Medical Policy

This document addresses the use of panel and multi-gene tests for polymorphisms to determine drug-metabolizer status.

Note: For additional information regarding single (individual) gene tests for polymorphisms, please see:

• CG-GENE-11 Genotype Testing for Individual Genetic Polymorphisms to Determine Drug-Metabolizer Status

Note: For additional information regarding panel tests please see:

• GENE.00052 Whole Genome Sequencing, Whole Exome Sequencing, Gene Panels, and Molecular Profiling

Investigational and Not Medically Necessary:

The use of panel and multi-gene tests for polymorphisms to determine drug-metabolizer status is considered investigational and not medically necessary.

Testing for polymorphisms can identify variants of specific genes associated with abnormal and normal drug metabolism. The use of such testing is based on the theory that individuals with certain gene variants may potentially be able to receive higher or lower doses of some drugs, or should avoid some drugs altogether, to improve the likelihood of achieving clinical goals as well as lessening the risk of adverse drug effects. A number of proprietary panels and multi-gene tests have been developed to assess multiple variants and alleles across genes involved in drug metabolism.

Several commercial laboratories market multi-gene panels for genetic polymorphisms related to drug metabolizer status. While the use of some individual tests included in these panels may be reasonable under specific circumstances, analysis of multiple genes within a panel should be justified in that there is clinical evidence that testing each gene provides information that leads to meaningful impact on treatment. Many genetic and pharmacogenetic association studies have not been reproducible or replicated by independent (non-industry) sponsored studies. Replication of results from genome-wide association studies requires identification of a large and appropriate sample. Furthermore, various pharmacogenetic profiles have not been compared to each other, and thus, testing concordance remains unknown; in other words, it is possible for different proprietary tests to produce conflicting results for the same individual. At this time, the available published evidence addressing the use of such multi-gene tests and panels is limited to a few multi-gene tests and panels and condition-specific studies (Altar, 2015; Hall-Flavin, 2012, 2013; Winner, 2013a, 2013b). The results of these studies are limited by the study designs utilized by the investigators, with each having some combination of no blinding, small study population, retrospective methodology, selection bias, short follow-up periods, and subjective study outcomes. The data from these studies is weakly supported, and further investigation is warranted using better designed, larger study samples and double-blind randomized controlled methodology.

In 2018 Bradley and others reported the results of a randomized controlled trial involving 685 subjects with depression or anxiety treated with either standard of care (n=333) or guided by the results of the NeuroIDgenetix^® panel test (n=352). Subjects were evaluated at 4, 8 and 12 weeks post-initiation of therapy. The authors reported that subjects with depression had significantly higher response rates (p=0.001; odds ratio [OR], 4.72) and remission rates (p=0.02; OR, 3.54) vs. control subjects at 12 weeks. They also reported that experimental group subjects diagnosed with anxiety showed a meaningful improvement in Hamilton Rating Scale for Anxiety scores at both 8 and 12 weeks (p=0.02 and p=0.02, respectively) as well as higher response rates (p=0.04; OR, 1.76). These results are promising. However, the follow-up time of the study was short, and the authors reported a significant loss to follow-up (15.5%). Additionally, there were numerous medication changes reported throughout the study, which makes it difficult to determine the true clinical utility of this test. Finally, the reported results do not appear to be an intent to treat analysis, which may lead to selection bias, as dropped subjects are typically thought to be at risk of being failures or developed troubling side effects. Further investigation into the potential benefits of this test would be welcome.

In a 2019 study by Greden and colleagues, the authors presented data from the Genomics Used to Improve DEpression Decisions (GUIDED) trial. A total of 1167 participants diagnosed with major depressive disorder were included and were randomized to treatment as usual or guided care. Active treatment guided by pharmacogenomic panel testing (GeneSight^® Psychotropic, Assurex Health, Inc.) was compared to unguided active treatment in participants who had failed to respond to at least one adequate prior medication trial. GeneSight Psychotropic is a proprietary test that assesses the genotypes of 59 alleles and variants across 8 genes (CYP1A2, CYP2C9, CYP2C19, CYP3A4, CYP2B6, CYP2D6, HTR2A, SLC6A4) by a pharmacogenomic algorithm. Symptom improvement, response, and remission were monitored over 24 weeks with the primary endpoint at week 8. A total of 1167 participants completed the study through the blinded week 8 endpoint (607 participants in treatment as usual, 560 participants in the guided-care arm). Prior to treatment, 79.4% (456/574) of participants in the guided-care arm and 77.5% (476/614) of participants in the treatment as usual arm were prescribed medications that were congruent with the pharmacogenomics test report; at week 8 the proportion increased to 91.2% (508/557) for the guided-care arm and remained relatively unchanged in the treatment as usual arm. Symptom improvement (change in 17-item Hamilton Depression Rating Scale [HAM-D17]) was the primary outcome at week 8; secondary outcomes were response (≥ 50% decrease in HAM-D17) and remission (HAM-D17 ≤ 7) at week 8. For those in the guided-care arm, there was a 27.2% decrease in HAM-D17 scores at the 8 week visit compared to a 24.4% decrease in the treatment as usual arm (non-significant: p=0.107). In the guided-care arm response rate was 146/560 (26.0%) at week 8 compared to 121/607 (19.9%) in the treatment as usual arm and the rate of remission for participants in the guided-care arm was 86/560 (15.3%) compared to 61/607 (10.1%) in the treatment as usual group (both statistically significant: p=0.013 and p=0.007 respectively). As noted by the authors, overall improvement of symptom improvement, response and remission was modest. The authors note that larger, controlled studies still need to be performed for those receiving their initial treatment. Limitations of this study include failure to meet the primary outcome, a short follow-up period (unblinded assessments in participants did not exceed week 8, not long enough to assess sustained remission), inadequate blinding (the treating clinician was not blinded to study arm), participant homogeneity (the majority of the participants were Caucasian), and a study population that only included participants with moderate to severe major depressive disorder. The long-term incremental clinical significance of pharmacogenomics testing remains unproven. Furthermore, it is unclear how pharmacogenetics testing influences long-term depression outcomes as compared to usual care, which ultimately relies upon symptom improvement and clinical response to a medication, irrespective of pharmacogenetics status.

A 2020 meta-analysis by Brown and colleagues reported on four prospective, controlled trials which evaluated the clinical utility of a pharmacogenomic test on subjects with major depressive disorder who had failed at least one prior medication. The objective of the included studies was assessment of outcomes using HAM-D17. There were 1556 subjects from the four studies examined. Outcome evaluations occurred after 8 or 10 weeks during the studies. There were two randomized controlled trials and two open-label trials. This meta-analysis reported multiple sources of bias among the studies including no blinding of treating physicians in the randomized controlled trials, no blinding of physicians or subjects in the open-label trials. Additionally, there was high recruitment bias and further potential bias due to industry sponsorship and involvement with study design, execution and analysis. Overall, among the studies, guided care had a 33.8% reduction in HAM-D17 score and the unguided care had a 23.7% reduction. Overall response using the HAM-D17 showed a higher response among the subjects in the guided care arms in all four studies. The limitations of the studies include lack of ethnic diversity, risk of bias with industry sponsorship, and insufficient follow-up duration to determine clinical utility.

In a 2022 retrospective study by Dagar and colleagues, the authors reviewed the records of 281 children and adolescents with major depressive disorder or generalized anxiety disorder who were taking psychotropic medications. All subjects had undergone GeneSight testing and the authors evaluated the presumed impact of combinatorial pharmacogenomic testing on medication management and clinical outcomes. The pre-baseline visit was when the pharmacogenomic testing was ordered. The authors considered the clinic visit in which psychotropic medications were changed (presumably based on pharmacogenomic testing) as the baseline visit. At this baseline visit, medication management was categorized as added, replaced, or unchanged. A follow-up visit 8 weeks later was considered the post-baseline visit. Clinical Global Impression scores (which includes the severity scale, improvement scale, and efficacy index) were retrospectively calculated from clinic visit documentation. In terms of severity scores, pre-baseline score was 4.23, baseline visit score was 4.18, and post-baseline score was 3.92. Efficacy scores ranged from 8.16 at pre-baseline visit, 8.75 at baseline visit, and 6.34 at post-baseline visit. Global improvement scores were 3.95 at pre-baseline, 4.27 at baseline, and 3.26 at post-baseline. The average number of medications at the pre-baseline visit was 2.1 which increased to 2.4 at the post-baseline visit; during the assessment period, 123 subjects had medications added, 92 subjects had medication replaced, and 66 subjects had medication unchanged. Given the study design, including a lack of control group, it is difficult to directly assess the role of pharmacogenomic testing on medical changes and health outcomes.

A 2021 systematic review and meta-analysis by David and colleagues reported on hospitalization rates and medication changes among participants who had pharmacogenomic testing and those who received treatment as usual. The authors included five studies which addressed hospital admissions and five studies which addressed medication changes. There was one case-control study, three cohort studies, and five randomized controlled trials. In the pharmacogenomic tested group, 54.7% of participants had a medication change versus 41.5% of participants in the treatment as usual group. The quantitative data were combined for meta-analysis which showed a statistically significant increase in medications changes in the pharmacogenomic testing group across 749 participants and 825 participants in the treatment as usual group, with an odds ratio of 1.91. There were unplanned hospital admissions in four out of the five studies. The all-cause hospital admissions occurred significantly less frequently in those in the pharmacogenomic group. This incorporated data was from 2,957 participants in the pharmacogenomic group and 6,783 participants in the treatment as usual group, with an odds ratio of 0.5. In the pharmacogenomic testing group, there were 11.5% of participants with a hospital admission, compared to 20.1% of changes in the treatment as usual group. The included studies provided little detail whether or not the recommendations in the pharmacogenomic reports were actioned.

In 2022, Brown and colleagues performed a systematic review on clinical trials to evaluate the efficacy of pharmacogenomic testing as a way to inform antidepressant treatment. They also conducted a meta-analysis to see if the pharmacogenomic testing strategy is associated with remission of depressive symptoms. There were 13 studies included (10 randomized controlled trials and 3 open label trials). All of the trials except two exclusively enrolled participants with a diagnosis of major depressive disorder. There were 9/13 trials which measured their primary endpoint at 8 weeks. There were variances of which genes were tested across all the trials. Across all the randomized controlled trials, performance bias was judged as high. There were 11/13 trials which were industry sponsored. The pooled risk ratio for the open label trials was 1.26 (95% CI = 0.84–1.88, P = 0.26) and 1.46 (95% CI = 1.13–1.88, P = 0.003) for the randomized controlled trials. The pooled risk ratio for all trials was 1.41 (95% CI = 1.15–1.74, P = 0.001). Clinical generalizability of the findings is limited and .there was a high risk for performance bias due to lack of blinding of the prescribing physicians.

In a 2022 pragmatic, randomized controlled trial, Oslin and colleagues assessed whether pharmacogenomic testing affects antidepressant medication selection and if that testing leads to improved clinical outcomes. This study included 1944 participants with major depressive disorder. Participants were randomized 1:1 to either the pharmacogenomic-guided group (n=966) or the comparison group of usual care (n=978). Those in the usual care group had access to pharmacogenomic results after 24 weeks. Remission of depressive symptoms was measured by the Patient Health Questionnaire–9 (PHQ-9). Outcomes were assessed at 4, 8, 12, 18, and 24 weeks after randomization. Among those who received an antidepressant prescription, those in the pharmacogenomic-guided group were more likely to receive a medication with a lower potential drug-gene interaction for no drug-gene vs. moderate/substantial interaction (4.32 [95%CI, 3.47 to 5.39]; P < .001) and no/moderate vs substantial interaction (OR, 2.08 [95%CI, 1.52 to 2.84]; P = .005) (P < .001 for overall comparison). The estimated risks of none, moderate, and substantial interaction for the pharmacogenomic-guided group were 59.3%, 30.0%, and 10.7% compared with 25.7%, 54.6%, and 19.7% for the usual care group, respectively. At 24 weeks, there were 130 participants in the pharmacogenomic-guided group and 126 participants in the usual care group who met remission criteria. There was no significant differences in response rates (32.1% vs 27.5%) at24weeks. However, symptom improvement was larger at 24 weeks in the pharmacogenomic-guided group (mean, 5.4) compared to mean of 4.8 in the usual care group. Limitations include lack of blinding with potential for a placebo-type effect, lack of generalizability with other pharmacogenomic test products, and the trial was not powered to evaluate outcomes such as the effect of changes in dosing, the presence of adverse drug reactions, the effect of medication adherence or the effect of antidepressant switches after randomization. 

A 2023 study by Swen and colleagues assessed the clinical utility of pre-emptive genotype testing by pharmacogenomic panel testing. The primary outcome was the incidence of causal and clinically relevant adverse drug reactions reported for the index drug within the 12-week follow-up period. There were 3342 participants assigned to the study group with 830 participants noted to have an actionable drug-gene interaction. There were 105 participants without available data, 92 were lost to follow-up, and 13 withdrew consent. Of the 2512 participants without an actionable drug-gene interaction, 314 did not have data available, 275 were lost to follow-up, and 39 withdrew consent. There were 3602 assigned to the control group; 923 had an actionable drug-gene interaction and 90 didn’t have data available, 79 were lost to follow-up, and 11 withdrew consent. Of the 2679 participants without an actionable drug-gene interaction, 242 didn’t have available data, 206 were lost to follow-up, and 36 withdrew consent. In the group of participants who had actionable test results, a clinically relevant adverse drug reaction occurred in 152 (21.0%) of 725 participants in the study group and 231 (27.7%) of 833 participants in the control group. Limitations include the use of participant-reported adverse drug reactions. The investigators relied on participants contacting the study team when another drug was started during follow-up. The study only looked at the effect of pharmacogenomic panel testing on the reduction of adverse drug events.

In October 2018, 23andMe received Food and Drug Administration (FDA) approval as a direct-to-consumer test to provide information regarding genetic variants that can be associated with medication metabolism. The FDA authorization notes that consumers should not use this test to make treatment decisions on their own. Any medical decisions should be made only after discussing the results with a licensed health care provider and results have been confirmed using clinical pharmacogenetic testing.

A number of proprietary multi-gene and panel tests have been developed to assess multiple variants and alleles across genes involved in drug metabolism; some examples and uses are addressed here.

Per the manufacturer’s website (Genomind^®), Genecept assay (also referred to as Genomind Professional PGx^™) is a genetic test which aims to identify specific genetic markers to assist with decision making for mental disorders. The test aims to guide treatment for several psychiatric conditions including major depression, anxiety disorder, obsessive compulsive disorder, bipolar disorder, and schizophrenia. In a naturalistic unblinded study by Brennan and colleagues (2015), the purpose was to collect data from participants using the Genecept Assay to determine the effectiveness of the test based on clinician-rated and participant-rated measures and assess the influence on treatment decisions. The deoxyribonucleic acid (DNA) sample was collected via saliva and then participants completed online questionnaires at baseline, 1 month and 3 months. There were 137 participants who also had corresponding clinician assessments. Diagnoses included primary mood disorder, attention-deficit disorder, schizophrenia, cognitive disorder, substance-related disorder, developmental disorders, and personality disorders. The surveyed physicians reported that the assay results influenced medication decisions for 93% of participants and the physicians made a change to the medication regimen congruent with the assay report in 94% of participants. The participant surveys demonstrated decreases in depression and anxiety symptoms, decreased side effect of medications, and increased quality of life. However, while there was overall improvement following pharmacogenetics testing, without a control group the results cannot be attributed to a specific benefit of testing. With a short follow-up period, lack of heterogeneity of participants, and lack of control group, further studies of randomized controlled designs are needed to fully ascertain the magnitude of clinical utility of genetic testing.

A 2020 industry-sponsored study by Perlis and colleagues reported on the efficacy of assay-guided treatment versus treatment-as-usual for subjects with major depressive disorder. Outcomes were measured by change in HAM-D17 after 8 weeks. Treating physicians were unblinded while subjects and raters of HAM-D17 were blinded. There were 304 participants randomized to assay-guided treatment (n=151) or treatment-as-usual (n-153). After 8 weeks, there were no significant differences detected between assay-guided treatment and treatment-as-usual. There were also no significant differences in response between the two groups (58/146 in assay-guidance versus 72/150 in treatment-as-usual). Longer term studies may be necessary to further assess impact on health outcomes.

GeneSight has developed several pharmacogenomic tests. GeneSight Analgesic aims to analyze how genes might affect an individual’s response to opioids and muscle relaxants. GeneSight ADHD aims to help identify and avoid ADHD medications which are likely to cause side effects based on genetics. The GeneSight Psychotropic panel, according to the manufacturer website, uses an algorithm to analyze different genes to ascertain an individual’s response to different psychotropic medications. As noted in the GUIDED study, there were several limitations including failure to meet the primary outcome, short follow-up period, inadequate blinding and participant homogeneity.

The NeuroIDgenetix test, according to the AltheaDx website, is a neuropsychiatric test that uses a panel of genetic tests reported to analyze variants of receptor and transporter genes associated with response to psychiatric medications. Studies are underway to assess the use of these genetic panel tests, however as noted above in the Bradley 2018 study, there were several methodological issues which led to difficulty determining the true clinical utility of the test.

The SureGene Test for Antipsychotic and Antidepressant Response (STA2R) aims to use genetic information to assist with behavioral health medication decisions. A genetic variant in the sulfotransferase 4A1 haplotype 1 (SULT4A1-1) gene has been reported to be a predictor of risk of hospitalization for individuals with schizophrenia who are treated with olanzapine or who might be switched to olanzapine. Genetic variation in the SULT4A1-1 gene is associated with severity of clinical symptoms and response to antipsychotic treatment. The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study has looked at genetic studies of antipsychotic treatment response and prediction of individual difference in treatment outcomes for those with schizophrenia. Liu and colleagues (2012) looked at the CATIE data to see if there is influence of SULT4A1-1 haplotype status on hospitalization due to exacerbation of schizophrenia. Using a Kaplan-Meier survival analysis of the CATIE data, the authors found that participants with negative SULT4A1-1 had a higher risk of hospitalization than participants with positive SULT4A1-1. In this industry-sponsored study, the number of participants who were positive SULT4A1-1 was low as well as the overall number of hospitalizations. Prospective studies are necessary with larger participant population and heterogeneity to replicate clinical significance.

The RightMed^® comprehensive test, marketed by OneOme, claims to analyze multiple genes involved in drug metabolism, drug targets, drug receptors and drug transports to categorize more than 300 medications into four recommendations: “major gene-drug interaction”, “moderate gene-drug interactions”, “minimal gene-drug interactions”, and “limited pharmacogenetics impact.” According to the manufacturer, the RightMed test was developed using proprietary algorithms based on available clinical evidence from scientific literature and public and private databases; however, the test has not been evaluated in a prospective, controlled study to assess the benefits and potential harms of using the test report to guide medication use. Currently published evidence is limited to several feasibility studies; as well, a number of clinical trials appear ongoing. To this end, evidence of clinical utility and net health benefit are lacking.

Evidence evaluating the clinical utility of pharmacogenetic multi-gene testing panels for polymorphisms for the purpose of guiding drug treatment remains limited and additional data is required to demonstrate that multi-gene panel testing results in improved net health outcomes.

Drug efficacy and toxicity vary substantially between individuals. Because drugs and doses are typically adjusted to meet individual requirements as needed by using trial and error, clinical consequences may include a prolonged time to optimal therapy and serious adverse events. It has been found that inherited DNA sequence variation (polymorphisms) in genes for drug-metabolizing enzymes may have a significant effect on the efficacy or toxicity of a drug. This field of research is referred to as pharmacogenomics. 

It has been proposed that testing for certain genes to detect polymorphisms will allow physicians to predict side effects to drugs and to tailor a drug regimen based on an individual’s genetic make-up. It may be that this testing will improve the choice of drug, or the dose of the drug, when the drug in question has a narrow therapeutic dose range, when the consequences of treatment failure are severe, and/or when serious adverse reactions are more likely in individuals with certain polymorphisms. 

Critical elements of assessing the effectiveness of such genetic tests include: (1) analytic (diagnostic) validity; (2) clinical validity; and (3) clinical utility. Analytic validity measures the technical performance of the test, in terms of accurately identifying the genetic markers to be measured. Clinical validity measures the strength of association between genetic test results and clinical parameters such as dose, therapeutic efficacy, or adverse events. Clinical utility, the ultimate goal of genetic testing, measures the ability of the test to improve clinical outcomes, such as whether prescribing or dosing based on information from genetic testing improves therapeutic efficacy or adverse event rate as compared with treatment without genetic testing.

Therefore, when considering whether or not a test to determine drug metabolizer status is appropriate in the treatment of individuals prescribed certain medications, specific issues need to be evaluated, including:

• A specific mutation, or set of mutations, has been established in the scientific literature to be reliably associated with drug metabolism status; AND
• A biochemical or other non-genetic test is identified but the results are indeterminate, or the genetic status cannot be identified through such biochemical or other non-genetic testing; AND
• The results of the genetic test could impact the medical management of the individual with a resulting improvement in health outcomes.

Multi-gene panel testing has been proposed to guide treatment, member evaluations and decisions based on the ability to predict response to treatment in particular clinical contexts. Pharmacogenetic tests intend to assess how a drug is metabolized, including evaluation of variation in genes that encode drug-metabolizing enzymes, drug transporters, and drugs targets, as well as other genes involved in the drug’s mechanism of action. Next generation sequencing, (including but not limited to massively parallel sequencing, and microarray testing) has made it possible to conduct multi-gene panel testing which involves the analysis of multiple genes for multiple mutations simultaneously. Multi-gene panel testing has the potential benefit of analyzing multiple genes more rapidly and thereby providing the results of the genetic work-up in a more timely fashion. However, the newer sequencing techniques may be associated with a higher error rate and lower diagnostic accuracy than direct sequencing which could affect the clinical validity of testing. Another potential drawback of the newer technologies is that they may provide information on genetic mutations which is of uncertain clinical significance.

The impact of polymorphisms has been the focus of study with a wide variety of drugs. The use of this type of science is just starting to be investigated, and its impact on actual medical practice is not yet fully understood.

Metabolize: Refers to breaking down a drug so that it is no longer clinically active.

Polymorphisms: Refers to genetic variation between individuals resulting in differences in gene expression, in this case differing activity of various enzymes.

The following codes for treatments and procedures applicable to this document are included below for informational purposes. Inclusion or exclusion of a procedure, diagnosis or device code(s) does not constitute or imply member coverage or provider reimbursement policy. Please refer to the member’s contract benefits in effect at the time of service to determine coverage or non-coverage of these services as it applies to an individual member.

When Services are Investigational and Not Medically Necessary:

Peer Reviewed Publications:

1. Altar CA, Carhart JM, Allen JD, et al. Clinical validity: Combinatorial pharmacogenomics predicts antidepressant responses and healthcare utilizations better than single gene phenotypes. Pharmacogenomics J. 2015; 15(5):443-451.
2. Bradley P, Shiekh M, Mehra V, et al. Improved efficacy with targeted pharmacogenetic-guided treatment of patients with depression and anxiety: a randomized clinical trial demonstrating clinical utility. J Psychiatr Res. 2018; 96:100-107.
3. Brennan FX, Gardner KR, Lombard J, et al. A naturalistic study of the effectiveness of pharmacogenetic testing to guide treatment in psychiatric patients with mood and anxiety disorders. Prim Care Companion CNS Disord. 2015; 17(2):1-17.
4. Brown L, Vranjkovic O, Li J, et al. The clinical utility of combinatorial pharmacogenomic testing for patients with depression: a meta-analysis. Pharmacogenomics. 2020; 21(8):559-569.
5. Brown LC, Stanton JD, Bharthi K, et al. Pharmacogenomic testing and depressive symptom remission: a systematic review and meta-analysis of prospective, controlled clinical trials. Clinical pharmacology and therapeutics. 2022; 112(6):1303-1317.
6. Dagar A, Cherlopalle S, Ahuja V, et al. Real-world experience of using combinatorial pharmacogenomic test in children and adolescents with depression and anxiety. J Psychiatr Res. 2022; 146:83-86.
7. David V, Fylan B, Bryant E, et al. An analysis of pharmacogenomic-guided pathways and their effect on medication changes and hospital admissions: a systematic review and meta-analysis. Frontiers in genetics. 2021; 12:698148.
8. Greden JF, Parikh SV, Rothschild AJ, et al. Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: a large, patient- and rater-blinded, randomized, controlled study. J Psychiatr Res. 2019; 111:59-67.
9. Hall-Flavin DK, Winner JG, Allen JD, et al. Using a pharmacogenomic algorithm to guide the treatment of depression. Transl Psychiatry. 2012; 2:e172.
10. 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(10):535-548.
11. Liu Q, Ramsey TL, Meltzer HY, et al. Sulfotransferase 4A1 haplotype 1 (SULT4A1-1) is associated with decreased hospitalization events in antipsychotic-treated patients with schizophrenia. Prim Care Companion CNS Disord. 2012; 14(3): PCC.11m01293.
12. Oslin DW, Lynch KG, Shih MC, et al. Effect of pharmacogenomic testing for drug-gene interactions on medication selection and remission of symptoms in major depressive disorder: the PRIME care randomized clinical trial. JAMA. 2022; 328(2):151-161.
13. Perlis RH, Dowd D, Fava M, et al. Randomized, controlled, participant- and rater-blind trial of pharmacogenomic test-guided treatment versus treatment as usual for major depressive disorder. Depress Anxiety. 2020; 37(9):834-841.
14. Swen JJ, van der Wouden CH, Manson LE, et al. A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study. Lancet (London, England), 2023, Vol 401 (10374), 347-356.
15. Winner J, Allen JD, Altar CA, Spahic-Mihajlovic A. Psychiatric pharmacogenomics predicts health resource utilization of outpatients with anxiety and depression. Transl Psychiatry. 2013; 3:e242.
16. 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(89):219-227.

Government Agency, Medical Society, and Other Authoritative Publications:

1. U.S. Food and Drug Administration (FDA). FDA News Release. FDA authorizes first direct-to-consumer test for detecting genetic variants that may be associated with medication metabolism. Last updated October 31, 2018. Available at: https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm624753.htm. Accessed on April 14, 2023.

Genecept Assay
GeneSight Analgesic
GeneSight Psychotropic
GeneSight ADHD
Genomind^® Professional PGx Express^™ CORE
NeuroIDgenetix test
Psych HealthPGx Panel
RightMed
SureGene Test for Antipsychotic and Antidepressant Response (STA2R)

The use of specific product names is illustrative only. It is not intended to be a recommendation of one product over another, and is not intended to represent a complete listing of all products available.