Back in the April 2018 issue of The Inside Story®, we described the promise of pharmacogenomics as a way to guide prescribing decisions in order to provide patients with the most optimal drug treatment. At that time, we were in the early stages of a clinical study designed to answer the question: Does the use of pharmacogenomic testing affect outcomes in patients with mental health conditions? To be honest, we expressed a significant amount of skepticism given the limited evidence available, but we also felt compelled to address that gap in research and knowledge.
The study concluded late last year, and since then we’ve been analyzing the data. Before revealing what we learned, let’s briefly review what pharmacogenomics is all about and provide some of the context for our investigation.
What is pharmacogenomics?
Pharmacogenomics is a form of “personalized medicine” and is one of several types of genetic testing available for medical purposes. The test results determine whether a person has certain genetic mutations that are known to influence their response to a drug in a certain way. The goal is to predict who will benefit from a medication, who will not respond at all, and who will experience negative side-effects. Based on that information, a physician or pharmacist could choose medications better suited to that individual.
What does genetics have to do with drugs?
The way a person’s body metabolizes or breaks down a drug and their subsequent response to the drug is in part determined by that person’s genes. Currently there are about 150 drugs that have been linked to specific genetic variations affecting an individual’s response to therapy, including widely prescribed medications, such as antidepressants, cholesterol-lowering statins, and blood thinners.
However, there are many other factors in addition to an individual’s genes that play a part in their response to any particular drug, such as demographics, lifestyle, co-morbidities, and other drug therapy.
More information about pharmacogenomics and the background for GSC’s investigation can be found in The Inside Story, April 2018. Keep reading for the details of our study and the results.
Why GSC chose to focus on depression…
Improving the treatment of depression is commonly considered to be a constructive way that pharmacogenomics testing could show its value for benefit plans. This is due to a number of factors:
- Depression impacts many GSC plan members, particularly in the 30-50 age band.
- Mental illness is the leading cause of disability across Canada.1
- There is low adherence to antidepressant medications due to unpleasant side-effects
and other issues.2
- Up to one-third of patients do not respond to treatment for depression.3
- It has been estimated that a substantial percentage of patients do not achieve remission
of symptoms even after several trials of antidepressant medication.4
- A number of pharmacogenomic tests currently in use have already led to guidelines for
using the test results to recommend dosing and type of antidepressant.
While there has been some limited research that examines pharmacogenomics as a potential tool to support more effective antidepressant use, the wide range of different types of studies and different pharmacogenomic tests make the results difficult to compare or verify. Furthermore, the findings themselves have been highly variable, with certain studies showing positive impacts on outcomes and others failing to demonstrate those findings. Ultimately, given that much of the research to date had been conducted by the test vendors themselves, we noted a crucial need for an independent evaluation.
Our study was in the form of a prospective, single-blinded randomized, controlled trial design where we evaluated the impact of pharmacogenomics-guided antidepressant treatment versus “treatment as usual” for depression and anxiety. The treatment was implemented by pharmacists in three large community pharmacies in Toronto, featuring collaboration with patients’ physicians.
METHOD AND MEASURES…
We recruited 213 patients who were taking antidepressants and randomly assigned them to either the control group or the intervention group. While all patients were cheek swabbed and tested for their pharmacogenomic profile, the patients were unaware of their group assignment. Both groups received standard clinical pharmacy services but only the intervention group’s drug therapy was optimized on the basis of their pharmacogenomics test results. The patient’s personalized pharmacogenomics test report helped pharmacists identify potential problems with that patient’s drug therapy and make recommendations to the prescribing physician. For the patients in the control group, the results of the pharmacogenomics test were supressed by the test vendor from both the pharmacists and the patients. The control group’s drug therapy was instead based purely on the pharmacist’s clinical judgment regarding the prescribed medication, in other words, “treatment as usual.”
Over a six-month period, we evaluated the impact of testing on the identification of drug therapy problems and on the short-term and long-term patient-reported outcomes of depression, anxiety, functional impairment, and treatment satisfaction. We hypothesized that participants in the intervention group receiving pharmacogenomics-guided treatment would report greater improvement of their depression and/or generalized anxiety compared to those receiving treatment as usual.
To evaluate patient response to the treatment, the following self-reporting questionnaires were given to the patients in both groups when they first joined the study (which is referred to as the baseline) then again at months one, three, and six:
- Patient Health Questionnaire (PHQ-9) – The PHQ-9 is used to assess the nine diagnostic criteria of depression. Items were scored using a four-point scale from 0 (“not at all”) to 3 (“nearly every day”). The total score reflects symptom frequency and severity, with cut-offs of 5, 10, and 15 indicating mild, moderate, and severe symptoms, respectively.
- General Anxiety Disorder – 7 (GAD-7) – The GAD-7 is used to assess anxiety symptom severity. Items were scored using a four-point scale to score from 0 (“not at all”) to 3 (“nearly every day”), with cut-offs of 5, 10, and 15 indicating mild, moderate, and moderately severe anxiety, respectively.
- Sheehan Disability Scale (SDS) – The SDS assesses functional disability and impairment. It measures the symptomatic impact on work/school, social life, family life / home responsibilities. Items are scored on a 11-point scale to evaluate disability from 0 (“not at all”), 1-3 (“mild”), 4-6 (“moderate”), 7-9 (“markedly”), and 10 (“extremely”).
- Treatment Satisfaction with Medicines Questionnaire (SATMED-Q) – The SATMED-Q assesses six domains of patient satisfaction including side-effects, drug efficacy, convenience of use, impact on activities of daily living, medical care, and general satisfaction. This measure was designed for patients undergoing prolonged use of pharmacological treatment for a chronic illness; items are rated using a five-point scale from 0 (“not at all”) to 4 (“very much”). The SATMED-Q was used primarily as a screening tool to determine which patients were eligible for entry into the trial.
Snapshot of the study participants:
As you can see in the following graphs, the intervention group shows a notable improvement in average score over six months. (A lower score indicates improvement.) While the control group also shows an improvement for each measure, the gaps between the two curves widen, meaning that the intervention group reported a greater improvement as the patients became optimized on the drugs over time. Note that it is an expected and positive result that the control group also improved as these patients received clinically appropriate treatment and had their care closely overseen by a clinical pharmacist and physician.
For the PHQ-9 measure, both groups started with a similar baseline score. Over the sixmonth period, the average score for the intervention group dropped from 13.9 to 8.9 indicating an improvement in the severity of depression symptoms from moderate to mild. And at six months, there was a sizeable difference of 2.1 points between the average score of the intervention group and the control group.
The GAD-7 baseline measure for both groups was also almost the same at the outset with the intervention group experiencing a dramatic drop after only one month of treatment. After six months, the intervention group’s score fell from 11.7 to 6.8 showing an improvement in the severity of anxiety from moderate to mild.
On the SDS Scale, although the intervention group started with a slightly higher baseline score, the groups effectively reached the same score after one month of treatment. At three months, the control group leveled off, but the intervention group continued to improve, falling from 18.2 to 10.2 over the six-month period. This eight-point drop indicated a striking improvement in this group’s functioning in everyday life.
What did we learn?
We went into this investigation with one key question: Does clinician access to pharmacogenomic test results during routine clinical care improve patient outcomes relative to care provided in the absence of that information?
We posed this important question because we recognized there were substantial gaps in knowledge regarding the impact of pharmacogenomic testing and the value of this testing for benefit plans. While every study, including this one, has some limitations, the evidence generated provides an important contribution to research in this area.
Utilizing a strong study design allowed us to observe that over a six-month period, patients’ mental health conditions improved significantly more when their treatment was guided by a pharmacogenomics profile rather than purely by clinician judgment. Our investigation results also support the role of pharmacists in pharmacogenomic testing and treatment recommendations for mental health difficulties. Pharmacists had an opportunity to share the insights revealed by the pharmacogenomics testing with the prescribing physicians who accepted vast majority of pharmacist recommendations.
GSC recognizes that the evidence for pharmacogenomics is growing, and we will continue to monitor emerging investigations. In the meantime, we are comfortable supporting pharmacogenomics testing. The results of this important study give us strong reassurance that pharmacogenomics has an important role to play as part of benefit plans with the ultimate goal of optimizing drug therapy and improving patient health.