Escitalopram and Sertraline Population Pharmacokinetic Analysis in Pediatric Patients

Investigating Remedial Strategies for Missed or Delayed Dose of Sertraline in Chinese Adolescent Patients with Depressive Disorders via Population Pharmacokinetics Modeling and Simulation Approaches

Background

Sertraline is a commonly used medication for adolescent patients with depression, and missed or delayed dose has been frequently observed during long-term treatment. However, little is known about the remedial strategies for missed or delayed dose of sertraline. Hence, we designed the remedial strategies of different missed dosage scenarios based on population pharmacokinetics (PPK) model simulation, aiming to provide medication guidance to Chinese adolescent depressive patients.

Methods

A total of 221 sertraline concentration monitoring data were collected from 103 sertraline-treated adolescent patients with depression. Data analysis was performed using nonlinear mixed-effects model, and situations of different missed doses were simulated in the sertraline PPK model, including one single missed dose and double/triple missed doses under different therapeutic dosages.

Results

A one-compartment model without covariates was established to characterize the PPK of sertraline in adolescent patients with depression. Population typical values of sertraline clearance and apparent distribution volume were 65.8 L/h and 1570 L, respectively. Our simulation results revealed that when adolescent patients missed or delayed sertraline administration, the scheduled dose should be administered immediately. Additionally, our results suggested that the dose of the next remedial time should be adjusted according to the duration of the delay and the frequency of missed doses.

Conclusion

In this study, we developed remedy strategies for missed or delayed dosing of sertraline in Chinese adolescents with depression based on our PPK model simulations. These findings may provide valuable clinical guidance for sertraline treatment in this population.

Deprescribing Antidepressants in Children and Adolescents: A Systematic Review of Discontinuation Approaches, Cross-Titration, and Withdrawal Symptoms

Background: Antidepressant medications, including selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs), are commonly used to treat depressive, anxiety, and obsessive-compulsive disorders in youth. Yet, data on discontinuing these medications, withdrawal symptoms, and strategies to switch between them are limited. Methods: We searched PubMed and ClinicalTrials.gov through June 1, 2024, to identify randomized controlled trials assessing antidepressant discontinuation in youth. We summarized pediatric pharmacokinetic data to inform tapering and cross-titration strategies for antidepressants and synthesized these data with reports of antidepressant withdrawal. Results: Our search identified 528 published articles, of which 28 were included. In addition, 19 records were obtained through other methods, with 14 included. The corpus of records included 13 randomized, double-blind, placebo-controlled trials (3026 patients), including SSRIs (K = 10), SNRIs (K = 4), and TCAs (K = 1), ranging from 4 to 35 weeks. Deprescribing antidepressants requires considering clinical status, treatment response, and, in cross-titration cases, the pharmacokinetics and pharmacodynamics of both medications. Antidepressant withdrawal symptoms are related to the pharmacokinetics of the medication, which vary across antidepressants and may include irritability, palpitations, anxiety, nausea, sweating, headaches, insomnia, paresthesia, and dizziness. These symptoms putatively involve changes in serotonin transporter expression and receptor sensitivity, impacting the serotonin, dopamine, and norepinephrine pathways. Conclusions: Although approaches to deprescribing antidepressants in pediatric patients are frequently empirically guided, accumulating data related to the course of relapse and withdrawal symptoms, as well as the pharmacokinetic and pharmacodynamic properties of medications, should inform these approaches. Recommendations within this review support data-informed discussions of deprescribing-including when and how-that are critically important in the clinician-family-patient relationship.

Insights into prescribing patterns for antidepressants: an evidence-based analysis

BACKGROUND

Antidepressants are a primary treatment for depression, yet prescribing them poses significant challenges due to the absence of clear guidelines for selecting the most suitable option for individual patients. This study aimed to analyze prescribing patterns for antidepressants across healthcare providers, including physicians, physician assistants, nurse practitioners, and pharmacists, to better understand the complex factors influencing these patterns in the management of depression.

METHODS

Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify variables that explained the variation in the prescribed antidepressants, utilizing a large number of claims. Models were created to identify the prescription patterns of the 14 most common antidepressants, including amitriptyline, bupropion, citalopram, desvenlafaxine, doxepin, duloxetine, escitalopram, fluoxetine, mirtazapine, nortriptyline, paroxetine, sertraline, trazodone, and venlafaxine. The accuracy of predictions was measured through the Area under the Receiver Operating Curve (AROC).

RESULTS

Our analysis revealed several key factors influencing prescribing patterns, including patients' comorbidities, previous medications, age, and gender. A history of high antidepressant use (four or more prior medications) was the most common factor across antidepressants. Age influenced prescribing patterns, with mirtazapine and trazodone more frequent among older patients, while fluoxetine and sertraline were more common in younger individuals. Condition-specific factors included trazodone for insomnia, and amitriptyline or nortriptyline for headaches. Paroxetine, venlafaxine, and sertraline more often prescribed to females, while bupropion and doxepin were commonly prescribed for patients with tobacco use disorder and opioid dependence. Predictive factors per medicine ranged from 51 (doxepin) to 168 (citalopram), with cross-validated AROC scores averaging 76.3%.

CONCLUSIONS

Our findings provide valuable insights into the nuanced factors that shape evidence-based antidepressant prescribing practices, offering a foundation for more personalized, effective depression treatment. Further research is needed to validate these models in other extant databases. These findings contribute to a more comprehensive understanding of antidepressant prescribing practices and have the potential to improve patient outcomes in the management of depression.

The effect of zopiclone co-administration on sertraline initial dosage optimization in pediatric major depressive disorder patients based on model-informed precision dosing

Objective

The present study aims to explore the initial dosage optimization of sertraline in pediatric major depressive disorder (MDD) patients based on model-informed precision dosing (MIPD).

Methods

A total of 111 pediatric MDD patients treated with sertraline were included for analysis using MIPD. Sertraline concentration levels, physiological and biochemical indexes of pediatric MDD patients, combined drug information were included in the construction of model.

Results

Weight and zopiclone co-administration influenced sertraline clearance in pediatric MDD patients. With the same weight, the sertraline clearance rates were 0.453:1 in patients with, or without zopiclone, respectively. Furthermore, without zopiclone, for once-daily sertraline scheme, the dosages of 4.0, and 3.0 mg/kg/day were suggested for pediatric MDD patients with body weight of 30-38.5, and 38.5-80 kg, respectively; for twice-daily sertraline scheme, the dosage of 2.0 mg/kg/day was suggested for pediatric MDD patients with body weight of 30-80 kg. With zopiclone, for once-daily sertraline scheme, the dosage of 1.0 mg/kg/day was suggested for pediatric MDD patients with body weight of 30-80 kg; for twice-daily sertraline scheme, the dosage of 0.5 mg/kg/day was suggested for pediatric MDD patients with body weight of 30-80 kg.

Conclusion

This study first explored the initial dosage optimization of sertraline in pediatric MDD patients based on MIPD, and recommended the optimal sertraline initial dosage in pediatric MDD patients based on zopiclone co-administration.

Population pharmacokinetic modeling study and discovery of covariates for the antidepressant sertraline, a serotonin selective reuptake inhibitor

The purpose of this study was to discover effective covariates related to explanation of inter-individual pharmacokinetic (PK) variations through population pharmacokinetic (Pop-PK) modeling for sertraline and to provide insight into establishing scientific regimen. The bioequivalence results of sertraline performed on 24 healthy Korean men and the physiological and biochemical parameters derived from each individual were used as data to develop a Pop-PK model of sertraline for Koreans. And the relevant effectiveness of ∗10 allele polymorphisms of CYP2D6 in sertraline PK polymorphisms was further confirmed through a modeling approach. The Pop-PK profiles of sertraline were explained by the basic structure of sequential 2-absorption with 1-compartment, and in terms of inter-individual PK diversity, the volume of distribution could be significantly correlated with estimated glomerular filtration rate (eGFR) and clearance with total protein levels. CYP2D6∗10 allele was not significant in interpreting sertraline PK diversity. As a result of model simulation, the concentration of sertraline in serum significantly increased as total protein and eGFR levels became higher and lower, respectively. The mean serum concentrations of sertraline at steady-state differed by up to 2.12 times from 10.36 to 22.02 ng/mL depending on changes in total protein and eGFR levels, and the fluctuations between the maximum and minimum concentration values ranged from 2.02 to 29.51 to 4.31-63.78 ng/mL. The factor that significantly influenced change in mean serum concentration of sertraline at steady-state was the total protein level, which was interpreted to be closely related to the change in clearance due to the high serum protein binding of sertraline.

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.

Population pharmacokinetic approach to guide personalized sertraline treatment in Chinese patients

Object: Sertraline is a first-line SSRI for the treatment of depression and has the same effectiveness along with a superior safety profile compared to other medications. There are few population pharmacokinetic (PPK) studies of sertraline and a lack of studies in the Chinese population. Therefore, we performed a PPK analysis of Chinese patients treated with sertraline to identify factors that can influence drug exposure. In addition, the dosing and discontinuation regimen of sertraline when applied to adolescents was explored. Methods: Sertraline serum drug concentration data were collected from 140 hospitalized patients to generate a sertraline PPK dataset, and data evaluation and examination of the effects of covariates on drug exposure in the final model were performed using nonlinear mixed-effects models (NONMEM) and first-order conditional estimation with interaction (FOCE-I). Examining rational medication administration and rational withdrawal of sertraline based on significant covariates and final modeling. Results: A one-compartment model with first-order absorption and elimination of sertraline was developed for Chinese patients with psychiatric disorders. Analysis of covariates revealed that age was a covariate that significantly affected sertraline CL/F (P < 0.01) and that sertraline clearance decreased progressively with aging, whereas other factors had no effect on CL/F and V/F of sertraline. In the age range of 11-79, there were 54 adolescent patients (about 1/3) aged 13-18 years, and the safe and effective optimal daily dose for adolescent patients based on the final model simulations was 50-250 mg/d. For adolescent patients, serum concentration fluctuations were moderate for OD doses of 50 mg and 100 mg, using a fixed dose-descent regimen. For patients with OD doses of 150-200 mg and BID doses of 100-200 mg, a more gradual decrease in serum concentration was achieved with a fixed dose interval of 7 or 14 days for 25 mg as the regimen of descent. Conclusions: To our knowledge, this may be the first PPK study of sertraline in Chinese patients. We found that age was an important factor affecting clearance in Chinese patients taking sertraline. Patients taking sertraline may be exposed to increased amounts of sertraline due to decreased clearance with increasing age. The rational dosing and safe discontinuation of sertraline in adolescent patients can be appropriately referenced to the results of the model simulation, thus providing assistance for individualized dosing in adolescents.

Pharmacogenetics in Child and Adolescent Psychiatry: Background and Evidence-Based Clinical Applications

The efficacy and tolerability of psychotropic medications can vary significantly among children and adolescents, and some of this variability relates to pharmacogenetic factors. Pharmacogenetics (PGx) in child and adolescent psychiatry can potentially improve treatment outcomes and minimize adverse drug reactions. This article reviews key pharmacokinetic and pharmacodynamic genes and principles of pharmacogenetic testing and discusses the evidence base for clinical decision-making concerning PGx testing. This article reviews current guidelines from the United States Food and Drug Administration (FDA), the Clinical Pharmacogenetics Implementation Consortium (CPIC), and the Dutch Pharmacogenetics Working Group (DPWG) and explores potential future directions. This review discusses key clinical considerations for clinicians prescribing psychotropic medications in children and adolescents, focusing on antidepressants, antipsychotics, stimulants, norepinephrine reuptake inhibitors, and alpha-2 agonists. Finally, this review synthesizes the practical use of pharmacogenetic testing and clinical decision support systems.

Artificial intelligence-based model for dose prediction of sertraline in adolescents: a real-world study

BACKGROUND

Variability exists in sertraline pharmacokinetic parameters in individuals, especially obvious in adolescents. We aimed to establish an individualized dosing model of sertraline for adolescents with depression based on artificial intelligence (AI) techniques.

METHODS

Data were collected from 258 adolescent patients treated at the First Hospital of Hebei Medical University between December 2019 to July 2022. Nine different algorithms were used for modeling to compare the prediction abilities on sertraline daily dose, including XGBoost, LGBM, CatBoost, GBDT, SVM, ANN, TabNet, KNN, and DT. Performance of four dose subgroups (50 mg, 100 mg, 150 mg, and 200 mg) were analyzed.

RESULTS

CatBoost was chosen to establish the individualized medication model with the best performance. Six important variables were found to be correlated with sertraline dose, including plasma concentration, PLT, MPV, GL, A/G, and LDH. The ROC curve and confusion matrix exhibited the good prediction performance of CatBoost model in four dose subgroups (the AUC of 50 mg, 100 mg, 150 mg, and 200 mg were 0.93, 0.81, 0.93, and 0.93, respectively).

CONCLUSION

The AI-based dose prediction model of sertraline in adolescents with depression had a good prediction ability, which provides guidance for clinicians to propose the optimal medication regimen.