Utility of pharmacogenetic testing to optimise antidepressant pharmacotherapy in youth: a narrative literature review

Abstract

Pharmacogenetics (PGx) is the study and application of how interindividual differences in our genomes can influence drug responses. By evaluating individuals’ genetic variability in genes related to drug metabolism, PGx testing has the capabilities to individualise primary care and build a safer drug prescription model than the current “one-size-fits-all” approach. In particular, the use of PGx testing in psychiatry has shown promising evidence in improving drug efficacy as well as reducing toxicity and adverse drug reactions. Despite randomised controlled trials demonstrating an evidence base for its use, there are still numerous barriers impeding its implementation. This review paper will discuss the management of mental health conditions with PGx-guided treatment with a strong focus on youth mental illness. PGx testing in clinical practice, the concerns for its implementation in youth psychiatry, and some of the barriers inhibiting its integration in clinical healthcare will also be discussed. Overall, this paper provides a comprehensive review of the current state of knowledge and application for PGx in psychiatry and summarises the capabilities of genetic information to personalising medicine for the treatment of mental ill-health in youth.

Full text

Utility of pharmacogenetic testing
to optimise antidepressant
pharmacotherapy in youth: a
narrative literature review
Bradley Roberts
1
,
2
*
‡
, Zahra Cooper
1
*
‡
, Stephanie Lu
3
,
Susanne Stanley
4
, Bernadette T. Majda
5
, Khan R. L. Collins
6
,
Lucy Gilkes
5
,
7
, Jennifer Rodger
1
,
2
, P. Anthony Akkari
1
,
8
,
9
,
10
and
Sean D. Hood 
4†§
1
The Perron Institute for Neurological and Translational Science, Nedlands, WA, Australia,
2
School of
Biological Sciences, University of Western Australia, Crawley, WA, Australia,
3
School of Psychological
Science, University of Western Australia, Crawley, WA, Australia,
4
Division of Psychiatry, School of
Medicine, University of Western Australia, Crawley, WA, Australia,
5
School of Medicine, University of Notre
Dame, Fremantle, WA, Australia,
6
Western Australian Department of Health, North Metropolitan Health
Service, Perth, WA, Australia,
7
Divison of General Practice, School of Medicine, University of Western
Australia, Crawley, WA, Australia,
8
School of Human Sciences, University of Western Australia, Crawley,
WA, Australia,
9
Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University,
Murdoch, WA, Australia,
10
Division of Neurology, Duke University Medical Centre, Duke University,
Durham, United States
Pharmacogenetics (PGx) is the study and application of how interindividual
differences in our genomes can influence drug responses. By evaluating
individuals’genetic variability in genes related to drug metabolism, PGx testing
has the capabilities to individualise primary care and build a safer drug prescription
model than the current “one-size-fits-all”approach. In particular, the use of PGx
testing in psychiatry has shown promising evidence in improving drug efficacy as
well as reducing toxicity and adverse drug reactions. Despite randomised
controlled trials demonstrating an evidence base for its use, there are still
numerous barriers impeding its implementation. This review paper will discuss
the management of mental health conditions with PGx-guided treatment with a
strong focus on youth mental illness. PGx testing in clinical practice, the concerns
for its implementation in youth psychiatry, and some of the barriers inhibiting its
integration in clinical healthcare will also be discussed. Overall, this paper provides
a comprehensive review of the current state of knowledge and application for PGx
in psychiatry and summarises the capabilities of genetic information to
personalising medicine for the treatment of mental ill-health in youth.
KEYWORDS
pharmacogenetics, pharmacogenomics, personalised medicine, cytochrome P450, drug
metabolism, youth mental health, depression, anxiety
1 Introduction
Interindividual differences in our genomes occur within and between demographic
populations, leading to genomic variation and individual phenotype presentation (Vallejos-
Vidal et al., 2019). Variations in genes associated with drug absorption and metabolism may
have significant effects on a person’s response to medication. Understanding these genetic
variations has the potential to improve drug selection and dosage in clinical practice and may
OPEN ACCESS
EDITED BY
Sujit Nair,
Viridis BioPharma Pvt. Ltd., India
REVIEWED BY
Ingrid Fricke-Galindo,
Instituto Nacional de Enfermedades
Respiratorias-México (INER), Mexico
Caroline Dalton,
Sheffield Hallam University,
United Kingdom
*CORRESPONDENCE
Bradley Roberts,
brad.roberts@perron.uwa.edu.au
Zahra Cooper,
zahra.cooper@perron.uwa.edu.au
‡
These authors have contributed equally
to this work and share first authorship
§
These authors share senior authorship
RECEIVED 26 July 2023
ACCEPTED 30 August 2023
PUBLISHED 19 September 2023
CITATION
Roberts B, Cooper Z, Lu S, Stanley S,
Majda BT, Collins KRL, Gilkes L, Rodger J,
Akkari PA and Hood SD (2023), Utility of
pharmacogenetic testing to optimise
antidepressant pharmacotherapy in
youth: a narrative literature review.
Front. Pharmacol. 14:1267294.
doi: 10.3389/fphar.2023.1267294
COPYRIGHT
© 2023 Roberts, Cooper, Lu, Stanley,
Majda, Collins, Gilkes, Rodger, Akkari and
Hood. This is an open-access article
distributed under the terms of the
Creative Commons Attribution License
(CC BY). The use, distribution or
reproduction in other forums is
permitted, provided the original author(s)
and the copyright owner(s) are credited
and that the original publication in this
journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
Frontiers in Pharmacology frontiersin.org01
TYPE Review
PUBLISHED 19 September 2023
DOI 10.3389/fphar.2023.1267294

ultimately better the quality of medical care (Theoktisto et al., 2009;
Wall et al., 2010). The study of variations within those genes
involved in drug metabolism and how they influence an
individual’s biological response to medication is known as
“pharmacogenetics”(PGx) (Pirmohamed, 2001); a field of
research showing promise as an interventional application for
personalised and precision medicine (Roden and Tyndale, 2013;
Zgheib and Patrinos, 2020).
The factors affecting drug efficacy and toxicity can have
significant consequences for patient health outcomes and
contribute to personal and national economic loss associated
with avoidable hospital admission. In regard to the United States
Food and Drug Administration’s (FDA) top ten highest-grossing
drugs, for every person experiencing a therapeutic benefit, it is
estimated that between three and 24 individuals fail to show any
response (Schork, 2015). A recent review on PGx perspectives
quoted Allen Roses, MD, stating: “The vast majority of
drugs—more than 90%—only work in 30% or 50% of people”
(Pirmohamed, 2023). Side effects and adverse events to
medication are believed to account for ~6.5% of hospital
admissions (Pirmohamed et al., 2004;Soiza, 2020), with Australia
alone reporting that ~7.2% of all medical admissions are medication
related (Roughead et al., 2016). Though not the only contributing
factor, four out of five people are believed to carry genetic variations
that may alter drug efficacy and safety (Schärfe et al., 2017), with
some experts estimating genetic factors to account for up to 95% of
treatment response (Oates and Lopez, 2018). Furthermore, patient
risk of adverse events and serious side effects from many drugs varies
with respect to geographical ancestry (Shah and Gaedigk, 2018). The
application of PGx aims to move drug prescription away from the
current “one-size-fits-all”model to a more personalised and precise
manner of medication choice tailored to each individual
(Pirmohamed, 2023).
Mental health disorders represent a significant proportion of the
burden of disease in youth worldwide (Malhi and Mann, 2018).
People under the age of 24 years are particularly vulnerable to
mental illness, with 30%–50% of this age group suffering from
depressive and anxiety disorders non-responsive to cognitive
behavioural therapy and primary medication-based treatment
(Kessler et al., 2005;Lefaucheur et al., 2020). Research shows that
an untreated mental health condition in younger years can have
chronic, long-lasting effects, shaping the lives of young people into
adulthood (Goodsell et al., 2017). Furthermore, a rise in mental
health prevalence in recent years has caused a dramatic increase in
the use and prescription rates of antidepressants (de Oliveira Costa
et al., 2023). Between 2015 and 2019, de Olivera Costa et al. reported
over 50% of young Australian males and females aged between
10 and 17 years received a new antidepressant prescription. Given
the rising incidence and the stark increase in psychiatric medication
use, there is an urgent need to ensure young people with mental
health issues receive effective care. Guiding antidepressant selection
and dosage with PGx testing has been suggested to improve mental
health outcomes and limit adverse medication-related events
(Bousman et al., 2019).
This review comprises three sections. The first section presents
an overview of PGx and its role in clinical practice, with a particular
focus on the role of cytochrome P450 (CYP) drug-metabolizing
enzymes. The second section investigates the involvement of CYP
enzymes in the metabolism of antidepressant medication and the
use of PGx in improving mental health outcomes in randomized
clinical trials. The third section addresses the application of PGx
testing in youth mental health, evaluating the existing barriers
hindering the implementation of PGx in primary healthcare. It
explores the gaps in the literature pertaining to PGx in psychiatric
care and provides recommendations for future research required to
progress PGx into routine clinical practice.
2 Pharmacogenetics
2.1 What is Pharmacogenetics and
Pharmacogenomics?
PGx comes under the umbrella term “pharmacogenomics”,
encompassing the understanding of how an individual’s genetic
makeup (genotype) may influence their metabolic status
(phenotype) and overall response to a drug, in the context of
both efficacy and toxicity (Pirmohamed, 2023).
Pharmacogenomics includes two main determinants of drug
response, pharmacodynamics and pharmacokinetics, with
pharmacodynamics studying the drug’s effect on the body and
pharmacokinetics studying the body’s effects on the drug
(Adams, 2008). More specifically, pharmacodynamics is the study
of a drug’s effects both biochemically and physiologically on its
molecular target, observing the downstream effects that are elicited
by the drug-target interaction (Marino et al., 2023). In contrast,
pharmacokinetics is the study of how the body interacts with and
affects the elicited outcome of an administered substance such as a
pharmaceutical drug (Grogan and Preuss, 2023). The four main
components of pharmacokinetics are the processes of absorption,
distribution, metabolism, and excretion, which are all affected by an
individual’s genetics. Analysing the variations present in one’s genes
affecting these processes (PGx testing), allows us to better
understand how their body may respond to different medication
at varying doses. Thus, PGx testing has become a way to adapt and
optimise drug prescription and dosing in a clinical environment, by
predicting the interactions between pharmacokinetic parameters
and genetic variability.
2.2 The role of cytochrome P450 enzymes in
pharmacogenetic variation
Genetics influences pharmacodynamic and pharmacokinetic
properties. In particular, affecting how a drug is absorbed,
distributed, metabolised, and excreted in an individual, all factors
contributing to drug efficacy and tolerability (Figure 1)(Kalow,
1990;Weinshilboum, 2003;Horstmann and Binder, 2009).
Variation in genes involved in drug response (PGx genes) is
common within the human population, with a recent review
providing evidence that 97.8% of people worldwide are likely to
carry an actionable genetic variant in a PGx gene (Pirmohamed,
2023). Furthermore, over half the drugs currently prescribed in
clinical practice are metabolised by PGx genes and are affected by
one or more PGx variants (Dunnenberger et al., 2015;Kimpton
et al., 2019).
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Variations in genes encoding drug metabolizing enzymes,
transporters, and/or pharmacological receptors have been shown
to have a profound impact on an individual’s drug response (Shah,
2005). Involved in more than 90% of all enzymatic reactions, CYP
enzymes catalyse iron-induced oxidation reactions to convert lipid-
soluble compounds to more water-soluble compounds for excretion
(Guengerich, 2018). CYPs are a family of genes that encode enzymes
responsible for key roles in the metabolism of drugs and other
foreign compounds (i.e., xenobiotics). CYPs are some of the most
diverse catalysts in biochemistry and contribute to large
intraindividual variability in the safety, efficacy, and tolerability
of drug response (Zhao et al., 2021).
CYP gene sequences are classified by a family number, subfamily
letter, and then at the protein level further differentiated by isoform
number (e.g., CYP2C9,CYP1A2,etc.)(McDonnell and Dang, 2013).
Currently there are 57 known CYP genes with just six CYP genes
encoding enzymes that metabolise ~90% of all drugs (Lynch et al.,
2007). A number of CYP genes have functional polymorphisms,
yielding enzyme isoforms that vary in their ability to metabolise
drugs, therefore influencing the biologically available concentration
of the active drug compound (Turner, 2013). Recognizing how these
polymorphisms affect drug tolerability and which individuals carry
these mutations is important when trying to understand and predict
how individual alleles may influence drug-drug interactions, the
likelihood of adverse events, and drug therapeutic outcomes. For
example, a recent study by Koopmans et al. (2021) suggests that
~36% of people worldwide are likely to at least one actionable
variant in the CYP2D6 enzyme-encoding gene, and ~62% are likely
to one in the CYP2C19 encoding gene, altering their metabolic
efficiency of drugs digested by these enzymes. Through PGx testing,
an individual’s drug metabolic phenotype can be classified into four
main subtypes: poor metabolisers, intermediate metabolisers,
extensive metabolisers, or ultrarapid metabolisers (Bousman
et al., 2019). This information can help clinicians make informed
decisions on drug prescription and dosing for each patient based on
known personalised genetic data, rather than average population
statistics. As these variations are encoded in the human genome,
PGx testing is suggested as an efficient and effective tool to tailor
prescription medication to patients.
Considerable CYP allelic carriage differences are not only seen
between individuals but have previously been noted between people
of varying ethnic backgrounds (McGraw and Waller, 2012;Shah and
Gaedigk, 2018). For example, a higher proportion of poor
metabolisers of CYP2C19 has been reported in Asian populations
(~15–30%) compared to populations of European, African, and
Arab decent (~3–6%) (Poolsup et al., 2000). Similarly, the
proportion of poor metabolisers of the CYP2D6 gene appears to
be higher in Europeans and Caucasian Americans (~7%) than in
Asian populations (~1%). Differences in CYP allele carriage such as
these may possibly lead to large variances in clinical drug responses
between ethnically diverse groups, disadvantaging minority
backgrounds (Poolsup et al., 2000).
Allelic variation in CYPs is not likely due to de novo mutations,
but more likely to provide advantageous evolutionary traits (van der
Weide and Hinrichs, 2006). Although genetic influence and
inherited attributes are considered key contributors to drug
FIGURE 1
Pharmacogenetics is the study of how our individual genetic makeup can affect the properties of pharmacokinetics and pharmacodynamics. More
specifically, how our genetics can influence our body’s absorption, distribution, metabolism, and excretion of medication for all health conditions.
Variation in genes encoding drug metabolizing enzymes, transporters, and/or pharmacological receptors found predominantly in the liver, but also found
in the lungs, kidneys, and small intestine, can provide clinical information on a person’s drug-metabolic status, and may predict how they will
respond to pharmacotherapy.
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response (Xie et al., 2001;Murphy et al., 2013;Shah, 2015), research
suggests that drug-metabolising enzymes have evolved due to our
interaction with our environment. Diet then, is likely as important as
genetic ancestry when understanding a person’s response to
medication (Wilson et al., 2001). For instance, foods, including
fruits, alcohol, teas, and herbs are commonly known to induce or
inhibit the activity of drug metabolising enzymes, such as CYPs
(Fujita, 2004). For example, St John’s wort (Hypericum perforatum)
extract has long been associated with relieving the symptoms of mild
depression, demonstrating a high safety and efficacy profile
(Canenguez Benitez et al., 2022). However, research has shown
that St John’s wort is a potent inducer of CYP enzyme CYP3A4,
resulting in the enhanced metabolism and decreased blood
concentration of drugs metabolised by this enzyme, including
common antidepressants citalopram, fluoxetine, and sertraline
(Zhou et al., 2004). Because of this interaction, St John’s wort is
a major safety concern when consumed in combination with
antidepressants.
These considerations show that whilst PGx testing to guide
treatment is important for the integration of precision medicine into
primary care, the discussion of the results of genetic testing should
not disregard a patient’s ethnicity and cultural background when
used to prescribe medication (Shah and Gaedigk, 2018).
2.3 Pharmacogenetic testing in clinical
practice
PGx testing has been commercially available for nearly
two decades, yet its implementation into clinical practice has
been slow. Currently, PGx testing has been restricted to a few
specialist clinics at the forefront of the field, predominantly in
North America and Europe
1
(Volpi et al., 2018;Haidar et al.,
2019;Maruf et al., 2020;Smith et al., 2020). The lack of adoption
is surprising as PGx testing has been demonstrated in clinical trials
to significantly increase the efficacy and tolerability of prescribed
drugs (Pirmohamed et al., 2013). The most commonly prescribed
drugs with FDA-approved PGx recommendations are anti-
inflammatory medication, anti-blood clotting medication, and
pain relief medication (Smith et al., 2020).
In addition to improving safety and efficacy, the use of PGx in
clinical practice has the potential to reduce costs to both the
individual and the economy. In Australia, it is estimated that
~400,000 patients are admitted to hospital emergency
departments with adverse drug reactions (ADRs) related to their
prescription medication (Phillips et al., 2014). By current estimates,
Australian healthcare expenditure for ADRs is estimated at AUD
~$1.4 billion per annum
2
. However, only 6% of all ADRs that occur
are reported, suggesting that the real cost to the Australian economy
is substantially higher (Bailey et al., 2016). Incorporating routine
PGx testing into clinical use would likely reduce the frequency and
severity of ADRs, minimise the prescription of ineffective
medications and streamline treatment (Verbelen et al., 2017;
Swen et al., 2023). Furthermore, with the availability of simple
buccal swab genetic testing, PGx-guided treatment has become
an accessible form of personalised medicine at relatively low cost
to patients (Blumberger et al., 2018). The incorporation of PGx
testing into clinical practice is integral for the promotion of patient-
centered care and addressing populace health disparities by ensuring
that all patients receive the best possible care, regardless of their
genetic background (Hicks et al., 2019).
3 Pharmacogenetics in the treatment
for mental health
Mental health disorders are among the most prevalent health
conditions in Western society (De Vaus et al., 2018). The World
Health Organisation estimated 1 in 8 people were suffering from a
diagnosable mental illness prior to the COVID-19 pandemic, with
numbers rising by ~27% in following years
3
. By 2030, mental health
disorders are projected to be the first ranked cause of disease burden
worldwide (Friedrich, 2017), with ~1 in 5 people expected to
experience an episode of mental ill-health at some stage in their
lifetime (Malhi and Mann, 2018). Depression and anxiety are
amongst the most common mental health disorders observed in
general practice, often seen as comorbid conditions that are treated
similarly by primary healthcare practitioners (Tiller, 2013).
Psychological therapy is frequently used as a first port of call for
treating patients with depression and anxiety at a mild to moderate
severity, however, patients with a more severe condition often
undergo psychological treatment in conjunction with
pharmacotherapy. As most second-generation antidepressant
medications also show promising results in reducing levels of
anxiety (Cassano et al., 2002), both conditions can typically be
treated through the prescription of one or more antidepressants
(Ballenger, 2000).
3.1 Understanding the role of cytochrome
P450s in psychiatry
Although antidepressants are the most frequently prescribed
medication for mental health disorders (Lunghi et al., 2022), their
efficacy in relieving symptoms of depression and anxiety are variable
(Maslej et al., 2021). Whilst a considerable number of patients will
undergo remission within 2 months of treatment, over half
experience minimal improvement, with some patient’s depressive
symptoms worsening (Thomas et al., 2013). It is believed that
between ~42–50% of this variability in antidepressant drug
response is accounted for by genetic variation in PGx genes
(Crisafulli et al., 2011;Tansey et al., 2013). Although there are
over 50 CYP genes involved in drug metabolism (Ingelman-
Sundberg et al., 2007;Lynch et al., 2007;Zhao et al., 2021), only
a small number have major influences on the metabolism of
antidepressants (Table 1)(Zemanova et al., 2022). Approximately
1https://news.vumc.org/2020/04/30/predict%E2%80%88program-
expands-opens-new-genomics-clinic/
2https://www.psa.org.au/wp-content/uploads/2019/01/PSA-Medicine-
Safety-Report.pdf 3https://vizhub.healthdata.org/gbd-results/
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TABLE 1 Royal Australian and New Zealand College of Psychiatrists approved and recommended antidepressants, their primary mechanism of action and
cytochrome P450 metabolic pathways. Minor metabolic pathways in parentheses (Cacabelos, 2012;Samer et al., 2013;Malhi et al., 2015;Malhi et al., 2021;
Zemanova et al., 2022).
Drug class Generic drug
name
Cytochrome
P450 metabolism
Primary mechanism of action References
Monoamine Oxidase
Inhibitors (MAOIs)
Moclobemide
a
CYP2C19 (CYP1A2,
CYP2D6)
Inhibits the oxidative deamination of
monoamines A and B to increase
concentrations of 5-HT, NA, and DA within
the presynaptic neuron
Bonnet (2003);Bunney and Davis (1965);
Gillman, (2011);Schildkraut, (1965);Sub
Laban and Saadabadi (2022)
Phenelzine (CYP3A4, CYP2C19)
Tranylcypromine CYP2A6 Last-line antidepressants due to irreversible
nature of inhibition
Tricyclic Antidepressants
(TCAs)
Amitriptyline CYP2C19, CYP3A4 (CYP1A2) Diverse mechanisms of action though most
inhibit the reuptake of NA and 5-HT to
increase neurotransmitter concentrations in
the synaptic cleft for postsynaptic uptake.
High affinity for NA reuptake transporters
over 5-HT. Some TCAs also act as DA
receptor antagonists
Gillman (2007);Hillhouse and Porter
(2015);Sheffler et al. (2022)
c
Clomipramine CYP2C19 (CYP1A2, CYP3A4) Secondary antidepressants due to off-target
effects caused by antagonism for
postsynaptic histamine, muscarinic, and
adrenergic receptors
Dosulepin CYP2C19, CYP3A4, CYP1A2
(CYP2D6)
Doxepin CYP2C19, CYP3A4, CYP1A2,
CYP2C9 (CYP2D6)
Imipramine CYP2C19 (CYP1A2)
Nortriptyline CYP2D6 (CYP1A2, CYP2C19,
CYP3A4)
Amoxapine
b
CYP2D6, CYP1A2, CYP2C9,
CYP2C19, CYP3A4
Selective Serotonin
Reuptake Inhibitors
(SSRIs)
Citalopram CYP2C19, CYP3A4
(CYP2D6)
Selectively inhibits 5-HT reuptake
transporters, increasing 5-HT concentration
in the synaptic cleft. Weak affinity for NA
reuptake transporters
Castrén and Rantamäki (2010);Hillhouse
and Porter (2015);Sangkuhl et al. (2009);
Santarelli et al. (2003);Shaw et al. (1967);
Wong et al. (1975);Wong et al. (1974);
Castrén and Rantamäki (2010)
Escitalopram CYP2C19, CYP3A4, CYP2D6 Increases the expression of neurotrophic
factors such as brain-derived neurotrophic
factor (BDNF), enhancing hippocampal
neurogenesis and neuroplasticity
Fluoxetine CYP2D6, CYP2C9, CYP3A4
(CYP2C19)
Primary first-line treatment option due to
their strong tolerability and safety profile
Fluvoxamine CYP2D6 (CYP1A2)
Paroxetine CYP2D6, CYP2B6
Sertraline CYP2B6 (CYP2C19, CYP2C9,
CYP3A4, CYP2D6)
Serotonin-Noradrenaline
Reuptake Inhibitors
(SNRIs)
Desvenlafaxine CYP3A4 Inhibits the reuptake of both NA and 5-HT
with little to no off-target effects on
histamine, muscarinic, and adrenergic
receptors. Reuptake inhibition leads to an
increase in prefrontal DA concentration
Bymaster et al. (2001);Liebowitz and
Tourian (2010);Millan et al. (2001);
Papakostas et al. (2007);Paris et al.
(2009);Vaishnavi et al. (2004)
Duloxetine CYP2D6, CYP1A2
Milnacipran CYP2B6, CYP3A4
Levomilnacipran CYP3A4 (CYP2C8, CYP2C19,
CYP2D6, CYP2J2)
Primary first-line treatment option
Venlafaxine CYP2D6 (CYP2C19,
CYP3A4)
Selective Noradrenaline
Reuptake Inhibitors
(NRIs)
Reboxetine CYP 3A4 Selectively inhibits the reuptake of NA by the
presynaptic membrane to increase prefrontal
DA and NA levels without significantly
affecting subcortical DA concentrations
Brunello et al. (2002);Bymaster et al.
(2002);Dillon and Pizzagalli (2018);
Nestler et al. (2002);Steiger and
Pawlowski (2019)
Atomoxetine CYP2D6
(Continued on following page)
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TABLE 1 (Continued) Royal Australian and New Zealand College of Psychiatrists approved and recommended antidepressants, their primar y mechanism of action
and cytochrome P450 metabolic pathways. Minor metabolic pathways in parentheses (Cacabelos, 2012;Samer et al., 2013;Malhi et al., 2015;Malhi et al., 2021;
Zemanova et al., 2022).
Drug class Generic drug
name
Cytochrome
P450 metabolism
Primary mechanism of action References
Well tolerated first-line antidepressants used
primarily for their stimulating effects such as
cortical arousal and increased energy levels
Teniloxazine ?
Serotonin Modulators Trazodone CYP3A4 High doses: inhibits 5-HT reuptake via 5-HT
2A/2C
Ellingrod and Perry (1995);Malhi et al.
(2015);Rotzinger and Baker (2002);
Wang et al. (2013)
Low doses: antagonises 5-HT 2A, adrenergic,
and histamine
Vortioxetine CYP2D6, (CYP3A4, CYP2A4,
CYP2C19, CYP2C9)
Inhibits 5-HT transporters, agonists for 5-
HT1a and 1b, and antagonists for 5-HT3a
and 7
Nefazodone CYP3A4 Inhibits 5-HT transporters to increase
cortical neurotransmitters (NA, DA, and
BDNF)
Vilazodone CYP3A4 (CYP2C19,
CYP2D6)
Selective inhibitor for 5-HT reuptake and
partial agonist on 5-HT1a receptors
Atypical Antidepressants Agomelatine CYP1A2, (CYP2C9,
CYP2C19)
First antidepressant to directly increase
melatonin levels, acting as a melatonin
agonist and 5-HT antagonist to further
promote the increased release of both DA
and NA.
Bymaster et al. (2002)
Mirtazapine
b
CYP2D6, CYP3A4, (CYP1A2) Inhibits 5-HT reuptake transporters,
antagonises adrenergic receptors to increase
NA, and antagonises 5-HT to increase
cortical NA and DA. Mirtazapine is
commonly used as first-line treatment
Harmer et al. (2017);Hickie and Rogers
(2011)
Mianserin
b
CYP2D6, CYP1A2 (CYP3A4)
Bupropion
b
CYP2B6 Inhibits the reuptake of both NA and DA,
prolonging their duration of action within
the synaptic cleft and promoting
downstream effects
Horst and Preskorn (1998)
Maprotiline
b
CYP2D6 Predominantly a NA and DA reuptake
inhibitor but also antagonises histamine and
adrenergic receptors. Maprotiline also acts as
an inhibitor to amine transporters to delay
NA reuptake
Llorca (2010);Montejo et al. (2010)
NMDA-Glutamatergic
Receptor Antagonists
Esketamine CYP2B6, CYP3A4 (CYP2C9,
CYP2C19)
Selective antagonists of glutamate NMDA
receptors that bind to the glutamate site of
the receptor, thereby inhibiting the release of
calcium into the neuron and increasing
prefrontal and hippocampal glutamate
concentrations
Edinoff et al. (2021)
Ketamine CYP2C9, CYP2B6, CYP3A4 Esketamine: non-competitive; ketamine:
competitive
Lener et al. (2017);Lin et al. (2015);Singh
et al. (2016)
d
Brexanolone Non-CYP based pathways Enhances the inhibitory effects of GABA by
antagonising GABA
A
receptors to promote
stimulation of glutamate production
Atypical Antipsychotics Aripiprazole CYP2D6, CYP3A4 Acts as a partial agonist at D2 receptors
acting on both postsynaptic DA2 receptors
and presynaptic autoreceptors to enhance
DA activity in the mesocortical pathway.
Also modulates 5-HT2A receptors
Bauman et al. (2008);DeLeon et al.
(2004);McNeil et al. (2022);Stahl (2016);
Thomas and Saadabadi (2022);Zubiaur
et al. (2021)
e
Brexpiprazole CYP2D6, CYP3A4
Lurasidone CYP3A4 Antagonist for DA2 and 5-HT2A receptors
to increase neurotransmitter concentrations
and normalise brain activity
(Continued on following page)
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24% of antidepressants are metabolised by CYP1A2-encoded
enzymes, 5% by CYP2B6, 38% by CYP2C19, 85% by CYP2D6,
and 38% by CYP3A4, however, this may vary subject to age, sex,
and ethnicity related differences (Cacabelos and Torrelas, 2015).
Most antidepressants are metabolised by numerous CYPs, whilst it is
possible for a single CYP to be involved in the metabolism of
multiple different antidepressants (Radosavljevic et al., 2023).
Variations in allelic carriage of these CYP enzyme-encoding
genes is known to alter enzymatic activity and affect drug
efficacy, therapeutic outcomes, and possible side effect
presentation (van Westrhenen et al., 2021).
Though the CYP3A family is known to play a major role in the
metabolism of ~30% of all pharmacological drugs (Zemanova et al.,
2022), the most well studied and important CYP genes in psychiatry are
CYP2D6 and CYP2C19.BothCYP2D6 and CYP2C19 are highly
polymorphic genes with extensive interactions with the metabolic
processes of tricyclic antidepressants (TCAs), selective serotonin
reuptake inhibitors (SSRIs), and serotonin-noradrenaline reuptake
inhibitors (SNRIs) (Müller et al., 2013;Shalimova et al., 2021;
Zemanova et al., 2022). The interindividual genetic variations within
thesegenesandthesubsequentlyalteredenzymaticandmetabolic
activity may explain some of the variation in drug response observed in
people prescribed antidepressants. For example, of the more than
150 CYP2D6 allelic variations designated to date by the
Pharmacogene Variation Consortium (PharmVar)
4
(Gaedigk et al.,
2018),over 40 variations are known to encode inactive or non-
functional enzymes, with other variations encoding ‘normal’or
increased enzymatic activity (Bertilsson et al., 2002). Similarly, of the
more than 30 CYP2C19 variations have been designated by the
PharmVar
4
, only seven have been reported to maintain normal
enzymatic function (Gaedigk et al., 2018). Furthermore, CYP2D6
and CYP2C19 phenotypes vary greatly across the global population
with studies suggesting that only 10% of people are poor metabolisers
and even fewer (3%) are ultrarapid metabolisers (Martis et al., 2013;
Gaedigk et al., 2017). With the implementation of PGx into clinical
practice, it is possible to obtain CYP2D6 and CYP2C19 metaboliser
status prior to prescribing antidepressants, accurately predicting dose
and improving treatment outcomes (Altar et al., 2015;Arranz et al.,
2019;Greden et al., 2019). Demonstrated in a recent study by Jukićet al.
(2018) investigation into CYP2C19-encoded enzymes and their
metabolism of escitalopram in 2,066 participants observed a
noticeable difference in drug tolerability. Ultrarapid and poor
metabolisers of CYP2C19 were more likely to cease taking
escitalopram due to both therapeutic failures in the case of
ultrarapid metabolisers, as well as poor metabolisers presenting with
ADRs. Jukićet al. (2018) conclude that by individualising escitalopram
therapy and tailoring dosage dependent on CYP2C19 status, PGx-
guided treatment may limit the presentation of ADRs and improve
therapeutic outcomes of escitalopram in clinical practice.
When investigating the interaction of CYP2D6 and CYP2C19
alleles with antidepressant metabolism in patients with depression,
intermediate metabolisers have shown better responses to
antidepressant medication with ultrarapid metabolisers presenting
a higher risk of suicide than other metabolic phenotypes (Zackrisson
et al., 2010). Additionally, poor metabolisers with reduced
enzymatic activity, have shown higher residual concentrations of
antidepressant levels in serum compared to extensive metabolisers,
leading to adverse side-effects (Schenk et al., 2010;Huezo-Diaz et al.,
2012;Chang et al., 2014;Milosavljevic et al., 2021). Finally, when
prescribing antidepressants, it is pertinent to understand and
recognise interactions with other drugs that are also metabolised
on the CYP2D6 and CYP2C19 pathways to maximise therapeutic
potential from all prescribed medications. For example, sertraline, a
first-line SSRI, is metabolised by CYP enzyme CYP2B6 and at a dose
of 50 mg it also acts as a mild inhibitor of CYP2D6 (Lynch et al.,
2007). When dosage is increased from 50 to 200 mg, sertraline
becomes a potent inhibitor, antagonising CYP2D6 and inhibiting
the metabolism of other medications via this drug pathway, such as
other common SSRIs (Sproule et al., 1997).
TABLE 1 (Continued) Royal Australian and New Zealand College of Psychiatrists approved and recommended antidepressants, their primar y mechanism of action
and cytochrome P450 metabolic pathways. Minor metabolic pathways in parentheses (Cacabelos, 2012;Samer et al., 2013;Malhi et al., 2015;Malhi et al., 2021;
Zemanova et al., 2022).
Drug class Generic drug
name
Cytochrome
P450 metabolism
Primary mechanism of action References
Quetiapine CYP3A4 (CYP2D6) Antagonists for 5-HT2A and D2 receptors,
increasing cortical concentration of DA and
5-HT.
Olanzapine CYP1A2 (CYP2D6) Secondary actions with 5-HT1A, histamine,
and adrenergic receptors
Risperidone CYP2D6 (CYP3A4) Olanzapine also antagonises muscarinic
receptors
NB: serotonin (5-HT), noradrenaline (NA), dopamine (DA).
a
Reversibile.
b
Bupropion is unicyclic; mirtazapine, mianserin, maprotiline, and amoxapine are tetracyclic.
c
https://pubchem.ncbi.nlm.nih.gov/pathway/PathBank:SMP0000641
d
https://s3-us-west-2.amazonaws.com/drugbank/fda_labels/DB11859.pdf?1553196718
e
https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling
?: Unknown metabolic pathway.
4https://www.pharmvar.org/
5https://www1.health.gov.au/internet/main/publishing.nsf/Content/
mental-pubsm-child2
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Research exploring drug-drug interactions in psychiatry and the
involvement of CYP genetic variation has shown that
polypsychopharmacotherapy increases a patient’s risk of potential
adverse outcomes and hospital admissions, as well as having been
retrospectively associated with an increased risk of suicidal
behaviours (Goffin et al., 2016;Kessler et al., 2016). These
retrospective studies have shown that individuals who have died
of suicide had a greater likelihood of carrying more than two active
copies of CYP gene CYP2D6 than those dying of natural causes, thus
corresponding to an ultrarapid metaboliser phenotype and failing to
reach drug therapeutic potential (Zackrisson et al., 2010;Peñas-
Lledó et al., 2012). Further studies associate not only high suicide
risk with ultrarapid CYP2D6 metabolism, but also implicate the
involvement of ultrarapid CYP2C19 metabolisers, and suggest that
these phenotypes and polypharmacy factors need to be taken in
account to prevent suicide attempt in patients with mental illness
(Peñas-Lledó et al., 2022).
These studies, amongst others in the literature, demonstrate the
benefit of implementing PGx testing for CYP2D6 and CYP2C19
metaboliser status in clinical practice. However, as PGx testing can
currently provide information only on the probability of potential
vulnerabilities, it should always be used in conjunction with clinical
assessment (Namerow et al., 2020). Further research is suggested by
most of the studies described above to advance the understanding of
CYP involvement in psychiatric medication metabolism.
3.2 Pharmacogenetic testing in clinical
psychiatric practice
It is not uncommon for people suffering from psychiatric
conditions such as depression to persist through a trial-and-error
process of therapies lacking efficacy to find the appropriate
psychotherapeutic agents and dosages (Slomp et al., 2022).
Nearly 60% of individuals suffering from depression are
estimated to not completely respond to first-line antidepressants
and around one in three people are predicted to not respond at all
(Crisafulli et al., 2011). Furthermore, this same third of people
treated for depression who do not show full remission following two
or more treatment trials of first-line antidepressants may develop a
persistent depressive disorder (Kverno and Mangano, 2021). This
may be, in part, due to unsuitable medication and ADRs and
possibly due to non-adherence to treatment recommendations
after recurring failures in the trial-and-error treatment process
(Howes et al., 2022). Furthermore, for each subsequent
antidepressant prescribed to a person with depression, the
likelihood of remission decreases significantly, with remission
rates for the first and second drug trials at 36.8% and 30.6%
respectively, and the third and fourth at 13.7% and 13.0% (Rush
et al., 2006). PGx testing and informed prescribing has been
suggested to improve treatment response and outcomes in those
patients resistant to numerous antidepressants, decreasing the risk
of drug-related side effects and patient non-compliance (Bousman
et al., 2019).
As described above for CYP2D6 and CYP2C19, many studies
have explored the role of PGx gene polymorphisms in modifying
psychiatric medication outcomes. However, there are few
randomised controlled trials (RCTs) assessing the clinical
utilisation of PGx testing as a predictive tool to guide
prescription. Two RCTs published by Hall-Flavin et al. (2012);
Hall-Flavin et al. (2013) found that patients who received PGx-
guided treatment and tailored prescription had a significantly
greater reduction in depressive symptoms after 8 weeks of
antidepressant treatment when compared to those who received
standard treatment with clinical guidelines. These studies utilised
information from PGx CYP genes CYP2D6,CYP2C19,CYP1A2,as
well as two genes encoding serotonin transporters (SLC6A4) and
receptors (HTR2A). Similarly, two recent meta-analyses examining
1,737 participants over five RCTs and 5,347 participants over
11 RCTs, showed that PGx-guided treatment and tailored
prescription and dosing were associated with higher remission
rates and faster response times when compared to standard
treatment in difficult-to-treat-depressive patients (Bousman et al.,
2019;Wang et al., 2023). Vos et al. (2023) recently conducted a RCT
exploring whether PGx-guided treatment would result in faster
attainment of therapeutic TCA serum levels than standard of
care treatment in 111 participants. They reported that
participants receiving PGx-guided treatment realised therapeutic
TCA concentrations faster than controls presenting with fewer and
less severe side effects. No change was noted in depressive
symptoms, however, indicating that whilst PGx-guided treatment
was effective, perhaps future studies should investigate the use of
second-generation antidepressants and not just TCAs.
Though the studies above suggest that PGx-guided treatment
may be a promising approach to tailor drug treatments for
patients with mental health disorders, there remain some
ambiguities in the literature. Solomon et al. (2019)
systematically reviewed 16 studies between 2013 and 2018 to
investigate whether PGx testing of CYP2D6 and CYP2C19 was
able to predict antidepressant response or ADRs. This study
reported mixed findings, suggesting that PGx testing may predict
ADRs in certain individuals, however, it was unclear if these
results would translate across a broader population. Solomon
et al. (2019) go on to mention that the lack of positive
associations between PGx-guided treatment and reducing
ADRs could range from explanations such as underpowered
studies and the lack of ethnic diversity, to uncontrolled
concomitant use of herbal medicines. Further RCTs should be
conducted with adequate sample sizes to clarify whether CYP
PGx-guided treatment can yield positive outcomes for mental
health (Solomon et al., 2019).
In conclusion, current evidence from RCTs suggests that
whilst PGx-guided treatment may aid in tailoring medication
choice and dosing for patients with mental health disorders,
further research is needed to fully understand the potential
benefits of PGx-guided treatment and to address the
challenges that currently limit its widespread use in clinical
practice. It is also important to note that, although treatment
with antidepressants has provided promising results for treating
mental ill-health such as depression and anxiety, only 60%–70%
of patients show an effective response to antidepressant therapy
(Kennedy and Giacobbe, 2007;Al-Harbi, 2012). Further studies
investigating the PGx profiles of patients who do not respond to
medication may help guide selection of alternative treatments
and identify the genetic profile of people with treatment-resistant
depression (Fabbri et al., 2021;McCarthy et al., 2021).
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4 Clinical application of
pharmacogenetics in youth mental
health
Mental ill-health in youth is a significant public health
concern with the most recent Young Minds Matter survey
estimating ~600,000 Australian children and adolescents are
currently living with some form of mental health disorder
such as depression
5
(Goodsell et al., 2017). Since the
beginning of the COVID-19 pandemic, the prevalence of
depression in youth significantly increased (Racine et al.,
2021), with nearly two in five Australian people (39.6%) aged
between 16- and 24-year reporting having mental ill-health
in2020
6
.Additionally,agreaterincidenceofworry,loneliness,
and depressive symptoms has been reported amongst Australian
high school students since the pandemic outbreak in 2019
(Houghton et al., 2022). Although the situation has slightly
improved since the end of 2021, the prevalence of mental ill-
health in youth remains high, with many young people unable to
access sufficient care and support for their mental health
condition
7
. Depression in youth is associated with reduced
quality-of-life outcomes such as school performance and social
growth, as well as increases in the likelihood of risk-taking and
self-harming behaviours (Goodsell et al., 2017). Adolescents
suffering from untreated depression may continue to
experience chronic depressive symptoms and impairment into
their adult years, with a greater risk of developing a depression-
related disability (Ghio et al., 2015). Furthermore, research
findings indicate a correlation between chronic depression
experienced during the ages of 12–17-year and subsequent
psychosocial outcomes may affect individuals into their
adulthood (Clayborne et al., 2019). These consequences
include a higher likelihood of failing to complete secondary
school, unemployment, and an increased in early/unwanted
pregnancies.
With an increasing incidence of youth depression in recent
years, lifetime healthcare costs covering mental health services
such as psychiatric and other allied health professional services,
hospitalisations and disability support services, and mental
health-related subsidised prescriptions are also rising (Cook,
2019). Current lifetime costs associated with depression are
AUD $43–70 billion/year to Australia’seconomy
8
,
encompassing not only healthcare costs, but also the loss of
opportunity to the workforce through unemployment and/or
loss of employment. Given the rising prevalence and
socioeconomic burden attributed to mental health disorders in
youth, there is a considerable need to reanalyse preliminary
treatment options and the current care pathways to limit the
mental health burden and to ensure that this population is
receiving effective care (Le et al., 2021).
4.1 Guided antidepressant treatment in
youth mental health
Although PGx-guided prescription is common in paediatric
oncology and gastroenterology (Ramos et al., 2021;Sandritter
et al., 2023), the majority of PGx studies to guide treatment for
mental health have been conducted in adults with results
extrapolated to youth populations (Wehry et al., 2018). Whilst
there are limitations to PGx-guided treatment in youth, PGx
testing has the potential to decrease morbidity, side effects and
adverse event presentations, increase treatment response, and
decrease hospital admissions, readmissions, and overall cost of care.
Studies observing metaboliser status and its association with
escitalopram in children and adolescents echo those mentioned
earlier by Jukićet al. (2018). Specifically, poor metabolisers for
CYP2C19 experienced more severe ADRs than other metaboliser
statuses and have a high likelihood of discontinuing the use of
escitalopram, citalopram, and sertraline (Aldrich et al., 2019;
Poweleit et al., 2019). Furthermore, ultrarapid metabolisers
tended to respond faster to both escitalopram and citalopram
and subsequently spent less time in hospital following drug
administration. These results reflect the observations of Strawn
et al. (2019) who showed that differing CYP2C19 metabolisers
required differing doses of escitalopram and sertraline in order to
maintain similar therapeutic benefits. CYP2D6 polymorphisms have
also been investigated in antidepressant prescription in youth. For
example, fluvoxamine concentrations have been shown to remain
higher for longer in CYP2D6 poor metabolisers, with these patients
therefore requiring dose adjustments (Chermá et al., 2011).
Similarly, CYP2D6 poor metabolisers are slower to metabolise
fluoxetine into its active metabolite, norfluoxetine, than other
metabolisers. As a result, at similar time points, fluoxetine
concentrations are higher in poor metabolisers compared to
other metabolisers (Gassó et al., 2014). A recent review
conducted by Strawn et al. (2023) explored the acute and chronic
ADRs often associated with SSRIs and SNRIs in children and
adolescent patients. Strawn et al. discuss the presentation of acute
gastrointestinal symptoms, long-term weight gain, and sexual
dysfunction, suggesting that PGx-guided treatment may address
these ADRs and inform discontinuation strategies from second-
generation antidepressants in those youth patients presenting with
symptoms.
Though the research above suggests PGx-guided treatment may be
beneficial in guiding the treatment of mental ill-health in children and
adolescents with antidepressant medication, not all studies have been as
promising. Namerow et al. (2022), reviews a prospective trial
investigating the use of PGx-guided treatment in adolescents with
depression (Vande Voort et al., 2022). This trial randomised
176 adolescents with moderate to severe major depressive disorder
to either receive PGx-guided treatment or treatment as usual, aiming to
evaluate the clinical impact and potential for PGx testing panels to
improve mental health outcomes in clinical practice in child and
adolescent psychiatry. Results showed that there was no significant
difference in symptom improvement, side effect burden, or satisfaction
between the two groups. However, the study found that the use of PGx
testing did influence clinical providers to more frequently prescribe
medications that are not considered first-line antidepressants due to
failed demonstrations in efficacy for the treatment of depression in
6https://www.abs.gov.au/statistics/health/mental-health/national-study-
mental-health-and-wellbeing/latest-release
7https://psychology.org.au/for-members/news-and-updates/news/2022/
australians-need-psychological-help-more-than-ever
8https://apo.org.au/node/309475
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youth. Namerow et al. (2022) go on to describe that whilst PGx testing
may not improve treatment efficacy, there is growing evidence that PGx
testing may be efficacious in specific medication prescription.
With mixed information surrounding the efficacy of PGx testing in
youth for mental health disorders, it is vital to consider other factors of
antidepressant therapy where PGx guided treatment may improve the
current treatment model. When finding the right medication, one
qualitative research study exploring young people’sviewson
medication reported that changing medication was often fraught
with anxiety and that the switching of antidepressants was
considered a frustrating and challenging process of trial-and-error,
likely to increase medication non-compliance (McMillan et al.,
2020). Recent research has shown that youth and the elderly,
particularly those patients with depressive disorders, are at an
extreme risk of lower medication adherence (Gast and Mathes,
2019). PGx provides an interventional tool to address these
adherence issues. Though the evidence is limited, and further
research is needed to critically analyse the effects of PGx-guided
treatment on patient medication adherence, one study observed PGx
testing was associated with a higher rate of adherence with 39 different
medications (Christian et al., 2020). This study suggests that by
prescribing more personalised medications using PGx testing and
avoiding unsuitable and potentially high-risk drugs, genetically
informed care may have a positive influence on medication
adherence. Though this study assessed PGx-guided treatment in an
adult population, this holds promise that personalised treatments in
youth will help to increase adherence rates for mental health conditions
and the use of psychiatric medication.
4.2 Challenges for pharmacogenetic
implementation in youth mental health
treatment
Whilst the research on PGx-guided treatment is promising,
implementing patient-specific genetic testing to guide
antidepressant prescription into clinical practice does not come
without its challenges (Gurwitz and Motulsky, 2007;Pinzón-
Espinosa et al., 2022). Precision medicine, or the individualised
and tailored approach to medical treatment, has been made a
government funding priority across the world, with countries
such as the United States, Canada, the United Kingdom, and
China making significant headway in their commitments to
implementation over the last 5 years
9,10,11
. In 2018, Innovation
and Science Australia, an independent board driving sustainable
economic growth and societal benefits, identified an ideal National
Mission to catalyse action around developing genetic-based
precision medicine tools to customise care for each individual by
2030 and provide the right treatment first
12
. However, this policy, as
well as other scientific research (Mutsatsa and Currid, 2013;Amato
et al., 2018;Corponi et al., 2018;Herbert et al., 2018;Corponi et al.,
2019;Goodspeed et al., 2019;Vest et al., 2020;Jameson et al., 2021;
Ramsey et al., 2021), note significant barriers that need addressing to
facilitate the adoption of precision medicine practices into routine
clinical practice. In the context of PGx-guided treatment, even if the
translation of PGx testing utilisation to personalise medicine in
psychiatry has accelerated, until these barriers are addressed, PGx
testing will likely remain research-use only (Bousman et al., 2017;
Blasco-Fontecilla, 2019).
4.2.1 Lack of clinical guidelines for youth
Currently, there are limited evidence-based guidelines for the
use of PGx in youth mental health (Abdullah-Koolmees et al., 2020),
causing a lack of consensus amongst primary care clinicians on the
best way to apply genetic testing to medication selection and dosing
(Gizer et al., 2009). Though the Clinical Pharmacogenetics
Implementation Consortium (CPIC) recently provided guidelines
on the interpretation of PGx testing for psychiatric medication
(Hicks et al., 2017;Bousman et al., 2023), it is noted that
children and adolescents were underrepresented in many of the
studies of the drugs listed in these guidelines. The CPIC therefore
recommends that the generalisability of recommendations to
paedeatric patients needs to be established. Furthermore, many of
the PGx trials in youth psychiatry have been small and not yet
replicated, limiting the applicability of their findings to govern these
CPIC guidelines. However, recent research may suggest that adult
data can be used to govern the guidelines for child and adolescent
PGx-guided treatment. Paediatric pharmacology has traditionally
held the idea that children are not small adults (Stephenson, 2005).
Children undergo phases of rapid growth and development, and
their bodies and organ systems function differently than those of
their adult counterparts. Considering this, it has always been
thought that pharmaceuticals may exhibit different effects and
safety profiles in paediatric patients, resulting in the
requirement for different prescriptions and dosages. However,
studies in pharmacokinetics suggest that in this domain, children
may indeed be treated as small adults (Stephenson, 2005;
Anderson and Holford, 2013). Though controversial,
Anderson and Holford explain how adjusting adult
pharmacological doses using a demographic covariate of size,
maturation, and organ function can scale doses from adults to
children (O’Hara, 2016). If possible, this then begs the question:
do we require separate evidence of efficacious PGx-guided
treatment in youth if evidence exists in adults (Barker et al.,
2022)? Stephenson reports that the perception of variable
responses in children compared to adults arises because drugs
are not adequately studied in youth populations of different ages
(Stephenson, 2005). Stephenson continues to discuss how the
response to drugs in youth have much in common with the
responses observed in adults. WiththemajorityofPGxresearch
conducted in adults and extrapolated to youth, the extent to
which we can rely on adult PGx to safely dose medication in
youth needs to be established. Therefore, more age-appropriate
RCTs with a youth focus using PGx-guided treatment in clinical
practice are required to compare to their adult counterparts to
build a robust evidence-based guideline for mental health
treatment (Brothers, 2013;Van Driest and McGregor, 2013).
9https://www.genomicsengland.co.uk/initiatives/100000-genomes-
project
10 https://allofus.nih.gov/
11 https://www.weforum.org/agenda/2017/11/3-ways-china-is-leading-
the-way-in-precision-medicine/
12 https://www.chiefscientist.gov.au/sites/default/files/Precision-
medicine-final.pdf
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4.2.2 Experience and attitudes of general
practitioners
General practitioners (GPs) are often the first level of contact
in the Australian healthcare system, with mental health related
issues representing one of the most common health issues
managed by GPs (Carey et al., 2020). However, implementing
PGx-guided mental health treatment poses many challenges to
primary healthcare providers–these include the limited
knowledge and experience of PGx testing, the evidence basis
for its use, and the concerns about how it can be incorporated
into current workflow (Vest et al., 2020;Jameson et al., 2021). As
a relatively new field in standard medical practice, many
healthcare professionals lack the training and expertise to
interpret PGx testing results and use them to inform
treatment decisions (Prows and Saldaña, 2009). Many
psychiatric medications are prodrugs, meaning they are
prescribed as inactive derivatives that when metabolised by
CYP enzymes, convert into active compounds (Lynch et al.,
2007). Conversely, some drugs, including numerous SSRIs, are
administered as active compounds that require deactivation by
CYP enzymes during the drug metabolising process. Having a
comprehensive knowledge of common psychiatric drugs is
paramount when properly interpreting PGx testing
information to ensure patient safety and avoid ADRs from
drug-drug interactions. A recent review by Ong et al. (2022)
investigated the experience, attitudes, and knowledge of GPs
towards the application of PGx in primary practice and found
that whilst most GPs understood the basic principles and
recognised the potential benefits of using PGx testing, many
were uncertain about how to use genetic testing to inform
treatment decisions. This was further supported in recent
findings, where the implementation of PGx education and
training efforts were shown to improve the comfort felt by
primary care providers, such as GPs, when ordering a PGx
test for their patients (Preys et al., 2023). These encouraging
results demonstrate an enthusiasm for PGx-guided treatment in
GP practices and welcome the idea that with proper support,
guidance, and education, primary care providers may implement
PGx testing into their clinical practice.
Although the studies above illustrate a keen understanding on
the usefulness for PGx testing in the primary care setting, with no
endorsed guidelines on the use of PGx testing in clinical practice,
many GPs have expressed the need for further research that
explores the translation of new genetic technologies into clinical
practice to help inform the development of such guidelines. The
review by Ong et al. (2022) emphasised the importance of
gathering a complete understanding of the experiences,
knowledge, and attitudes held by GPs towards genetic testing.
Gathering this information and tailoring the integration of PGx
testing to the requirements of primary care providers is integral
for successful adoption into clinical practice (Aboelbaha et al.,
2023).
4.2.3 Attitudes and expectations of young people
with mental ill-health
As well as those of GPs, the knowledge and attitudes of the
patient towards PGx testing are reported in the literature. Stancil
et al. (2021) explored adolescent perceptions of PGx testing and
noted the lack of research on this topic. This study emphasised the
importance of further research exploring youths’perceived values
and understandings of PGx testing, as well as strategies on
disseminating PGx results to youth. All participating youth in
this study were optimistic for the implementation of PGx-guided
treatment in clinical care, expressing an understanding for its use in
primary care, the low risk associated with testing, and the benefit for
them and their peers in their treatment (Stancil et al., 2021). Stancil
et al. conclude by noting the importance for including youth in the
decision-making process and engaging them in the discussion of
PGx testing results and the relevance of these results to the
medication they may be prescribed.
4.2.4 Societal expectations
Lastly, societal expectations around data privacy, equity, and
economic values may provide a hurdle in normalising PGx-guided
treatment
12
. Public awareness around data privacy has increased in
recent years and has become a sensitive issue in the healthcare sector
(Vimalachandran et al., 2020). Though there are many benefits to
PGx testing, the broader community will only have confidence in the
use of genetic data if privacy can be guaranteed. Precision medicine
tools and applications need also to be available to all peoples,
including people of different ethnicities and those living in
remote communities
12
. Finally, studies have found that a large
concern for both patients and healthcare professionals is the cost
of PGx testing (Jameson et al., 2021). Multiple studies have found
that costing was the largest factor influencing patients’decision
making (Chan et al., 2017;Liko et al., 2020), highlighting that
patients would be more likely to seek PGx testing if the cost was
lower or subsidised by Medicare.
To navigate the barriers and educate the public on the potential
benefits of PGx-guided treatment for improved mental health outcomes
in youth, it is important that researchers engage with the communities
that will benefit from the adoption of this technology.
5 Conclusion
Mental health conditions are becoming increasingly prevalent in
Australian society and throughout the Western world, particularly in
our youth. The management of mental health conditions in youth is
complex and, if not handled correctly, can exacerbate mental ill-health
and risk the persistence of these conditions into adulthood. Although
there have been improvements in symptom recognition, diagnostic
guidelines, and the availability of pharmacotherapies, there remain
many challenges in the management of mental health in young
people. PGx testing has been shown to provide an increased chance
of achieving a therapeutic outcome, minimising harmful ADR and their
flow on effects. Current research suggests that an informed knowledge
of youth’sspecific PGx characteristics could be expected to enhance the
treatment and recovery from mental illness. Although there are barriers
to the use of genomic testing in youth, the field of PGx continues to
develop and advances in this area have led to an increased accessibility
of DNA testing and PGx-guided treatment. Whilst emerging literature
has shown PGx testing may be utilised in the care of young people with
mental illness, there is a need for more large-scale age-appropriate
studies to resolve conflicting research. In addition, further studies are
needed to explore how this testing could be incorporated into standard
Frontiers in Pharmacology frontiersin.org11
Roberts et al. 10.3389/fphar.2023.1267294

clinical practice, navigating social, behavioural, and economic barriers.
With most mental illness in Australia managed in primary care, it is
important that the application of PGx testing in youth is extensively
studied in the context of primary healthcare settings. Therefore, future
research in PGx-guided treatment needs to acknowledge the
experiencesofyouthsthemselvesandtheconcernsoftheirprimary
care providers, involving key stakeholders in the research process to
ensure personalised and effective care.
Author contributions
BR: Conceptualization, Visualization, Writing–original draft,
Writing–review and editing. ZC: Conceptualization,
Writing–original draft, Writing–review and editing. SL:
Conceptualization, Writing–review and editing. SS:
Writing–review and editing. BM: Writing–review and editing.
KC: Writing–review and editing. LG: Writing–review and editing.
JR: Writing–review and editing. PA: Writing–review and editing.
SH: Conceptualization, Writing–review and editing.
Funding
The authors declare financial support was received for the
research, authorship, and/or publication of this article. BR was
supported by an Australian Government Research Training
Program (RTP), and a Perron Institute Byron Kakulas Prestige
scholarship. The funders had no role in the preparation of the
review and the decision to publish.
Acknowledgments
We would like to acknowledge the people with lived experience
of mental health that contribute to our ongoing work. We would also
like to acknowledge the lifetime of mentoring and wisdom that the
late Professor Allen Roses has given to our team and this field of
work. It is an honor and privilege to have worked with him for over
27 years and through our research we continue the work we started
together. The authors wish to acknowledge philanthropic support
from the Sarich family.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
The authors declared that they were an editorial board member
of Frontiers, at the time of submission. This had no impact on the
peer review process and the final decision.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
References
Abdullah-Koolmees, H., van Keulen, A. M., Nijenhuis, M., and Deneer, V. H. M.
(2020). Pharmacogenetics guidelines: Overview and comparison of the DPWG, CPIC,
CPNDS, and RNPGx guidelines. Front. Pharmacol. 11, 595219. doi:10.3389/fphar.2020.
595219
Aboelbaha, S., Zolezzi, M., Abdallah, O., and Eltorki, Y. (2023). Mental health
prescribers’perceptions on the use of pharmacogenetic testing in the management
of depression in the Middle East and North africa region. Pharmgenomics Pers. Med. 16,
503–518. doi:10.2147/pgpm.S410240
Adams, J. (2008). Pharmacogenomics and personalized medicine. Nat. Educ. 1
(1), 194.
Al-Harbi, K. S. (2012). Treatment-resistant depression: Thera peutic trends, challenges, and
future directions. Patient Prefer Adherence 6, 369–388. doi:10.2147/ppa.S29716
Aldrich, S. L., Poweleit, E. A., Prows, C. A., Martin, L. J., Strawn, J. R., and Ramsey, L.
B. (2019). Influence of CYP2C19 metabolizer status on escitalopram/citalopram
tolerability and response in youth with anxiety and depressive disorders. Front.
Pharmacol. 10, 99. doi:10.3389/fphar.2019.00099
Altar, C. A., Carhart, J. M., Allen, J. D., Hall-Flavin, D. K., Dechairo, B. M., and
Winner, J. G. (2015). Clinical validity: Combinatorial pharmacogenomics predicts
antidepressant responses and healthcare utilizations better than single gene
phenotypes. Pharmacogenomics J. 15 (5), 443–451. doi:10.1038/tpj.2014.85
Amato, R. J., Boland, J., Myer, N., Few, L., and Dowd, D. (2018). Pharmacogenomics
and psychiatric clinical care. J. Psychosoc. Nurs. Ment. Health Serv. 56 (1), 22–31. doi:10.
3928/02793695-20170928-01
Anderson, B. J., and Holford, N. H. (2013). Understanding dosing: Children are small
adults, neonates are immature children. Arch. Dis. Child. 98 (9), 737–744. doi:10.1136/
archdischild-2013-303720
Arranz, M. J., Gonzalez-Rodriguez, A., Perez-Blanco, J., Penadés, R., Gutierrez, B.,
Ibañez, L., et al. (2019). A pharmacogenetic intervention for the improvement of the
safety profile of antipsychotic treatments. Transl. Psychiatry 9 (1), 177. doi:10.1038/
s41398-019-0511-9
Bailey, C., Peddie, D., Wickham, M. E., Badke, K., Small, S. S., Doyle-Waters, M. M.,
et al. (2016). Adverse drug event reporting systems: A systematic review. Br. J. Clin.
Pharmacol. 82 (1), 17–29. doi:10.1111/bcp.12944
Ballenger, J. C. (2000). Anxiety and depression: Optimizing treatments. Prim. Care
Companion J. Clin. Psychiatry 2 (3), 71–79. doi:10.4088/pcc.v02n0301
Barker, C. I. S., Groeneweg, G., Maitland-van der Zee, A. H., Rieder, M. J., Hawcutt, D.
B., Hubbard, T. J., et al. (2022). Pharmacogenomic testing in paediatrics: Clinical
implementation strategies. Br. J. Clin. Pharmacol. 88 (10), 4297–4310. doi:10.1111/bcp.
15181
Bauman, J. N., Frederick, K. S., Sawant, A., Walsky, R. L., Cox, L. M., Obach, R. S.,
et al. (2008). Comparison of the bioactivation potential of the antidepressant and
hepatotoxin nefazodone with aripiprazole, a structural analog and marketed drug. Drug
Metab. Dispos. 36 (6), 1016–1029. doi:10.1124/dmd.108.020545
Bertilsson, L., Dahl, M. L., Dalén, P., and Al-Shurbaji, A. (2002). Molecular genetics of
CYP2D6: Clinical relevance with focus on psychotropic drugs. Br. J. Clin. Pharmacol. 53
(2), 111–122. doi:10.1046/j.0306-5251.2001.01548.x
Blasco-Fontecilla, H. (2019). Clinical utility of pharmacogenetic testing in children
and adolescents with severe mental disorders. J. Neural Transm. (Vienna) 126 (1),
101–107. doi:10.1007/s00702-018-1882-4
Blumberger, D. M., Vila-Rodriguez, F.,Thorpe,K.E.,Feffer,K.,Noda,Y.,
Giacobbe, P., et al. (2018). Effectiveness of theta burst versus high-frequency
repetitive transcranial magnetic stimulation in patients with depression (THREE-
D): A randomised non-inferiority trial. Lancet 391 (10131), 1683–1692. doi:10.
1016/S0140-6736(18)30295-2
Bonnet, U. (2003). Moclobemide: Therapeutic use and clinical studies. CNS Drug Rev.
9 (1), 97–140. doi:10.1111/j.1527-3458.2003.tb00245.x
Bousman, C. A., Arandjelovic, K., Mancuso, S. G., Eyre, H. A., and Dunlop, B. W.
(2019). Pharmacogenetic tests and depressive symptom remission: A meta-analysis of
randomized controlled trials. Pharmacogenomics 20 (1), 37–47. doi:10.2217/pgs-2018-
0142
Frontiers in Pharmacology frontiersin.org12
Roberts et al. 10.3389/fphar.2023.1267294

Bousman, C. A., Forbes, M., Jayaram, M., Eyre, H., Reynolds, C. F., Berk, M., et al.
(2017). Antidepressant prescribing in the precision medicine era: A prescriber’s primer
on pharmacogenetic tools. BMC Psychiatry 17 (1), 60. doi:10.1186/s12888-017-1230-5
Bousman, C. A., Stevenson, J. M., Ramsey, L. B., Sangkuhl, K., Hicks, J. K., Strawn,
J. R., et al. (2023). Clinical pharmacogenetics implementation consortium (CPIC)
guideline for CYP2D6, CYP2C19, CYP2B6, SLC6A4, and HTR2A genotypes and
serotonin reuptake inhibitor antidepressants. Clin. Pharmacol. Ther. 114 (1), 51–68.
doi:10.1002/cpt.2903
Brothers, K. B. (2013). Ethical issues in pediatric pharmacogenomics. J. Pediatr.
Pharmacol. Ther. 18 (3), 192–198. doi:10.5863/1551-6776-18.3.192
Brunello,N.,Mendlewicz,J.,Kasper,S.,Leonard,B.,Montgomery,S.,Nelson,J.,etal.(2002).
The role of noradrenaline and selective noradrenaline reuptake inhibition in depression. Eur.
Neuropsychopharmacol. 12 (5), 461–475. doi:10.1016/s0924-977x(02)00057-3
Bunney, W. E., Jr., and Davis, J. M. (1965). Norepinephrine in depressive reactions. A
review. Arch. Gen. Psychiatry 13 (6), 483–494. doi:10.1001/archpsyc.1965.
01730060001001
Bymaster, F. P., Dreshfield-Ahmad, L. J., Threlkeld, P. G., Shaw, J. L., Thompson, L.,
Nelson, D. L., et al. (2001). Comparative affinity of duloxetine and venlafaxine for
serotonin and norepinephrine transporters in vitro and in vivo, human serotonin
receptor subtypes, and other neuronal receptors. Neuropsychopharmacology 25 (6),
871–880. doi:10.1016/s0893-133x(01)00298-6
Bymaster, F. P., Katner, J. S., Nelson, D. L., Hemrick-Luecke, S. K., Threlkeld, P. G.,
Heiligenstein, J. H., et al. (2002). Atomoxetine increases extracellular levels of
norepinephrine and dopamine in prefrontal cortex of rat: A potential mechanism
for efficacy in attention deficit/hyperactivity disorder. Neuropsychopharmacology 27 (5),
699–711. doi:10.1016/s0893-133x(02)00346-9
Cacabelos, R. (2012). Pharmacogenomics of central nervous system (CNS) drugs.
Drug Dev. Res. 73 (8), 461–476. doi:10.1002/ddr.21039
Cacabelos, R., and Torrelas, C. (2015). Pharmacogenomics of antidepressants.
J. Psychiatr. Depress Anxiety 1 (001), 1–42. doi:10.24966/PDA-0150/100001
Canenguez Benitez, J. S., Hernandez, T. E., Sundararajan, R., Sarwar, S., Arriaga, A. J.,
Khan, A. T., et al. (2022). Advantages and disadvantages of using St. John’s wort as a
treatment for depression. Cureus 14 (9), e29468. doi:10.7759/cureus.29468
Carey, M., Sanson-Fisher, R., Zwar, N., Mazza, D., Meadows, G., Piterman, L., et al.
(2020). Improving depression outcomes among Australian primary care patients:
Protocol for a cluster randomised controlled trial. BMJ Open 10 (2), e032057.
doi:10.1136/bmjopen-2019-032057
Cassano,G.B.,BaldiniRossi,N.,andPini,S.(2002).Psychopharmacologyofanxiety
disorders. Dialogues Clin. Neurosci. 4 (3), 271–285. doi:10.31887/DCNS.2002.4.3/gcassano
Castrén, E., and Rantamäki, T. (2010). The role of BDNF and its receptors in
depression and antidepressant drug action: Reactivation of developmental plasticity.
Dev. Neurobiol. 70 (5), 289–297. doi:10.1002/dneu.20758
Chan, C. Y., Chua, B. Y., Subramaniam, M., Suen, E. L., and Lee, J. (2017). Clinicians’
perceptions of pharmacogenomics use in psychiatry. Pharmacogenomics 18 (6),
531–538. doi:10.2217/pgs-2016-0164
Chang,M.,Tybring,G.,Dahl,M.L.,andLindh,J.D.(2014).ImpactofcytochromeP450
2C19 polymorphisms on citalopram/escitalopram exposure: A systematic review and meta-
analysis. Clin. Pharmacokinet. 53 (9), 801–811. doi:10.1007/s40262-014-0162-1
Chermá,M.D.,Ahlner,J.,Bengtsson,F.,andGustafsson,P.A.(2011).Antidepressant
drugs in children and adolescents: Analytical and demographic data in a naturalistic, clinical
study. J. Clin. Psychopharmacol. 31 (1), 98–102. doi:10.1097/JCP.0b013e318205e66d
Christian, C., Borden, B. A., Danahey, K., Yeo, K. J., van Wijk, X. M. R., Ratain, M. J.,
et al. (2020). Pharmacogenomic-based decision support to predict adherence to
medications. Clin. Pharmacol. Ther. 108 (2), 368–376. doi:10.1002/cpt.1838
Clayborne, Z. M., Varin, M., and Colman, I. (2019). Systematic review and meta-
analysis: Adolescent depression and long-term psychosocial outcomes. J. Am. Acad.
Child. Adolesc. Psychiatry 58 (1), 72–79. doi:10.1016/j.jaac.2018.07.896
Cook, L. (2019). Mental health in Australia: A quick guide. Australia: Parliamentary Li brary.
Corponi, F., Fabbri, C., and Serretti, A. (2019). Pharmacogenetics and depression: A
critical perspective. Psychiatry Investig. 16 (9), 645–653. doi:10.30773/pi.2019.06.16
Corponi, F., Fabbri, C., and Serretti, A. (2018). Pharmacogenetics in psychiatry. Adv.
Pharmacol. 83, 297–331. doi:10.1016/bs.apha.2018.03.003
Crisafulli,C.,Fabbri,C.,Porcelli,S.,Drago,A.,Spina,E.,DeRonchi,D.,etal.(2011).
Pharmacogenetics of antidepressants. Front. Pharmacol. 2, 6. doi:10.3389/fphar.2011.00006
de Oliveira Costa, J., Gillies, M. B., Schaffer, A. L., Peiris, D., Zoega, H., and Pearson, S.
A. (2023). Changes in antidepressant use in Australia: A nationwide analysis (2015-
2021). Aust. N. Z. J. Psychiatry 57 (1), 49–57. doi:10.1177/00048674221079740
De Vaus, J., Hornsey, M. J., Kuppens, P., and Bastian, B. (2018). Exploring the east-
west divide in prevalence of affective disorder: A case for cultural differences in coping
with negative emotion. Personality Soc. Psychol. Rev. 22 (3), 285–304. doi:10.1177/
1088868317736222
DeLeon, A., Patel, N. C., and Crismon, M. L. (2004). Aripiprazole: A comprehensive
review of its pharmacology, clinical efficacy, and tolerability. Clin. Ther. 26 (5), 649–666.
doi:10.1016/s0149-2918(04)90066-5
Dillon, D. G., and Pizzagalli, D. A. (2018). Mechanisms of memory disruption in
depression. Trends Neurosci. 41 (3), 137–149. doi:10.1016/j.tins.2017.12.006
Dunnenberger, H. M., Crews, K. R., Hoffman, J. M., Caudle, K. E., Broeckel, U.,
Howard, S. C., et al. (2015). Preemptive clinical pharmacogenetics implementation:
Current programs in five US medical centers. Annu. Rev. Pharmacol. Toxicol. 55,
89–106. doi:10.1146/annurev-pharmtox-010814-124835
Edinoff, A. N., Odisho, A. S., Lewis, K., Kaskas,A.,Hunt,G.,Cornett,E.M.,etal.(2021).
Brexanolone, a GABA(A) modulator, in the treatment of postpartum depression in adults: A
comprehensive review. Front. Psychiatry 12, 699740. doi:10.3389/fpsyt.2021.699740
Ellingrod, V. L., and Perry, P. J. (1995). Nefazodone: A new antidepressant. Am.
J. Health Syst. Pharm. 52 (24), 2799–2812. doi:10.1093/ajhp/52.24.2799
Fabbri, C., Hagenaars, S. P., John, C., Williams, A. T., Shrine, N., Moles, L., et al.
(2021). Genetic and clinical characteristics of treatment-resistant depression using
primary care records in two UK cohorts. Mol. Psychiatry 26 (7), 3363–3373. doi:10.
1038/s41380-021-01062-9
Friedrich, M. J. (2017). Depression is the leading cause of disability around the world.
Jama 317 (15), 1517. doi:10.1001/jama.2017.3826
Fujita, K. (2004). Food-drug interactions via human cytochrome P450 3A (CYP3A).
Drug Metabol. Drug Interact. 20 (4), 195–217. doi:10.1515/dmdi.2004.20.4.195
Gaedigk, A., Ingelman-Sundberg, M., Miller, N. A., Leeder, J. S., Whirl-Carrillo, M.,
Klein, T. E., et al. (2018). The Pharmacogene variation (PharmVar) consortium:
Incorporation of the human cytochrome P450 (CYP) allele nomenclature database.
Clin. Pharmacol. Ther. 103 (3), 399–401. doi:10.1002/cpt.910
Gaedigk, A., Sangkuhl, K., Whirl-Carrillo, M., Klein, T., and Leeder, J. S. (2017).
Prediction of CYP2D6 phenotype from genotype across world populations. Genet. Med.
19 (1), 69–76. doi:10.1038/gim.2016.80
Gassó, P., Rodríguez, N., Mas, S., Pagerols, M., Blázquez, A., Plana, M. T., et al. (2014).
Effect of CYP2D6, CYP2C9 and ABCB1 genotypes on fluoxetine plasma concentrations
and clinical improvement in children and adolescent patients. Pharmacogenomics J. 14
(5), 457–462. doi:10.1038/tpj.2014.12
Gast, A., and Mathes, T. (2019). Medication adherence influencing factors-an
(updated) overview of systematic reviews. Syst. Rev. 8 (1), 112. doi:10.1186/s13643-
019-1014-8
Ghio, L., Gotelli, S., Cervetti, A., Respino, M., Natta, W., Marcenaro, M., et al. (2015).
Duration of untreated depression influences clinical outcomes and disability. J. Affect
Disord. 175, 224–228. doi:10.1016/j.jad.2015.01.014
Gillman, P. K. (2011). Advances pertaining to the pharmacology and interactions of
irreversible nonselective monoamine oxidase inhibitors. J. Clin. Psychopharmacol. 31
(1), 66–74. doi:10.1097/JCP.0b013e31820469ea
Gillman, P. K. (2007). Tricyclic antidepressant pharmacology and therapeutic
drug interactions updated. Br.J.Pharmacol.151 (6), 737–748. doi:10.1038/sj.bjp.
0707253
Gizer, I. R., Ficks, C., and Waldman, I. D. (2009). Candidate gene studies of ADHD: A
meta-analytic review. Hum. Genet. 126 (1), 51–90. doi:10.1007/s00439-009-0694-x
Goffin, K. C., Dell’Osso, B., Miller, S., Wang, P. W., Holtzman, J. N., Hooshmand, F.,
et al. (2016). Different characteristics associated with suicide attempts among bipolar I
versus bipolar II disorder patients. J. Psychiatr. Res. 76, 94–100. doi:10.1016/j.jpsychires.
2016.02.006
Goodsell, B., Lawrence, D., Ainley, J., Sawyer, M., Zubrick, S., and Maratos, J. (2017).
Child and adolescent mental health and educational outcomes. An analysis of
educational outcomes from young Minds matter: The second Australian child and
adolescent survey of mental health and wellbeing. Perth: Graduate School of
Education, The University of Western Australia.
Goodspeed, A., Kostman, N., Kriete, T. E., Longtine, J. W., Smith, S. M., Marshall, P.,
et al. (2019). Leveraging the utility of pharmacogenomics in psychiatry through clinical
decision support: A focus group study. Ann. Gen. Psychiatry 18, 13. doi:10.1186/s12991-
019-0237-3
Greden, J. F., Parikh, S. V., Rothschild, A. J., Thase, M. E., Dunlop, B. W., DeBattista,
C., et al. (2019). Impact of pharmacogenomics on clinical outcomes in major depressive
disorder in the guided trial: A large, patient- and rater-blinded, randomized, controlled
study. J. Psychiatr. Res. 111, 59–67. doi:10.1016/j.jpsychires.2019.01.003
Grogan, S., and Preuss, C. V. (2023). Pharmacokinetics.
Guengerich, F. P. (2018). Mechanisms of cytochrome P450-catalyzed oxidations. ACS
Catal. 8 (12), 10964–10976. doi:10.1021/acscatal.8b03401
Gurwitz, D., and Motulsky, A. G. (2007). Drug reactions, enzymes, and biochemical
genetics’: 50 years later. Pharmacogenomics 8 (11), 1479–1484. doi:10.2217/14622416.8.
11.1479
Haidar, C. E., Relling, M. V., and Hoffman, J. M. (2019). Preemptively precise:
Returning and updating pharmacogenetic test results to realize the benefits of
preemptive testing. Clin. Pharmacol. Ther. 106 (5), 942–944. doi:10.1002/cpt.1613
Hall-Flavin, D. K., Winner, J. G., Allen, J. D., Carhart, J. M., Proctor, B., Snyder, K. A.,
et al. (2013). Utility of integrated pharmacogenomic testing to support the treatment of
major depressive disorder in a psychiatric outpatient setting. Pharmacogenet Genomics
23 (10), 535–548. doi:10.1097/FPC.0b013e3283649b9a
Frontiers in Pharmacology frontiersin.org13
Roberts et al. 10.3389/fphar.2023.1267294

Hall-Flavin, D. K., Winner, J. G., Allen, J. D., Jordan, J. J., Nesheim, R. S., Snyder, K.
A., et al. (2012). Using a pharmacogenomic algorithm to guide the treatment of
depression. Transl. Psychiatry 2 (10), e172. doi:10.1038/tp.2012.99
Harmer, C. J., Duman, R. S., and Cowen, P. J. (2017). How do antidepressants work?
New perspectives for refining future treatment approaches. Lancet Psychiatry 4 (5),
409–418. doi:10.1016/s2215-0366(17)30015-9
Herbert, D., Neves-Pereira, M., Baidya, R., Cheema, S., Groleau, S., Shahmirian, A.,
et al. (2018). Genetic testing as a supporting tool in prescribing psychiatric medication:
Design and protocol of the IMPACT study. J. Psychiatr. Res. 96, 265–272. doi:10.1016/j.
jpsychires.2017.09.002
Hickie, I. B., and Rogers, N. L. (2011). Novel melatonin-based therapies: Potential
advances in the treatment of major depression. Lancet 378 (9791), 621–631. doi:10.
1016/S0140-6736(11)60095-0
Hicks, J. K., Aquilante, C. L., Dunnenberger, H. M., Gammal, R. S., Funk, R. S., Aitken,
S. L., et al. (2019). Precision pharmacotherapy: Integrating pharmacogenomics into
clinical pharmacy practice. J. Am. Coll. Clin. Pharm. 2 (3), 303–313. doi:10.1002/jac5.
1118
Hicks, J. K., Sangkuhl, K., Swen, J. J., Ellingrod, V. L., Müller, D. J., Shimoda, K., et al.
(2017). Clinical pharmacogenetics implementation consortium guideline (CPIC) for
CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants: 2016 update.
Clin. Pharmacol. Ther. 102 (1), 37–44. doi:10.1002/cpt.597
Hillhouse, T. M., and Porter, J. H. (2015). A brief history of the development of
antidepressant drugs: From monoamines to glutamate. Exp. Clin. Psychopharmacol. 23
(1), 1–21. doi:10.1037/a0038550
Horst, W. D., and Preskorn, S. H. (1998). Mechanisms of action and clinical
characteristics of three atypical antidepressants: Venlafaxine, nefazodone, bupropion.
J. Affect Disord. 51 (3), 237–254. doi:10.1016/s0165-0327(98)00222-5
Horstmann, S., and Binder, E. B. (2009). Pharmacogenomics of antidepressant drugs.
Pharmacol. Ther. 124 (1), 57–73. doi:10.1016/j.pharmthera.2009.06.007
Houghton,S.,Kyron,M.,Hunter,S.C.,Lawrence,D.,Hattie,J.,Carroll,A.,etal.
(2022). Adolescents’longitudinal trajectories of mental health and loneliness: The
impact of COVID-19 school closures. J. Adolesc. 94 (2), 191–205. doi:10.1002/jad.
12017
Howes, O. D., Thase, M. E., and Pillinger, T. (2022). Treatment resistance in
psychiatry: State of the art and new directions. Mol. Psychiatry 27 (1), 58–72.
doi:10.1038/s41380-021-01200-3
Huezo-Diaz, P., Perroud, N., Spencer, E. P., Smith, R., Sim, S., Virding, S., et al. (2012).
CYP2C19 genotype predicts steady state escitalopram concentration in GENDEP.
J. Psychopharmacol. 26 (3), 398–407. doi:10.1177/0269881111414451
Ingelman-Sundberg, M., Sim, S. C., Gomez, A., and Rodriguez-Antona, C. (2007).
Influence of cytochrome P450 polymorphisms on drug therapies: Pharmacogenetic,
pharmacoepigenetic and clinical aspects. Pharmacol. Ther. 116 (3), 496–526. doi:10.
1016/j.pharmthera.2007.09.004
Jameson,A.,Fylan,B.,Bristow,G.C.,Sagoo,G.S.,Dalton,C.,Cardno,A.,etal.(2021).
What are the barriers and enablers to the implementation of pharmacogenetic testing in
mental health care settings?. Front. Genet. 12, 740216. doi:10.3389/fgene.2021.740216
Jukić, M. M., Haslemo, T., Molden, E., and Ingelman-Sundberg, M. (2018). Impact of
CYP2C19 genotype on escitalopram exposure and therapeutic failure: A retrospective
study based on 2,087 patients. Am. J. Psychiatry 175 (5), 463–470. doi:10.1176/appi.ajp.
2017.17050550
Kalow, W. (1990). The Pennsylvania state university College of medicine
1990 bernard B. Brodie lecture. Pharmacogenetics: Past and future. Life Sci. 47 (16),
1385–1397. doi:10.1016/0024-3205(90)90517-u
Kennedy, S. H., and Giacobbe, P. (2007). Treatment resistant depression--advances in
somatic therapies. Ann. Clin. Psychiatry 19 (4), 279–287. doi:10.1080/
10401230701675222
Kessler, C., Ward, M. J., and McNaughton, C. D. (2016). Reducing adverse drug
events: The need to rethink outpatient prescribing. Jama 316 (20), 2092–2093. doi:10.
1001/jama.2016.16392
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., and Walters, E. E.
(2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the
national comorbidity survey replication. Arch. Gen. Psychiatry 62 (6), 593–602. doi:10.
1001/archpsyc.62.6.593
Kimpton,J.E.,Carey,I.M.,Threapleton,C.J.D.,Robinson,A.,Harris,T.,Cook,D.G.,etal.
(2019). Longitudinal exposure of English primary care patients to pharmacogenomic drugs:
An analysis to inform design of pre-emptive pharmacogenomic testing. Br. J. Clin. Pharmacol.
85 (12), 2734–2746. doi:10.1111/bcp.14100
Koopmans, A. B., Braakman, M. H., Vinkers, D. J., Hoek, H. W., and van Harten, P. N.
(2021). Meta-analysis of probability estimates of worldwide variation of CYP2D6 and
CYP2C19. Transl. Psychiatry 11 (1), 141. doi:10.1038/s41398-020-01129-1
Kverno, K. S., and Mangano, E. (2021). Treatment-resistant depression: Approaches
to treatment. J. Psychosoc. Nurs. Ment. Health Serv. 59 (9), 7–11. doi: 10.3928/02793695-
20210816-01
Le, L. K., Shih, S., Richards-Jones, S., Chatterton, M. L., Engel, L., Stevenson, C., et al.
(2021). The cost of Medicare-funded medical and pharmaceutical services for mental
disorders in children and adolescents in Australia. PLoS One 16 (4), e0249902. doi:10.
1371/journal.pone.0249902
Lefaucheur, J. P., Aleman, A., Baeken, C., Benninger, D. H., Brunelin, J., Di Lazzaro,
V., et al. (2020). Evidence-based guidelines on the therapeutic use of repetitive
transcranial magnetic stimulation (rTMS): An update (2014-2018). Clin.
Neurophysiol. 131 (2), 474–528. doi:10.1016/j.clinph.2019.11.002
Lener, M. S., Niciu, M. J., Ballard, E. D., Park, M., Park, L. T., Nugent, A. C., et al.
(2017). Glutamate and gamma-aminobutyric acid systems in the pathophysiology of
major depression and antidepressant response to ketamine. Biol. Psychiatry 81 (10),
886–897. doi:10.1016/j.biopsych.2016.05.005
Liebowitz, M. R., and Tourian, K. A. (2010). Efficacy, safety, and tolerability of
Desvenlafaxine 50 mg/d for the treatment of major depressive disorder:a systematic
review of clinical trials. Prim. Care Companion J. Clin. Psychiatry 12 (3), PCC.09r00845.
doi:10.4088/PCC.09r00845blu
Liko, I., Lai, E., Griffin, R. J., Aquilante, C. L., and Lee, Y. M. (2020). Patients’
perspectives on psychiatric pharmacogenetic testing. Pharmacopsychiatry 53 (6),
256–261. doi:10.1055/a-1183-5029
Lin, F., He, Y., Zhang, L., Zhang, M., Zhang, Y., and Wen, C. (2015). Assessment of the
effect of ketamine on cytochrome P450 isoforms activity in rats by cocktail method. Int.
J. Clin. Exp. Med. 8 (3), 4335–4341.
Llorca, P.-M. (2010). The antidepressant agomelatine improves the quality of life of
depressed patients: Implications for remission. J. Psychopharmacol. 24 (2), 21–26.
doi:10.1177/1359786810372978
Lunghi, C., Dugas, M., Leclerc, J., Poluzzi, E., Martineau, C., Carnovale, V., et al.
(2022). Global prevalence of antidepressant drug utilization in the community: Protocol
for a systematic review. BMJ Open 12 (5), e062197. doi:10.1136/bmjopen-2022-062197
Lynch, T., and Price, A. (2007). The effect of cytochrome P450 metabolism on drug
response, interactions, and adverse effects. Am. Fam. Physician 76 (3), 391–396.
Malhi, G. S., Bassett, D., Boyce, P., Bryant, R., Fitzgerald, P. B., Fritz, K., et al. (2015).
Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines
for mood disorders. Aust. N. Z. J. Psychiatry 49 (12), 1087–1206. doi:10.1177/
0004867415617657
Malhi, G. S., Bell, E., Bassett, D., Boyce, P., Bryant, R., Hazell, P., et al. (2021). The
2020 Royal Australian and New Zealand College of Psychiatrists clinical practice
guidelines for mood disorders. Aust. N. Z. J. Psychiatry 55 (1), 7–117. doi:10.1177/
0004867420979353
Malhi, G. S., and Mann, J. J. (2018). Depression. Lancet 392 (10161), 2299–2312.
doi:10.1016/S0140-6736(18)31948-2
Marino, M., Jamal, Z., and Zito, P. M. (2023). Pharmacodynamics. (2023/01/29 ed.).
Martis, S., Peter, I., Hulot, J. S., Kornreich, R., Desnick, R. J., and Scott, S. A. (2013).
Multi-ethnic distribution of clinically relevant CYP2C genotypes and haplotypes.
Pharmacogenomics J. 13 (4), 369–377. doi:10.1038/tpj.2012.10
Maruf, A. A., Fan, M., Arnold, P. D., Müller, D. J., Aitchison, K. J., and Bousman, C. A.
(2020). Pharmacogenetic Testing Options Relevant to Psychiatry in Canada: Options de
tests pharmacogénétiques pertinents en psychiatrie au Canada. Can. J. Psychiatry 65 (8),
521–530. doi:10.1177/0706743720904820
Maslej, M. M., Furukawa, T. A., Cipriani, A., Andrews, P. W., Sanches, M.,
Tomlinson, A., et al. (2021). Individual differences in response to antidepressants: A
meta-analysis of placebo-controlled randomized clinical trials. JAMA Psychiatry 78 (5),
490–497. doi:10.1001/jamapsychiatry.2020.4564
McCarthy, M. J., Chen, Y., Demodena, A., Leckband, S. G., Fischer, E., Golshan, S.,
et al. (2021). A prospective study to determine the clinical utility of pharmacogenetic
testing of veterans with treatment-resistant depression. J. Psychopharmacol. 35 (8),
992–1002. doi:10.1177/02698811211015224
McDonnell, A. M., and Dang, C. H. (2013). Basic review of the cytochrome
p450 system. J. Adv. Pract. Oncol. 4 (4), 263–268. doi:10.6004/jadpro.2013.4.4.7
McGraw, J., and Waller, D. (2012). Cytochrome P450 variations in different ethnic
populations. Expert Opin. Drug Metab. Toxicol. 8 (3), 371–382. doi:10.1517/17425255.
2012.657626
McMillan, S. S., Stewart, V., Wheeler, A. J., Kelly, F., and Stapleton, H. (2020).
Medication management in the context of mental illness: An exploratory study of young
people living in Australia. BMC Public Health 20 (1), 1188. doi:10.1186/s12889-020-
09237-9
McNeil, S. E., Gibbons, J. R., and Cogburn, M. (2022). Risperidone.
Millan, M. J., Gobert, A., Lejeune, F., Newman-Tancredi, A., Rivet, J. M., Auclair, A.,
et al. (2001). S33005, a novel ligand at both serotonin and norepinephrine transporters:
I. Receptor binding, electrophysiological, and neurochemical profile in comparison with
venlafaxine, reboxetine, citalopram, and clomipramine. J. Pharmacol. Exp. Ther. 298
(2), 565–580.
Milosavljevic, F., Bukvic, N., Pavlovic, Z., Miljevic, C., Pešic, V., Molden, E., et al.
(2021). Association of CYP2C19 and CYP2D6 poor and intermediate metabolizer status
with antidepressant and antipsychotic exposure: A systematic review and meta-analysis.
JAMA Psychiatry 78 (3), 270–280. doi:10.1001/jamapsychiatry.2020.3643
Montejo, A. L., Prieto, N., Terleira, A., Matias, J., Alonso, S., Paniagua, G., et al. (2010).
Better sexual acceptability of agomelatine (25 and 50 mg) compared with paroxetine
Frontiers in Pharmacology frontiersin.org14
Roberts et al. 10.3389/fphar.2023.1267294

(20 mg) in healthy male volunteers. An 8-week, placebo-controlled study using the
PRSEXDQ-SALSEX scale. J. Psychopharmacol. 24 (1), 111–120. doi:10.1177/
0269881108096507
Müller, D. J., Kekin, I., Kao, A. C., and Brandl, E. J. (2013). Towards the
implementation of CYP2D6 and CYP2C19 genotypes in clinical practice: Update
and report from a pharmacogenetic service clinic. Int. Rev. Psychiatry 25 (5),
554–571. doi:10.3109/09540261.2013.838944
Murphy, E., Hou, L., Maher, B. S., Woldehawariat, G., Kassem, L., Akula, N., et al.
(2013). Race, genetic ancestry and response to antidepressant treatment for major
depression. Neuropsychopharmacology 38 (13), 2598–2606. doi:10.1038/npp.2013.166
Mutsatsa, S., and Currid, T. J. (2013). Pharmacogenetics: A reality or misplaced
optimism?. J. Psychiatr. Ment. Health Nurs. 20 (4), 314–320. doi:10.1111/j.1365-2850.
2012.01910.x
Namerow, L. B., Ramsey, L. B., Malik, S., Cortese, S., and Strawn, J. R. (2022).
Editorial: Beyond red light, green light: Examining the role of pharmacogenomics in
evidence-based care in child and adolescent psychiatry. J. Am. Acad. Child. Adolesc.
Psychiatry 61 (1), 29–31. doi:10.1016/j.jaac.2021.11.001
Namerow, L. B., Walker, S. A., Loftus, M., Bishop, J. R., Ruaño, G., and Malik, S.
(2020). Pharmacogenomics: An update for child and adolescent psychiatry. Curr.
Psychiatry Rep. 22 (5), 26. doi:10.1007/s11920-020-01145-4
Nestler,E.J.,Barrot,M.,DiLeone,R.J.,Eisch,A.J.,Gold,S.J.,andMonteggia,L.M.(2002).
Neurobiology of depression. Neuron 34 (1), 13–25. doi:10.1016/s0896-6273(02)00653-0
O’Hara, K. (2016). Paediatric pharmacokinetics and drug doses. Aust. Prescr. 39 (6),
208–210. doi:10.18773/austprescr.2016.071
Oates, J. T., and Lopez, D. (2018). Pharmacogenetics: An important part of drug
development with A focus on its application. Int. J. Biomed. Investig. 1 (2), 111. doi:10.
31531/2581-4745.1000111
Ong, C. S. B., Fok, R. W., Tan, R. C. A., Fung, S. M., Sun, S., and Ngeow, J. Y. Y. (2022).
General practitioners’(GPs) experience, attitudes and needs on clinical genetic services:
A systematic review. Fam. Med. Community Health 10 (4), e001515. doi:10.1136/fmch-
2021-001515
Papakostas, G. I., Thase, M. E., Fava, M., Nelson, J. C., and Shelton, R. C. (2007). Are
antidepressant drugs that combine serotonergic and noradrenergic mechanisms of
action more effective than the selective serotonin reuptake inhibitors in treating major
depressive disorder? A meta-analysis of studies of newer agents. Biol. Psychiatry 62 (11),
1217–1227. doi:10.1016/j.biopsych.2007.03.027
Paris,B.L.,Ogilvie,B.W.,Scheinkoenig,J.A.,Ndikum-Moffor,F.,Gibson,R.,and
Parkinson, A. (2009). In vitro inhibition and induction of human liver cytochrome
p450 enzymes by milnacipran. Drug Metab. Dispos. 37 (10), 2045–2054. doi:10.1124/
dmd.109.028274
Peñas-Lledó, E. M., Blasco-Fontecilla, H., Dorado, P., Vaquero-Lorenzo, C., Baca-
García, E., and Llerena, A. (2012). CYP2D6 and the severity of suicide attempts.
Pharmacogenomics 13 (2), 179–184. doi:10.2217/pgs.11.146
Peñas-Lledó, E. M., Guillaume, S., de Andrés, F., Cortés-Martínez, A., Dubois, J.,
Kahn, J. P., et al. (2022). A one-year follow-up study of treatment-compliant suicide
attempt survivors: Relationship of CYP2D6-CYP2C19 and polypharmacy with suicide
reattempts. Transl. Psychiatry 12 (1), 451. doi:10.1038/s41398-022-02140-4
Phillips, A. L., Nigro, O., Macolino, K. A., Scarborough, K. C., Doecke, C. J., Angley,
M. T., et al. (2014). Hospital admissions caused by adverse drug events: An Australian
prospective study. Aust. Health Rev. 38 (1), 51–57. doi:10.1071/AH12027
Pinzón-Espinosa, J., van der Horst, M., Zinkstok, J., Austin, J., Aalfs, C., Batalla, A.,
et al. (2022). Barriers to genetic testing in clinical psychiatry and ways to overcome
them: From clinicians’attitudes to sociocultural differences between patients across the
globe. Transl. Psychiatry 12 (1), 442. doi:10.1038/s41398-022-02203-6
Pirmohamed, M., Burnside, G., Eriksson, N., Jorgensen, A. L., Toh, C. H., Nicholson,
T., et al. (2013). A randomized trial of genotype-guided dosing of warfarin. N. Engl.
J. Med. 369 (24), 2294–2303. doi:10.1056/NEJMoa1311386
Pirmohamed, M., James, S., Meakin, S., Green, C., Scott, A. K., Walley, T. J., et al.
(2004). Adverse drug reactions as cause of admission to hospital: Prospective analysis of
18 820 patients. BMJ 329 (7456), 15–19. doi:10.1136/bmj.329.7456.15
Pirmohamed, M. (2001). Pharmacogenetics and pharmacogenomics. Br. J. Clin.
Pharmacol. 52 (4), 345–347. doi:10.1046/j.0306-5251.2001.01498.x
Pirmohamed, M. (2023). Pharmacogenomics: Current status and future perspectives.
Nat. Rev. Genet. 24, 350–362. doi:10.1038/s41576-022-00572-8
Poolsup, N., Li Wan Po, A., and Knight, T. L. (2000). Pharmacogenetics and
psychopharmacotherapy. J. Clin. Pharm. Ther. 25 (3), 197–220. doi:10.1046/j.1365-
2710.2000.00281.x
Poweleit, E. A., Aldrich, S. L., Martin, L. J., Hahn, D., Strawn, J. R., and Ramsey, L. B.
(2019). Pharmacogenetics of sertraline tolerability and response in pediatric anxiety and
depressive disorders. J. Child. Adolesc. Psychopharmacol. 29 (5), 348–361. doi:10.1089/
cap.2019.0017
Preys, C. L., Blout Zawatsky, C. L., Massmann, A., Heukelom, J. V., Green, R. C.,
Hajek, C., et al. (2023). Attitudes about pharmacogenomic testing vary by healthcare
specialty. Pharmacogenomics 24, 539–549. doi:10.2217/pgs-2023-0039
Prows, C. A., and Saldaña, S. N. (2009). Nurses’genetic/genomics competencies when
medication therapy is guided by pharmacogenetic testing: Children with mental health
disordersasanexemplar.J. Pediatr. Nurs. 24 (3), 179–188. doi:10.1016/j.pedn.2008.02.033
Racine, N., McArthur, B. A., Cooke, J. E., Eirich, R., Zhu, J., and Madigan, S. (2021).
Global prevalence of depressive and anxiety symptoms in children and adolescents
during COVID-19: A meta-analysis. JAMA Pediatr. 175 (11), 1142–1150. doi:10.1001/
jamapediatrics.2021.2482
Radosavljevic, M., Svob Strac, D., Jancic, J., and Samardzic, J. (2023). The role of
pharmacogenetics in personalizing the antidepressant and anxiolytic therapy. Genes
(Basel) 14 (5), 1095. doi:10.3390/genes14051095
Ramos, K. N., Gregornik, D., and Ramos, K. S. (2021). Pharmacogenomics insights
into precision pediatric oncology. Curr. Opin. Pediatr. 33 (6), 564–569. doi:10.1097/
mop.0000000000001065
Ramsey,L.B.,Namerow,L.B.,Bishop,J.R.,Hicks,J.K.,Bousman,C.,Croarkin,P.E.,etal.
(2021). Thoughtful clinical use of pharmacogenetics in child and adolescent
psychopharmacology. J. Am. Acad. Child. Adolesc. Psychiatry 60 (6), 660–664. doi:10.
1016/j.jaac.2020.08.006
Roden, D. M., and Tyndale, R. F. (2013). Genomic medicine, precision medicine,
personalized medicine: what’s in a name?. Clin. Pharmacol. Ther. 94 (2), 169–172.
doi:10.1038/clpt.2013.101
Rotzinger, S., and Baker, G. B. (2002). Human CYP3A4 and the metabolism of
nefazodone and hydroxynefazodone by human liver microsomes and heterologously
expressed enzymes. Eur. Neuropsychopharmacol. 12 (2), 91–100. doi:10.1016/s0924-
977x(02)00005-6
Roughead,E.E.,Semple,S.J.,andRosenfeld,E.(2016).Theextentofmedicationerrorsand
adverse drug reactions throughout the patient journey in acute care in Australia. Int. J. Evid.
Based Healthc. 14 (3), 113–122. doi:10.1097/XEB.0000000000000075
Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W.,
Warden, D., et al. (2006). Acute and longer-term outcomes in depressed outpatients
requiring one or several treatment steps: A STAR*D report. Am. J. Psychiatry 163 (11),
1905–1917. doi:10.1176/ajp.2006.163.11.1905
Samer, C. F., Lorenzini, K. I., Rollason, V., Daali, Y., and Desmeules, J. A. (2013).
Applications of CYP450 testing in the clinical setting. Mol. Diagn Ther. 17 (3), 165–184.
doi:10.1007/s40291-013-0028-5
Sandritter, T., Chevalier, R., Abt, R., and Shakhnovich, V. (2023). Pharmacogenetic
testing for the pediatric gastroenterologist: Actionable drug–gene pairs to know. Pharm.
(Basel) 16, 889. doi:10.3390/ph16060889
Sangkuhl, K., Klein, T. E., and Altman, R. B. (2009). Selective serotonin reuptake
inhibitors pathway. Pharmacogenet Genomics 19 (11), 907–909. doi:10.1097/FPC.
0b013e32833132cb
Santarelli, L., Saxe, M., Gross, C., Surget, A., Battaglia, F., Dulawa, S., et al. (2003).
Requirement of hippocampal neurogenesis for the behavioral effects of antidepressants.
Science 301 (5634), 805–809. doi:10.1126/science.1083328
Schärfe, C. P. I., Tremmel, R., Schwab, M., Kohlbacher, O., and Marks, D. S. (2017).
Genetic variation in human drug-related genes. Genome Med. 9 (1), 117. doi:10.1186/
s13073-017-0502-5
Schenk, P. W., van Vliet, M., Mathot, R. A., van Gelder, T., Vulto, A. G., van Fessem,
M. A., et al. (2010). The CYP2C19*17 genotype is associated with lower imipramine
plasma concentrations in a large group of depressed patients. Pharmacogenomics J. 10
(3), 219–225. doi:10.1038/tpj.2009.50
Schildkraut, J. J. (1965). The catecholamine hypothesis of affective disorders: A review
of supporting evidence. Am. J. Psychiatry 122 (5), 509–522. doi:10.1176/ajp.122.5.509
Schork, N. J. (2015). Personalized medicine: Time for one-person trials. Nature 520
(7549), 609–611. doi:10.1038/520609a
Shah, R. R., and Gaedigk, A. (2018). Precision medicine: Does ethnicity information
complement genotype-based prescribing decisions?. Ther. Adv. Drug Saf. 9 (1), 45–62.
doi:10.1177/2042098617743393
Shah, R. R. (2015). Inter-ethnic differences in drug response: Implications for drug
development and complying with drug regulation. Review 32 (1), 88–98. doi:10.3109/
10601333.2015.1064131
Shah, R. R. (2005). Pharmacogenetics in drug regulation: Promise, potential and pitfalls.
Philos. Trans. R. Soc. Lond B Biol. Sci. 360 (1460), 1617–1638. doi:10.1098/rstb.2005.1693
Shalimova, A., Babasieva, V., Chubarev, V. N., Tarasov, V. V., Schiöth, H. B., and
Mwinyi, J. (2021). Therapy response prediction in major depressive disorder: Current
and novel genomic markers influencing pharmacokinetics and pharmacodynamics.
Pharmacogenomics 22 (8), 485–503. doi:10.2217/pgs-2020-0157
Shaw, D. M., Camps, F. E., and Eccleston, E. G. (1967). 5-Hydroxytryptamine in the
hind-brain of depressive suicides. Br. J. Psychiatry 113 (505), 1407–1411. doi:10.1192/
bjp.113.505.1407
Sheffler, Z. M., Patel, P., and Abdijadid, S. (2022). Antidepressants.
Singh, J. B., Fedgchin, M., Daly, E., Xi, L., Melman, C., De Bruecker, G., et al. (2016).
Intravenous esketamine in adult treatment-resistant depression: A double-blind,
double-randomization, placebo-controlled study. Biol. Psychiatry 80 (6), 424–431.
doi:10.1016/j.biopsych.2015.10.018
Frontiers in Pharmacology frontiersin.org15
Roberts et al. 10.3389/fphar.2023.1267294

Slomp, C., Morris, E., Edwards, L., Hoens, A. M., Landry, G., Riches, L., et al. (2022).
Pharmacogenomic testing for major depression: A qualitative study of the perceptions
of people with lived experience and professional stakeholders. Can. J. Psychiatry 68,
436–452. doi:10.1177/07067437221140383
Smith, D. M., Peshkin, B. N., Springfield, T. B., Brown, R. P., Hwang, E., Kmiecik, S.,
et al. (2020). Pharmacogenetics in practice: Estimating the clinical actionability of
pharmacogenetic testing in perioperative and ambulatory settings. Clin. Transl. Sci. 13
(3), 618–627. doi:10.1111/cts.12748
Soiza, R. L. (2020). Global pandemic-the true incidence of adverse drug reactions. Age
Ageing 49 (6), 934–935. doi:10.1093/ageing/afaa165
Solomon,H.V.,Cates,K.W.,andLi,K.J.(2019).DoesobtainingCYP2D6and
CYP2C19 pharmacogenetic testing predict antidepressant response or adverse
drug reactions?. Psychiatry Res. 271, 604–613. doi:10.1016/j.psychres.2018.12.053
Sproule, B. A., Otton, S. V., Cheung, S. W., Zhong, X. H., Romach, M. K., and Sellers,
E. M. (1997). CYP2D6 inhibition in patients treated with sertraline. J. Clin.
Psychopharmacol. 17 (2), 102–106. doi:10.1097/00004714-199704000-00007
Stahl, S. M. (2016). Mechanism of action of brexpiprazole: Comparison with
aripiprazole. CNS Spectr. 21 (1), 1–6. doi:10.1017/s1092852915000954
Stancil, S. L., Berrios, C., and Abdel-Rahman, S. (2021). Adolescent perceptions of
pharmacogenetic testing. Pharmacogenomics 22 (6), 335–343. doi:10.2217/pgs-2020-0177
Steiger, A., and Pawlowski, M. (2019). Depression and sleep. Int. J. Mol. Sci. 20 (3),
607. doi:10.3390/ijms20030607
Stephenson, T. (2005). How children’s responses to drugs differ from adults. Br.
J. Clin. Pharmacol. 59 (6), 670–673. doi:10.1111/j.1365-2125.2005.02445.x
Strawn, J. R., Mills, J. A., Poweleit, E. A., Ramsey, L. B., and Croarkin, P. E. (2023).
Adverse effects of antidepressant medications and their management in children and
adolescents. Pharmacotherapy 43, 675–690. doi:10.1002/phar.2767
Strawn,J.R.,Poweleit,E.A.,andRamsey,L.B.(2019).CYP2C19-Guidedescitalopramand
sertraline dosing in pediatric patients: A pharmacokinetic modeling study. J. Child. Adolesc.
Psychopharmacol. 29 (5), 340–347. doi:10.1089/cap.2018.0160
Sub Laban, T., and Saadabadi, A. (2022). Monoamine oxidase inhibitors (MAOI).
Swen,J.J.,vanderWouden,C.H.,Manson,L. E., Abdullah-Koolmees, H., Blagec, K.,
Blagus, T., et al. (2023). A 12-gene pharmacogenetic panel to prevent adverse drug reactions:
An open-label, multicentre, controlled, cluster-randomised crossover implementation study.
Lancet 401 (10374), 347–356. doi:10.1016/s0140-6736(22)01841-4
Tansey, K. E., Guipponi, M., Hu, X., Domenici, E., Lewis, G., Malafosse, A., et al.
(2013). Contribution of common genetic variants to antidepressant response. Biol.
Psychiatry 73 (7), 679–682. doi:10.1016/j.biopsych.2012.10.030
Theoktisto, K. M. (2009). Pharmacokinetic considerations in the treatment of
pediatric behavioral issues. Pediatr. Nurs. 35 (6), 369–374; quiz 374-375. quiz 374-365.
Thomas, K., and Saadabadi, A. (2022). Olanzapine.
Thomas, L., Kessler, D., Campbell, J., Morrison, J., Peters, T. J., Williams, C., et al.
(2013). Prevalence of treatment-resistant depression in primary care: Cross-sectional
data. Br. J. Gen. Pract. 63 (617), e852–e858. doi:10.3399/bjgp13X675430
Tiller, J. W. (2013). Depression and anxiety. Med. J. Aust. 199 (S6), S28–S31. doi:10.
5694/mja12.10628
Turner, R. M. (2013). From the lab to the prescription pad: Genetics,
CYP450 analysis, and medication response. J. Child. Adolesc. Psychiatr. Nurs. 26 (2),
119–123. doi:10.1111/jcap.12028
Vaishnavi, S. N., Nemeroff, C. B., Plott, S. J., Rao, S. G., Kranzler, J., and Owens, M. J.
(2004). Milnacipran: A comparative analysis of human monoamine uptake and
transporter binding affinity. Biol. Psychiatry 55 (3), 320–322. doi:10.1016/j.biopsych.
2003.07.006
Vallejos-Vidal, E., Reyes-Cerpa, S., Rivas-Pardo, J. A., Maisey, K., Yáñez, J. M.,
Valenzuela, H., et al. (2019). Single-nucleotide polymorphisms (SNP) mining and their
effect on the tridimensional protein structure prediction in a set of immunity-related
expressed sequence tags (EST) in atlantic salmon (Salmo salar). Front. Genet. 10, 1406.
doi:10.3389/fgene.2019.01406
van der Weide, J., and Hinrichs, J. W. (2006). The influence of cytochrome
P450 pharmacogenetics on disposition of common antidepressant and antipsychotic
medications. Clin. Biochem. Rev. 27 (1), 17–25.
Van Driest, S. L., and McGregor, T. L. (2013). Pharmacogenetics in clinical pediatrics:
Challenges and strategies. Per Med. 10 (7), 661–671. doi:10.2217/pme.13.70
van Westrhenen, R., van Schaik, R. H. N., van Gelder, T., Birkenhager, T. K., Bakker,
P. R., Houwink, E. J. F., et al. (2021). Policy and practice review: A first guideline on the
use of pharmacogenetics in clinical psychiatric practice. Front. Pharmacol. 12, 640032.
doi:10.3389/fphar.2021.640032
VandeVoort,J.L.,Orth,S.S.,Shekunov,J.,Romanowicz,M.,Geske,J.R.,Ward,J.A.,etal.
(2022). A randomized controlled trial of combinatorial pharmacogenetics testing in
adolescent depression. J. Am. Acad. Child. Adolesc. Psychiatry 61 (1), 46–55. doi:10.1016/
j.jaac.2021.03.011
Verbelen, M., Weale, M. E., and Lewis, C. M. (2017). Cost-effectiveness of
pharmacogenetic-guided treatment: Are we there yet?. Pharmacogenomics J. 17 (5),
395–402. doi:10.1038/tpj.2017.21
Vest, B. M., Wray, L. O., Brady, L. A., Thase, M. E., Beehler, G. P., Chapman, S. R.,
et al. (2020). Primary care and mental health providers’perceptions of implementation
of pharmacogenetics testing for depression prescribing. BMC Psychiatry 20 (1), 518.
doi:10.1186/s12888-020-02919-z
Vimalachandran, P., Liu, H., Lin, Y., Ji, K., Wang, H., and Zhang, Y. (2020).
Improving accessibility of the Australian My Health Records while preserving
privacy and security of the system. Health Inf. Sci. Syst. 8 (1), 31. doi:10.1007/
s13755-020-00126-4
Volpi, S., Bult, C. J., Chisholm, R. L., Deverka, P. A., Ginsburg, G. S., Jacob, H. J., et al.
(2018). Research directions in the clinical implementation of pharmacogenomics: An
overview of US programs and projects. Clin. Pharmacol. Ther. 103 (5), 778–786. doi:10.
1002/cpt.1048
Vos, C. F., ter Hark, S. E., Schellekens, A. F. A. , Spijker, J., van der Meij,A.,Grotenhuis,A.J.,
et al. (2023). Effectiveness of genotype-specific tricyclic antidepressant dosing in patients with
major depressive disorder: A randomized clinical trial. JAMA Netw. Open 6 (5), e2312443.
doi:10.1001/jamanetworkopen.2023.12443
Wall, C. A., Oldenkamp, C., and Swintak, C. (2010). Safety and efficacy pharmacogenomics
in pediatric psychopharmacology. Prim. Psychiatry 17 (5), 53–58.
Wang, S. M., Han, C., Lee, S. J., Patkar, A. A., Masand, P. S., and Pae, C. U. (2013). A
review of current evidence for vilazodone in major depressive disorder. Int. J. Psychiatry
Clin. Pract. 17 (3), 160–169. doi:10.3109/13651501.2013.794245
Wang, X., Wang, C., Zhang, Y., and An, Z. (2023). Effect of pharmacogenomics
testing guiding on clinical outcomes in major depressive disorder: A systematic review
and meta-analysis of RCT. BMC Psychiatry 23 (334), 334. doi:10.1186/s12888-023-
04756-2
Wehry, A. M., Ramsey, L., Dulemba, S. E., Mossman, S. A., and Strawn, J. R. (2018).
Pharmacogenomic testing in child and adolescent psychiatry: An evidence-based
review. Curr. Probl. Pediatr. Adolesc. Health Care 48 (2), 40–49. doi:10.1016/j.
cppeds.2017.12.003
Weinshilboum, R. (2003). Inheritance and drug response. N. Engl. J. Med. 348 (6),
529–537. doi:10.1056/NEJMra020021
Wilson, J. F., Weale, M. E., Smith, A. C., Gratrix, F., Fletcher, B., Thomas, M. G., et al.
(2001). Population genetic structure of variable drug response. Nat. Genet. 29 (3),
265–269. doi:10.1038/ng761
Wong, D. T., Bymaster, F. P., Horng, J. S., and Molloy, B. B. (1975). A new selective
inhibitor for uptake of serotonin into synaptosomes of rat brain: 3-
(p-trifluoromethylphenoxy). N-methyl-3-phenylpropylamine. J. Pharmacol.
Exp. Ther. 193 (3), 804–811.
Wong, D. T., Horng, J. S., Bymaster, F. P., Hauser, K. L., and Molloy, B. B. (1974). A
selective inhibitor of serotonin uptake: Lilly 110140, 3-(p-trifluoromethylphenoxy)-N-
methyl-3-phenylpropylamine. Life Sci. 15 (3), 471–479. doi:10.1016/0024-3205(74)
90345-2
Xie, H. G., Kim, R. B., Wood, A. J., and Stein, C. M. (2001). Molecular basis of ethnic
differences in drug disposition and response. Annu. Rev. Pharmacol. Toxicol. 41,
815–850. doi:10.1146/annurev.pharmtox.41.1.815
Zackrisson, A. L., Lindblom, B., and Ahlner, J. (2010). High frequency of
occurrence of CYP2D6 gene duplication/multiduplication indicating ultrarapid
metabolism among suicide cases. Clin. Pharmacol. Ther. 88 (3), 354–359. doi:10.
1038/clpt.2009.216
Zemanova, N., Anzenbacher, P., and Anzenbacherova, E. (2022). The role of
cytochromes P450 in the metabolism of selected antidepressants and anxiolytics
under psychological stress. Biomed. Pap. Med. Fac. Univ. Palacky. Olomouc Czech
Repub. 166 (2), 140–149. doi:10.5507/bp.2022.019
Zgheib,N.K.,andPatrinos,G.P.(2020).Editorial: Pharmacogenetics research
and clinical applications: An international landscape of the accomplishments,
challenges, and opportunities. Front. Pharmacol. 11, 1217. doi:10.3389/fphar.2020.
01217
Zhao, M., Ma, J., Li, M., Zhang, Y., Jiang, B., Zhao, X., et al. (2021). Cytochrome
P450 enzymes and drug metabolism in humans. Int. J. Mol. Sci. 22 (23), 12808. doi:10.
3390/ijms222312808
Zhou, S., Chan, E., Pan, S. Q., Huang, M., and Lee, E. J. (2004). Pharmacokinetic
interactions of drugs with St John’s wort. J. Psychopharmacol. 18 (2), 262–276. doi:10.
1177/0269881104042632
Zubiaur, P., Soria-Chacartegui, P., Villapalos-García, G., Gordillo-Perdomo, J. J., and
Abad-Santos, F. (2021). The pharmacogenetics of treatment with olanzapine.
Pharmacogenomics 22 (14), 939–958. doi:10.2217/pgs-2021-0051
Frontiers in Pharmacology frontiersin.org16
Roberts et al. 10.3389/fphar.2023.1267294