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Knowledge of the Pharmacology of Antidepressants and Antipsychotics Yields Results Comparable With Pharmacogenetic Testing

MACALUSO, MATTHEW, DO; PRESKORN, SHELDON H., MD

Journal of Psychiatric Practice®: November 2018 - Volume 24 - Issue 6 - p 416–419
doi: 10.1097/PRA.0000000000000345
COLUMNS

Several companies offer pharmacogenetic testing for psychiatry on the basis of the claim that the outcome of drug selection is better when guided by such testing than when such testing is not used. This column examines the results of the GeneSight Psychotropic Test which groups various antidepressants and antipsychotics into 3 bins: green (“use as directed”), yellow (“use with caution”), and red (“use with increased caution and more frequent monitoring”). The authors examined how frequently the same drugs appeared in these different bins in 19 patients. They found that of the 22 antidepressants evaluated, 2 were virtually always (>90%) in the green bin: desvenlafaxine and levomilnacipran; and 8 were almost never (≤10.5%) in the green bin: citalopram, duloxetine, escitalopram, fluoxetine, fluvoxamine, mirtazapine, paroxetine, and sertraline. Of the 16 antipsychotics evaluated, they found that 4 were virtually always (>90%) in the green bin: asenapine, lurasidone, paliperidone, and ziprasidone; and 2 were almost never (≤10.5%) in the green bin: chlorpromazine and thioridazine. What was common among those drugs almost always in the green bin versus those almost never in the green bin were newer versus older marketed drugs and those not dependent versus dependent on oxidative metabolism for their clearance. The authors concluded that the results of this pharmacogenetic testing could be predicted on the basis of knowledge of the pharmacology of the drugs, particularly whether their clearance was dependent on oxidative drug metabolism.

MACALUSO and PRESKORN: University of Kansas School of Medicine, Wichita, KS

S.H.P. has worked with over 135 pharmaceutical companies in the United States and throughout the world. Over the past year, he has received grants/research support from or has served as a consultant, on the advisory board, or on the speaker’s bureau for Alkermes, Allergan, BioXcel, Food and Drug Administration, Janssen, National Institute of Mental Health, Merck, Pfizer, and Sunovion. M.M. has conducted clinical trials research as principal investigator for the following pharmaceutical companies over the last 12 months: Acadia, Allergan, AssureRx, Eisai, Lundbeck, Janssen, Naurex/Aptinyx, Neurim, and Suven. All clinical trial and study contracts were with and payments made to the Kansas University Medical Center Research Institute, a research institute affiliated with Kansas University School of Medicine-Wichita (KUSM-W).

Please send correspondence to: Matthew Macaluso, DO, University of Kansas Medical Center, 1010 N. Kansas, Wichita, KS 67217 (e-mail: mmacaluso@kumc.edu).

Several companies offer pharmacogenetic tests marketed as “decision support tools” to be used in selecting and dosing antidepressant and antipsychotic medications in clinical practice.1 These pharmacogenetic tests provide data principally on patient genotype relative to cytochrome P450 (CYP) enzymes and, to a lesser extent, relevant to pharmacodynamic targets (eg, the serotonin transporter). These “decision support tools” can advise when a specific patient will have faster or slower clearance of a drug. This information can be used to predict when a patient will have a higher or lower blood level of a specific medication relative to the dose prescribed and compared with the usual patient population. Such predictions are based on genotypic expression of specific CYP enzymes and the known metabolic pathways of specific drugs.1

Different companies marketing genetic tests use different formats for presenting the findings. Assurex Health’s “GeneSight” test2 presents the findings as follows: each test report groups specific drugs into “red bins,” “yellow bins,” and “green bins,” which, respectively, translate into “use with increased caution and more frequent monitoring,” “use with caution,” and “use as directed,” respectively. The drugs in the “red bins” and “yellow bins” are typically metabolized significantly through the liver and can have higher than anticipated blood levels in patients who lack functional copies of alleles for specific CYP enzymes. The authors hypothesized that one could predict which drugs are classified as “red bin” versus “yellow bin” versus “green bin” simply on the basis of knowledge of the metabolic pathways for each drug and without the genetic testing results.

This column will focus solely on the GeneSight Psychotropic Test, which analyzes genes that can affect a patient’s response to antidepressant and antipsychotic medications, because it is the test with which the authors have experience. The results presented here may be generalizable to other pharmacogenetic tests examining the same classes of medication, and they may also be generalizable to tests of other classes of drugs per se to the degree that they are also based primarily on differences in oxidative drug metabolism. However, the results presented here are not generalizable to those medications both in psychiatry and in other areas of medicine for which the US Food and Drug Administration (FDA) recommends genetic testing, such as drugs to treat certain cancers that involve companion genetic testing.

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METHODS

The authors reviewed pharmacogenetic testing reports from the last 19 patients who received the GeneSight test under our care. The reports were sent to the authors by Assurex Health, the manufacturer of the GeneSight test. Each patient had a diagnosis of major depressive disorder and had been nonresponsive to or intolerant of previous antidepressant treatments. We compiled the data by drug from all 19 reports to determine the frequency with which each drug appeared in the “red bin,” “yellow bin,” and “green bin.”

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RESULTS

Tables 1 and 2 show the complete results from the pharmacogenetic testing for the green, yellow, and red bins.

TABLE 1

TABLE 1

TABLE 2

TABLE 2

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Antidepressant Medications

As shown in Table 1, of the 22 antidepressants evaluated, 2 were virtually always (>90%) in the green bin: desvenlafaxine and levomilnacipran; 8 of 22 antidepressants were almost never (≤10.5%) in the green bin: citalopram, duloxetine, escitalopram, fluoxetine, fluvoxamine, mirtazapine, paroxetine, and sertraline. Vilazodone appeared in the “green bin” 84.2% of the time. Paroxetine appeared in the red bin 68.4% of the time, which was the highest percentage for any of the antidepressants or antipsychotics in the red bin.

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Antipsychotic Medications

As shown in Table 2, of the 16 antipsychotics evaluated, 4 were virtually always (>90%) in the green bin: asenapine, lurasidone, paliperidone, and ziprasidone; 2 of the 16 antipsychotics were almost never (10.5%) in the green bin: chlorpromazine and thioridazine.

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DISCUSSION

Given these results, one could predict which drugs are found in the “green bin” >90% of the time simply on the basis of knowledge of their oxidative drug metabolism without knowing the results of the patient’s genetic testing. As expected, drugs such as desvenlaxine and ziprasidone, which do not rely on CYP enzyme-mediated metabolism, were in the “green bin” 100% of the time. Medications that are metabolized approximately equally by multiple CYP and non-CYP enzymes and hence have little variability in blood level based on genetics were found in the “green bin” between 84.2% and 94.7% of the time and included levomilnacipran, vilazodone, asenapine, lurasidone, and paliperidone. For example, vilazodone is metabolized by several CYP enzymes and undergoes metabolism through non-CYP enzyme pathways, limiting its reliance on CYP-mediated drug metabolism. Hence, there is little risk with these medications that the blood level will be higher or lower than anticipated given the nature of their metabolism.

Drugs that are more reliant on CYP enzymes for their metabolism generally appeared in the “yellow” and “red” bins. The greater the reliance on hepatic metabolism and the narrower the therapeutic index, the more likely a drug would appear in the “red bin” versus the “yellow bin.” For example, amitriptyline appeared in the “red bin” 47.4% of the time. As amitriptyline relies heavily on CYP 2D6 for its metabolism, its blood levels vary widely depending upon an individual’s CYP 2D6 genotype. In addition, tricyclic antidepressants have a narrow therapeutic index, and genotypic CYP 2D6 poor metabolizers (ie, individuals who are genetically deficient in CYP 2D6 activity) are at risk for potentially toxic blood levels of such medications. For this reason, one could predict amitriptyline would frequently appear in the “red bin” because of its heavy reliance on this polymorphic CYP enzyme.

Therefore, if a patient did not respond to initial treatment with an antidepressant or had adverse effects related to that agent, providers could use information about the metabolic pathways of potential alternative medications along with the probability that the patient is deficient at specific metabolic pathways based on their ethnicity to choose a medication that has a lower risk of variability in blood level. Such an approach may be more cost-effective than genetic testing and may perhaps also be more practical, given that based on the results of this study, such logic would be consistent with the predictability of the genetic testing results.

Broadening the discussion to review the earlier published data on the effectiveness of GeneSight testing helps put the findings of this paper into perspective. Previously published studies on genetic testing had methodological flaws, including being underpowered or having an open-label design, which could bias the results in favor of the utility of pharmacogenetic testing. Keeping these limitations in mind, 2 open-label studies found statistically significant reduction in symptoms in patients whose treatment was guided by GeneSight testing compared with treatment as usual.3,4 Conversely, a double-blind randomized controlled trial found a trend for greater improvement in the GeneSight group but the differences were not statistically significant, although this study but may have been underpowered to detect a difference.5 Subsequently, GeneSight conducted a large (N=1200 patients) double-blind randomized clinical trial which included patients with treatment-resistant depression.6 This study found statistically significant higher rates of response and remission (P=0.01 and P<0.01, respectively) and greater reduction in symptoms in patients whose treatment was guided by GeneSight testing compared with those whose treatment was not; however, the number needed to treat was small (18 for response and 13 for remission at 8 wk).7

Two important caveats should be considered when examining results from this latest GeneSight study. First, the study did include patients with treatment-resistant depression and those patients may simply not be responsive to currently marketed antidepressants targeted at a biogenic amine mechanism of action regardless of how drug selection is performed, as was shown in the largest National Institute of Mental Health funded study on antidepressants, the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study.8 Second, the small but positive results in this study and the open-label studies may have occurred, in part, because treatment as usual may not necessarily be guided by a knowledge of the differences in oxidative drug metabolism of the drugs involved, and hence may be inferior to treatment guided by genetic testing. The results presented in this column suggest that a study comparing drug selection based on oxidative drug metabolism versus drug selection guided by pharmacogenetic testing would yield comparable results because of the high predictability of which drugs are classified as “green bin” versus “yellow bin” versus “red bin.” Consistent with this finding, in a 2018 statement, the American College of Neuropsychopharmacology stated that “While the genomics revolution may eventually justify genetic testing in psychiatric patients for clinically meaningful genetic variations, such scientifically supported and responsibly administered testing lies in the future for these patients.”9

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CONCLUSIONS

Although pharmacogenetic testing will almost undoubtedly prove critical in customizing treatment in psychiatry, the critical question is whether that time is now.10 On the basis of the results presented in this column, pharmacogenetic testing in terms of guiding the selection of specific antidepressant and antipsychotic medications may not yet have reached the threshold of clinical utility.

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ACKNOWLEDGMENT

The authors acknowledge Maggie Searight, MS4, for help with data entry.

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REFERENCES

1. Rosenblat JD, Lee Y, McIntyre RS, et al. Does pharmacogenomic testing improve clinical outcomes for major depressive disorder? A systematic review of clinical trials and cost-effectiveness studies. J Clin Psychiatry. 2017;78:720–729.
2. Assurex Health Website. Available at: https://assurexhealth.com. Accessed August 1, 2018.
3. Hall-Flavin DK, Winner JG, Allen JD, et al. Utility of integrated pharmacogenomic testing to support the treatment of major depressive disorder in a psychiatric outpatient setting. Pharmacogenet Genomics. 2013;23:535–548.
4. Hall-Flavin DK, Winner JG, Allen JD, et al. Using a pharmacogenomic algorithm to guide the treatment of depression. Transl Psychiatry. 2012;2:e172.
5. Winner JG, Carhart JM, Altar CA, et al. A prospective, randomized, double-blind study assessing the clinical impact of integrated pharmacogenomic testing for major depressive disorder. Discov Med. 2013;16:219–227.
6. Genomics Used to Improve DEpression Decisions (GUIDED) Study. Information available on the National Institutes of Health, US National Library of Medicine, Clinicaltrials.gov website. Available at: https://clinicaltrials.gov/ct2/show/NCT02109939?term=Genesight&rank=1. Accessed August 21, 2018.
7. Greden JF, Parikh SV, Rothschild AJ, et al. Combinatorial pharmacogenomics to guide treatment selection for major depressive disorder: congruency matters. Poster presented at the Annual Meeting of the American Psychiatric Association, New York, NY, May 2018.
8. Preskorn SH. Results of the STAR*D study: implications for clinicians and drug developers. J Psychiatr Pract. 2009;15:45–49.
9. American College of Neuropsychopharmacology (ACNP). ACNP statement on genetic testing for neuropsychiatric disorders. Available at: https://acnp.org/wp-content/uploads/2018/07/ACNP-Statement-on-Genetic-Testing-for-Neuropsychiatric-Disorders.pdf. Accessed August 26, 2018.
10. Preskorn SH, Hatt CR. How pharmacogenomics (PG) are changing practice: implications for prescribers, their patients, and the healthcare system (PG series part I). J Psychiatr Pract. 2013;19:142–149.
Keywords:

pharmacogenetic tests; drug metabolism; cytochrome P450 enzymes; antidepressants; antipsychotics; blood levels; GeneSight test

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