“BUT IT’S NOT SO SIMPLE!”
DOES PHARMACOGENETIC TESTING MAKE PRACTICE OF
PSYCHIATRY DIFFICULT?
Andrius Baskys, MD, PhD
Website: mindinspector.com
Email: moodnoteservice@gmail.com
Recent advances in molecular genetics offer greatly increased understanding of
the relationship between genetic variations, and drug effectiveness or their side effects.
This relationship is often referred to as pharmacogenetics and pharmacogenomics and is
in part revealed by pharmacogenomic testing (PGx). PGx is a relatively new technology
that has recently become commercially available for psychiatrists and other providers
who wish to prescribe medications based on the patient’s genotype. Since the adverse
drug effect-related mortality ranks the 5th in the overall US mortality statistics (Lazarou et
al, 1998), the attractiveness of this new tool should be difficult to contest. Nevertheless,
implementation of the PGx into clinical practice has been slow and acceptance of PGx
testing by prescribers has been wavering (Patel et al, 2014, Hess et al, 2015, Peterson et
al 2017).
To understand the reasons of prescriber reluctance, it is important to realize that a
distinguishing feature of PGx testing is a comparatively large number of data points
produced. Thus, in a somewhat simplified scenario, all genetic variation effects could be
roughly divided into those possessing either pharmacodynamic (PD) or pharmacokinetic
(PK) properties. Gene variants associated with PD properties are usually directly linked
to the drug target. For example, carriers of a mutant G allele in the opiate mu-receptor 1
(OPMR1) have greater sensitivity to pain and require 2-4 times more of an analgesic drug
to achieve a comparable degree of analgesia as non-carriers (Ren et al., 2015). Gene
variants associated with PK properties of a drug usually involve drug-metabolizing
enzymes and can slow or speed up drug metabolism. For example, the enzyme produced
by Cytochrome P450 (CYP) 2C19 gene metabolizes several commonly prescribed
antidepressants (Mrazek, 2010). Mutations in one or both alleles of the CYP 2C19 gene
could render the enzyme partially or completely ineffective and significantly increase the
likelihood of medication side effects. Alternatively, a mutation known as CYP 2C19 *17/
*17 results in increased drug metabolism and lack of efficacy.
Most PGx testing reports available today include data on 4 to 8 PK genes affecting
CYP enzyme activity and 4 to 5 PD genes linked to medication effectiveness. While
these numbers are miniscule compared to those produced by whole genome sequencing,
they are nearly impossible for a prescriber to meaningfully integrate into the clinical
decision, especially when the interactions with other medications that the patient may be
taking are considered. This could be illustrated in a following example. Let’s think of a
depressed patient who requires treatment with an antidepressant. PGx testing reveals that
the patient has a homozygote loss-of-function mutation in the CYP 2C19 gene (Poor
metabolizer) and a heterozygote loss-of-function mutation in the CYP 2C9 gene
(Intermediate metabolizer). In light of these data why not choose bupropion that is not
primarily metabolized by CYP 2C19 and 2C9 and consider the problem solved? Not so
fast! The patient is on an antiplatelet drug clopidogrel, a strong inhibitor of CYP 2B6, a
major metabolic pathway of bupropion that, when inhibited, could potentially increase a
risk of seizures. Furthermore, some antidepressants (including bupropion) can alter CYP
enzyme activity in a way that turns an intermediate metabolizer into a poor metabolizer
potentially affecting the other drugs (e.g. antipsychotics or anxiolytics). It is difficult to
escape the impression of The Queen’s Croquet-Ground from Alice in Wonderland by
Lewis Carroll, a curious version of croquet played by the Queen where the croquet balls
are live hedgehogs, and the mallets are live flamingos, each with the mind of its own,
making Alice to come to a conclusion that it was “a very difficult game”.
And it is a difficult game indeed. Adding PGx data to the
“ordinary” drug-drug interactions add a new layer of
complexity. Predicting the outcome of these interactions is less
momentous if the prescriber has enough time or the necessary
tools. But how much time does the prescriber really have?
Ferreri et al (2014) addressed this question in a study, which
found that a clinical pharmacist on average spends 76.6 minutes
with the patient “coordinating the care and communicating
results and any changes”. It is the experience of this writer that
on average it takes 30 minutes to explain genotyping results and
the rationale for medication selection to the patient.
In summary, molecular techniques including PGx testing are
coming to the clinic. They improve outcomes and patient
experiences, save money, and they are here to stay.
Successful implementation of PGx testing depends on the
development of new software tools that can rapidly process and
present genetic and drug interaction data to a clinician
in a meaningful way. For this, our EHR systems, systems of
communication between health care providers, reimbursement and
outcomes research paradigms will need to evolve to adapt to the
new technology.
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Hess et al. Pharmacogenomic and pharmacogenetic-guided therapy as a tool in precision
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