Bousman et al. BMC Psychiatry (2017) 17:60
DOI 10.1186/s12888-017-1230-5
REVIEW
Open Access
Antidepressant prescribing in the precision
medicine era: a prescriber’s primer on
pharmacogenetic tools
Chad A. Bousman1,2,3,4*, Malcolm Forbes1, Mahesh Jayaram1, Harris Eyre1,5,6, Charles F. Reynolds7, Michael Berk1,4,5,
Malcolm Hopwood1 and Chee Ng1
Abstract
About half of people who take antidepressants do not respond and many experience adverse effects. These detrimental
outcomes are in part a result of the impact of an individual’s genetic profile on pharmacokinetics and pharmcodynamics.
If known and made available to clinicians, this could improve decision-making and antidepressant therapy outcomes. This
has spurred the development of numerous pharmacogenetic-based decision support tools. In this article, we provide an
overview of pharmacogenetic decision support tools, with particular focus on tools relevant to antidepressants. We briefly
describe the evolution and current state of antidepressant pharmacogenetic decision support tools in clinical
practice, followed by the evidence-base for their use. Finally, we present a series of considerations for clinicians
contemplating use of these tools and discuss the future of antidepressant pharmacogenetic decision support tools.
Keywords: Precision medicine, Pharmacogenomics, Major depressive disorder, Psychiatry, Decision support
Background
Antidepressant use has increased over the past decade
[1] but only half of those taking them will respond [2]
and about 55% will experience at least one bothersome
side effect [3]. In the largest and longest evaluation of
antidepressants, the Sequenced Treatment Alternatives
to Relieve Depression (STAR*D) trial, it took more than
50 weeks and at least four trials to obtain a cumulative
remission rate of 67% [4]. Such suboptimal outcomes as
these has resulted in a recent call for better use of
antidepressants, including searching for predictors of
response and reducing usage in people with situational
and personality based problems [5]. Current pharmacological strategies include swifter dose escalation and
medication changes as well as augmentation strategies [6].
An emerging and promising strategy is to utilise a person’s
pharmacokinetic and pharmacodynamic genetic profile to guide individualised antidepressant therapy
* Correspondence: cbousman@unimelb.edu.au
1
Department of Psychiatry, Melbourne Neuropsychiatry Centre, University of
Melbourne, 161 Barry Street, Level 3, Parkville, VIC 3053, Australia
2
Department of General Practice, The University of Melbourne, Parkville, VIC,
Australia
Full list of author information is available at the end of the article
decisions. Increasingly evidence indicates that genetic
factors play a critical role (42–50%) in determining
the differences in both response to and adverse effects of
antidepressants [7, 8] and this evidence has in part served
as the foundation of precision medicine.
Precision medicine is a novel approach to disease
prevention and treatment. It is based on an appreciation of the heterogeneity of disease entities and individual difference in genetic make-up. This approach
has had its fair share of criticism, since use of genetics
alone can be construed as stigmatising or unaffordable
to most. On the other hand, others have argued that
this may be a means to better understand issues
related to treatment response or lack of due to specific
genetic characteristics and could be a useful tool eventually enabling universal access [9]. Pharmacogenetic
application to antidepressant prescription aims to both
improve remission rates for depression and reduce adverse effects associated with antidepressants by identifying genetic markers that could be utilized as clinical
tools for tailoring treatment. Currently, there are a
number of pharmacogenetic decision support tools
that are commercially available [10] but recent commentary
within the field suggests the widespread adoption of these
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Bousman et al. BMC Psychiatry (2017) 17:60
tools in practice may be premature [11–15]. However, pharmacogenetic tools continue to be refined, developed, and
marketed to clinicians who have varying degrees of knowledge of the nature and/or the current evidence of these
tools. As such, this paper aims to provide an introduction
to pharmacogenetic decision support tools relevant to antidepressants and raise a number of considerations for
clinicians who may be contemplating use of these tools
in their practice.
The evolution of pharmacogenetic decision support tools
Pharmacogenetic decision support tools have evolved
rapidly over the past decade. In 2004, the first-generation of
clinical pharmacogenetic tools was born when Roche’s
CYP2D6 and CYP2C19 Amplichip (Basel, Switzerland) was
made available. First-generation tools test individual genes/
variants and provide genotype and accompanying phenotype (e.g. metaboliser status) information. However, they do
not account for potential synergies between genetic variants
and may not offer clinical interpretation/recommendations
or flag drug-drug interactions. Although individual gene
tests remain available for clinical use, second-generation
tools now utilise combinatorial or polygene testing. The
combinatorial/polygene approach is based on evidence that
most antidepressants and other psychiatric medications
interact with multiple pharmacodynamic and pharmacokinetic pathways [16]. Thus, unlike their firstgeneration predecessors, second-generation tools [see
recent reviews: [10, 17] account for synergies between genes included in their testing panels and
often provide drug-drug interaction information to
aide in drug selection and/or dosing decisions.
Page 2 of 7
Ideally, pharmacogenetic-based decision support tools
would include information from a wide variety of genomic,
personal, and environmental factors implicated in drug
response and toxicity variability, yet current tools typically
only include genetic and sometimes drug-drug interaction
information (Fig. 1). This is an issue since many of the most
robust predictors of response are clinical and psychosocial
in nature [18]. The genetic content varies considerably from
tool to tool, although what is consistent across all antidepressant pharmacogenetic tools is a focus on pharmacokinetic genes, specifically CYP2D6 and CYP2C19 [10]. The
focus on CYP2D6 and CYP2C19 is primarily a result of
expert groups such as the Clinical Pharmacogenetics Implementation Consortium [19] that have developed dosing
guidelines for serotonin selective reuptake inhibitors and
tricyclic antidepressants based exclusively on CYP2D6 and
CYP2C19 genetic variation [20–22]. Despite these guidelines, a majority of tools also include other pharmacokinetic
and/or pharmacodynamics genes in their testing panels
with varying degrees of evidence [10]. These gene panels
are then subjected to the tool’s decision algorithm, which in
turn produces an interpretative report. Interpretative
reports vary in the depth of content but at minimum include a snapshot of the patient’s pharmacogenetic status
along with recommendations and/or considerations aimed
at optimising efficacy and/or reducing adverse events associated with antidepressant therapy.
Current state of pharmacogenetic decision support tools
in practice
The menu of tools available to clinicians depends on geography (Fig. 2). Clinicians in the United States have the
Fig. 1 Overview of current and future development of pharmacogenetic-based decision support tools. *These factors are typically included
in decision algorithms of currently available pharmacogenetic-based decision support tools
Bousman et al. BMC Psychiatry (2017) 17:60
Page 3 of 7
Fig. 2 Number of pharmacogenetic-based decision support tools available by country/region
largest selection of tools to choose from, although the
availability of tools in other countries and regions of the
world is growing. As a result, pharmacogenetic testing for
psychotropic medication use is increasing among physicians in the United States [23] and Canada [24]. However,
there is less support for direct-to-consumer genetic testing
and three-quarters of US psychiatrists believed genetic
counselling would be needed for patients having testing
[23]. A recent study reported that 6% of psychiatrists in
the United States ordered a pharmacogenetic test in the
past six months, representing 47% of all genetic tests ordered by psychiatrists [25]. In other parts of the developed
world, similar studies have not yet been conducted but
there is no reason to think the opinions and rates of
genetic testing are likely to differ. However, it remains unclear at what stage of treatment these tests are ordered or
what underlying clinical circumstances led to the decision
to order a pharmacogenetic test.
Developers of pharmacogenetic tools advocate for preemptive (i.e. prior to prescribing) use of their tools but
we [10] have argued these tools are more likely to be ordered in a reactive (post-prescribing) fashion. A recent
case series has shown the utility of pharmacogenetic
testing for a patient that had failed to respond to multiple medications and for another that was experiencing
a high side-effect burden [26], limitations of this evidence type notwithstanding, and that other mechanisms
such as the nocebo effect may be operative [27]. As such,
the evidence base for both pre-emptive and reactive use of
pharmacogenetic tools for antidepressant therapy remains
limited. Furthermore, there is debate around whether
pharmacogenetic tools will boost antidepressant treatment
adherence, with some arguing pharmacogenetic tools may
assist in doctor-patient shared decision making, improve
health literacy, reduce the perception of side effects and
poor efficacy or treatment, and reduce health care costs
[28]. In fact, a recent retrospective study of claims data
implied an increase in adherence among patients for
whom a polygene pharmacogenetic tool was ordered compared to those who received standard treatment [29].
Although prospective trials will be need to confirm these
findings, promoting adherence is clearly desirable given
data suggesting 42% of patients discontinue antidepressant
treatment after 12 weeks [30] and long-term adherence is
estimated to be 45% [31].
Evidence-base for pharmacogenetic decision support
tools
Three recent reviews have assessed the evidence-base of
pharmacogenetic-guided antidepressant prescribing using
different evaluation frameworks [10, 17, 32]. All reviews
highlight that only a small proportion (<20%) of current
pharmacogenetic tools have been empirically evaluated and
suggest that pharmacogenetics has potential for clinical
utility but there are numerous gaps in the current evidence.
For example, it is unclear whether these pharmacogeneticguided tools can shorten the time to remission and/or
sustain the duration of remission from depression. Furthermore, the utility of these tools for assisting in the decision
to switch or augment a patient’s current prescription
remains uncertain, although preliminary evidence suggests
these tools may assist with medication changes [33]. Finally,
the cost-effectiveness of pharmacogenetic decision support
tools remains unclear [34] and until robust economic
studies are conduced, firm clinical guidelines or recommendations cannot be made.
Bousman et al. BMC Psychiatry (2017) 17:60
Considerations about the use of pharmacogenetic
decision support tools
There are a number of factors to consider if and when
selecting a pharmacogenetic decision support tool and a
variety of evaluation frameworks are available to clinicians to guide this process. One of the most well-known
is the Oxford Centre for Evidence-Based Medicine
(CEBM) Levels of Evidence [35]. The CEBM Levels of
Evidence assists clinicians in identifying and appraising
evidence using a hierarchy of the likely best evidence. In
this hierarchy, systematic reviews of RCTs and individual
RCTs are the preferred sources of evidence for making
appraisals about a particular intervention. As mentioned
above, three systematic reviews of these tools for antidepressant therapy have been completed [10, 17, 32] and
two individual RCTs have been conducted [36, 37]. The
CEBM advises that even in cases where supportive evidence exists, clinicians should consider at minimum four
questions before choosing to use or adopt the intervention. Below we present these questions and provide
salient information to assist clinicians in forming their
own conclusions.
1) Do you have good reason to believe that your
patients are sufficiently similar to the patients
in the studies? It has previously been noted that
the studies conducted to date have primarily
included Caucasian females in their forties who
lacked common comorbidities (e.g. substance use
disorder) among patients with depression [17].
Although the over-representation of females is
common in depression clinical trials (and clinical
practice) and may limit the application in real
world clinical settings, the under-representation
of non-Caucasian patients is particularly noteworthy
given the multi-ethnic populations of most
developed countries.
Ethnicity and its accompanying cultural and environmental factors account for some inter-individual pharmacokinetic and pharmacodynamic genetic variation relevant to
antidepressant therapy [38]. For example, the alleles used to
predict CYP2D6 and CYP2C19 metaboliser status vary considerably in frequency between ethnicities [39–41] and
tools including CYP2D6 and CYP2C19 do not necessarily
measure the same alleles. This is a particularly important
issue when contemplating whether to order a pharmacogenetic test for a non-Caucasian patient, in that most of the
tools were developed and tested in Caucasian populations
and may not include alleles that are rare in in this population but more frequent in people of Asian and/or African
descent. As a result, non-Caucasians may be reported as
normal (i.e. extensive) metabolisers by default when in fact
they are poor or ultra-rapid metabolisers. Thus, it is
Page 4 of 7
important to be aware that ‘predicted’ metaboliser status
provided by all pharmacogenetic tools should always be
interpreted in the context of diverse cultural and
environmental factors. In fact, a recent comparison of
genotype-predicted and ‘true’ CYP2D6 metabolism
showed genotype-predicted metaboliser status missed
43% to 64% of true poor metabolisers, depending on
ethnicity [42]. It should also be noted that within the
broad Caucasian, Asian, and African ethnic groupings,
allelic differences in key pharmacogenes (e.g. CYP2D6
and CYP2C19) have been observed, although more
subtle than difference observed between these broad
groupings [43–45]. Such ethnic and cultural variations
need to be addressed in future pharmacogenetic tool
development.
2) Does the tool have a clinically relevant benefit that
outweighs the harms? Evidence to date suggests
there may be potential benefits associated with the
use of pharmacogenetic decision support tools,
such as increased remission rates [36, 46–48],
reduced adverse effects [36, 49], and cost-savings
[36, 50–52]. However, the clinical utility of these
tools remains uncertain due to a lack of high quality
randomized clinical trials with adequate statistical
power. Nonetheless, the prevailing opinion is that
antidepressant pharmacogenetic testing is of
relatively low risk. Exceptions arise when preemptive testing results may take a significant
period (range: one day – three weeks) to be
available. Delaying initiation of antidepressant
therapy whilst awaiting results of the test may
not be ethically appropriate and may lead to
clinical deterioration. These potential harms
could be mitigated via the deployment of pointof-care testing. Unfortunately, none of the antidepressant pharmacogenetic tools offer point-of-care
testing but examples from antiplatelet prescribing
[53] suggest such testing is likely to become feasible
and applicable to antidepressant prescribing.
Another potential risk is loss of genetic privacy.
Although privacy concerns are not unique to pharmacogenetic testing, it has been argued that genetic data is
perceived as being higher quality and more definitive
than other laboratory data, suggesting special protections are warranted [54]. However, not all genetic data is
equal. Most antidepressant pharmacogenetic tools do
not included genetic variants that are used to identify
risk or diagnosis of disease and as such likely do not
require additional privacy measures beyond those in
place for other laboratory and clinical data. However, we
are aware of pharmacogenetic tools that measure genetic
variation in apolipoprotein E (APOE), a gene with potential
Bousman et al. BMC Psychiatry (2017) 17:60
risk implications for Alzheimer disease, as well as emerging
tools that will employ genome and exome sequencing technology that have the capability of identifying disease-related
mutations. Thus, mitigation of perceived and real genetic
privacy concerns will continually need to be addressed by
regulators, developers, and end-users of pharmacogenetic
tools. To date, these issues have been addressed in some
countries via genetic discrimination legislation such as the
US Genetic Information Nondiscrimination Act of 2008
[55]. Most pharmacogenetic companies and laboratories
offering pharmacogenetic testing use encrypted emails and
password-protected websites to gain access to genetic information. However, the potential for this information to be
inadvertently shared outside of the patient-clinician relationship is not trivial and for some patients could reduce
their enthusiasm to be tested [56].
3) Is another tool better? To our knowledge, no
comparative effectiveness trial of pharmacogenetic
decision support tools has been conducted.
However, given that multiple tools are available
for use worldwide, such a trial is a priority and
would ideally be funded and conducted independent
of the tool developers to avoid potential biases.
4) Are the patient’s values and circumstances
compatible with use of pharmacogenetic decision
support tools? Such consideration will obviously
vary from patient to patient and may be influenced
by cultural, spiritual, and/or historical factors. A
recent U.S. national survey of public attitudes toward
pharmacogenetic testing suggested the majority
(>73%) of people are interested in pharmacogenetic
testing to assist with drug selection, guide dosing and/
or predict side effects. A survey of 910 undergraduate
medicine and science students showed 90% were in
favor of pharmacogenetic testing [57]. Furthermore,
a telephone survey of US adults reported younger
Caucasians with a college education and history of
side effects from medication were more likely to be
interested in testing but most (73%) respondents’
would not agree to pharmacogenetic testing if there
was a risk that their genetic material or information
would be shared without their permission [56].
Conclusion
In the precision medicine era, the supply and demand for
antidepressant and other drug pharmacogenetic testing is
anticipated to increase. With this in mind, it is feasible
that clinicians will soon be able to obtain genetic information and generate a report that provides personalised
treatment recommendations within a single consultation.
Given the potential to improve patient treatment outcomes, even a modest increase in remission rates of
depression or reduction of adverse event risk would
Page 5 of 7
significantly reduce the growing disease burden of depression at the population level. However, the availability of
pharmacogenetic testing and genetic information to clinicians does not guarantee clinical applicability. More independent research related to the effectiveness and utility of
pharmacogenetic tools in real world practice is needed,
particularly within the primary care setting, where the
majority of patients with depression are diagnosed and
treated [58]. In the next five years, results will be available
from a number of randomized clinical trials currently
underway in the U.S and Canada that will allow for a better evaluation of the clinical utility of antidepressant pharmacogenetic decision support tools. In the meantime, new
tools will continue to emerge and diffuse into practice. As
such, clinicians are encouraged to consider the evidencebase of these tools in the context of their practice and
their diverse patient needs.
Acknowledgements
None.
Funding
CB was supported by a University of Melbourne Research Fellowship. MB is
supported by a NHMRC Senior Principal Research Fellowship (1059660).
Availability of data and materials
Data sharing is not applicable to this article as no datasets were generated
or analysed during the current study.
Authors’ contributions
CB and MF wrote the first draft of the manuscript and MJ, HE, CR, MB, MH
and CN contributed to the final draft. All authors read and approved the
final manuscript.
Competing interests
CB, MF, MJ, CR, MB, MH and CN declares that they have no competing
interests. HE owns shares in Baycrest Technology Pty Ltd.
Consent for publication
Not Applicable.
Ethics approval and consent to participate
Not Applicable.
Author details
1
Department of Psychiatry, Melbourne Neuropsychiatry Centre, University of
Melbourne, 161 Barry Street, Level 3, Parkville, VIC 3053, Australia.
2
Department of General Practice, The University of Melbourne, Parkville, VIC,
Australia. 3Centre for Human Psychopharmacology, Swinburne University of
Technology, Hawthorne, VIC, Australia. 4Florey Institute of Neuroscience and
Mental Health, The University of Melbourne, Parkville, VIC, Australia. 5Deakin
University, IMPACT Strategic Research Centre, School of Medicine, Geelong,
Australia. 6Discipline of Psychiatry, The University of Adelaide, Adelaide,
South Australia, Australia. 7University of Pittsburgh School of Medicine,
Pittsburgh, USA.
Received: 23 November 2016 Accepted: 4 February 2017
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