Pharmacogenomic Testing in Psychiatry: Ready for Primetime?ublication by articles

ORIGINAL ARTICLE

Pharmacogenomic Testing in Psychiatry Ready for Primetime? Gopalkumar Rakesh, MD,* Calvin R. Sumner, MD,† Jeanne Leventhal Alexander, MD, ABPN, FRCPC, FAPA, FACPsych,‡ Lawrence S. Gross, MD,§ Janet Pine, MD,§ Andrew Slaby, MD,|| Amir Garakani, MD,}# and David Baron, MSEd, DO** Abstract: Pharmacogenomic testing in clinical psychiatry has grown at an accelerated pace in the last few years and is poised to grow even further. Despite robust evidence lacking regarding efficacy in clinical use, there continues to be growing interest to use it to make treatment decisions. We intend this article to be a primer for a clinician wishing to understand the biological bases, evidence for benefits, and pitfalls in clinical decision-making. Using clinical vignettes, we elucidate these headings in addition to providing a perspective on current relevance, what can be communicated to patients, and future research directions. Overall, the evidence for pharmacogenomic testing in psychiatry demonstrates strong analytical validity, modest clinical validity, and virtually no evidence to support clinical use. There is definitely a need for more double-blinded randomized controlled trials to assess the use of pharmacogenomic testing in clinical decisionmaking and care, and until this is done, they could perhaps have an adjunct role in clinical decision-making but minimal use in leading the initial treatment plan. Key Words: Pharmacogenomic, psychotropic medications, psychotropic, medications, metabolism (J Nerv Ment Dis 2020;208: 127–130)

ith advances in genetics, it is now possible to examine how patients respond to psychotropic medications. Genes code for enzymes and receptors that psychotropic medications act on. Enzymes metabolize these medications, and receptors help produce downstream effects. In addition to enzymes, metabolism of psychotropic medications depends on physiological factors like age, sex, and circadian rhythm. Environmental factors such as exposure to chemicals, stress, and interaction with other ingested biological agents can also influence metabolism of medications (Kalow, 2004; Weinshilboum and Wang, 2006). Pharmacogenomics can predict with certain limitations the probability of adverse effects with psychotropic medications based on identifying specific genes and their variants. The field is an attempt in precision psychiatry. Stated in a reductionist manner, the field examines the genetic bases for differences in a person's enzymatic metabolism status compared to normal variants in the population (Kalow, 2004; Kitzmiller et al., 2011).

*Department of Psychiatry, University of Kentucky, Lexington, Kentucky; †Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, Florida; ‡Private Practice, Psychoneuroendocrinology and Women's Health, Berkeley; §Department of Psychiatry and the Behavioral Sciences, Keck School of Medicine of USC, Los Angeles, California; ||Department of Psychiatry, New York University Medical School; ¶Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; #Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut; and **Faculty of Health Sciences, Western University of Health Sciences, Pomona, California. Send reprint requests to David Baron, MSEd, DO, Faculty of Health Sciences, Western University of Health Sciences, Pomona, CA. E‐mail: dbaron@westernu.edu. Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved. ISSN: 0022-3018/20/20802–0127 DOI: 10.1097/NMD.0000000000001107

History and Current Relevance of Pharmacogenomic Testing Initially, pharmacogenomic testing was presented to psychiatrists as an opportunity to assess genetic bases for adverse effects in patients treated with tricyclic antidepressants and monoamine oxidase inhibitors. Current testing kits advertised by companies look at genes responsible for cytochrome P450 (CYP450) enzymes. In addition, they also look at genes responsible for serotonin and dopamine receptors (Bousman and Hopwood, 2016). From a business standpoint, the global profitability of testing across specialties including mental health is predicted to be US $15 to 25 billion by 2024 (Newswire, 2017). The primary markets for pharmacogenomic testing in psychiatry are in North America, followed by smaller markets in Europe and Asia Pacific (Newswire, 2017). However, the cost utility of performing tests is controversial (Peterson et al., 2017; Rosenblat et al., 2017). Most psychotropic medications including antidepressants, antipsychotics, and mood stabilizers metabolized by the liver encounter one of the CYP450 enzymes. Depending on speed and strength of metabolism, medications may accumulate in plasma, causing more side effects than expected. They may also be cleared out very quickly, needing higher doses than usual for therapeutic levels (Kitzmiller et al., 2011; Zanger and Schwab, 2013; Zanger et al., 2014). Except for the CYP450 enzymes, evidence for the other genes is minimal to modest (Bousman and Hopwood, 2016). Broadly speaking, the current state of pharmacogenomic testing in psychiatry is cautionary. However, expected advances in pharmacogenomic technology need more evidence to support the use of testing. To elucidate the advantages and possible pitfalls of pharmacogenomic testing in clinical decision-making in psychiatry, we describe two case vignettes below.

Case Vignette 1 D. S. (name changed) is a 19-year-old woman who presented with debilitating symptoms, consistent with generalized anxiety disorder (GAD) and panic disorder. She was stable on 30 mg of escitalopram. She developed purpuric patches, and these were correlated with escitalopram as workup did not reveal any other cause for the purpura. She was transitioned to fluoxetine after tapering escitalopram. She was able to tolerate 20 mg of the medication; however, her anxiety symptoms were not optimally controlled on this dose of fluoxetine. Her anxiety symptoms are suggestive of GAD and panic disorder. After an initial concomitant trial with buspirone at total daily doses of 30 to 60 mg, she was transitioned to clonazepam 0.5 mg to be used twice daily. The anxiety symptoms do not respond to these additions. With increase in dose of fluoxetine to 40 mg, she developed nausea, vomiting, and fatigue. Side effects continued to persist and gradually became intolerable. Pharmacogenomic testing revealed low metabolizer status for CYP2C9; she is, however, a rapid metabolizer for CYP2D6, and based on this, she is started venlafaxine extended release. Venlafaxine works well for her. She was stabilized on 150 mg with concomitant clonazepam and continues to do well with optimal control of both anxiety and panic symptoms.

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Case Vignette 2 S. A. (Name changed) is a 34-year-old man who presented with depressive symptoms and was diagnosed with major depressive disorder. On a regimen comprising sertraline 100 mg/d and bupropion extended release 300 mg/d, he reported optimal response. He presented to the office requesting a reevaluation of his medication regimen after a few instances of worrying, noting that although he has not been depressed or anxious, he thinks that the medications are “just not right.” S. A. requested pharmacogenomic testing, having heard about it from an online advertisement. He wants an “objective measure” that would help him figure out the “best medications” for him. A full medication history revealed that he has not been tried on other psychotropic medications in the past. He has no significant medical history, and records from his internist indicated that recent routine laboratory work, electrocardiogram, and physical examination are all within normal limits. Pharmacogenomic testing showed that the patient is an ultrarapid metabolizer of CYP1A2, a poor metabolizer of CYP2D6, and an intermediate metabolizer of CYP2C9. He is a normal metabolizer of the other cytochrome genes. The report also stated that the patient is homozygous for the short promoter polymorphism of the serotonin transporter gene (SLC6A4), which means that he would have lower probability of responding to selective serotonin reuptake inhibitors and would benefit from another medication class. The color-coded sheet indicates that he has a moderate gene-drug interaction with sertraline, stating that his genotype may affect drug mechanism and reduce its efficacy. He has a significant gene-drug interaction with bupropion, with the report stating that he may have too-high serum levels, which may require lower doses, and that he is at higher risk of side effects. After discussing the report and its limitations, S. A. wanted to change his antidepressants. Despite the psychiatrist's recommendation to lower the bupropion and continue the sertraline, S. A. insisted on making a change. He asked to be put on desvenlafaxine, a serotonin norepinephrine reuptake inhibitor with no gene-drug interactions as noted on the testing. S. A. was first tapered off the bupropion and then tapered off the sertraline while desvenlafaxine was added. After 5 weeks on desvenlafaxine monotherapy, he noted that his mood was starting to decline and started experiencing increased severity of depressive symptoms. S. A. regretted the change and requested to go back to his previous medication regimen comprising bupropion extended release 150 mg/d and sertraline 100 mg/d. S. A. noted that his mood and anxiety symptoms have abated with concomitant improvement in his sleep and appetite.

What Can We Infer from Pharmacogenomic Decision Support Tools? Testing kits collect saliva samples from patients and then run gene analyses results through proprietary decision trees to produce various interpretations. These decision tree algorithms are software based and trained with previous samples and results. Through extensive training, they provide results that cluster medications based on their metabolic profile. For the same reason, these tests are called pharmacogenomic decision support tools. The interpretations vary in content depth and candidate gene markers across support tools manufactured by various companies (Bousman and Hopwood, 2016; Bousman and Müller, 2018; Butler, 2018). Elucidating on the principles behind the relationship between gene variants and metabolizer status, a normal gene variant (or allele) for cytochrome enzymes results in optimal or normal metabolizer status. This leads to expected serum drug levels for recommended medication doses. Duplications or multiplication of the functional allele leads to rapid or ultrarapid metabolizer status, leading to low serum levels with recommended doses and needing higher doses for optimal effectiveness. Deletions lead to poor metabolizer status and, consequently, elevated serum levels with recommended or lower than recommended medication doses. Rapid or ultrarapid metabolizer status leads to accelerated 128

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breakdown products of drug metabolism, leading to suboptimal serum levels for recommended drug doses (Seeringer and Kirchheiner, 2008). Although pharmacogenomic testing could be useful in certain situations as highlighted in the first case scenario, there has been a certain degree of misinformation on their use when marketed by companies that use or market the support tool (Abbasi, 2018). Current plethora of tests are limited to assessing pharmacokinetic mechanisms (what our body does to the drug, which encompasses metabolism and absorption) and pharmacodynamic mechanisms (drug binding to receptors and mediating downstream effects) (Franconi and Campesi, 2014; Gumbo, 2008). Outputs from these support tools use differences in these genes and their variants to hypothesize differences in drug response between subjects who take the candidate drug. Ironically, there has been only one study showing a steady correlation between blood levels of psychotropic medications and response/adverse effect profiles. This presents a flaw with the process. This study used a pharmacogenomic decision support tool to estimate target dosing of desvenlafaxine, and this was relatively close to the clinically tolerable dose (Bousman et al., 2017b). This is limited to one particular decision tool. More trials are needed to generalize this principle across other kits and medications. For psychotropic medications, genetic variants that examine how medications affect physiological parameters are far and few between. Concordance between genotype (i.e., genetic profile) and phenotype (i.e., physical equivalent of genotype that is expressed) has been found to be inconsistent across studies (Bousman and Dunlop, 2018). A notable example of a drug–receptor gene interaction is serotonin transporter gene and antidepressant response; the transporter gene influences serotonin receptor status, which can be changed by antidepressants. Some other examples of genes that code for receptors include serotonin 2A receptor gene (HTR2A) and dopamine 2 receptor (DRD2) gene variants (Bousman and Dunlop, 2018; Bousman and Hopwood, 2016). Studies looking at these genes and response/adverse effect profiles to psychotropic medications have yielded inconsistent results (Bousman and Müller, 2018; Bousman et al., 2017a, 2019a, 2019b). In our second vignette, the decision support tool was not able to optimally predict which antidepressant would work best for S. A. It is reasonable to infer that a thorough clinical history is crucial for making initial pharmacologic recommendations, as currently we have no evidence to use pharmacogenomic testing at the outset of treatment. They could, however, be an adjunct to therapy, especially in patients who are deemed to be sensitive to side effects from previous trials of psychotropic medications (Bousman and Müller, 2018; Bousman et al., 2019a). However, there needs to be a steady and consistent consensus on when they need to be used. Recently, the following genes have been made mandatory across support tools: CYP2C9, CYP2C19, CYP2D6, HLA-A, and HLA-B. The human leukocyte antigen genes are relevant to mood stabilizer medications, specially carbamazepine (Ferrell and McLeod, 2008; McCormack et al., 2011).

What Can Be Conveyed to Patients Regarding These Tests? The tests assume that there is a correlation between serum drug levels and its effectiveness, which is not evidence based (Bousman and Hopwood, 2016; Bousman and Müller, 2018). This needs to be conveyed to the client seeking the test. In addition, the discordance between genotype and phenotype needs to be mentioned and explained as well. In the second scenario, S. A. sought to have the test, with the physician opining that the likelihood of it influencing prescribing is minimal. Prescribing pharmacogenomic testing by a provider needs to be a judicious decision that is based on rationale and demonstration of definitive need, which encompasses a clinical scenario wherein a patient has intolerable adverse effects to multiple antidepressant trials. In such scenarios, pharmacogenomic testing could, perhaps, provide pointers on medication selection. However, they have minimal use in leading the initial treatment © 2020 Wolters Kluwer Health, Inc. All rights reserved.

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plan or choosing medications during the first visit (Bousman and Müller, 2018). Patients should also be informed about differences in how different gene testing support tools produce results. In addition, multiple gene variants may also be tested by different support tools and although required to be listed on result reports, does not happen (Bousman et al., 2017a). A study tested 37 support tools and found none of them fulfilled reporting recommendations laid down by the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Center for Disease Control (Bousman et al., 2017a). Most decision tool results would include a summary of psychotropics the patient can take and ones that cannot be taken; however, there is no consistent pattern of how testing results are displayed across them. Given the lack of robust association between genes tested in the support tools and the effectiveness of psychotropic medications, it would not be surprising for clinicians to encounter instances wherein medications classified as recommended by the support tool cause adverse effects and are intolerable to patients (Abbasi, 2018; Bousman et al., 2017a).

What Does Evidence Say? Clinical and research evidence is suboptimal for pharmacogenomic testing (Abbasi, 2018; Bousman and Müller, 2018; Bousman et al., 2019a; Butler, 2018). The standard of proof assumed by most clinicians is that a diagnostic product has quality evidence to support analytical validity, clinical validity, and clinical use. The evidence for pharmacogenomic testing in psychiatry demonstrates strong analytical validity, modest clinical validity, and virtually no evidence to support clinical use (Pendergast, 2008). In the United States, the standards for evidence required for medical products are enforced by regulatory agencies: primarily the Food and Drug Administration (FDA) and the Centers for Medicare and Medicaid Services. Currently, only a subset of pharmacogenomic tests fall under regulatory guidance from the FDA (Pendergast, 2008). With the rapid growth of the industry and expansion of broad claims that may exceed the evidence, regulatory oversight in North America and Europe may be changing (Pendergast, 2008). Measures of clinical validity are included in the CPIC guidelines. Current evidence shows a need for more genomics research to develop optimal and standardized decision trees. There is a need for research to optimize predictive capability of these decision trees, not just with regard to adverse effects but effectiveness as well. We also need more doubleblinded randomized controlled trials (RCTs) to assess the use of pharmacogenomic testing in clinical decision-making and care. Although there seems to be some evidence for use (Bousman and Müller, 2018; Bousman et al., 2017b, 2019b), more RCTs are necessary (Abbasi, 2018; Bousman and Müller, 2018). The CPIC lists various polymorphisms associated with each psychotropic medication and dosing strategies that need to be considered. The International Society of Psychiatric Genetics has encouraged the direction of testing and development, although stating that we need more RCTs. The development model for the pharmacogenomic testing industry resembles commercialization of digital technology more than traditional business models of traditional pharmaceuticals or medical devices. The initial acceptance of pharmacogenomic testing in mental health probably benefited from pent-up demand for “breakthroughs” in psychiatry and the halo effect of substantial successes in pharmacogenomic testing in other medical areas. In general, the quick-to-market strategy, particularly in mental health, is not well suited to a complex and heavily regulated market like health care. Pharmacogenomic testing is, at least for the time being, taking advantage of minimal oversight by regulators (Pendergast, 2008).

Future Directions An ideal pipeline for development and validation of pharmacogenomic testing needs to precede commercial use of any gene testing

Pharmacogenomic Testing

toolkit (Tonk et al., 2017). As detailed in this article, examining the correlation between genotype and phenotype requires extensive basic science research and correlation with blood levels, quantification, and modeling (Tonk et al., 2017). In addition, there needs to be a minimum set of genes and alleles needed for consistency across pharmacogenomic decision support tools so that these results may serve to be adjunctive tools that clinicians use for patient care (Bousman et al., 2019a). Larger initiatives like Implementing Genomics in Practice (IGNITE), which is an NIH-funded initiative (Cavallari et al., 2017), the Dutch Pharmacogenetics Working Group, and the Pharmacogenomic Resource for Enhanced Decisions in Care & Treatment (Van Driest et al., 2014) have attempted to optimize pharmacogenomic testing so as to better integrate it into clinical decision-making in psychiatry. As in other fields of science and medicine, pharmacogenomic testing needs to transcend anecdotal case reports and prove itself from a clinical validity standpoint. The vision of pharmacogenomic testing as expressed by pioneers in the field at Mayo Clinic was the hope of being able to select the right drug at the right dose for every patient every time. The strength of current pharmacogenomic testing in psychiatry is the ability to detect genetic variations that potentially affect metabolism of medications and the potential that the patient might experience intolerable side effects when taking these medications. The evidence remains to be developed to demonstrate correlation between these specific genetic variations provided by pharmacogenomic testing and patient response to medication.

CONCLUSIONS Dr Fred Goodwin, former Director of the NIH Institute for Alcohol, Drug Abuse and Mental Health (later to become NIHM, NIDA, and NIAAA) proclaimed the 1990s “The Decade of the Brain.” Psychiatry has sought biomarkers and genomic analysis to better understand the etiology and treatment of psychopathology. The initiation of the Human Genome Project in 1990 by the NIH added a potential clinical application of gene sequencing to assist clinicians in creating personalized medical care. Over the past 15 years, the concept of pharmacogenomic testing has advanced patient care in a number of medical specialties, most notably oncology. The fact that similar findings have not been demonstrated in the treatment of psychopathology is understandable and, in fact, predictable. Psychiatric illness is affected by the complex gene interactions of many genes and the modulating effect of the environment. This is more often seen in certain neurologic diseases, such as Huntington's disease. Translational research has demonstrated these gene interactions are affected by the environment, as well as a geneenvironment interaction. This level of complexity has proven to be a significant challenge in developing clinically relevant, valid, and reliable genomic markers to assist in the treatment/management of psychopathology. As discussed in this article, the marketing of pharmacogenomic testing as a clinical tool has gone beyond the current extant clinical science, despite a number of promising areas such as adverse drug-drug interactions. The desire to have a laboratory test determine the most effective and safest medication to prescribe to treat psychopathology is a worthy goal. Unfortunately, we are not there yet. It is likely that genomic testing will ultimately be an important piece of a large, complex clinical management puzzle, offering clinicians and patients more effective treatment strategies. Future research will benefit from following the model established by oncology and cardiology: through very large, prospective, well-conducted multisite clinical trials. The establishment of Clinical Trials Networks has resulted in significant advances in both oncology and cardiology. This model could work in psychiatry as well. Refraining from “overselling” the clinical use of pharmacogenomic will be important to maintain public confidence in the long run. There is little doubt that

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pharmacogenomics will likely play a role in the effective treatment of psychopathology in the future. We are just not there yet. Future well-conducted clinical trials will determine what that role will be. ACKNOWLEDGMENT This work was performed by the Group for Advancement of Psychiatry (GAP) Psychopharmacology Committee, which includes all the authors. DISCLOSURE The authors declare no conflict of interest. REFERENCES Abbasi J (2018) Companies tout psychiatric pharmacogenomic testing, but is it ready for a store near you? JAMA. 320:1627–1629. Bousman C, Maruf AA, Müller DJ (2019a) Towards the integration of pharmacogenetics in psychiatry: A minimum, evidence-based genetic testing panel. Curr Opin Psychiatry. 32:7–15. Bousman CA, Arandjelovic K, Mancuso SG, Eyre HA, Dunlop BW (2019b) Pharmacogenetic tests and depressive symptom remission: A meta-analysis of randomized controlled trials. Pharmacogenomics. 20:37–47. Bousman CA, Dunlop BW (2018) Genotype, phenotype, and medication recommendation agreement among commercial pharmacogenetic-based decision support tools. Pharmacogenomics J. 18:613–622. Bousman CA, Hopwood M (2016) Commercial pharmacogenetic-based decisionsupport tools in psychiatry. Lancet Psychiatry. 3:585–590. Bousman CA, Jaksa P, Pantelis C (2017a) Systematic evaluation of commercial pharmacogenetic testing in psychiatry: A focus on CYP2D6 and CYP2C19 allele coverage and results reporting. Pharmacogenet Genomics. 27:387–393. Bousman CA, Müller DJ (2018) Pharmacogenetics in psychiatry: A companion, rather than competitor, to protocol-based care. JAMA Psychiatry. 75:1090. Bousman CA, Müller DJ, Ng CH, Byron K, Berk M, Singh AB (2017b) Concordance between actual and pharmacogenetic predicted desvenlafaxine dose needed to achieve remission in major depressive disorder: A 10-week open-label study. Pharmacogenet Genomics. 27:1–6. Butler MG (2018) Pharmacogenetics and psychiatric care: A review and commentary. J Ment Health Clin Psychol. 2:17–24. Cavallari LH, Beitelshees AL, Blake KV, Dressler LG, Duarte JD, Elsey A, Eichmeyer JN, Empey PE, Franciosi JP, Hicks JK, Holmes AM, Jeng L, Lee CR, Lima JJ, Limdi NA, Modlin J, Obeng AO, Petry N, Pratt VM, Skaar TC, Tuteja S, Voora D, Wagner M, Weitzel KW, Wilke RA, Peterson JF, Johnson JA (2017) The IGNITE Pharmacogenetics Working Group: An opportunity for building evidence with pharmacogenetic implementation in a real-world setting. Clin Transl Sci. 10:143–146. Ferrell PB Jr., McLeod HL (2008) Carbamazepine, HLA-B*1502 and risk of StevensJohnson syndrome and toxic epidermal necrolysis: US FDA recommendations. Pharmacogenomics. 9:1543–1546.

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