What is the difference between traditional “single-gene” testing and the GeneSight^® test’s proprietary CPGx^® approach?

Summary: Single-gene pharmacogenetic testing is a common way of analyzing gene-drug interactions for clinical implementation and personalizing patients’ medication regimens. However, many medications are processed by multiple metabolic pathways, with genomic variability being capable of influencing the rate of metabolism and the likelihood of response or risk of side effects. The GeneSight Psychotropic test uses a weighted multi-gene approach that measures multiple genomic variants for each individual and weighs them together to provide comprehensive information about how an individual’s genetic variation may impact their outcomes with certain psychotropic medications. This weighted multi-gene approach has been found to better predict antidepressant blood levels and clinical outcomes in comparison to single-gene pharmacogenetic testing.^1-4

How is weighted multi-gene pharmacogenomic testing different from single-gene testing?

Weighted multi-gene pharmacogenomic testing is the process of simultaneously assessing the effects of variation found in multiple genes associated with medication metabolism or mechanism of action, and then conveying the results in an integrated fashion to help inform medication selection, or dosing.5 This differs from single-gene pharmacogenetic testing which evaluates how genetic variation found in one gene associated with one or more medications may influence medication metabolism, efficacy, or tolerability.6 While single-gene testing may help inform prescribing decisions in certain scenarios, it is worth acknowledging that the majority of commonly prescribed medications in psychiatric practice are metabolized by multiple enzymes where genetic variation may arise in these pathways resulting in variable medication breakdown, and potentially differential pharmacologic effects. This may limit the capability of single-gene testing information to improve patient outcomes in psychiatric practice, and trying to combine information from multiple single-gene test results may not be feasible for clinical implementation.

What is the weighted multi-gene approach used by the GeneSight Psychotropic test?

The GeneSight Psychotropic test addresses limitations of single-gene testing in psychiatric practice by employing a weighted multi-gene pharmacogenomic approach. This weighted multi-gene approach is comprised of evidence derived from comprehensive literature and data reviews, which is integrated into an algorithm to analyze and weigh the importance of multiple genes associated with a medication. For an overview of the data selection process, the inputs, and steps involved in the GeneSight algorithm, please see our GeneSight^® Psychotropic Weighted Multi-Gene Algorithm white paper. (Figure 1A)

Based on an individual’s genetic test results, the GeneSight weighted multi-gene algorithm generates a report categorizing 64 FDA approved medications into three color-coded categories according to the expected level of gene-drug interactions: green (use as directed), yellow (moderate gene-drug interaction), and red (significant gene-drug interaction). Report categorization and assignment of clinical consideration annotations provides insight into the weighted multi-gene effects of gene-drug interactions on medications to help inform on prescribing decisions. (Figure 1B)

Figure 1: The Algorithmic Process and example of weighted multi-gene Impact of Genetic Variability on Drug X

Are there examples showing the importance of weighted multi-gene pharmacogenomic testing compared to single-gene testing?

One example highlighting the importance of weighted multi-gene pharmacogenomic testing is with (es)citalopram. (Es)citalopram is extensively metabolized by CYP2C19, in which genetic variation can result in changes in (es)citalopram blood levels.^7-13 The correlation between CYP2C19 genetic variation and altered (es)citalopram blood levels has led to subsequent CYP2C19 single gene-drug interaction recommendations by the Clinical Pharmacogenetics Implementation Consortium (CPIC) with (es)citalopram, and Food and Drug Administration (FDA) with citalopram, specifically.^14,15 However, data have also shown involvement of CYP3A4 and CYP2D6 in the metabolism of (es)citalopram (Figure 2).^15-22 Multiple studies have evaluated the impact of genetic polymorphisms in multiple CYP enzymes on (es)citalopram blood levels including a post-hoc analysis of the GUIDED study.^1,19-21 This post hoc analysis evaluated the ability of the GeneSight weighted multi-gene approach to predict (es)citalopram blood levels compared to CPIC single-gene recommendations. Results showed that the weighted multi-gene approach used by the GeneSight Psychotropic test was a better predictor of (es)citalopram blood levels than CYP2C19 single-gene testing, and also identified more patients who could benefit from clinically actionable recommendations in contrast to CYP2C19 single-gene testing and CPIC classifications.¹

Another example highlighting the value of weighted multi-gene pharmacogenomic testing is with sertraline (Figure 3). Multiple cytochrome P450 (CYP450) enzymes have been characterized in the metabolism of sertraline, along with evidence supporting that genetic variation in these enzymatic pathways results in altered sertraline blood levels.^23-34 Previously, CPIC guidelines provided gene-drug interaction recommendations for sertraline with only CYP2C19.³⁵ However, during an updated evidence review, recommendations for both CYP2C19 and CYP2B6 were provided for sertraline.14 A post hoc analysis of the GUIDED study evaluated the ability of the GeneSight weighted multi-gene approach to predict sertraline blood levels when looking at the weighted assessment of CYP2C19, CYP2B6, and CYP3A4 variation in the algorithm compared to CYP2C19 single-gene testing, showing that the weighted multi-gene algorithm was a significant predictor of variation in sertraline blood levels.² These findings support the importance of a weighted multi-gene pharmacogenomic approach with sertraline in comparison to single-gene recommendations.

Does weighted multi-gene pharmacogenomic testing predict better patient outcomes than single-gene testing?

Single-gene pharmacogenetic testing in depression has historically produced mixed results regarding clinical outcomes³⁶, although recent guidelines have brought attention to clinically important single gene-drug interactions^14-37 A potential reason for the lack of overwhelming evidence for single-gene testing in depression is that the effects of multiple genes assessed together may give a more complete pharmacologic picture for a given medication. ⁵ Therefore, an important question clinically is, “Do the effects of multiple genes, assessed through a weighted multi-gene approach, predict improved patient outcomes compared to each gene individually?”

To address this question, two analyses have been published evaluating the ability of the weighted multi-gene approach used by the GeneSight Psychotropic test to predict patient outcomes compared to single-gene testing.^3,4 The first analysis used combined data from the standard of care arms of three prospective clinical outcomes trials of the GeneSight Psychotropic test. By assessing the standard of care (i.e., control arm or treatment as usual) group of patients, no patient or patient’s healthcare provider was aware of their pharmacogenomic information, thus creating blinded outcomes.

In this analysis, patients were separated into groups based on the primary metabolizing enzyme of their medication, and single-gene phenotypes (i.e., CYP2D6 ultrarapid metabolizer, extensive (normal) metabolizer, intermediate metabolizer, and poor metabolizer) were assessed for improvement in HAM-D17 from baseline. When only CYP2D6 genotype was considered for medications primarily, though not exclusively, metabolized by CYP2D6, the individual phenotypes were not predictive of patient outcomes, suggesting that single-gene testing was unable to accurately predict patient outcomes.³ However, when the same analysis was performed using the weighted multi-gene approach of the GeneSight Psychotropic test, significant differences were found. Patients taking medications in the red Significant Gene-Drug Interaction category showed significantly less improvement in their symptoms compared to those taking medications in the green Use as Directed or yellow Moderate Gene-Drug Interaction categories.³ This analysis showed that the GeneSight test predicted which patients were likely to have poor depression outcomes with their treatment regimen.

The second analysis evaluated the ability of the weighted multi-gene approach used by the GeneSight Psychotropic test to predict patient outcomes (and medication blood levels) compared to CPIC single-gene guideline recommendations for CYP2D6 and CYP2C19. This post-hoc study analyzed patients from GUIDED taking any medication included on the GeneSight test and a subset of patients taking a medication with single-gene CPIC guidelines. The predictive ability of each pharmacogenetic approach was measured according to medication congruence and correlation with treatment outcomes (symptom improvement, response, and remission). For GeneSight, medications were considered congruent if they were in the no or moderate gene-drug interaction categories, while incongruent medications were those in the significant gene-drug interaction category. For single-gene guidelines, congruent medications were those with no actionable therapeutic recommendations. Medications with no available guidelines were also considered congruent as there are no published guidelines to say that those medications have a meaningful gene-drug interaction. Medications with actionable recommendations in single-gene guidelines were considered incongruent. Congruence analyses were conducted for all medications on the GeneSight report, and a subset of medications with CPIC single-gene guidelines, specifically. The results showed that only the weighted multi-gene approach used by the GeneSight Psychotropic test, not single-gene analysis, was able to predict symptom improvement, response, and remission.⁴ (Figure 4)

Conclusion

The GeneSight test employs a unique method of predicting patient medication outcomes using a weighted multi-gene pharmacogenomic approach. Weighted multi-gene pharmacogenomics uses knowledge of each medication’s unique set of pharmacokinetic and pharmacodynamic characteristics to incorporate and appropriately weigh genetic variation at multiple loci to produce better predictions of patient outcomes than testing solely for the primary metabolic pathway of a medication.

For more information, contact the GeneSight Medical Information Department at:

PHONE: 855.891.9415

EMAIL: medinfo@genesight.com

References

1. Shelton, R.C., et al., Combinatorial Pharmacogenomic Algorithm is Predictive of Citalopram and Escitalopram Metabolism in Patients with Major Depressive Disorder. Psychiatry Res, 2020. 290: p. 113017.
2. Parikh, S.V., et al., Combinatorial pharmacogenomic algorithm is predictive of sertraline metabolism in patients with major depressive disorder. Psychiatry Res, 2022. 308: p. 114354.
3. Altar, C.A., et al., Clinical validity: Combinatorial pharmacogenomics predicts antidepressant responses and healthcare utilizations better than single gene phenotypes. Pharmacogenomics J, 2015. 15(5): p. 443-51.
4. Rothschild, A.J., et al., Clinical validation of combinatorial pharmacogenomic testing and single-gene guidelines in predicting psychotropic medication blood levels and clinical outcomes in patients with depression. Psychiatry Res, 2021. 296: p. 113649.
5. Winner, J.G. and B. Dechairo, Combinatorial Versus Individual Gene Pharmacogenomic Testing in Mental Health: A Perspective on Context and Implications on Clinical Utility. Yale J Biol Med, 2015. 88(4): p. 375-82.
6. Maruf, A.A., Approaches and Hurdles of Implementing Pharmacogenetic Testing in the Psychiatric Clinic. Psychiatry and Clinical Nuerosciences Reports, 2022.
7. Tsuchimine, S., et al., Effects of Cytochrome P450 (CYP) 2C19 Genotypes on Steady-State Plasma Concentrations of Escitalopram and its Desmethyl Metabolite in Japanese Patients With Depression. Ther Drug Monit, 2018. 40(3): p. 356-361.
8. Uckun, Z., et al., The impact of CYP2C19 polymorphisms on citalopram metabolism in patients with major depressive disorder. J Clin Pharm Ther, 2015. 40(6): p. 672-9.
9. Yu, B.N., et al., Pharmacokinetics of citalopram in relation to genetic polymorphism of CYP2C19. Drug Metab Dispos, 2003. 31(10): p. 1255-9.
10. Yin, O.Q., et al., Phenotype-genotype relationship and clinical effects of citalopram in Chinese patients. J Clin Psychopharmacol, 2006. 26(4): p. 367-72.
11. Jin, Y., et al., Effect of age, weight, and CYP2C19 genotype on escitalopram exposure. J Clin Pharmacol, 2010. 50(1): p. 62-72.
12. Jukic, M.M., et al., Impact of CYP2C19 Genotype on Escitalopram Exposure and Therapeutic Failure: A Retrospective Study Based on 2,087 Patients. Am J Psychiatry, 2018. 175(5): p. 463-470.
13. Rudberg, I., et al., Impact of the ultrarapid CYP2C19*17 allele on serum concentration of escitalopram in psychiatric patients. Clin Pharmacol Ther, 2008. 83(2): p. 322-7.
14. Bousman, C.A., et al., Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6, CYP2C19, CYP2B6, SLC6A4, and HTR2A Genotypes and Serotonin Reuptake Inhibitor Antidepressants. Clin Pharmacol Ther, 2023. 114(1): p. 51-68.
15. Celexa [package insert]. St. Louis, MO: Forest Pharmaceuticals, Inc.; 2011.
16. Lexapro [package insert]. St. Louis, MO: Forest Laboratories, Inc.;m 2007.
17. von Moltke, L.L., et al., Escitalopram (S-citalopram) and its metabolites in vitro: cytochromes mediating biotransformation, inhibitory effects, and comparison to R-citalopram. Drug Metab Dispos, 2001. 29(8): p. 1102-9.
18. Olesen, O.V. and K. Linnet, Studies on the stereoselective metabolism of citalopram by human liver microsomes and cDNA-expressed cytochrome P450 enzymes. Pharmacology, 1999. 59(6): p. 298-309.
19. Fudio, S., et al., Evaluation of the influence of sex and CYP2C19 and CYP2D6 polymorphisms in the disposition of citalopram. Eur J Pharmacol, 2010. 626(2-3): p. 200-4.
20. Ji, Y., et al., Citalopram and escitalopram plasma drug and metabolite concentrations: genome-wide associations. Br J Clin Pharmacol, 2014. 78(2): p. 373-83.
21. Huezo-Diaz, P., et al., CYP2C19 genotype predicts steady state escitalopram concentration in GENDEP. J Psychopharmacol, 2012. 26(3): p. 398-407.
22. Sangkuhl, K., T.E. Klein, and R.B. Altman, PharmGKB summary: citalopram pharmacokinetics pathway. Pharmacogenet Genomics, 2011. 21(11): p. 769-72.
23. Braten, L.S., et al., Impact of CYP2C19 genotype on sertraline exposure in 1200 Scandinavian patients. Neuropsychopharmacology, 2020. 45(3): p. 570-576.
24. Hamelin, B.A., et al., The disposition of fluoxetine but not sertraline is altered in poor metabolizers of debrisoquin. Clin Pharmacol Ther, 1996. 60(5): p. 512-21.
25. Huddart, R., et al., PharmGKB summary: sertraline pathway, pharmacokinetics. Pharmacogenet Genomics, 2020. 30(2): p. 26-33.
26. Kobayashi, K., et al., Sertraline N-demethylation is catalyzed by multiple isoforms of human cytochrome P-450 in vitro. Drug Metab Dispos, 1999. 27(7): p. 763-6.
27. Lloret-Linares, C., et al., Phenotypic Assessment of Drug Metabolic Pathways and P-Glycoprotein in Patients Treated With Antidepressants in an Ambulatory Setting. J Clin Psychiatry, 2018. 79(2).
28. Obach, R.S., L.M. Cox, and L.M. Tremaine, Sertraline is metabolized by multiple cytochrome P450 enzymes, monoamine oxidases, and glucuronyl transferases in human: an in vitro study. Drug Metab Dispos, 2005. 33(2): p. 262-70.
29. Palacharla, R.C., et al., Quantitative in vitro phenotyping and prediction of drug interaction potential of CYP2B6 substrates as victims. Xenobiotica, 2018. 48(7): p. 663-675.
30. Rudberg, I., et al., Serum concentrations of sertraline and N-desmethyl sertraline in relation to CYP2C19 genotype in psychiatric patients. Eur J Clin Pharmacol, 2008. 64(12): p. 1181-8.
31. Saiz-Rodriguez, M., et al., Effect of Polymorphisms on the Pharmacokinetics, Pharmacodynamics and Safety of Sertraline in Healthy Volunteers. Basic Clin Pharmacol Toxicol, 2018. 122(5): p. 501-511.
32. Wang, J.H., et al., Pharmacokinetics of sertraline in relation to genetic polymorphism of CYP2C19. Clin Pharmacol Ther, 2001. 70(1): p. 42-7.
33. Xu, Z.H., et al., Evidence for involvement of polymorphic CYP2C19 and 2C9 in the N-demethylation of sertraline in human liver microsomes. Br J Clin Pharmacol, 1999. 48(3): p. 416-23.
34. Yuce-Artun, N., et al., Influence of CYP2B6 and CYP2C19 polymorphisms on sertraline metabolism in major depression patients. Int J Clin Pharm, 2016. 38(2): p. 388-94.
35. Hicks, J.K., et al., Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6 and CYP2C19 Genotypes and Dosing of Selective Serotonin Reuptake Inhibitors. Clin Pharmacol Ther, 2015. 98(2): p. 127-34.
36. Matchar, D.B., et al., Testing for cytochrome P450 polymorphisms in adults with non-psychotic depression treated with selective serotonin reuptake inhibitors (SSRIs). Evid Rep Technol Assess (Full Rep), 2007(146): p. 1-77.
37. Hicks, J.K., et al., Clinical pharmacogenetics implementation consortium guideline (CPIC) for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants: 2016 update. Clin Pharmacol Ther, 2017. 102(1): p. 37-44.