CYP2D6 functional enzyme status, the poor (PM) and intermediate status (IM) are much
more common, and the normal metabolizer (NM) status is less common compared to the
genotype-inferred phenotype [12]
[13]
[14].
For example, a patient genotyped as CYP2D6 NM treated with bupropion will
phenoconvert to a CYP2D6 PM. Not considering PC in the interpretation of PGx results
can lead to an inappropriate drug selection or false dosing recommendation, which
in
turn increases the risk for adverse drug reactions or nonresponse. Consequently, the
phenoconversion effects of CYP2D6 are relevant [12]
[13]
[14]; however, integration in clinical routine
is currently rare [5].
As of today, data on the relevance of PC for CYP2C19 are missing. One study described
a decrease in CYP2C19 NM and an increase in IM when considering PC; however, the
authors did not report statistical significance [13]. Unlike CYP2D6, different methods are available for CYP2C19 to
correct for PC effects, taking into account the presence of an inducer or a moderate
or strong inhibitor [7]
[15]
[16].
According to Bousman et al. [7], in the
presence of a moderate CYP2C19 inhibitor, the phenotype is converted to the next
lower activity, whereas a concomitant strong inhibitor leads to a conversion into
a
PM functional enzyme status regardless of the genotype-derived status. If an inducer
is present, the phenotype is converted to the next higher activity phenotype. On the
other hand, according to Hahn and Roll [17],
in the presence of a moderate or strong inhibitor, NM and IM are phenoconverted to
PM, whereas rapid (RM) and ultrarapid metabolizers (UM) are both converted to IM,
respectively. In the presence of a moderate or strong inducer, NM and RM are
phenoconverted to UM whereas IM is converted to NM. Thus, the latter method is
stricter in the presence of a moderate CYP2C19 inhibitor. However, there is
currently no consensus on any approach to adjust CYP2C19 phenotypes [5]
[7]
[16]
[18]. Also, physiologically based
pharmacokinetics modeling is an approach to predict phenoconversion effects [19]. A model predicting the phenoconversion of
CYP2C19 by esomeprazole is available [19];
however, besides that, available models mainly focus on specific drug-drug
interactions.
Aside from CYP2D6, CYP2C19 is an important enzyme in the metabolism of psychotropic
drugs [20], and its phenotype affects serum
concentrations of many drugs [21]. Mainly
selective serotonin reuptake inhibitors and tricyclic antidepressants serum
concentrations are affected by the CYP2C19 phenotypes [2]
[4]; in
addition, in a previous study, CYP2C19 phenotypes also affected venlafaxine serum
concentration [22]. So far, studies reporting
the pharmacokinetics of the drugs with respect to the CYP2C19 functional enzyme
status in a clinical setting are missing.
To address these prevailing issues and therefore to improve the interpretation of
PGx
result on CYP2C19, the primary goal was to investigate how considering PC
alters the CYP2C19 phenotype status. Different methods of including phenoconversion
effects were applied to compare the effect of the correction method. According to
Mostafa et al. [13]
[15], PC should be calculated rather than
measured to relieve psychiatric patients, but also to obtain results applicable to
routine clinical practice. Second, as an exploratory goal, this study investigates
how the CYP2C19 functional enzyme status affects serum concentrations and
metabolite-to-parent ratios of psychotropic drugs.
Methods
Patients
Wuerzburg Sample
In the Wuerzburg sample, 212 inpatients at the Department of Psychiatry,
Psychosomatics, and Psychotherapy of the University Hospital of Wuerzburg,
with available genotype data, as well as therapeutic drug monitoring (TDM)
results, were included in the analyses. Only adult patients (≥18
years of age) were included. Genotyping of CYP2D6 and CYP2C19,
as well as TDM, were performed according to the physician’s choice
as part of the clinical routine. TDM was performed according to the
guidelines of the TDM expert group of the Arbeitsgemeinschaft für
Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP) [20]. Genotyping for CYP2D6 and
CYP2C19 was performed according to recommendations of the German
Genetic Diagnostics Commission [23]
[24] and according to
the procedures of the German Genetic Diagnostics Act with written informed
consent. Genotypes and serum concentrations were determined between January
2020 and December 2021. To avoid bias in case of multiple serum
concentration determinations for one drug in the same patient, only the
latest determination per analyte was included in the analyses. The
retrospective analysis of clinical routine data was approved by the
Wuerzburg ethics committee (20220120 02) and was performed in accordance
with the principles of the declaration of Helsinki.
Frankfurt Sample
Adult inpatients (≥18 years of age) admitted to the Department of
Psychiatry, Psychosomatic Medicine and Psychotherapy of the University
Hospital Frankfurt due to a depressive episode (single major depressive
episode, recurrent depression) were genotyped for CYP2D6 and
CYP2C19 as part of the FACT-PGx study. TDM was performed as part
of the clinical routine according to the physician’s choice
according to the guidelines of the TDM expert group of the AGNP [20]. Data of 104 patients who took part
in the FACT-PGx study with available TDM data were included in the analyses.
Genotypes and serum concentrations were determined between July 2021 and
March 2022. To avoid bias in case of multiple serum concentration
determinations for one drug in the same patient, only the latest
determination per analyte was included in the analyses. The study was
approved by the local ethics committee of the University of Frankfurt
(2021–138) and carried out in accordance with the ethical principles
of the Helsinki Declaration version 2013. Written informed consent was
obtained from each participant.
Genotyping and therapeutic drug monitoring
Genotyping and serum concentration determinations in both cohorts were performed
at the Department of Psychiatry, Psychosomatics, and Psychotherapy of the
University Hospital of Wuerzburg. Details about the methods are available in
Supplement 1.
Haplotypes were defined for all analyzed single nucleotide polymorphisms
according to gene-specific haplotype tables from the PharmVar homepage
(https://www.pharmvar.org/genes; Supplement 1).
Phenotypes were determined according to the Clinical Pharmacogenetics
Implementation Consortium (CPIC) specifications [25].
Dose-corrected serum concentrations (serum concentration/dose, CD) of
either the active moiety of the drug (serum concentration parent
drug+active metabolite; CDAM) or the parent drug alone,
depending on the relevance for treatment response [20] and metabolite-to-parent ratios (MPR)
were calculated [20].
Dimensional outliers (≥3 SD from the mean) from CD and MPR were
set as missing data.
Phenoconversion effects
As there is no consensus on how to correct for the phenoconversion effects of
CYP2C19 without using a “drug-cocktail” [11], two available methods were used and
compared to each other. The phenoconversion effects were assessed according to
Bousman et al. [7] and Hahn and Roll [17]. For details, see Introduction, and
Supplement 1.
According to Bousman et al. [7],
concomitant drugs with the propensity to cause phenoconversion due to inhibitory
or inducing effects on CYP2C19 were derived from the Flockhart table (Supplement
2) [6]. For supplemental analysis, drugs
with inhibitory and inducing effects on CYP2C19 were derived from the FDA table
[26] (Supplement 3).
Statistical analyses
Statistical analyses were conducted in R v4.0.4 [27].
Differences in the CYP2C19 functional enzyme status obtained by different
correction methods were investigated by performing McNemar tests with continuity
correction. We performed Benjamini-Hochberg correction, as Bonferroni correction
tends to be too conservative for genomic analysis due to the linkage equilibrium
between individual genotypes [28]. A
p-value<0.05 was considered significant.
Differences in CD and MPR depending on the CYP2C19 functional enzyme status, were
investigated by performing linear regression analyses, corrected for age and
sex. In the amitriptyline, venlafaxine, and risperidone samples, the CYP2D6
functional enzyme status was also included in the regression analyses, as the
serum concentrations of these drugs are also dependent on CYP2D6 functional
enzyme status [12]. Chi-squared tests or
Fisher’s exact tests were performed to investigate the association
between the phenotype and the serum concentration being below, above, or within
the therapeutic reference range [20] for
the respective drug. To obtain reliable statistic results, groups (below, above,
or within the therapeutic reference range) with less than five patients were
excluded from analyses. A p-value<0.05 was considered significant.
Results
Patient Samples
The combined sample comprised 316 patients, which were 44.2±15.4
(mean±standard deviation (SD)) years old, and 54.1% female.
Among these, 144 patients were nonsmokers, 99 were smokers, and from 73
patients, no information on smoker status was available. Patients received
between 0 and 18 additional drugs in combination (mean±SD
4.1±3.5). A more detailed demographic overview is given in [Table 1]. Eighteen patients were
identified as CYP2C19 UM (genotype-inferred phenotype), 95 patients as RM, 129
as NM, 69 as IM, and 5 as PM.
Table 1 Demographic data of the patients included in the
sample. Genotypic phenotypes were the phenotypes according to the
PGx results.
|
|
Combined Sample
|
|
|
N
|
Mean±SD (range)
|
INCLUDED PATIENTS
|
|
316
|
|
|
|
AGE [YEARS]
|
|
316
|
44.2±15.4 (18–84)
|
|
|
MALE/FEMALE
|
|
145/171
|
|
|
|
NONSMOKER/SMOKER
|
|
144/99
|
|
|
|
|
NONSMOKER M/F
|
57/87
|
|
|
|
|
SMOKER M/F
|
56/43
|
|
|
|
MEDICATION WITH TDM
|
ANTIDEPRESSANTS
|
N
|
ANTIPSYCHOTICS
|
N
|
ANTIEPILEPTICS
|
N
|
Venlafaxine
|
117
|
Quetiapine
|
125
|
Pregabaline
|
25
|
Amitriptyline
|
100
|
Risperidone
|
73
|
Pipamperone
|
20
|
Mirtazapine
|
85
|
Aripiprazole
|
32
|
Valproic Acid
|
15
|
Sertraline
|
64
|
Olanzapine
|
20
|
Lamotrigine
|
13
|
Escitalopram
|
52
|
Cariprazine
|
10
|
Oxcarbazepine
|
9
|
Bupropion
|
38
|
Clozapine
|
8
|
Gabapentine
|
3
|
Trazodon
|
32
|
Chlorprothixene
|
7
|
Carbamazepine
|
2
|
Duloxetine
|
28
|
Amisulpride
|
5
|
Topiramate
|
2
|
Clomipramine
|
27
|
Haloperidol
|
5
|
Levetiracetam
|
1
|
Milnacipran
|
19
|
Perazine
|
4
|
|
|
Doxepine
|
16
|
Melperone
|
3
|
|
|
Trimipramine
|
5
|
Benperidol
|
2
|
|
|
Fluoxetine
|
4
|
Flupentixol
|
2
|
|
|
Moclobemid
|
3
|
Fluphenazine
|
1
|
|
|
Citalopram
|
2
|
|
|
|
|
Maprotiline
|
1
|
|
|
|
|
Opipramole
|
1
|
|
|
|
|
COMEDICATION
|
|
4.1±3.5 (0–18)
|
GENOTYPIC PHENOTYPES
|
CYP2C19
|
|
316
|
|
UM/RM/NM/IM/PM
(%)
|
18/95/129/69/5
(5.7/30.1/40.8/21.8/1.6)
|
N, number of patients; (%), percentage number; SD, standard
deviation; M, male; F, female; UM, ultrarapid metabolizer; RM, rapid
metabolizer; NM, normal metabolizer; IM, intermediate metabolizer; PGx,
pharmacogenetic; PM, poor metabolizer.
The number of serum concentration determinations is listed in [Table 1]. Only patients who received
venlafaxine (N=117), amitriptyline (N=100), mirtazapine
(N=85), sertraline (N=64), escitalopram (N=52),
risperidone (N=73), and quetiapine (N=125) were included in the
analyses to limit the type II error probability. Demographic data of these
patients are given in Supplement 4. To increase statistical power, all analyses
were performed in the combined sample.
Phenoconversion Effect
Results on phenoconversion effects are given per TDM request, as concomitant
drugs with each TDM request affect the genotype-inferred phenotype.
At baseline, 40.9% of the patients were classified as CYP2C19 NM ([Table 2]); after accounting for PC,
according to Bousman et al. (PCBousman) [7], the number significantly decreased, and
39.5% were classified as NMPC (p=0.05) ([Table 3]). According to Hahn and Roll
(PCHahn&Roll) [17],
the number of NM changed not significantly (p=0.08) ([Table 3]); however, the number of PM
significantly increased from 1.1% to 2.7% (p<0.001). The
number of IM, UM, and RM did not change significantly with either of the
correction methods ([Table 2], [3]; [Figure 1]). Patients prone to PC are summarized in Supplement 5. As
only five patients with CYP2C19 affecting concomitant medications according to
the FDA phenoconversion list were included, the number of NM, IM, PM, RM, and UM
did not change significantly after considering PC (Supplement 3).
Figure 1 (a) Sankey Plot showing the changes in CYP2C19
phenotypes when considering phenoconversion effects assessed by
different methods (Hahn and Roll [17], and Bousman et al. [7]). (b) Frequencies of predicted CYP2C19 phenotype
before (pre) and functional enzyme status after (post), including
phenoconversion effects, are shown.
Table 2 Number of CYPC19 phenotypes and CYP2C19 functional
enzyme status assessed by different methods (Hahn&Roll [17], and Bousman et al. [5]).
|
ALL PATIENTS (PER TDM)
|
|
N (%)
|
CYP2C19 nonPC
|
633
|
|
UM/RM/NM/IM/PM
|
29/185/259/153/7
(4.6/29.2/40.9/24.2/1.1)
|
CYP2C19 PC
Hahn&Roll
|
633
|
|
UM/RM/NM/IM/PM
|
32/182/251/150/17
(5.1/28.9/39.7/23.7/2.7)
|
CYP2C19 PC
Bousman
|
633
|
|
UM/RM/NM/IM/PM
|
30/185/250/160/8
(4.7/29.2/39.5/25.3/1.3)
|
N, number of patients; (%), percentage number; nonPC,
non-phenoconversion; PC, phenoconversion; UM, ultrarapid metabolizer;
RM, rapid metabolizer; NM, normal metabolizer; IM, intermediate
metabolizer; PM, poor metabolizer.
Table 3 CYP2C19 genotype-inferred phenotypes compared to
functional enzyme status assessed by different methods (Hahn and
Roll [17], and Bousman et al.
[5]). McNemar test with
continuity correction was used to describe significant differences
in the number of phenotypes/functional enzyme status.
Benjamini-Hochberg correction was performed, as Bonferroni
correction tends to be too conservative for genomic analysis due to
the linkage equilibrium between individual genotypes [30].
|
|
N(nonPC)
|
N(PCHahn&Roll)
|
Adjusted p-value (unadjusted)
|
N(nonPC)
|
N(PCBousman)
|
Adjusted p-value (unadjusted)
|
UM
|
UM nonUM
|
29 604
|
32 601
|
0.13 (0.08)
|
29 604
|
30 603
|
0.4 (0.32)
|
RM
|
RM nonRM
|
185 448
|
183 450
|
0.18 (0.16)
|
185 448
|
185 448
|
1.0 (1.0)
|
NM
|
NM nonNM
|
259 374
|
251 382
|
0.08 (0.03)
|
259 374
|
250 383
|
0.05 (0.01)
|
IM
|
IM nonIM
|
153 480
|
150 483
|
0.18 (0.18)
|
153 480
|
160 473
|
0.08 (0.03)
|
PM
|
PM nonPM
|
7 626
|
17 616
|
7.85*10
–3
(1.57*10
–3
)
|
7 626
|
8 625
|
0.4 (0.32)
|
N, number of patients; nonPC, non-phenoconversion; PC, phenoconversion;
UM, ultrarapid metabolizer; RM, rapid metabolizer; NM, normal
metabolizer; IM, intermediate metabolizer; PM, poor metabolizer.
Venlafaxine
CDAM and MPR of venlafaxine were not associated with genotype-inferred
CYP2C19 phenotypes, functional enzyme statusBousman, and functional
enzyme statusHahn&Roll (Supplement 6).
Genotype-inferred CYP2C19 phenotypes, as well as the functional enzyme status,
were not associated with serum concentrations below, above, or within the
therapeutic reference range (Supplement 6).
Amitriptyline
CDAM of amitriptyline was associated with genotype-inferred CYP2C19
phenotypes, with RM and UM showing lower CD compared to NM
(ßstd=−0.52, p=0.02;
ßstd=−0.68 p=0.04) (Supplement
6). MPR was not associated with genotype-inferred CYP2C19 phenotypes, and these
were not associated with serum concentrations below, above, or within the
therapeutic reference range. Considering PCBousman or
PCHahn&Roll did not change the number of NM, IM, PM, RM,
and UM. Consequently, analyses on functional enzyme status were not performed
(Supplement 6).
Mirtazapine
CD, as well as MPR of mirtazapine, were not associated with genotype-inferred
CYP2C19 phenotypes, nor with functional enzyme status. Serum concentrations of
mirtazapine within, above or below the respective therapeutic reference range
were not associated with genotype-inferred CYP2C19 phenotypes, nor with
functional enzyme status (Supplement 6).
Sertraline
CD of sertraline was associated with genotype-inferred CYP2C19 phenotypes with
higher CD in PM compared to NM (ßstd=2.67;
p=0.005). A trend towards higher and lower CD in IM and UM,
respectively, compared to NM, was observed
(ßstd=0.74, p=0.06;
ßstd=−0.94, p=0.06). The number of
NM, IM, PM, RM, and UM considering PCBousman was concordant with the
number considering PCHahn&Roll. CD was associated with
functional enzyme status with higher CD in PM compared to NM
(ßstd=2.37, p<0.001).
Metabolites were not measured; thus, analyses on MPR were not possible. Only one
patient showed serum concentrations below the therapeutic reference range, and
no patient showed concentrations above the reference range; therefore, further
analyses could not be conducted (Supplement 6).
Escitalopram
CD of escitalopram was associated with genotype-inferred CYP2C19 phenotypes with
lower CD in UM compared to NM (ßstd=−1.96,
p=0.05). MPR of escitalopram was not associated with genotype-inferred
CYP2C19 phenotypes. Genotype-inferred CYP2C19 phenotypes were associated with
serum concentrations below or within the therapeutic reference range
(p<0.001). Post-hoc tests showed that frequencies of RM compared to IM
were significantly different (p=0.007). Considering PCBousman
or PCHahn&Roll did not change the number of NM, IM, and PM;
RM, and UM, therefore, analyses on functional enzyme status were not performed
(Supplement 6).
Risperidone
CDAM, as well as MPR of risperidone, were not associated with
genotype-inferred CYP2C19 phenotypes, nor with functional enzyme status. Serum
concentrations of risperidone within, above, and below the respective
therapeutic reference range were not associated with genotype-inferred CYP2C19
phenotypes, nor with functional enzyme status (Supplement 6).
Quetiapine
CD and MPR of quetiapine were not associated with genotype-inferred CYP2C19
phenotypes; also, serum concentrations of quetiapine within, above, and below
the respective therapeutic reference range were not associated with
genotype-inferred CYP2C19 phenotypes. Considering PCBousman or
PCHahn&Roll did not change the number of NM, IM, PM; RM,
and UM, therefore, analyses on functional enzyme status were not performed
(Supplement 6).
Discussion
In this naturalistic setting, we investigated how correcting for PC alters the
CYP2C19 phenotype/functional enzyme status in a clinical routine setting. We
applied different methods to correct for the phenoconversion effects, as there is
no
consensus on how to adjust CYP2C19 phenotypes yet [5]
[7]
[18]. Depending on the correction method, our
findings reveal a significant decrease in CYP2C19 NM and a significant increase in
PM. We explored the association between CYP2C19 functional enzyme status and the
pharmacokinetics of antidepressant and antipsychotic drugs and found significant
associations between drug exposure of amitriptyline, sertraline, and escitalopram
and CYP2C19 phenotypes, as well as functional metabolizer status
(PCBousman and PCHahn&Roll).
We applied two methods to calculate PC rather than measuring PC, e. g., by
using the “Geneva Micrococktail” [11], to relieve the psychiatric patients, but still obtain results
applicable to routine clinical practice.
CYP2C19 phenotype frequencies in our clinical routine sample are in concordance with
the phenotype frequency for Europeans [29].
Less than one in two patients were CYP2C19 NM. When including PCBousman,
in accordance with a previous study, the number of NM decreased; however, no
statistical results were reported previously [13]. When applying PCHahn&Roll, due to the stricter
classification when taking a moderate CYP2C19 inhibitor, the number of PMs
increased. Thus, the method of correction for PC significantly affected the
frequencies of the functional enzyme status.
As including PC altered the frequencies of phenotypes/functional enzyme
status of CYP2C19, PC is relevant for CYP2D6 [5]
[12], and for CYP2C19; however,
they may be less pronounced. PC rates in the present study seem much lower than in
previous studies [13]
[15]. Mostafa et al. included not only
psychiatric patients [15]; in addition,
esomeprazole was used more often in the previous study [13] compared to the present one. In clinical
practice in Würzburg and Frankfurt, pantoprazole is preferred over
(es)omeprazole due to the preferable drug interaction profile.
Compared to CYP2D6, CYP2C19-affecting drugs were less often used; only 17 patients
(5.4%) were prone to CYP2C19 PC; in contrast, 24.1% of the patients
were prone to CYP2D6 PC [12]. Thus, due to the
limited use of CYP2C19-affecting drugs, PCs are less common; nevertheless, PCs are
relevant for an individual treated with CYP2C19-inhibiting/inducing drugs,
especially esomeprazole [6]
[30]. Therefore, we suggest considering PC not
only for CYP2D6, but also for CYP2C19 as part of individualized treatment in
psychiatry.
Considering the FDA phenoconversion table, the number of NM, IM, PM, RM, and UM did
not change significantly after taking into account PC. However, in the FDA
phenoconversion table, esomeprazole is not considered a CYP2C19 inhibitor. This is
in contrast to the product information of the European Medicines Agency (EMA) that
esomeprazole is a CYP2C19 inhibitor, and when starting or ending treatment with
esomeprazole, the potential for interactions with drugs metabolized through CYP2C19
should be considered [30]. Moreover, also
clinical data showed that esomeprazole inhibits CYP2C19 clinically relevant [31]
[32].
For CYP2D6, there is consensus among experts that if the patient is taking a strong
or moderate inhibitor, the activity score of CYP2D6 should be multiplied by 0 or
0.5, respectively. Administration of a weak inhibitor does not require adjustment,
as the area under the curve is only minimally affected by weak inhibitors [5]
[33].
This concurs with the definition of the relevance of drug interactions in general,
which are only considered relevant with moderate and strong inhibitors. In contrast
to CYP2D6, there are no activity scores for CYP2C19; therefore, establishing a
method for including PC of CYP2C19 is more challenging. Currently, there is no
consensus about dealing with weak/moderate/strong inhibitors. Prior
to including CYP2C19 PC into clinical routine processes, studies must show that the
serum concentrations correlate better with the functional enzyme status than with
the genotype-inferred phenotype; if relevant, a consensus on how to adjust for PC
has to be developed. In the meantime, to ensure an effective and safe
pharmacotherapy in patients affected by CYP2C19 PC and treated with drugs
metabolized by CYP2C19, therapy should be closely monitored by TDM to prevent
adverse drug reactions.
We explored the association between pharmacokinetics and CYP2C19 phenotypes and
functional enzyme status using linear regression analyses to control for age and
sex. In analyses on venlafaxine, amitriptyline, and risperidone, we also controlled
for CYP2D6 functional enzyme status, as CYP2D6 has previously been shown to impact
drug exposure of these drugs [12].
Venlafaxine is primarily metabolized by CYP2D6 and, to a lesser extent, by CYP2C19
[34]
[35], making the impact of CYP2C19 alone harder to assess as a single
gene. Therefore, for better accuracy, we evaluated the CYP2D6/CYP2C19
combination. CDAM of venlafaxine was not associated with CYP2C19
phenotypes nor with functional enzyme status. This contrasts with initial results
that CYP2C19 phenotypes affected the active moiety serum concentration of
venlafaxine [22]. However, previously, CYP2C19
was assessed as a single gene, not CYP2D6/CYP2C19 in combination. Thus, the
combined approach showed that CYP2D6 rather than CYP2C19 impacted CDAM of
venlafaxine (CDAM was associated with CYP2D6 functional enzyme status
with higher CDAM in CYP2D6 IM compared to NM (Supplement 6)), which is in
accordance with PGx dosing guidelines for venlafaxine [4].
According to venlafaxine, in the metabolism of amitriptyline, CYP2D6 is primarily
involved and should be considered in combination with CYP2C19 [2]. Therefore, corrected for the CYP2D6
functional enzyme status, CYP2C19 was associated with CDAM of
amitriptyline with lower CDAM in RM and UM compared to NM. This concurs
with dosing guidelines, considering CYP2D6 and CYP2C19 phenotypes for the treatment
with amitriptyline [2]. None of the patients
on amitriptyline had been taking medications with relevant inhibition or induction
effects on CYP2C19 to cause PC. Therefore, it is not possible to determine the
impact of PC.
In our clinical routine setting, we found that CD of sertraline was associated with
CYP2C19 phenotypes and functional enzyme status. The number of NM, IM, PM, RM, and
UM did not differ when applying PCBousman and
PCHahn&Roll. This highlighted the major role of CYP2C19 in
the metabolism, more precisely in the N-demethylation of sertraline
in-vivo, even if other CYP enzymes are also involved [36]
[37]
[38]. This result supports
clinical guidelines giving dosing recommendations based on CYP2C19 phenotypes [1]
[4]
[39]
[40]
[41].
Additionally, escitalopram is mainly metabolized by CYP2C19 [4]; it has been recommended that in
escitalopram-treated patients, CYP2C19 phenotypes should be considered for dose
adjustments [4]
[41]. This is in accordance with our results
that CYP2C19 phenotypes were associated with CD of escitalopram. In addition to
these results, CYP2C19 was associated with serum concentrations below, above
or within the therapeutic reference range of escitalopram. Patients with serum
concentrations below the therapeutic reference range are more often RM, compared to
IM; in contrast patients with serum concentrations within the therapeutic reference
range were more often IM than RM. Thus, CYP2C19 RM may have an increased risk for
low serum concentrations. However, according to amitriptyline, no patients on
escitalopram were taking medications with relevant inhibition or induction effects
on CYP2C19 to cause PC.
As serum concentrations of mirtazapine, risperidone, and quetiapine were not
associated with CYP2C19 phenotypes/functional enzyme status, we demonstrated
that CYP2C19 does not affect the serum concentrations of these drugs in a
clinically relevant way [31]. This is in
accordance with the knowledge that CYP2C19 is not involved in the metabolism of
these drugs [31]. In consequence, drug-drug
interactions with respect to CYP2C19 are likely negligible for mirtazapine,
risperidone, and quetiapine.
This shows that enzymes with altered function can possibly be compensated by other
enzymes involved in the metabolism of the drug. In consequence, as shown previously
for sertraline, a combined pharmacogenomics algorithm including more than two genes
may predict the serum concentrations more precisely than one or two individual genes
[42]. Bousman, therefore, proposed
evidence-based panel testing with a minimum gene set (CYP2C19, CYP2D6, CYP2C9,
HLA-A, HLA-B) [43].
Strengths and limitations
The major strength of our analysis is the relevance for a routine clinical
setting. Our retrospective naturalistic study in two independent cohorts
provides clinical routine real-life data, including a high number of patients.
Pharmacokinetic analyses were controlled for age and sex and, if relevant, for
the CYP2D6 functional enzyme status. However, due to the limited number of
patients who received CYP2C19-affecting drugs and whose phenotype was
consequently corrected for PC, it cannot be assessed whether correction for PC
and if so, if PCBousman or PCHahn&Roll is better
associated with serum concentrations than the genotype-inferred phenotype.
Inhibitors and inducers derived from index drugs were categorized as
weak/moderate/strong [6].
This categorization of inhibitor/inducer strength, however, is not
consistent among different sources. Nevertheless, using the Flockhart table was
in line with a previous study by Bousman et al. [7]. Clinical data, for example, clinical improvement, were not
available in both cohorts. A limitation of our study is that daily doses of the
inhibitors/inducers of CYP2C19 were not recorded due to the
retrospective nature of this study. However, a recent study showed that the
phenoconversion effect might be dose-dependent [44]. Also, the phenoconversion was calculated based on the genetic
phenotype, not on haplotypes due to the low number of patients; however, a study
of de Jong showed that the phenoconversion might depend upon the specific
polymorphism (e. g.,*1/*17
vs.*2/*17) [45].
Moreover, patients were not restricted to a diet, thus, nutrition may have
affected enzyme inhibition/induction. Comorbidities and ethnicities were
not recorded. Inclusion criteria in both samples were not the same; the
Wuerzburg cohort included all patients from which TDM and PGx were available; in
contrast, in the Frankfurt cohort, only patients suffering from a depressive
episode were included. In addition, drugs are not metabolized by one enzyme but
by multiple enzymes in combination; however, we considered only CYP2C19, if
relevant, in combination with CYP2D6. Nevertheless, as such real-life data on
PGx are rare, our results are important for supporting routine PGx-testing to
provide precision medicine.
Conclusion
Phenoconversion effects are relevant for CYP2C19; however, occur less often due
to the limited use of CYP2C19 perturbating drugs, compared to CYP2D6. Including
PC effects for both enzymes in clinical routine processes may maximize the
potential benefits of PGx testing due to an improvement in the prediction of
pharmacokinetics, as not only the genotype-inferred phenotype but the more
specific (dynamic) functional status of the enzyme is taken into account.
However, before including CYP2C19 PC in routine clinical processes, studies with
large numbers of patients and sufficient power must show that the serum
concentrations correlate better with the functional enzyme status than with the
genotype-inferred phenotype. If relevant, a consensus on how to adjust for PC
has to be developed. In our study with limited sample size, PC of CYP2C19
changes phenotypes but does not provide superior correlations with serum
concentrations. Based on our results, we suggest therapeutic drug monitoring to
ensure adequate serum concentrations in individual patients treated with
CYP2C19-affecting drugs, for example, esomeprazole and fluoxetine.
Ethical approvalAll procedures performed in the analysis involving human
participants were in accordance with the ethical standards of the institutional
research committees and with the 1964 Helsinki Declaration and its later
amendments or comparable ethical standards.
Author contributions
Project administration: J. Deckert, M. Scherf-Clavel, and M. Hahn; data collection:
M. Scherf-Clavel, A. Eckert, M. Hahn, and A. Frantz; analysis and interpretation of
the data: M. Scherf-Clavel; writing—original draft preparation: M.
Scherf-Clavel; writing—review and editing: M. Scherf-Clavel, H. Weber, S.
Unterecker, J. Deckert, A. Reif, M. Hahn, A. Eckert, and A. Frantz.
All authors have approved of the contents of this manuscript and provided consent
for
publication.