Key words
precision medicine - pharmacogenetics - prescribing
Introduction
The treatment of psychiatric disorders commonly involves the use of psychotropic
medications such as antidepressants, antipsychotics, mood stabilizers,
anxiolytics/hypnotics, stimulants, or anti-addiction medications. However,
recipients of these medications often experience a lengthy trial-and-error process
marked by poorly managed symptoms and/or adverse drug reactions before the
right medications and doses are established. As such, strategies to predict or
mitigate these poor responses are needed. Current pharmacological strategies include
scheduled titrations over time (sometimes guided by therapeutic drug monitoring)
[1] until a patient receives a standard
target dose thought to be sufficient for clinical efficacy. Yet the same dose may
not be the correct one for all individuals. Another emerging and complementary
strategy is the implementation of pharmacogenomic (PGx) testing to inform medication
selection and dosing decisions [2]. PGx
testing examines genetic variation involved in medication metabolism and action to
facilitate individualized prescribing, thus reducing undesirable outcomes. To date,
this strategy has been implemented in a growing number of medical centres around the
world and has fueled a burgeoning commercial PGx testing sector [3]
[4]
[5]. However, widespread implementation and
adoption of this strategy has not yet occurred in psychiatry, in part due to
diverging perceptions of the quality and completeness of the PGx evidence base,
variable knowledge among psychiatrists about genetics, and mixed views related to
the utility of PGx testing in clinical practice. Recognizing the current lack of
consensus within the field, the International Society of Psychiatric Genetics (ISPG)
assembled a group of experts to provide an overview of PGx mechanisms, summarize the
current evidence and treatment recommendations related to PGx in psychiatry, and
provide consensus recommendations for the use of PGx testing in clinical practice
[6]. This review discusses the evidence
that was considered by the ISPG and provides an up-to-date summary of recent
developments that clinicians should know when considering PGx testing for their
patients.
Pharmacogenomic Mechanisms
Pharmacogenomic Mechanisms
Pharmacokinetics
The majority of medications used to treat psychiatric conditions undergo hepatic
metabolism, although some, such as lithium, are eliminated only through the
kidneys. A number of genes encoding oxidative (Phase 1) and conjugative (Phase
2) metabolizing enzymes contain variants known to influence enzymatic activity.
In addition, genetic variation in drug transporters expressed in the liver, gut,
and at the blood brain barrier may alter the distribution of drugs and thereby
alter their pharmacokinetic profile. The drug metabolizing enzymes that are
currently the most clinically relevant to commonly used psychiatric medications
are the cytochrome P450 (CYP) enzymes CYP2C9, CYP2C19, and CYP2D6 [7]. While genes encoding conjugative
enzymes, such as UDP-glucuronosyltransferase (UGT) and
catechol-O-methyltransferase (COMT) enzymes along with the P-glycoprotein
(ABCB1) drug transporter, may also be relevant, their clinical
utility has not yet been established.
The CYP superfamily is arguably the most important enzyme system for drug
metabolism. Allelic variants of CYP genes are commonly referred to using
the star (*) nomenclature [8]
[9]. Genotypes (reported as star
diplotypes, e. g., CYP2D6*1/*2) are then
translated into metabolizer phenotypes. The most widely used phenotype
classification system includes: ultrarapid metabolizers (UMs), rapid
metabolizers (RMs), normal metabolizers (NMs, activity of reference or sum of
allelic variants with activity that is similar to that of the reference),
intermediate metabolizers (IMs), and poor metabolizers (PMs, little or no enzyme
activity) [10]. In this context,
“activity” refers to the metabolic capacity of an enzyme, which
broadly includes catalytic activity and enzyme abundance [10].
Pharmacodynamics
Pharmacodynamics refers to the biochemical, cellular, and physiologic effects of
medications and their mechanism of action [11]. In psychiatric PGx, the focus has historically been on variation
in genes encoding neurotransmitter receptors and reuptake transporters that are
located on the pre- or postsynaptic cell membranes. More recently, the focus has
expanded to include genes involved in signal transduction, gene transcription,
and protein folding and trafficking. However, our understanding of how genetic
variation affects the pharmacodynamics of psychiatric medications is still
evolving.
Immunologic mechanisms
Immunologic mechanisms are often involved in drug hypersensitivity reactions.
Variations in some human leukocyte antigen (HLA) genes are implicated in
the risk for potentially severe and fatal hypersensitivity reactions to certain
anticonvulsants/mood stabilizers [12]. More details are provided below in the section related to mood
stabilizers.
Pharmacogenomic Evidence and Guidelines for Psychiatry
Pharmacogenomic Evidence and Guidelines for Psychiatry
Antidepressants
Evidence
The bulk of antidepressant PGx evidence has been derived from studies on
major depressive disorder and has focused on pharmacokinetic mechanisms,
which have been reviewed in detail elsewhere [13]. In brief, findings have shown that
genetic variants in CYP2C19 and CYP2D6 are associated with
antidepressant blood concentrations, adverse drug reactions, and, to a
lesser extent, clinical outcomes such as treatment discontinuation or
symptom response [14]
[15]. From a pharmacodynamic
perspective, the Sequenced Treatment Alternatives to Relieve Depression
(STAR*D) study [16], the
Genome-based Therapeutic Drugs for Depression (GENDEP) project [17]
[18], and the Munich Antidepressant
Response Signature (MARS) [19], as
well as the International SSRI Pharmacogenomics Consortium GWAS analysis
[20], have not consistently
supported any single pharmacodynamic gene variant as a significant predictor
of antidepressant treatment response. The Pharmacogenomics Knowledgebase
(PharmGKB) contains clinical annotations summarizing literature findings for
associations between antidepressant efficacy and potentially relevant genes
such as SLC6A4 (serotonin transporter), HTR2A (serotonin 2A
receptor), GRIK4 (glutamate ionotropic receptor kainate 4), and
FKBP5 (FK506 binding protein 5). However, the associations
have only moderate or low levels of evidence [21].
Guidelines
There is disagreement about the role of PGx testing in antidepressant
prescribing. A recent safety communication from the US Food and Drug
Administration (FDA) cautioned against using PGx testing to guide
antidepressant prescribing, citing lack of evidence [22]. However, as clarified by us
previously [23], 17 antidepressants
have been included in published PGx-based prescribing guidelines [13]
[24] or product labels for
associations with CYP2C19 and/or CYP2D6 ([Table 1]). The Clinical
Pharmacogenetic Implementation Consortium (CPIC) guidelines for
CYP2C19 PMs suggest a 50% reduction of the recommended
starting dose of citalopram, escitalopram, sertraline, and tertiary amine
tricyclic antidepressants (e. g., amitriptyline); whereas
RMs/UMs treated with citalopram, escitalopram, and tertiary amine
tricyclic antidepressants would likely have inadequate treatment response
due to inadequate circulating antidepressant blood levels and thus may
benefit from an alternative antidepressant [13]
[24]. For CYP2D6 PMs, CPIC
recommends up to a 50% reduction of most tricyclic antidepressants,
fluvoxamine, and paroxetine doses, while for UMs, it is advised to select an
alternative antidepressant that is not predominantly metabolized by
CYP2D6
[13]
[24]. In addition, the Dutch
Pharmacogenetics Working Group (DPWG) recommends reduced dosing (amount
unspecified) of venlafaxine for CYP2D6 PMs and up to 150%
increased dosing for UMs [25]
[26].
Table 1 Actionable pharmacogenetic guidelines and product
labels by antidepressants.
|
Actionable Guideline Available1
|
Product Label2
|
Antidepressant
|
CPIC
|
DPWG
|
FDA
|
EMA
|
PMDA
|
HCSC
|
Amitriptyline
|
CYP2C19, CYP2D6
|
CYP2D6
|
CYP2D6
|
–
|
–
|
–
|
Amoxapine
|
–
|
–
|
CYP2D6
|
–
|
–
|
–
|
Citalopram
|
CYP2C19
|
CYP2C19
|
CYP2C19
|
–
|
–
|
CYP2C19
|
Clomipramine
|
CYP2C19, CYP2D6
|
CYP2D6
|
CYP2D6
|
–
|
–
|
–
|
Desipramine
|
CYP2D6
|
–
|
CYP2D6
|
–
|
–
|
–
|
Doxepin
|
CYP2C19, CYP2D6
|
CYP2D6
|
CYP2C19, CYP2D6
|
–
|
–
|
–
|
Duloxetine
|
–
|
–
|
CYP2D6
|
CYP2D6
|
–
|
–
|
Escitalopram
|
CYP2C19
|
CYP2C19
|
–
|
–
|
CYP2C19
|
–
|
Fluvoxamine
|
CYP2D6
|
–
|
CYP2D6
|
–
|
–
|
–
|
Imipramine
|
CYP2C19, CYP2D6
|
CYP2C19, CYP2D6
|
CYP2D6
|
–
|
–
|
–
|
Nortriptyline
|
CYP2D6
|
CYP2D6
|
CYP2D6
|
–
|
–
|
CYP2D6
|
Paroxetine
|
CYP2D6
|
CYP2D6
|
–
|
–
|
–
|
–
|
Protriptyline
|
–
|
–
|
CYP2D6
|
–
|
–
|
–
|
Sertraline
|
CYP2C19
|
CYP2C19
|
–
|
–
|
–
|
–
|
Trimipramine
|
CYP2C19, CYP2D6
|
–
|
CYP2D6
|
–
|
–
|
–
|
Venlafaxine
|
–
|
CYP2D6
|
CYP2D6
|
–
|
–
|
–
|
Vortioxetine
|
–
|
–
|
CYP2D6
|
CYP2D6
|
–
|
CYP2D6
|
CPIC: Clinical Pharmacogenetics Implementation Consortium; DPWG:
Dutch Pharmacogenetics Working Group; EMA: European Medicines
Agency; FDA: US Food and Drug Administration; HCSC: Health Canada
(Santé Canada); PMDA: Pharmaceuticals and Medical Devices
Agency, Japan. 1Only guidelines where a clinical action
has been recommended were included. 2Product label
information was extracted from the Pharmacogenomics Knowledgebase
(PharmGKB), only labels coded as “actionable,”
“test recommended,” or “test
required” by PharmGKB curators were included. For a
description of these categories (PGx levels) and the drug label
curation process, see
https://www.pharmgkb.org/page/drugLabelLegend.
Drugs reviewed that did not have an actionable guideline or product
label included: agomelatine, buproprion, desvenlafaxine, fluoxetine,
levomilnacipran, mianserin, mirtazapine, milnacipran, nefazodone,
phenelzine, reboxetine, selegiline, tranylcypromine, trazodone, and
vilazodone.
Antipsychotics
Evidence
Most antipsychotics are hepatically metabolized by one or more CYP450
enzymes. To date, studies of pharmacokinetic genes have predominantly
focused on CYP2D6 genetic variation, with risperidone and
aripiprazole receiving the most recent attention [27]
[28]
[29]. In contrast, evidence relating
to the impact of pharmacodynamic genes on antipsychotics drug response is
still emerging. It is well established that antipsychotics act primarily via
antagonism [30] or partial-antagonism
[31] of the dopamine D2
receptor. However, evidence linking genetic variation in the dopamine
D2 receptor (DRD2) gene to antipsychotic efficacy or
adverse reactions has been inconsistent [32].
Guidelines
To date, 10 antipsychotics have product labels or prescribing guidelines
[25] that offer selection or
dosing recommendations based on CYP2D6 metabolizer status ([Table 2]). For all of these drugs, the
guidelines or product labels recommend that CYP2D6 PMs receive lower
starting doses or an alternative drug not primarily metabolized by
CYP2D6. In addition, the DPWG guidelines recommend reductions in
the starting dose for pimozide and zuclopenthixol among CYP2D6 IMs,
while for UMs they recommend the use of an alternative drug or titration to
the maximum dose for haloperidol, risperidone, and zuclopenthixol. Of note,
the FDA product label for clozapine suggest CYP2D6 PMs may require a
dose reduction, despite CYP2D6’s minor role (6%) in the
metabolism of clozapine [33], and a
recent study that showed CYP2D6 genotype-predicted enzyme activity
explained a minimal amount of the variance (3%–7%)
in dose-adjusted clozapine levels and psychotic symptom severity [34]. In addition, the FDA product label
for pimozide states CYP2D6 genetic testing should be performed if
doses above 0.05 mg/kg/day in children or above 4
mg/day in adults will be used. However, other regulatory agencies
seem not to mention testing for CYP2D6 on their pimozide labels
([Table 2]).
Table 2 Actionable pharmacogenetic guidelines and product
labels by antipsychotics.
|
Actionable Guideline Available1
|
Product Label2
|
Antipsychotic
|
CPIC
|
DPWG
|
FDA
|
EMA
|
PMDA
|
HCSC
|
Aripiprazole
|
–
|
CYP2D6
|
CYP2D6
|
CYP2D6
|
–
|
CYP2D6
|
Brexpiprazole
|
–
|
CYP2D6
|
CYP2D6
|
CYP2D6
|
–
|
–
|
Clozapine
|
–
|
–
|
CYP2D6
|
–
|
–
|
–
|
Haloperidol
|
–
|
CYP2D6
|
–
|
–
|
–
|
–
|
Iloperidone
|
–
|
–
|
CYP2D6
|
–
|
–
|
–
|
Perphenazine
|
–
|
–
|
CYP2D6
|
–
|
CYP2D6
|
–
|
Pimozide
|
–
|
CYP2D6
|
CYP2D6
|
–
|
–
|
–
|
Risperidone
|
–
|
CYP2D6
|
–
|
–
|
–
|
–
|
Thioridazine
|
–
|
–
|
CYP2D6
|
–
|
–
|
–
|
Zuclopenthixol
|
–
|
CYP2D6
|
–
|
–
|
–
|
–
|
CPIC: Clinical Pharmacogenetics Implementation Consortium; DPWG:
Dutch Pharmacogenetics Working Group; EMA: European Medicines
Agency; FDA: US Food and Drug Administration; HCSC: Health Canada
(Santé Canada); PMDA: Pharmaceuticals and Medical Devices
Agency, Japan. 1Only guidelines where a clinical action
has been recommended were included. 2Product label
information was extracted from the Pharmacogenomics Knowledgebase
(PharmGKB), only labels coded as “actionable,”
“test recommended,” or “test
required” by PharmGKB curators were included. For a
description of these categories (PGx levels) and the drug label
curation process, see
https://www.pharmgkb.org/page/drugLabelLegend.
Drugs reviewed that did not have an actionable guideline or product
label included: asenapine, cariprazine, chlorpromazine,
fluphenazine, loxapine, lurasidone, olanzapine, paliperidone,
promethazine, quetiapine, thiothixene, trifluoperazine, and
ziprasidone.
Mood stabilizers/anticonvulsants
Evidence
In contrast to antidepressants and antipsychotics, there is limited evidence
supporting a link between genetic variation in pharmacokinetic genes and
mood stabilizer/anticonvulsant treatment outcomes. An exception is
the strong associations between CYP2C9 IMs and PMs and increased
phenytoin plasma concentrations [35].
Furthermore, there are no robust associations between pharmacodynamic gene
variants and mood stabilizer/anticonvulsant treatment outcomes.
Three independent GWASs have identified SNPs associated with lithium
response, but each study implicates a different locus [36]
[37]
[38]. Polygenic risk scores derived
from schizophrenia and depression GWAS have been associated with lithium
response [39]
[40], but none of these findings have
been replicated.
The immunologic genes HLA-A and HLA-B are robustly linked to
rare, but potentially fatal, severe cutaneous adverse reactions (SCARs)
(e. g., Stevens-Johnson syndrome [SJS] and toxic epidermal
necrolysis [TEN]) following exposure to carbamazepine, oxcarbazepine, and
phenytoin [41]. Specifically,
HLA-A*31:01 and HLA-B*15:02 alleles are
associated with a higher risk of SCARs if exposed to carbamazepine [12], while only the
HLA-B*1502 allele is linked to a higher risk of SCARs
following exposure to oxcarbazepine and phenytoin [42]. Notably, a recent meta-analysis of
11 studies in Asian (Chinese, Korean, and Thai) populations found a pooled
odds ratio of 2.4 for risk of lamotrigine-induced SJS/TEN in
HLA-B*15:02 carriers [43].
Guidelines
Product labels and prescribing guidelines are available for carbamazepine,
oxcarbazepine, and phenytoin ([Table
3]). For carbamazepine and oxcarbazepine, the FDA-approved labels
recommend testing for the HLA-B*1502 allele prior to
prescribing these medications to “genetically at-risk
populations.” Current evidence suggests at-risk individuals are
those of Han Chinese, Thai, Vietnamese, Indonesian, Malay, Filipino, or
Indian descent, who carry this allele more frequently
(3–36%) [44]
[45]. In fact, in Taiwan [46], Hong Kong [47], and Thailand [48], HLA testing prior to prescribing
carbamazepine and oxcarbazepine is standard practice. The FDA-approved label
for carbamazepine also provides information about HLA-A*31:01
and HLA-B*1502. Other regulatory agencies such as Health
Canada (HCSC) and the Pharmaceuticals and Medical Devices Agency (PMDA) in
Japan also note the risk of prescribing carbamazepine to individuals that
carry the HLA-A*31:01 or HLA-B*1502 alleles.
Aligned with these product label recommendations, CPIC recommends use of
alternative medications for individuals who test positive for
HLA-A*31:01 (carbamazepine) or HLA-B*1502
(carbamazepine, oxcarbazepine, and phenytoin). Furthermore, the CPIC
guideline for phenytoin recommends a 50% dose reduction for
CYP2C9 PMs, assuming the individual is not a carrier of the
HLA-B*1502 allele [35]
[44].
Table 3 Actionable pharmacogenetic guidelines and product
labels by mood stabilizers/anticonvulsants.
|
Actionable Guideline Available1
|
Product Label2
|
Mood stabilizers/ anticonvulsants
|
CPIC
|
DPWG
|
FDA
|
EMA
|
PMDA
|
HCSC
|
Carbamazepine
|
HLA-A, HLA-B
|
–
|
HLA-A, HLA-B
|
–
|
HLA-A, HLA-B
|
HLA-A, HLA-B
|
Oxcarbazepine
|
HLA-B
|
–
|
HLA-B
|
–
|
–
|
HLA-B
|
Phenytoin
|
CYP2C9, HLA-B
|
CYP2C9
|
HLA-B
|
–
|
–
|
HLA-B
|
Valproic acid
|
–
|
–
|
OTC, POLG
|
–
|
CPS1, OTC
|
OTC, POLG
|
CPIC: Clinical Pharmacogenetics Implementation Consortium; DPWG:
Dutch Pharmacogenetics Working Group; EMA: European Medicines
Agency; FDA: US Food and Drug Administration; HCSC: Health Canada
(Santé Canada); PMDA: Pharmaceuticals and Medical Devices
Agency, Japan. 1Only guidelines where a clinical action
has been recommended were included. 2Product label
information was extracted from the Pharmacogenomics Knowledgebase
(PharmGKB), only labels coded as “actionable,”
“test recommended,” or “test
required” by PharmGKB curators were included. For a
description of these categories (PGx levels) and the drug label
curation process, see
https://www.pharmgkb.org/page/drugLabelLegend.
Drugs reviewed that did not have an actionable guideline or product
label included: eslicarbazepine, gabapentin, lamotrigine,
levetiracetam, lithium, phenobarbital, pregabalin, topiramate,
vigabatrin, and zonisamide.
Finally, FDA, HCSC, and the PMDA product labels include language that
valproic acid is contraindicated or recommend genetic tests before
prescribing valproic acid to individuals suspected (e. g., by family
history) of having certain rare metabolic disorders. Sequencing of the gene
POLG (mitochondrial DNA polymerase γ) is recommended in
patients suspected of having a mitochondrial disorder, while patients
suspected of having a urea cycle disorder should be screened for mutations
in the genes OTC (ornithine transcarbamylase) and CPS1
(carbamoyl-phosphate synthase 1). Use of valproic acid by these individuals
can induce liver toxicity, hyperammonemia, and encephalopathy [49].
Anxiolytics/hypnotics
Evidence
Most anxiolytic/hypnotic medications are preferentially metabolized
by CYP3A4, CYP3A5, and CYP2C19
[21]. Links between
anxiolytic/hypnotic treatment outcomes and CYP3A4 or
CYP3A5 genetic variation have been inconsistent [21], while associations between
CYP2C19 allelic variation and anxiolytic/hypnotic
concentrations are more robust. This is particularly the case for clobazam
and, to a lesser extent, diazepam. Serum concentrations of clobazam were
increased 30–50% and norclobazam (active metabolite)
concentrations were up to 7-fold higher in CYP2C19 PMs relative to
other metabolizer groups [50], with
single and repeated dosing half-lives in PMs of 130 hours and 289 hours,
respectively [51]. Likewise, for
diazepam and its active metabolite (nordiazepam), CYP2C19 PMs had
40% and 75% higher plasma half-lives compared to NMs,
respectively [52]. There is also some
evidence linking the UGT2B15 (UDP-glucuronosyltransferase 2B15)
rs1902023:AA genotype with reduced clearance of lorazepam and oxazepam [21]. In contrast, the limited available
data do not suggest that any pharmacodynamic gene or variant is robustly
associated with response to anxiolytic/hypnotic medications [53].
Guidelines
There are 2 actionable gene-drug pairs included on FDA-approved product
labels, CYP2C19 and clobazam and CYP2C19 and diazepam. For
clobazam, the FDA recommends that CYP2C19 PMs receive a starting dose
of 5mg/day, with up-titrations proceeding slowly according to body
weight. For diazepam, the label does not provide specific dosing
recommendations but does note that CYP2C19 PMs could present with
marked differences in drug clearance, suggesting caution and additional
monitoring is warranted when prescribing this drug to CYP2C19
PMs.
ADHD medications
Evidence
Stimulants, including methylphenidate and amphetamine, and the non-stimulant
atomoxetine, are generally the first line treatments to alleviate core ADHD
symptoms. To date, the strongest evidence for the impact of CYP2D6
genotype on atomoxetine has come from pharmacokinetic studies and clinical
outcomes in large fixed-dose treatment trials. This body of work, recently
reviewed by CPIC and summarized in their consensus guideline [54], demonstrates that using standard
dosing approaches, non-PMs are less likely than PMs to achieve blood
concentrations (>~200ng/ml) necessary for clinical
effectiveness. In contrast, PMs are more likely to experience improvement in
ADHD symptoms, but due to their absence of CYP2D6 metabolic activity, they
are at also at increased risk of having side effects from atomoxetine and
may therefore require lower doses. From a pharmacodynamic perspective, there
are a number of interesting findings warranting further investigation
related to dopamine and norepinephrine disposition in the brain
(e. g., COMT), as well as the contribution of genetic
variability in CES1 (carboxylesterase 1) to methylphenidate
metabolism [55]. However, the clinical
efficacy and utility of testing for these genes remains unknown.
Guidelines
At the present time, only CYP2D6 is noted as a PGx biomarker that may
be helpful in guiding treatment with atomoxetine. Official FDA product
labeling, CPIC [54], and DPWG [25] all note the clinical relevance of
CYP2D6 genetic variation for atomoxetine prescribing. In the
product labeling, patients taking a CYP2D6 inhibitor or who are known
CYP2D6 PMs are recommended to start at the same dose as NMs, but
to approach dose escalation differently by only considering increases after
4 weeks if the drug is tolerated and symptoms do not improve. CPIC
guidelines offer more specific recommendations with respect to CYP2D6
genotype-informed therapy (i. e., specific starting doses,
titration, and drug exposure/plasma verification recommendations for
children and adults) [54].
Addiction medications
Evidence
Among substance use disorders and behaviors, several pharmacokinetic and
pharmacodynamic genes have been studied, some of which are promising.
Markers in the nicotine-metabolizing gene CYP2A6 have repeatedly been
associated with cessation treatment success [56]
[57]
[58] and a randomized, double-blind
placebo-controlled trial suggested that CYP2A6 genotype-guided
therapy could help improve outcomes for various smoking cessation
interventions [59]. Likewise, for
CYP2B6, particularly the *6 decreased function
allele, has repeatedly been associated with higher methadone plasma
concentrations [60], but the magnitude
of this effect casts doubt upon the suitability of this marker for use in
the clinic [61].
Beyond pharmacokinetics, a number of GWAS have identified candidate variants
for tobacco, alcohol, and opioid use behaviors [62]
[63], although replication of these
findings is still required. However, recent work has demonstrated that
variation in the α5 nicotinic cholinergic receptor (CHRNA5)
gene has prognostic significance for smoking cessation and response to
nicotine replacement therapy [56]
[57]
[58]. Specifically, individuals with
CHRNA5 genetic variants that increase the risk for heavy smoking
and tobacco use disorder are also more likely to benefit from
pharmacotherapy for smoking cessation, compared to those who lack the risk
variants. In people with alcohol dependence, a variant of the mu opioid
receptor gene (OPRM1), rs1799971 (A118G), has been repeatedly
associated with reduced analgesic response to exogenous opioids as well as
reduced relapse rates during naltrexone treatment [61]. However, a large meta-analysis
study has indicated that the effect of the A118G variant on substance
dependence per se is only modest [64].
Guidelines
At the time of this review, there were no PGx guidelines or product labels
for addiction medications due to the relatively limited evidence base.
Pharmacogenomic Testing in Psychiatry
Pharmacogenomic Testing in Psychiatry
The PGx evidence to date suggests genetic variation in CYP2D6, CYP2C19,
CYP2C9, HLA-A, and HLA-B should be considered when prescribing
several medications used in psychiatry. However, to facilitate the implementation of
PGx into clinical practice, the mechanisms for testing, reporting, and interpreting
the genomic variations associated with the tested genes, as well as understanding
the complexities and limitations of testing, are required [65]. In this section, we provide an overview of
PGx testing as it relates to psychiatry and highlight some of the challenges and
limitations one should consider when using PGx in clinical practice.
Test providers
PGx test providers are typically classified into 2 groups: commercial and
non-commercial. The number of test providers in each of these groups is
difficult to estimate. Recent estimates suggest there are over 75 laboratories
in the US that offer PGx testing [3]. In
addition, many laboratories participate in the Genetic Testing Registry that is
maintained by the National Center for Biotechnology Information
(https://www.ncbi.nlm.nih.gov/gtr/), and CPIC
lists a growing number of clinics, medical centers, and healthcare
organizations/systems around the world that have implemented PGx into
clinical practice.
The 2 most frequently used implementation models by commercial providers are the
gatekeeper and the direct-to-consumer (DTC) models [66]. The major difference between these 2
models is the degree to which a healthcare provider’s involvement is
required to order and/or interpret test results. Within the gatekeeper
model, a healthcare provider must be involved in the ordering and
interpretation, or in some cases only the ordering or only the interpretation of
the test. In contrast, the DTC model does not require the involvement of a
healthcare provider in the ordering or interpretation process, although some DTC
companies offer consultation/interpretation services with an in-house
pharmacist or physician. Non-commercial PGx test providers (i. e.,
healthcare organizations/systems) typically restrict testing to their
specific patient population and require ordering and interpretation of test
results by a healthcare provider. However, delivery of the test results varies
by non-commercial providers due to differences in the clinical workflows,
reimbursement environment, and information technology resources available [66].
Test content
PGx tests may include a single gene or a panel of genes, although multiple-gene
panels have become the norm [3]
[4]. Evaluations of commercial PGx testing
panels have shown that gene content varies from test-to-test and often includes
genes lacking sufficient evidence to guide prescribing in psychiatry
(e. g., COMT, CYP1A2, DRD2, SLC6A4) [4]
[67]
[68]. Thus, the number of genes included
on a testing panel is not an adequate metric for test selection. In psychiatry,
the gene content most relevant to clinical practice, as discussed in the
preceding sections, includes CYP2D6, CYP2C19, CYP2C9, HLA-A, and HLA-B
B
[65] and most commercial and
non-commercial providers test for CYP2D6, CYP2C19, and CYP2C9
[67]. However, even when the same genes
appear on a testing panel, the number of sequence variations, or alleles,
assayed within those genes can substantially vary among tests [68]. Unfortunately, regulatory standards
for PGx test content have not been established. The FDA has recently issued
warnings related to PGx testing that has specifically questioned the testing of
particular gene-drug pairs to inform prescribing of psychiatric medications
[22], which has commenced a discussion
on FDA’s role in the regulation of PGx testing [23] and has raised concerns related to the
content validity and potential detrimental impact of PGx testing panels that
include genes with limited supporting evidence [69]. However, the Association for Molecular Pathology (AMP) and
College of American Pathologists (CAP) have published recommendations for
clinical genotyping allele selection for CYP2C9
[70] and CYP2C19
[71] with a CYP2D6 allele selection
guide underway. To enable full use of these guides, test providers should be
transparent about which SNPs are tested and not just provide genotype calls or
genotype-derived phenotype assignments. A decision tree for guiding test
selection is provided elsewhere [72].
Test analytical validity
PGx testing is ideally performed in laboratories that have been evaluated and
accredited according to national regulatory standards to ensure a high level of
analytical validity (i. e., ability of a test to detect whether a
specific genetic variant is present or absent). However, analytical validity
does vary among accredited laboratories. This variability stems from challenges
in accurately calling “star” alleles (or haplotypes) from the
variants tested, identification of structural variation (e. g., gene
copy number variants, or CNVs), and the presence of novel or rare allelic
variants that might affect PCR-based amplification and subsequent
genotyping/sequencing. Genotyping technologies are less uniform in the
detection of structural variants than in the detection of SNPs or short
insertion/deletion polymorphisms. For example, many tests that detect
CYP2D6 CNVs often only report the presence of a
“duplication” without specifying which allele is
“duplicated” and default the copy number to “2”
without determining how many copies of the gene are actually present. This can
lead to inaccurate phenotype assignments, which in turn may lead to inaccurate
recommendations. There are also numerous so-called hybrid genes that are part
CYP2D6 and part CYP2D7 and do not usually encode a functional
enzyme. Detailed descriptions of these structural variants and their impact for
psychiatry are described elsewhere [73]
[74].
Another challenge for PGx testing is the detection of rare variants. Current PGx
testing panels do not typically include rare variants and are also not designed
to detect novel variants. Sequencing has the advantage of detecting rare
variants that are not part of PGx panels. It has been estimated that rare
variants may account for up to 20–30% of the variance in
interindividual response to medications [75]. However, it needs to be emphasized that the functional impact of
a rare or novel allele may be uncertain or unknown, and thus clinical
interpretation of genotypes containing such variants is often difficult.
Test feasibility
The feasibility of PGx testing can be a challenge in clinical settings and is
dependent on 1) availability of testing, 2) patient and provider acceptability
of testing, 3) testing turnaround times, and 4) testing affordability. The
exponential growth of PGx testing over the last decade, particularly in the US,
has resulted in an increase in testing availability. Likewise, providers and the
general public report positive opinions related to PGx testing [76]
[77]
[78]
[79]
[80], and patient’s perception of
care improves when testing is delivered [81]. However, strategies for reducing turnaround times and the
monetary costs of performing PGx testing are still evolving. Turnaround times
range from 1 day to 3 weeks [4], which can
reduce the practicality of testing particularly in acute care settings, where
expedited prescribing decisions are required. This situation will improve as
rapid testing technologies delivering results within an hour emerge [82]
[83]. From a cost perspective, PGx testing
remains unattainable for many due to the high out-of-pocket expense and limited
third-party reimbursement, although several third-party payers have recently
announced limited coverage of testing or are actively evaluating the value of
offering such coverage [84].
Test clinical efficacy and cost-effectiveness
Establishing clinical efficacy and cost-effectiveness of PGx testing is vital to
widespread clinical uptake and adoption. Two meta-analytic evaluations of the
clinical efficacy of commercial PGx testing in psychiatry have been conducted
for prospective and retrospective clinical trials and showed that testing
improves the likelihood of achieving symptom remission compared to treatment as
usual [85]
[86]. However, recent inconclusive or
negative trial findings have been reported [87]
[88], leading some to conclude that
commercial PGx testing is not ready for widespread use in psychiatry [89]. Furthermore, evidence of clinical
efficacy has primarily been constrained to adults of European-ancestry with
major depressive disorder who had a history of antidepressant non-response or
adverse drug reactions, suggesting evaluations of clinical efficacy in other
clinical populations (e. g., non-Europeans, treatment-naïve,
children, schizophrenia) are required.
The cost-effectiveness of PGx testing has been evaluated in retrospective [90]
[91] and prospective clinical trials [92]
[93]
[94] for both psychotropic and
non-psychotropic drugs in diverse clinical settings. The majority of these
evaluations have concluded that PGx testing is a cost-effective or cost-saving
strategy relative to treatment as usual [90]
[95], although limitations have been noted
[92], and most economic studies have
been completed by providers of commercial PGx testing. Nevertheless, findings to
date are aligned with the notion that tailoring drug therapy to an
individual’s PGx profile can reduce visits to healthcare providers and
pharmacy costs related to medication switching as well as emergency room visits
and hospitalizations due to adverse drug reactions.
Test results interpretation and delivery
For most psychiatrists and other healthcare professionals, the interpretation of
PGx test results can be a challenge without accompanying clinical decision
support. Clinical decision support can be provided in a variety of forms, most
commonly through interpretative clinical reports that translate raw PGx data
into clinical recommendations and in ideal cases interruptive alerts implemented
within the electronic medical record.
The translation process, however, is not trivial. The process includes assigning
a function to the alleles possessed by an individual and then combining those
functions to derive a phenotype. For some genes, such as CYP2D6,
recommendations have been published with the goal to standardize the genotype to
phenotype translation [96]. However, this
process remains inconsistent across test providers and no gold standard approach
exists. Some providers combine information from several genes (combinatorial
approach) and employ proprietary algorithms that utilize—to varying
degrees—the published literature, product labels, and/or
guidelines developed by expert groups to derive recommendations [97]. This variability in genotype to
phenotype translation and clinical decision support from one test provider to
another can lead to potential discordant recommendations [98]. In addition, third-party analytic
applications are now ubiquitously available and are capable of analyzing the raw
data available from DTC providers, although the validity of the results produced
by these applications have been questioned [99].
Beyond PGx information, other factors such as age [100], sex [101], concomitant medications [102], renal/hepatic function [103], inflammation [104]
[105], lifestyle (e. g., smoking,
diet), and weight [106] are also important
considerations when applying PGx test results (see [107] for a detailed review of these
factors). However, most PGx test providers do not typically account for these
factors in their clinical decision support, and as such, it is the
responsibility of the healthcare provider to be aware and understand how these
factors may influence the PGx-based recommendations being offered. For example,
an individual genotyped as a NM for a CYP enzyme who is taking a strong
inhibitor of that enzyme will phenotypically resemble a PM, while a UM may
convert to an IM. Weak inhibitors may convert a NM to an IM and a UM to a NM.
This phenomenon is known as phenocopying. Likewise, an individual genotyped as a
NM for a CYP enzyme who is taking a potent inducer of that enzyme will
phenotypically resemble an UM. In these clinical scenarios, recommendations
provided by a typical PGx test report, which does not account for the presence
of concomitant inhibitors or inducers, could be misleading or lead to
inappropriate medication selection or dosing. When possible, the use of
therapeutic drug monitoring in conjunction with PGx testing in these scenarios
can confirm suspected phenocopying and ensure more appropriate medication
selection or dosing [108]
[109].
Finally, ancestry is an important factor to consider when interpreting PGx
results. There are marked differences in allele frequencies across ancestry
groups for most of the genes of key drug metabolizing enzymes. In addition,
there are also many non-functional alleles that are relatively rare and have
been found in only some populations but not in others [110], resulting in notable differences in
phenotype frequencies ([Table 4]) [21]. This makes it particularly challenging
to design “one-size-fits-all” test panels, and in practice, most
panels are biased toward alleles observed in individuals of European ancestry.
As a consequence, PGx testing panels can inaccurately assign metabolizer
phenotypes. For example, the CYP2D6*29 decreased function allele
is uncommon among individuals of European ancestry (0.1%, range:
0–2%) but common among those of African ancestry (9%,
range: 4–20%) [111]. A PGx
panel that did not include this allele would incorrectly assign the
*1 or *2 alleles (depending on the other
variants being tested). The *1 allele is a default (not tested)
allele that is assigned when none of the tested alleles are detected, while the
*2 is a tested allele that has some overlap with the
*29 allele. Both the *1 and *2
alleles are interpreted as “normal,” and, as such, inadvertent
assignment of these alleles could lead to inaccurate metabolizer phenotype
predictions (e. g., assigning a person as a NM when they are an IM).
Thus, a “normal” genotype result for an individual, particularly
those of non-European ancestry, should be interpreted in the context of the
alleles that were tested to avoid potential inappropriate medication selection
or dosing decisions. Additional information and examples regarding the
assignment of alleles can be found in the CYP2C19
[112] and CYP2D6
[74] GeneFocus papers.
Table 4 Estimated phenotype frequency by ancestry for CYP2D6,
CYP2C19, CYP2C9, HLA-A and HLA-B.
Genotype-predicted phenotypes
|
African
|
African American
|
Caucasian (European+North American)
|
Near Eastern
|
East Asian
|
South/ Central Asian
|
Americas
|
Latino
|
Oceanian
|
CYP2D6
|
|
|
|
|
|
|
|
|
|
Ultrarapid Metabolizer
|
4.4%
|
4.5%
|
3.1%
|
9.5%
|
0.7%
|
2.2%
|
5.5%
|
4.4%
|
20.0%
|
Normal Metabolizer
|
43.4%
|
55.7%
|
51.1%
|
54.7%
|
51.9%
|
62.1%
|
63.6%
|
59.2%
|
67.0%
|
Intermediate Metabolizer
|
43.5%
|
36.2%
|
39.0%
|
29.9%
|
39.2%
|
29.5%
|
23.6%
|
29.1%
|
10.1%
|
Poor Metabolizer
|
1.5%
|
2.3%
|
6.5%
|
2.2%
|
0.9%
|
2.3%
|
2.2%
|
3.1%
|
0.4%
|
CYP2C19
|
|
|
|
|
|
|
|
|
|
Ultrarapid Metabolizer
|
3.0%
|
4.3%
|
4.7%
|
3.7%
|
0.0%
|
2.9%
|
0.7%
|
2.8%
|
0.3%
|
Rapid Metabolizer
|
19.0%
|
23.7%
|
27.2%
|
25.7%
|
2.5%
|
18.6%
|
13.6%
|
24.1%
|
2.1%
|
Normal Metabolizer
|
30.1%
|
32.8%
|
39.6%
|
45.2%
|
38.1%
|
29.6%
|
62.8%
|
52.5%
|
3.5%
|
Intermediate Metabolizer
|
36.2%
|
31.4%
|
26.0%
|
23.5%
|
45.9%
|
40.8%
|
21.4%
|
19.0%
|
36.9%
|
Likely Intermediate Metabolizer
|
4.0%
|
2.8%
|
0.1%
|
0.0%
|
0.1%
|
0.0%
|
0.0%
|
0.4%
|
0.0%
|
Poor Metabolizer
|
6.3%
|
4.1%
|
2.4%
|
1.9%
|
13.0%
|
8.2%
|
1.5%
|
1.1%
|
57.1%
|
Likely Poor Metabolizer
|
1.4%
|
0.7%
|
0.0%
|
0.0%
|
0.0%
|
0.0%
|
0.0%
|
0.0%
|
0.0%
|
CYP2C9
|
|
|
|
|
|
|
|
|
|
Normal metabolizer
|
73.1%
|
75.9%
|
62.9%
|
61.1%
|
83.8%
|
60.0%
|
83.1%
|
74.6%
|
91.2%
|
Intermediate metabolizer
|
26.3%
|
23.6%
|
34.5%
|
36.0%
|
15.2%
|
36.3%
|
16.4%
|
24.5%
|
8.7%
|
Poor metabolizer
|
0.5%
|
0.5%
|
2.6%
|
3.0%
|
0.6%
|
3.8%
|
0.4%
|
1.0%
|
0.1%
|
HLA
|
|
|
|
|
|
|
|
|
|
A*31:01
|
0.8%
|
1.0%
|
2.6%
|
1.1%
|
3.5%
|
3.3%
|
6.2%
|
4.5%
|
1.1%
|
B*15:02
|
0.0%
|
0.1%
|
0.0%
|
0.0%
|
4.6%
|
2.6%
|
0.2%
|
0.0%
|
0.8%
|
Frequency data retrieved from the PharmGKB:
https://www.pharmgkb.org/page/pgxGeneRef;
accessed 22-Sept-2020.
Conclusions
PGx testing should be viewed as a decision-support tool to assist in thoughtful
implementation of good clinical care, enhancing rather than offering an alternative
to standard treatment protocols. In this context, genetic markers can supplement
demographic (e. g., age, sex, family history), clinical (e. g.,
concomitant medications), and lifestyle (e. g., diet, smoking) information
to help guide treatment decisions. At this time, the published evidence, prescribing
guidelines, and product labels support use of PGx testing to guide medication
selection and dosing in several clinical contexts, particularly for antidepressants
(CYP2C19 and CYP2D6), antipsychotics (CYP2D6),
anticonvulsants (CYP2C9, HLA-A, and HLA-B), and the ADHD medication
atomoxetine (CYP2D6). The current evidence does not support the use of
genetic variants in pharmacodynamic genes (e. g., SLC6A4, COMT,
MTHFR) to inform prescribing of psychiatric medications. Clinicians and patients
are encouraged to educate themselves or consult an expert prior to ordering a PGx
test. This is particularly important given that PGx testing is currently not
regulated, and many of the available tests include genes that have little to no
support for clinical implementation. Recommendations produced by these tests could
lead to inappropriate medication selection and dosing decisions. Various resources
to assist in the interpretation and implementation of test results exist, but these
resources do not supplant clinical judgement.
A number of larger PGx studies, such as the Ubiquitous Pharmacogenomics Project in
Europe [113] and the Precision Medicine in
Mental Health Care Study in the United States (NCT03170362) are underway. We expect
with the completion of these studies and others that the PGx evidence will continue
to evolve, barriers to testing will be cleared, and the uptake of genome sequencing
and population-level precision medicine initiatives will increase. As such, we
anticipate PGx testing will become an important tool in psychiatry, mitigating the
trial-and-error process that too many individuals currently endure.
Author Contribution
All authors were involved in the conception of this work. All authors either drafted
or critically revised the content and approved the final version. All authors accept
accountability for all aspects of the work.
Data Availability
Data availability is not applicable to this article as no new data were created or
analyzed in this study.
Notice
This article was changed on December 11, 2020.
Conflict of Interest:
some details were incorrect and they have been corrected.