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In April 2019, Mozilla internet health report published an article “23 reasons not to reveal your DNA” [1]. The primary reasons why people should worry about sharing their DNA with strangers are quite obvious – privacy concerns, the potential for abuse and limited validity of the findings top the report’s list of reasons. While there are good motives why one should stay away from universal wavers that allow the use of DNA for research by third parties, such judgment is pouring the baby out with the bathwater. There is a great difference between genome-wide association studies and pharmacogenomic test panels. Targeted, limited and specific genetic tests are useful and can make patients safer.

The clinical pharmacology section of a drug label contains a wealth of information about how a drug is absorbed in the body, how it is distributed throughout the body and what kind of biotransformation it undergoes before it is excreted from the body. What the human body does to a pharmaceutical substance depends on the presence and activity of enzymes that metabolize the drugs. Cytochrome P450 (CYP) modulation affects drug efficacy through increasing or reducing plasma half-life. Toxicity occurs from increased levels of toxic metabolites. This CYP modulation effect is commonly observed when two or more drugs are administered together.
Pharmacokinetic, pharmacodynamic and metabolic properties of drugs are tested in vitro and in vivo. Evaluation of in vitro metabolism and cytochrome P450-related enzymatic activity is done using human liver metabolic fractions isolated from various parts of liver cells. Effects on hepatic metabolism after in vivo treatment on laboratory animals is determined histochemically and immunohistochemically in tissues. Information on clinical pharmacology is part of prescribing information of all marketed drugs.

There is a great individual variation in drug response due to genetic polymorphism and a nearly infinite number of possible combinations of pharmaceutical products people take. Individual variability in response to treatment is a major clinical problem. Major CYP polymorphisms, such as CYP2D6, CYP2C19, and CYP2C9, are responsible for a significant portion of commonly prescribed drugs including antidepressants, opioids, antiarrhythmics or anti-psychotics. While ultra-rapid metabolizers could find a drug ineffective, poor metabolizer could find the same dose to be toxic [2].

The same enzymatic system is also responsible for numerous drug interactions as some drugs function as substrates that compete for the same enzyme, or function as inducers or inhibitors of a specific enzymatic system. Enzymes most commonly involved in drug interactions are CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, CYP3A5, and CYP3A7. The Flockhart Table, designed by Indiana University as a hypothesis testing, teaching and reference tool for researchers and clinicians, shows all the major CYP450-dependent drug-drug interactions [3]. The most comprehensive resource on cytochrome P450 enzymes, web-based tool Transformer, formerly SuperCyp, was developed by Structural Bioinformatics Group Charite in Berlin, Germany [4].

Gradually, pharmacogenomic testing is making its way into clinical medicine. The Clinical Pharmacogenetics Implementation Consortium (CPIC) published a comprehensive database of actionable pharmacogenetic biomarkers that translate the results of genetic tests into prescribing decisions. CPIC guidelines help clinicians understand how genetic tests should be used to optimize therapy. Examples of existing guidelines include CYP2C19 and clopidogrel; CYP2D6, VKORC1 and CYP4F2 interaction with warfarin; CYP2D6 and CYP2C19 and Selective Serotonin Reuptake Inhibitors (SSRIs) and Tricyclic antidepressants or SLCO1B1 and simvastatin [5].
In 2019, the Veterans Health Administration Clinical Pharmacogenetics Subcommittee reviewed 30 drug-gene pairs for recommendations for routine testing in clinical practice. The top of the list of recommended tests includes HLA-B*15:02 for carbamazepine-associated Stevens-Johnston syndrome, G6PD for rasburicase-associated hemolytic anemia and CYP2D6 for codeine toxicity [6].

But clinical usefulness depends on other factors, not just clinical validity. In 2009, the Coverage and Analysis Group at the Centers for Medicare & Medicaid Services (CMS) reviewed coverage determination for pharmacogenomic testing that would predict response to warfarin. While there is no doubt about the validity of the test, the FDA approved drug label states that physicians should adjust dosing according to International Normalized Ratio (INR) results. Genotype might help with the initial dose selection, yet it cannot be the only consideration in older adults with multiple comorbidities and on multiple other, potentially interacting medications [7]. Clinical utility of such testing is, therefore, an important unknown that is difficult to determine.

In 2015, Kaiser Permanente asked their patients how they feel about pharmacogenomic testing. Responding patients were generally aware that not all medications work for everyone, and that some may be ineffective or toxic for some people. Patients understood that trial-and-error approach can cost them years of quality life on poor choice of medication. However, some patients felt that the testing would mainly protect the doctors from liability for patient injury rather than the patients from drug-related injury or could serve as a basis for discrimination and reason why to deny a specific medication to them. Patients on antidepressants and antipsychotics also expressed concerns over data sharing since mental illness is still associated with significant stigma and potential for discrimination. The authors concluded that the success of precision medicine depends on the broad acceptability of genomic testing and overcoming concerns over privacy, discrimination, overreliance on genomic results, and erosion of the physician-patient relationship [8].

There is little doubt about the scientific validity of pharmacogenomic testing in the selection of the most appropriate medications at the correct dose for the right patients. The uptake of these methods into clinical practice, however, depends greatly on the acceptance of such testing by patients and the confidence of payers that such tests reduce drug-related patient injury.


[1] The Internet Health Report 2019 (2019). 23 reasons not to reveal your DNA — The Internet Health Report 2019. [online] The Internet Health Report 2019. Available at: [Accessed 2 May 2019].

[2] Samer, C., Lorenzini, K., Rollason, V., Daali, Y. and Desmeules, J. (2013). Applications of CYP450 Testing in the Clinical Setting. Molecular Diagnosis & Therapy, [online] 17(3), pp.165-184. Available at: [Accessed 4 May 2019].

[3] Indiana University (2019). Drug Interactions Flockhart Table ™. [online] Indiana University School of Medicine. Available at: [Accessed 4 May 2019].

[4] Charite Institute for Physiology Structural Bioinformatics Group (2019). SuperCYP: a comprehensive database on Cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions.. [online] Available at: [Accessed 4 May 2019].

[5] Clinical Pharmacogenetics Implementation Consortium (CPIC®) (2019). Guidelines. [online] Available at: [Accessed 4 May 2019].

[6] Vassy, J., Stone, A., Callaghan, J., Mendes, M., Meyer, L., Pratt, V., Przygodzki, R., Scheuner, M., Wang-Rodriguez, J. and Schichman, S. (2018). Pharmacogenetic testing in the Veterans Health Administration (VHA): policy recommendations from the VHA Clinical Pharmacogenetics Subcommittee. Genetics in Medicine, [online] 21(2), pp.382-390. Available at: [Accessed 4 May 2019].

[7] Institute of Medicine. 2014. Assessing Genomic Sequencing Information for Health Care Decision Making: Workshop Summary. Washington, DC: The National Academies Press.

[8] Trinidad, S. (2015). “Getting off the Bus Closer to Your Destination”: Patients’ Views about Pharmacogenetic Testing. The Permanente Journal, [online] Summer 19(3), pp.21-27. Available at: [Accessed 4 May 2019].

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