Machine Learning-Based Prediction of Escitalopram and Sertraline Side Effects With Pharmacokinetic Data in Children and Adolescents.

Selective serotonin reuptake inhibitors (SSRI) are the first-line pharmacologic treatment for anxiety and depressive disorders in children and adolescents. Many patients experience side effects that are difficult to predict, are associated with significant morbidity, and can lead to treatment discontinuation. Variation in SSRI pharmacokinetics could explain differences in treatment outcomes, but this is often overlooked as a contributing factor to SSRI tolerability. This study evaluated data from 288 escitalopram-treated and 255 sertraline-treated patients ≤ 18 years old to develop machine learning models to predict side effects using electronic health record data and Bayesian estimated pharmacokinetic parameters. Trained on a combined cohort of escitalopram- and sertraline-treated patients, a penalized logistic regression model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% confidence interval (CI): 0.66-0.88), with 0.69 sensitivity (95% CI: 0.54-0.86), and 0.82 specificity (95% CI: 0.72-0.87). Medication exposure, clearance, and time since the last dose increase were among the top features. Individual escitalopram and sertraline models yielded an AUROC of 0.73 (95% CI: 0.65-0.81) and 0.64 (95% CI: 0.55-0.73), respectively. Post hoc analysis showed sertraline-treated patients with activation side effects had slower clearance (P = 0.01), which attenuated after accounting for age (P = 0.055). These findings raise the possibility that a machine learning approach leveraging pharmacokinetic data can predict escitalopram- and sertraline-related side effects. Clinicians may consider differences in medication pharmacokinetics, especially during dose titration and as opposed to relying on dose, when managing side effects. With further validation, application of this model to predict side effects may enhance SSRI precision dosing strategies in youth.

Influence of CYP2D6 Metabolizer Status on Risperidone and Paliperidone Tolerability in Children and Adolescents.

Background: Risperidone and, to a lesser extent, paliperidone are metabolized by CYP2D6; however, there are limited data related to variation in CYP2D6 phenotypes and the tolerability of these medications in children and adolescents. Furthermore, the impact of CYP2D6 on the association of risperidone and paliperidone with hyperprolactinemia in youth is not well understood. Methods: A retrospective chart review was performed in psychiatrically hospitalized children and adolescents prescribed risperidone (n = 263, age = 3-18 years, mean age = 13 ± 3 years, 49% female) or paliperidone (n = 124, age = 5-18 years, mean age = 15 ± 2 years, 44% female) who had CYP2D6 genotyping performed as part of routine care. CYP2D6 phenotypes were determined based on Clinical Pharmacogenetics Implementation Consortium guidelines and CYP2D6 inhibitors causing phenoconversion. Adverse effects were obtained from a review of the electronic health record, and patients were selected, in part, to enrich non-normal metabolizers. Results: Among risperidone-treated patients, 45% experienced an adverse effect, whereas 36% of paliperidone-treated patients experienced adverse effects. Discontinuation of risperidone due to lack of efficacy was more frequent in the CYP2D6 normal metabolizers and ultrarapid metabolizers compared with intermediate metabolizers (IMs) and phenoconverted poor metabolizers (pPMs) (54.5% vs. 32.7%, p < 0.001). Discontinuation due to weight gain was more common among risperidone- than paliperidone-treated patients (17% vs. 7%, p = 0.011). Among those taking paliperidone, CYP2D6 was associated with discontinuation due to side effects (p = 0.008), and youth with slower CYP2D6 metabolism (i.e., pPMs and IMs) were more likely to discontinue. Hyperprolactinemia was found in 10% of paliperidone-treated patients and 5% of risperidone-treated patients, and slower CYP2D6 metabolizers required higher risperidone doses to cause hyperprolactinemia (p = 0.011). Conclusions: CYP2D6 phenotype is associated with discontinuation of risperidone due to lack of efficacy and the dose of risperidone that induced hyperprolactinemia, as well as discontinuation of paliperidone due to adverse effects. Future studies should evaluate exposure-response and toxicity relationships in risperidone- and paliperidone-treated youth.

Escitalopram and Sertraline Population Pharmacokinetic Analysis in Pediatric Patients.

BACKGROUND AND OBJECTIVE: Escitalopram and sertraline are commonly prescribed for anxiety and depressive disorders in children and adolescents. The pharmacokinetics (PK) of these medications have been evaluated in adults and demonstrate extensive variability, but studies in pediatric patients are limited. Therefore, we performed a population PK analysis for escitalopram and sertraline in children and adolescents to characterize the effects of demographic, clinical, and pharmacogenetic factors on drug exposure. METHODS: A PK dataset was generated by extracting data from the electronic health record and opportunistic sampling of escitalopram- and sertraline-treated psychiatrically hospitalized pediatric patients aged 5-18 years. A population PK analysis of escitalopram and sertraline was performed using NONMEM. Concentration-time profiles were simulated using MwPharm++ to evaluate how covariates included in the final models influence medication exposure and compared to adult therapeutic reference ranges. RESULTS: The final escitalopram cohort consisted of 315 samples from 288 patients, and the sertraline cohort consisted of 265 samples from 255 patients. A one-compartment model with a proportional residual error model best described the data for both medications. For escitalopram, CYP2C19 phenotype and concomitant CYP2C19 inhibitors affected apparent clearance (CL/F), and normalizing CL/F and apparent volume of distribution (V/F) to body surface area (BSA) improved estimations. The final escitalopram model estimated CL/F and V/F at 14.2 L/h/1.73 m2 and 428 L/1.73 m2, respectively. For sertraline, CYP2C19 phenotype and concomitant CYP2C19 inhibitors influenced CL/F, and empirical allometric scaling of patient body weight on CL/F and V/F was significant. The final sertraline model estimated CL/F and V/F at 124 L/h/70 kg and 4320 L/70 kg, respectively. Normalized trough concentrations (Ctrough) for CYP2C19 poor metabolizers taking escitalopram were 3.98-fold higher compared to normal metabolizers (151.1 ng/mL vs 38.0 ng/mL, p < 0.0001), and normalized Ctrough for CYP2C19 poor metabolizers taking sertraline were 3.23-fold higher compared to normal, rapid, and ultrarapid metabolizers combined (121.7 ng/mL vs 37.68 ng/mL, p < 0.0001). Escitalopram- and sertraline-treated poor metabolizers may benefit from a dose reduction of 50-75% and 25-50%, respectively, to normalize exposure to other phenotypes. CONCLUSION: To our knowledge, this is the largest population PK analysis of escitalopram and sertraline in pediatric patients. Significant PK variability for both medications was observed and was largely explained by CYP2C19 phenotype. Slower CYP2C19 metabolizers taking escitalopram or sertraline may benefit from dose reductions given increased exposure.

Tutorial on model selection and validation of model input into precision dosing software for model-informed precision dosing.

There has been rising interest in using model-informed precision dosing to provide personalized medicine to patients at the bedside. This methodology utilizes population pharmacokinetic models, measured drug concentrations from individual patients, pharmacodynamic biomarkers, and Bayesian estimation to estimate pharmacokinetic parameters and predict concentration-time profiles in individual patients. Using these individualized parameter estimates and simulated drug exposure, dosing recommendations can be generated to maximize target attainment to improve beneficial effect and minimize toxicity. However, the accuracy of the output from this evaluation is highly dependent on the population pharmacokinetic model selected. This tutorial provides a comprehensive approach to evaluating, selecting, and validating a model for input and implementation into a model-informed precision dosing program. A step-by-step outline to validate successful implementation into a precision dosing tool is described using the clinical software platforms Edsim++ and MwPharm++ as examples.

Clinical covariates that improve the description of high dose methotrexate pharmacokinetics in a diverse population to inform MTXPK.org.

The MTXPK.org webtool was launched in December 2019 and was developed to facilitate model-informed supportive care and optimal use of glucarpidase following the administration of high-dose methotrexate (HDMTX). One limitation identified during the original development of the MTXPK.org tool was the perceived generalizability because the modeled population comprised solely of Nordic pediatric patients receiving 24-h infusions for the treatment of acute lymphoblastic leukemia. The goal of our study is to describe the pharmacokinetics of HDMTX from a diverse patient population (e.g., races, ethnicity, indications for methotrexate, and variable infusion durations) and identify meaningful factors that account for methotrexate variability and improve the model's performance. To do this, retrospectively analyzed pharmacokinetic and toxicity data from pediatric and adolescent young adult patients who were receiving HDMTX (>0.5 g/m2 ) for the treatment of a cancer diagnosis from three pediatric medical centers. We performed population pharmacokinetic modeling referencing the original MTXPK.org NONMEM model (includes body surface area and serum creatinine as covariates) on 1668 patients, 7506 administrations of HDMTX, and 30,250 concentrations. Our results support the parameterizations of short infusion duration (<8 h) and the presence of Down syndrome on methotrexate clearance, the parameterization of severe hypoalbuminemia (<2.5 g/dL) on the intercompartmental clearance (Q2 and Q3), and the parameterization of pleural effusion on the volume of distribution (V1 and V2). These novel parameterizations will increase the generalizability of the MTXPK.org model once they are added to the webtool.

Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing.

BACKGROUND: Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have greatly benefitted from computational and mathematical advances over the past 60 years. Furthermore, the use of artificial intelligence (AI) and machine learning (ML) approaches for supporting clinical research and support is increasing. However, AI and ML applications for precision dosing have been evaluated only recently. Given the capability of ML to handle multidimensional data, such as from electronic health records, opportunities for AI and ML applications to facilitate TDM and MIPD may be advantageous. METHODS: This review summarizes relevant AI and ML approaches to support TDM and MIPD, with a specific focus on recent applications. The opportunities and challenges associated with this integration are also discussed. RESULTS: Various AI and ML applications have been evaluated for precision dosing, including those related to concentration or exposure prediction, dose optimization, population pharmacokinetics and pharmacodynamics, quantitative systems pharmacology, and MIPD system development and support. These applications provide an opportunity for ML and pharmacometrics to operate in an integrated manner to provide clinical decision support for precision dosing. CONCLUSIONS: Although the integration of AI with precision dosing is still in its early stages and is evolving, AI and ML have the potential to work harmoniously and synergistically with pharmacometric approaches to support TDM and MIPD. Because data are increasingly shared between institutions and clinical networks and aggregated into large databases, these applications will continue to grow. The successful implementation of these approaches will depend on cross-field collaborations among clinicians and experts in informatics, ML, pharmacometrics, clinical pharmacology, and TDM.

Adverse Effects of Antidepressant Medications and their Management in Children and Adolescents.

INTRODUCTION: Selective serotonin reuptake inhibitors (SSRIs) and, to a lesser extent, serotonin-norepinephrine reuptake inhibitors (SNRIs) are the cornerstone of pharmacotherapy for children and adolescents with anxiety and depressive disorders. These medications alleviate symptoms and restore function for many youths; however, they are associated with a distinct adverse effect profile, and their tolerability may complicate treatment or lead to discontinuation. Yet, SSRI/SNRI tolerability has received limited attention in the pediatric literature. METHODS: This review examines the early- (e.g., activation, gastrointestinal symptoms, sedation) and late-emerging (e.g., weight gain) adverse effects of SSRIs and some SNRIs in pediatric patients. RESULTS: We provide a framework for discussing SSRI/SNRI tolerability with patients and their families and describe the pharmacologic basis, course, and predictors of adverse events in youth. Strategies to address specific tolerability concerns are presented. For selected adverse events, using posterior simulation of mean differences over time, we describe their course based on Physical Symptom Checklist measures in a prospective, randomized trial of anxious youth aged 7-17 years who were treated with sertraline (n = 139) or placebo (n = 76) for 12 weeks in the Child/Adolescent Anxiety Multimodal Study (CAMS). MAIN RESULTS: In CAMS, the relative severity/burden of total physical symptoms (p < 0.001), insomnia (p = 0.001), restlessness (p < 0.001), nausea (p = 0.002), abdominal pain (p < 0.001), and dry mouth (p = 0.024) decreased from baseline over 12 weeks of sertraline treatment, raising the possibility that these symptoms are transient. No significant changes were observed for sweating (p = 0.103), constipation (p = 0.241), or diarrhea (p = 0.489). Finally, we review the antidepressant withdrawal syndrome in children and adolescents and provide guidance for SSRI discontinuation, using pediatric pharmacokinetic models of escitalopram and sertraline-two of the most used SSRIs in youth. CONCLUSION: SSRI/SNRIs are associated with both early-emerging (often transient) and late-emerging adverse effects in youth. Pharmacokinetically-informed appraoches may address some adverse effects and inform SSRI/SNRI discontinuation strategies.

A retrospective examination of adjunctive L-methylfolate in children and adolescents with unipolar depression.

BACKGROUND: Adjunctive l-methylfolate is commonly prescribed for children and adolescents with treatment-resistant mood disorders; however, the relationship between l-methylfolate augmentation across methylenetetrahydrofolate reductase (MTHFR) genotypes in youths with depressive symptoms is unclear. METHODS: We retrospectively examined the electronic health records of patients (N = 412) with depressive symptoms associated with unipolar depressive disorders and their MTHFR C677T genotypes from 2013 to 2019. Patients were ≤18 years of age at the time of MTHFR pharmacogenetic testing. Treatment response was assessed with Clinical Global Impression-Improvement (CGI-I) score reported in the medical record. RESULTS: Patients with an MTHFR C677T C/T or T/T genotype were more likely to be prescribed l-methylfolate when the clinician knew their MTHFR genotype (p < 0.0001, OR: 15.1, 95 % CI: [5.1, 44.2]), but not when the clinician did not know their genotype (p = 0.4, OR: 2.1, 95 % CI: [0.4, 11.4]). Change in baseline and endpoint CGI-I scores between patients with an MTHFR C677T variant who were prescribed and not prescribed l-methylfolate did not significantly differ (p = 0.39). Response rate was not associated with l-methylfolate prescription (p = 0.17) or l-methylfolate dose (p = 0.69). LIMITATIONS: This was a retrospective study, which yielded a heterogeneous patient population and limited data availability (e.g., adherence). Patients are severely ill and may have a refractory illness that limits response to adjunctive l-methylfolate. CONCLUSION: Clinicians prescribe l-methylfolate to children and adolescents with depressive symptoms associated with unipolar depressive disorders who have an MTHFR C677T variant, although augmentation may not be associated with treatment response, regardless of MTHFR genotype or dose.

Selective Serotonin Reuptake Inhibitor Pharmacokinetics During Pregnancy: Clinical and Research Implications.

Pregnancy and associated physiologic changes affect the pharmacokinetics of many medications, including selective serotonin reuptake inhibitors-the first-line pharmacologic interventions for depressive and anxiety disorders. During pregnancy, SSRIs exhibit extensive pharmacokinetic variability that may influence their tolerability and efficacy. Specifically, compared to non-pregnant women, the activity of cytochrome P450 (CYP) enzymes that metabolize SSRIs drastically changes (e.g., decreased CYP2C19 activity and increased CYP2D6 activity). This perspective examines the impact of pharmacokinetic genes-related to CYP activity on SSRI pharmacokinetics during pregnancy. Through a simulation-based approach, plasma concentrations for SSRIs metabolized primarily by CYP2C19 (e.g., escitalopram) and CYP2D6 (e.g., fluoxetine) are examined and the implications for dosing and future research are discussed.

Systematic Review and Meta-Analysis of L-Methylfolate Augmentation in Depressive Disorders.

OBJECTIVES: Partial response to pharmacotherapy is common in major depressive disorder (MDD) and many patients require alternative pharmacotherapy or augmentation, including adjunctive L-methylfolate. Given that L-methylfolate augmentation is rarely included in major clinical practice guidelines, we sought to systematically review evidence for L-methylfolate augmentation in adults with MDD and to examine its efficacy meta-analytically. METHODS: We systematically searched PubMed for articles up to December 31, 2020, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations. Included studies were published in peer-reviewed, English-language journals and examined L-methylfolate adjunctive therapy in depressive disorders or its effect on antidepressant response. A fixed- and random-effects meta-analysis and risk of bias assessment using the Cochrane Risk of Bias Tool were conducted. RESULTS: Qualitative assessment of nine articles (N=6,707 patients) suggests that adjunctive L-methylfolate improved antidepressant response. In the meta-analysis of categorical Hamilton Rating Scale for Depression-17 response, (three studies, N=483) adjunctive L-methylfolate was associated with a small effect versus antidepressant monotherapy (relative risk: 1.25, 95% confidence interval [CI]=1.08 to 1.46, p=0.004). A meta-analysis of four studies (N=507) using a continuous measure of depressive symptoms showed a similar effect of adjunctive L-methylfolate (standardized mean difference=- 0.38, 95% CI=- 0.59 to-0.17, p=0.0003). CONCLUSION: Adjunctive L-methylfolate may have modest efficacy in antidepressant-treated adults with MDD.

Pharmacogenetically Guided Escitalopram Treatment for Pediatric Anxiety Disorders: Protocol for a Double-Blind Randomized Trial.

Current pharmacologic treatments for pediatric anxiety disorders (e.g., selective serotonin reuptake inhibitors (SSRIs)) frequently use "one size fits all" dosing strategies based on average responses in clinical trials. However, for some SSRIs, including escitalopram, variation in CYP2C19 activity produces substantial variation in medication exposure (i.e., blood medication concentrations). This raises an important question: would refining current SSRI dosing strategies based on CYP2C19 phenotypes increase response and reduce side effect burden? To answer this question, we designed a randomized, double-blind trial of adolescents 12-17 years of age with generalized, separation, and/or social anxiety disorders (N = 132). Patients are randomized (1:1) to standard escitalopram dosing or dosing based on validated CYP2C19 phenotypes for escitalopram metabolism. Using this approach, we will determine whether pharmacogenetically-guided treatment-compared to standard dosing-produces faster and greater reduction in anxiety symptoms (i.e., response) and improves tolerability (e.g., decreased risk of treatment-related activation and weight gain). Secondarily, we will examine pharmacodynamic variants associated with treatment outcomes, thus enhancing clinicians' ability to predict response and tolerability. Ultimately, developing a strategy to optimize dosing for individual patients could accelerate response while decreasing side effects-an immediate benefit to patients and their families. ClinicalTrials.gov Identifier: NCT04623099.

Pediatric Therapeutic Drug Monitoring for Selective Serotonin Reuptake Inhibitors.

Therapeutic drug monitoring (TDM) is uncommon in child and adolescent psychiatry, particularly for selective serotonin reuptake inhibitors (SSRIs)-the first-line pharmacologic treatments for depressive and anxiety disorders. However, TDM in children and adolescents offers the opportunity to leverage individual variability of antidepressant pharmacokinetics to shed light on non-response and partial response, understand drug-drug interactions, evaluate adherence, and characterize the impact of genetic and developmental variation in pharmacokinetic genes. This perspective aims to educate clinicians about TDM principles and examines evolving uses of TDM in SSRI-treated youths and their early applications in clinical practice, as well as barriers to TDM in pediatric patients. First, the impact of pharmacokinetic genes on SSRI pharmacokinetics in youths could be used to predict tolerability and response for some SSRIs (e.g., escitalopram). Second, plasma concentrations are significantly influenced by adherence, which may relate to decreased efficacy. Third, pharmacometric analyses reveal interactions with proton pump inhibitors, oral contraceptives, cannabinoids, and SSRIs in youths. Rapid developments in TDM and associated modeling have enhanced the understanding of variation in SSRI pharmacokinetics, although the treatment of anxiety and depressive disorders with SSRIs in youths often remains a trial-and-error process.

The Impact of Marijuana on Antidepressant Treatment in Adolescents: Clinical and Pharmacologic Considerations.

The neuropharmacology of marijuana, including its effects on selective serotonin reuptake inhibitor (SSRI)/antidepressant metabolism and the subsequent response and tolerability in youth, has received limited attention. We sought to (1) review clinically relevant pharmacokinetic (PK) and pharmacodynamic (PD) interactions between cannabinoids and selected SSRIs, (2) use PK models to examine the impact of cannabinoids on SSRI exposure (area under curve (AUC)) and maximum concentration (CMAX) in adolescents, and (3) examine the frequency of adverse events reported when SSRIs and cannabinoids are used concomitantly. Cannabinoid metabolism, interactions with SSRIs, impact on relevant PK/PD pathways and known drug-drug interactions were reviewed. Then, the impact of tetrahydrocannabinol (THC) and cannabidiol (CBD) on exposure (AUC24) and CMAX for escitalopram and sertraline was modeled using pediatric PK data. Using data from the Food and Drug Administration Adverse Events Reporting System (FAERS), the relationship between CBD and CYP2C19-metabolized SSRIs and side effects was examined. Cannabis and CBD inhibit cytochrome activity, alter serotonergic transmission, and modulate SSRI response. In PK models, CBD and/or THC increases sertraline and es/citalopram concentrations in adolescents, and coadministration of CBD and CYP2C19-metabolized SSRIs increases the risk of cough, diarrhea, dizziness, and fatigue. Given the significant SSRI-cannabinoid interactions, clinicians should discuss THC and CBD use in youth prescribed SSRIs and be aware of the impact of initiating, stopping, or decreasing cannabinoid use as this may significantly affect es/citalopram and sertraline exposure.

Escitalopram in Adolescents With Generalized Anxiety Disorder: A Double-Blind, Randomized, Placebo-Controlled Study.

BACKGROUND: Selective serotonin reuptake inhibitors (SSRIs) are commonly used to treat pediatric anxiety disorders, including generalized anxiety disorder (GAD); however, their efficacy and tolerability are difficult to predict. This study evaluated the efficacy and tolerability of escitalopram in adolescents with GAD (DSM-IV-TR) and the impact of variants in HTR2A and serotonin transporter (SLC6A4) genes and cytochrome P450 2C19 (CYP2C19) phenotypes on response as well as CYP2C19 phenotype on escitalopram pharmacokinetics from February 2015 through November 2018. METHODS: Patients were treated with escitalopram (forced titration to 15 mg/d, then flexible titration to 20 mg/d) (n = 26, mean ± SD age: 14.8 ± 1.7 years) or placebo (n = 25, mean ± SD age: 14.9 ± 1.6 years) for 8 weeks. Outcomes were the change in scores on the Pediatric Anxiety Rating Scale (PARS) and Clinical Global Impressions (CGI) scales as well as vital signs and adverse events. Plasma escitalopram and desmethylcitalopram area under the curve during 24 hours (AUC0-24) and maximum concentration (Cmax) were determined and compared across CYP2C19 phenotypes. RESULTS: Escitalopram was superior to placebo for mean ± SD baseline-to-endpoint change in PARS (-8.65 ± 1.3 vs -3.52 ± 1.1, P = .005) and CGI scores, and increasing CYP2C19 metabolism was associated with decreases in escitalopram Cmax (P = .07) and AUC0-24 (P < .05). Vital signs, corrected QT interval, and adverse events were similar in patients who received escitalopram and placebo. CONCLUSIONS: Escitalopram reduces anxiety symptoms, and pharmacogenetics variables influence the trajectory and magnitude of improvement. Variation in CYP2C19 metabolism accounts for significant differences in escitalopram pharmacokinetics, raising the possibility that CYP2C19 phenotype should be considered when prescribing escitalopram. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02818751.

Cariprazine in Youth with Bipolar and Psychotic Disorders: A Retrospective Chart Review.

Objective: To examine the potential effectiveness and tolerability of cariprazine in pediatric bipolar and psychotic disorders. Methods: We retrospectively reviewed the electronic health records of patients <21 years of age prescribed cariprazine to treat bipolar and psychotic disorders. Adverse effects, tolerability, therapeutic response (Clinical Global Impression-Improvement [CGI-I]), and severity of illness (Clinical Global Impression-Severity [CGI-S]) were determined through manual chart review. Results: We identified 16 patients aged 6-20 years who were treated with cariprazine (initial dose: 1.5 mg/day, interquartile range [IQR], 1.5-1.5; endpoint dose: 3 mg/day, IQR, 1.5-4.5). No serious adverse events were reported, but the most commonly reported side effect was weight gain (n = 3, 19%). Of the 14 patients for whom baseline and endpoint body mass index (BMI) data were available, neither changes in BMI (p = 0.391; 0.54 kg/m2, IQR, -0.33 to 1.38) nor BMI percentile (p = 0.71; 0.36%, IQR, -0.49 to 3.97) significantly differed between baseline and endpoint. However, patients receiving ≥4.5 mg/day had a significantly greater BMI increases during the course of treatment compared with those receiving ≤3 mg/day (p = 0.034; -1.14 kg/m2, IQR, -3.65 to 0.53 vs. 1.01 kg/m2, IQR, 0.17-4.88). CGI-S scores (p = 0.016; 4.5, IQR, 4-5 vs. 4, IQR, 3-4) significantly differed from baseline to endpoint. The response rate was 44% (n = 7/16), with responders being prescribed higher doses (p = 0.005; 6 mg/day, IQR, 4.875-6 vs. 3 mg/day, IQR, 3-4.125). Conclusions: Cariprazine may be well tolerated and effective for pediatric bipolar and psychotic disorders; however, compared with higher doses, total daily doses ≤3 mg/day appear to be more tolerable. Prospective controlled studies to further evaluate cariprazine in youth are needed.

Pharmacogenetics of Sertraline Tolerability and Response in Pediatric Anxiety and Depressive Disorders.

Objective: To determine whether genetic variants in a pharmacokinetic gene (the number of CYP2C19 reduced function alleles [RFAs]), and in pharmacodynamic genes (HTR2A, SLC6A4, and GRIK4) influence sertraline tolerability and response in a cohort of pediatric patients with anxiety and depressive disorders. Methods: A retrospective analysis was performed using the electronic medical record data of 352 patients <19 years of age being treated for anxiety and/or depressive disorders with sertraline and who underwent routine clinical CYP2C19 genotyping. Additional genotyping and analysis of variants in HTR2A, SLC6A4, and GRIK4 were conducted for 249 patients. Multivariate regression models testing for associations with CYP2C19 were adjusted for concomitant use of interacting medications. Combinatorial classification and regression tree (CART) analyses containing all pharmacokinetic and pharmacodynamic genes and clinical factors were performed. Results: The maximum sertraline dose during the initial titration period of sertraline was inversely associated with the number of CYP2C19 RFAs and sertraline dose at 60 (p = 0.025) and 90 days (p = 0.025). HTR2A rs6313 was associated with sertraline dose (p = 0.011) and time to the average maximum sertraline dose (p = 0.039). Regarding efficacy, the number of CYP2C19 RFAs was not associated with the sertraline dose at the time of response (p = 0.22), whereas for the pharmacodynamic genes, only HTR2A rs6313 was associated with response dose (p = 0.022). An association was observed between predicted expression levels of SLC6A4 and the duration on sertraline (p = 0.025). Combinatorial CART and multivariate regression analyses implicated that pharmacodynamic genes and clinical factors influence the maximum sertraline dose and response dose. The total number of side effects was not associated with any of the variants tested. Conclusion: Both pharmacokinetic and pharmacodynamic factors, in addition to clinical and demographic components, influence sertraline dose, response, and tolerability, thereby necessitating further research to assess for the validity of these pharmacogenetic associations in children and adolescents.

Influence of CYP2C19 Metabolizer Status on Escitalopram/Citalopram Tolerability and Response in Youth With Anxiety and Depressive Disorders.

In pediatric patients, the selective serotonin reuptake inhibitors (SSRIs) escitalopram and citalopram (es/citalopram) are commonly prescribed for anxiety and depressive disorders. However, pharmacogenetic studies examining CYP2C19 metabolizer status and es/citalopram treatment outcomes have largely focused on adults. We report a retrospective study of electronic medical record data from 263 youth < 19 years of age with anxiety and/or depressive disorders prescribed escitalopram or citalopram who underwent routine clinical CYP2C19 genotyping. Slower CYP2C19 metabolizers experienced more untoward effects than faster metabolizers (p = 0.015), including activation symptoms (p = 0.029) and had more rapid weight gain (p = 0.018). A larger proportion of slower metabolizers discontinued treatment with es/citalopram than normal metabolizers (p = 0.007). Meanwhile, faster metabolizers responded more quickly to es/citalopram (p = 0.005) and trended toward less time spent in subsequent hospitalizations (p = 0.06). These results highlight a disparity in treatment outcomes with es/citalopram treatment in youth with anxiety and/or depressive disorders when standardized dosing strategies were used without consideration of CYP2C19 metabolizer status. Larger, prospective trials are warranted to assess whether tailored dosing of es/citalopram based on CYP2C19 metabolizer status improves treatment outcomes in this patient population.

CYP2C19-Guided Escitalopram and Sertraline Dosing in Pediatric Patients: A Pharmacokinetic Modeling Study.

Objective: Cytochrome P4502C19 (CYP2C19) is a highly polymorphic gene that encodes an enzyme that metabolizes escitalopram and sertraline, two selective serotonin reuptake inhibitors (SSRIs) that are FDA approved for pediatric use and commonly used to treat anxiety and depressive disorders in youth. Using pharmacokinetic (PK) models in adolescents, we sought to (1) model SSRI dosing across CYP2C19 phenotypes to compare SSRI exposure (area under curve, AUC) and maximum concentration (Cmax), (2) evaluate the impact of b.i.d. dosing (in rapid metabolizers [RM] and ultrarapid metabolizers [UM]) on SSRI exposure and Cmax, and (3) determine pharmacogenomically-informed dosing strategies to provide similar exposure across CYP2C19 phenotypes in adolescents. Methods: Using PK parameters in CYP2C19 phenotype groups and previously reported pediatric PK data for escitalopram and sertraline, we modeled exposure (AUC0-24) and Cmax and determined CYP2C19-guided dosing strategies. Results: Compared with normal CYP2C19 metabolizers treated with either escitalopram or sertraline, Cmax and AUC0-24 were higher in slower metabolizers and lower in patients with increased CYP2C19 activity, although the magnitude of these differences was more pronounced for escitalopram than for sertraline. For escitalopram, poor metabolizers (PMs) require 10 mg/day and UMs require 30 mg/day to achieve an exposure that is equivalent to 20 mg/day in a normal metabolizer (NM). For sertraline, to achieve AUC0-24 and Cmax similar to NMs receiving 150 mg/day, PMs require 100 mg/day, whereas a dose of 200 mg/day was required in rapid and UMs. For UMs, b.i.d. escitalopram dosing was necessary to achieve comparable trough levels and exposure to NMs. Conclusions: This simulation study raises the possibility that achieving similar escitalopram and sertraline plasma concentrations could require dose adjustments in CYP2C19 poor metabolizers and UMs, although the magnitude of these differences were more pronounced for escitalopram than for sertraline. However, prospective trials of pharmacogenomically guided dosing in the pediatric population are needed to extend the findings of these modeling studies.