Can pharmacogenetics overcome barriers to medication adherence?

Can pharmacogenetics overcome barriers to medication adherence?

As many as half of patients don’t take medications as prescribed, and 20% to 30% of prescriptions are never filled 1,2. This nonadherence to recommended treatment has dramatic effects on patients and the health care system. Lack of patient adherence leads to poor therapeutic outcomes which result in increased use of other health services and overall healthcare costs 3,4. Approximately 125,000 deaths and at least 10% of hospitalizations are attributed to nonadherence annually in the United States 5.

Building patient trust before prescribing, as part of shared decision making, may uncover some adherence barriers 6. By tailoring drug options and dosage based on the genetic makeup of the individual patient being treated, and addressing patients’ concerns, adherence to a prescribed regimen can be improved 7. Genetic differences can cause a medication to work effectively in one person while failing to help, or even causing harm in another person. Pharmacogenetic testing detects these differences and can prevent adverse drug reactions (ADRs) and ineffective treatments 8. Following pharmacogenetic testing, patients felt more confident about taking their medication and believed it provided their physician with more insight on dosing 7.

Medication cost is another major deterrent for medication adherence. In a recent national online survey, more than one in five Canadians said that someone in their household did not take their medicines as prescribed, if at all, because of cost9. Part of the problem is that the cost of the prescriptions is often not taken into consideration when making medication choices. Patients often receive their prescriptions without knowing its cost which leads to unpleasant surprises when they go to the pharmacy.

Personalizing medications using pharmacogenetic and cost information

Providing drug cost as well as pharmacogenetic information in existing electronic health records and within a physician’s workflow, can facilitate the dialogue between healthcare providers and patients about affordability, differential cost among drugs, safety and efficacy 10. For example, ticagrelor (Brilinta) and clopidogrel (Plavix) are antiplatelet drugs used to inhibit the formation of blood clots and are a mainstay in the treatment and prevention of coronary artery disease, stroke, peripheral artery disease, or kidney problems. Clopidogrel is about four times cheaper than ticagrelor but as many as a third of patients using clopidogrel may be at risk of subsequent myocardial infarction (MI) and death because they are poor metabolizers for a gene called CYP2C19. A pharmacogenetic test can be used to identify CYP2C19 genetic variants and those at-risk patients. For CYPC19 poor metabolizers, the dosing guidelines 11 and the FDA-approved label recommends an alternative antiplatelet therapy, such as ticagrelor.

Personalizing medications for smoking cessation using pharmacogenetic testing can also help identify cost-effective and safe treatment options. Nicotine replacement therapy and varenicline (Champix) are common medications that help people quit smoking. Nicotine replacement therapy is usually covered by insurance plans and in some cases, it can be up to five times cheaper than varenicline. However, certain people may not respond as predicted to nicotine replacement therapy. For individuals who are CYP2A6 normal or ultrarapid metabolizers, nicotine replacement therapy may be less likely to help them quit smoking compared to varenicline. Alternatively, CYP2A6 intermediate or poor metabolizers may be more likely to experience side effects from varenicline such as nausea and abnormal dreams 12. Using pharmacogenetic testing can help predict whether people may have a better or worse than predicted response to medications for smoking cessation and identify the most cost-effective treatment option for the individual.

Smart tools for patient-centered care could increase medication adherence

A major challenge for healthcare providers is incorporating pharmacogenetic information with other important variables for prescribing, such as kidney and liver function, in the limited time given to deciding on a prescription. Intelligent decision support systems can enable prescribers to incorporate all these complex variables, together with medication costs, in their everyday clinical practice 13. Sifting through the enormous amounts of data using smart software can allow for safe and effective prescribing while engaging patients in their own health and improving communication among health professionals. A patient-centered approach to prescribing that incorporates patient preferences and barriers can lead to increase medication adherence and better clinical outcomes 14.

TreatGx decision support tool

TreatGx is an intelligent medication decision support system that provides safe and effective medication options based on the patients’ genetic profile, medical history, comorbidities and concomitant medications. TreatGx enables patient-centered care by fitting into a healthcare professional’s prescribing workflow, and providing alternative medications and/or doses. Using the highest level of evidence, TreatGx provides a list of optimal, individualized drug therapy options including price information. A recent feasibility study showed that family physicians and pharmacists found TreatGx easy to implement in their practices and to be a valuable tool to reduce inappropriate prescribing15. Pharmacogenetic testing is ready for use in the clinic, and TreatGx can help clinicians confidently implement the results and improve patient outcomes.

In Canada, TreatGxPlus is brought to you in partnership with LifeLabs



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12. Lerman, C. et al. Use of the nicotine metabolite ratio as a genetically informed biomarker of response to nicotine patch or varenicline for smoking cessation: a randomised, double-blind placebo-controlled trial. The Lancet Respiratory Medicine 3, 131–138 (2015).
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15. Dawes, M. et al. Introducing pharmacogenetic testing with clinical decision support into primary care: a feasibility study. cmajo 4, E528–E534 (2016).

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