Recommendations for prescribing SSRIs


Before turning to selective serotonin reuptake inhibitors (SSRIs) to treat anxiety and depressive disorders in children, check out the freely accessible, genotype-based, drug-dosing online knowledgebases for guidance.

Selective serotonin reuptake inhibitors (SSRIs) are a first-line pharmacologic intervention for major depressive and anxiety disorders, but also are indicated for obsessive-compulsive disorder (OCD), premenstrual dysphoric disorder, and bulimia nervosa, among other psychiatric conditions. Due to their relatively low adverse-effect profile-with the exception of a very low rate of self-injurious thoughts and behaviors-and the high prevalence of anxiety and depressive disorders in youth, a significant proportion of SSRIs are prescribed by pediatricians.

The mechanism of action of SSRIs consists of inhibiting the function of the transmembrane presynaptic serotonin transporter (SERT), which in turn increases the availability of serotonin to a myriad of postsynaptic serotonin receptors. Depending on the SSRI, the norepinephrine transporter (NET) and dopamine transporter (DAT) are also weakly inhibited.1 The ensuing serotonergic transmission cascade is still only poorly understood. In particular, neuroplasticity effects may be key in the therapeutic action of SSRIs.2

Pharmacogenomic data available to guide use of SSRIs is cataloged by the Clinical Pharmacogenomics Implementation Consortium (CPIC), with a guideline published in 2015 for CYP2D6 and CYP2C19 polymorphisms.3 Pharmacogenomic data can theoretically be used to personalize biologic information to guide clinicians in choosing among SSRIs to optimize response and decrease adverse effects. However, the data available is only emergent, and should be considered cautiously in its interpretation. In particular, the US Food and Drug Administration (FDA) has not approved pharmacogenomic tests for use in differentiating psychotropic drug choices.

Notwithstanding, CPIC data focuses on polymorphisms in the cytochrome P450 system, which metabolize SSRIs. These polymorphic alleles have been cataloged and may guide treatment, specifically in identifying patients who are refractory to SSRIs or have excessive adverse effects. This article reviews the 2015 CPIC guidelines for 2 CYP450 enzymes, CYP2D6 and CYP2C19, with the addition of recent data available since 2015.4 Of note, the 2015 CPIC guidelines caution using the information provided wholesale to children as most studies have only been performed in adults, with the caveat that cytochrome P450 system activity is generally fully mature by early childhood.

The CYP2D6 gene is highly polymorphic, with over 100 known allele variants and subvariants, which are not straightforward to genotype.5 The * (star) alleles should be read with long-read CYP2D6 sequencing to better detect duplicates compared with short-read methods, but these platforms are not widely available.6 In addition, CYP2D6 variants may provide information on metabolizer status but are less useful for predicting response as serotonin receptor variants also highly influence response. For example, 5-TH1beta receptor status may affect response to fluoxetine in children.7

The classification of metabolizers using CYP2D6 or CYP2C19 genotyping/phenotyping is as follows: a) ultrarapid metabolizers (UM) due to a duplication of functional alleles; b) normal or extensive metabolizers (NM) due to at least 1 normal or 2 decreased functional alleles; c) intermediate metabolizers (IM) due at 1 decreased functional and 1 nonfunctional allele; and d) poor metabolizers (PM) due to no functional alleles.8 Using this classification scheme based on allelic genotypes, pharmacogenomic data on the CYP450 system may be of use for those patients who are having difficulty with either nonresponse or a highly unusual adverse effect profile at usual doses.

It is also important to note that the most problematic phenotypes (UM and PM) are relatively uncommon in the general population and that they vary by race. Given the highly complex nature of the allelic data, therefore, only preliminary guidelines can be provided today even with the best efforts, and it is expected that pharmacogenomic research will clarify these uncertainties in the next decade. The reader is referred to the CPIC 2015 report for full details.4

In current practice, given a genetic data readout, the most common alleles expected for CYP2D6 are *1, *2 (both normal function); *6, *9, *10, *41 (all decreased function); and *3, *4, *5, *6 (no function). The CYP2D6 enzyme is the main metabolic agent for fluoxetine (Prozac), and partially for sertraline (Zoloft). Also, CYP2C19 is a polymorphic gene with over 30 known allele variants and subvariants that determine enzyme activity levels, in similar fashion to CYP2D6. The main alleles present in the population for CYP2C19 are *1 (functional), *2 (nonfunctional), and *17 (suprafunctional). In addition, CYP2C19 metabolizes citalopram (Celexa), escitalopram (Lexapro), and partially sertraline (Zoloft) into less pharmacologically active compounds.

Given that rare variants, and other less well-characterized alleles, are not in current databases, “negative” (normal) reports should be taken with caution. The CPIC suggests that CYP2D6 UM patients forego the use of fluoxetine for another SSRI that relies less on CYP2D6, while CYP2D6 PM patients lower the dosing to 30% to 50% for fluoxetine and observe closely for adverse effects. Similarly, for CYP2C19 UM patients, higher doses of escitalopram or citalopram will only produce low plasma levels and possible lack of efficacy, with a similar but lesser effect for sertraline. For CYP2C19 PM, the risk of cardiac arrhythmias is posed at higher doses of escitalopram or citalopram, which already may warrant clinical electrocardiographic (ECG) monitoring anyway. A similar consideration is present for sertraline, with usual doses possibly inducing an excess of adverse effects.

Although CPIC guidelines can assist in problematic cases of nonresponse and toxicity, standard pediatric SSRI prescribing practice follows the algorithm of initiating SSRIs at a low dose and increasing gradually to recommended doses until response or adverse effects are encountered. It is interesting to note that in one study pediatricians were more likely to prescribe SSRIs for childhood depression but less for childhood anxiety,9 despite effect sizes for SSRIs in anxiety disorders being much larger in controlled trials.

Only select studies have examined the use of CYP450 genetic data to guide SSRI treatment since 2015. Bishop and colleagues10 examined clinical outcomes based on CYP2C19 variants in a cohort of children and adults with autism spectrum disorder (ASD). Eighty-four participants with ASD aged 4 to 45 years completed a 6-week open-label escitalopram trial, using the Aberrant Behavior Checklist-Community Version (ABC-CV) score as an endpoint. There was no difference in outcomes for the different CYP450 genotype groups. However, UM participants showed a slower rate of change in dose over time.

In another study, Strawn and colleagues11 used pharmacokinetic (PK) parameters to model the dosing for escitalopram in different CYP450 genotypic groups. Based on PK modeling, poor metabolizers require a 10 mg/day dose and ultrarapid metabolizers require 30 mg/day to reach the equivalent 20 mg daily dose of normal metabolizers for similar drug exposure among the groups.

Finally, Aldrich and colleagues12 reported on a retrospective study of electronic medical record data from 263 youth aged younger than 19 years with anxiety and/or depressive disorders who were prescribed citalopram or escitalopram. As expected, patients with CYP2C19 PM genotypes had more adverse effects than CYP2C19 UM patients (P=0.015), including activation symptoms (P=0.029) and more rapid weight gain (P=0.018). In contrast, CYP2C19 PM patients discontinued treatment more frequently than CYP2C19 NM patients (P=0.007). Finally, faster metabolizers paradoxically responded more quickly (P=0.005) and trended toward less time spent in subsequent hospitalizations (P=0.06).

Databases for clinicians

The CPIC formed in 2009 as a joint collaboration between the Pharmacogenomics Research Network (PGRN) and the Pharmacogenomics Knowledgebase (PharmGKB). The PharmGKB, a National Institutes of Health-funded online knowledgebase, has been operating since 2000 to promote researchers’ understanding of the field of pharmacogenetics by serving as a database for storing peer-reviewed, freely accessible, genotype-based drug-dosing guidelines for clinicians.13

Another multidisciplinary group is the Dutch Pharmacogenetics Working Group (DPWG), which has developed pharmacogenetics-based therapeutic recommendations starting in 2005. Distinct gene-drug associations for well-known SSRIs have been catalogued by the DPGW ( Although both CPIC and DPWG guidelines show a high level of concordance, differences exist, possibly due to different initial selection of the relevant gene-drug pairs or dissimilar allele classification with subsequent conflicting genotype to phenotype conversions.14



In summary, given that anxiety and depressive disorders in children are etiologically complex conditions, a full assessment and consideration for psychosocial treatments is a key first step in management. If medication management is indicated, the use of CYP450 has promise for real world use in pediatric patients with need for SSRI treatment. The current practice of starting at lower doses and increasing gradually is supported as a reasonable approach to the large majority of patients. In those patients with early toxic effects at lower dosing or lack of response with higher dosing, CYP450 genotyping may provide additional guidance.


1. Zeppelin T, Ladefoged LK, Sinning S, Schiott B. Substrate and inhibitor binding to the serotonin transporter: insights from computational, crystallographic, and functional studies. Neuropharmacology. February 23, 2019; Epub ahead of print.

2. Umemori K, Winkel F, Didio G, Llach Pou M, Castrén E. iPlasticity: inducted juvenile-like plasticity in the adult brain as a mechanism of antidepressants. Psychiatry Clin Neurosci. 2018;72(9):633-653.

3. Clinical Pharmacogenetics Implementation Consortium (CPIC). Website. Available at: Accessed October 14, 2019.

4. Hicks JK, Bishop JR, Sangkuhl K, et al. Clinical Pharmacogenetics Implementation Consortium. Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2D6 and CYP2C19 genotypes and dosing of selective serotonin reuptake inhibitors. Clin Pharmacol Ther. 2015;98(2):127-134.

5. Yang Y, Botton MR, Scott ER, Scott SA. Sequencing CYP2D6 gene: from variant allele discovery to clinical pharmacogenetics testing. Pharmacogenomics. 2017;18(7):673-685.

6. Qiao W, Yang Y, Sebra R, et al. Long-read single molecule real-time full gene sequencing of cytochrome P450-2D6. Hum Mutat. 2016;37(3):315-323.

7. Gassó P, Rodrigueze N, Bláquez A, et al. Epigenetic and genetic variants in the HTR1B gene and clinical improvement in children and adolescents treated with fluoxetine. Prog Neuropsychopharmacol Biol Psychiatry. 2017;75:28-34.

8. Caudle KE, Bunnenberger HM, Freimuth RR, et al. Standardizing terms for clinical pharmacogenetics test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC). Genet Med. 2017;19(2):215-223.

9. Tulisiak AK, Klein JA, Harris E, et al. Antidepressant prescribing by pediatricians: a mixed-methods analysis. Curr Probl Pediatr Adolesc Health Care. 2017;47(1):15-24.

10. Bishop JR, Najjar F, Rubin LH, et al. Escitalopram pharmacogenetics: CYP2C19 relationships with dosing and clinical outcomes in autism spectrum disorder. Pharmacogenet Genomics. 2015;25(11):548-554.

11. Strawn JR, Poweleit EA, Ramsey LB. CYP2C19-guided escitalopram and sertraline dosing in pediatric patients: a pharmacokinetic modeling study. J Child Adolesc Psychopharmacol. 2019;29(5):340-347.

12. Aldrich SL, Poweleit EA, Prows CA, Martin LJ, Strawn JR, Ramsey LB. Influence of CYP2C19 metabolizer status of escitalopram/citalopram tolerability and response in youth with anxiety and depressive disorders. Front Pharmacol. 2019;10:99.

13. Caudle KE, Klein TE, Hoffman JM, et al. Incorporation of pharmacogenomics into routine clinical practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline development process. Curr Drug Metab. 2014;15(2):209-217.


14. Bank PCD, Caudle KE, Swan JJ, et al. Comparison of the guidelines of the Clinical Pharmacogenetics Implementation Consortium and the Dutch Pharmacogenetics Working Group. Clin Pharmacol Ther. 2018;103(4):599-618.

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