Therapeutic drug monitoring of sertraline in children and adolescents: A naturalistic study with insights into the clinical response and treatment of obsessive-compulsive disorder

Serum Concentration-Dose Relationship and Modulation Factors in Children and Adolescents Treated with Fluvoxamine

INTRODUCTION

Fluvoxamine is used in children and adolescents ('youths') for treating obsessive compulsive disorder (OCD) but also off-label for depressive and anxiety disorders. This study aimed to investigate the relationship between fluvoxamine dose and serum concentrations, independent correlates of fluvoxamine concentrations, and a preliminary therapeutic reference range (TRR) for youths with OCD and treatment response.

METHODS

Multicenter naturalistic data of a therapeutic drug monitoring service, as well as prospective data of the 'TDM Vigil study' (EudraCT 2013-004881-33), were analyzed. Patient and treatment characteristics were assessed by standardized measures, including Clinical Global Impressions-Severity (CGI-S) and -Change (CGI-I), with CGI-I of much or very much improved defining treatment response and adverse drug reactions using the Udvalg for Kliniske Undersogelser (UKU) Side Effect Rating Scale. Multivariable regression analysis was used to evaluate the influence of sex, age, body weight, body mass index (BMI), and fluvoxamine dose on fluvoxamine serum concentrations.

RESULTS

The study included 70 youths (age = 6.7-19.6 years, OCD = 78%, mean fluvoxamine dose = 140.4 (range = 25-300) mg/d). A weak positive correlation between daily dose and steady-state trough serum concentrations was found (rs = 0.34, p = 0.004), with dose variation explaining 16.2% of serum concentration variability. Multivariable correlates explaining 25.3% of the variance of fluvoxamine concentrations included higher fluvoxamine dose and lower BMI. Considering responders with OCD, the estimated TRR for youths was 55-371 ng/mL, exceeding the TRR for adults with depression of 60-230 ng/mL.

DISCUSSION

These preliminary data contribute to the definition of a TRR in youth with OCD treated with fluvoxamine and identify higher BMI as a moderator of lower fluvoxamine concentrations.

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.

Machine learning-based prediction of sertraline concentration in patients with depression through therapeutic drug monitoring

Background: Sertraline is a commonly employed antidepressant in clinical practice. In order to control the plasma concentration of sertraline within the therapeutic window to achieve the best effect and avoid adverse reactions, a personalized model to predict sertraline concentration is necessary. Aims: This study aimed to establish a personalized medication model for patients with depression receiving sertraline based on machine learning to provide a reference for clinicians to formulate drug regimens. Methods: A total of 415 patients with 496 samples of sertraline concentration from December 2019 to July 2022 at the First Hospital of Hebei Medical University were collected as the dataset. Nine different algorithms, namely, XGBoost, LightGBM, CatBoost, random forest, GBDT, SVM, lasso regression, ANN, and TabNet, were used for modeling to compare the model abilities to predict sertraline concentration. Results: XGBoost was chosen to establish the personalized medication model with the best performance (R 2 = 0.63). Five important variables, namely, sertraline dose, alanine transaminase, aspartate transaminase, uric acid, and sex, were shown to be correlated with sertraline concentration. The model prediction accuracy of sertraline concentration in the therapeutic window was 62.5%. Conclusion: In conclusion, the personalized medication model of sertraline for patients with depression based on XGBoost had good predictive ability, which provides guidance for clinicians in proposing an optimal medication regimen.

Psychopharmacology in children and adolescents: unmet needs and opportunities

Psychopharmacological treatment is an important component of the multimodal intervention approach to treating mental health conditions in children and adolescents. Currently, there are many unmet needs but also opportunities, alongside possible risks to consider, regarding the pharmacological treatment of mental health conditions in children and adolescents. In this Position Paper, we highlight and address these unmet needs and opportunities, including the perspectives of clinicians and researchers from the European College of Neuropsychopharmacology-Child and Adolescent Network, alongside those of experts by lived experience from national and international associations, via a survey involving 644 participants from 13 countries, and of regulators, through representation from the European Medicines Agency. We present and discuss the evidence base for medications currently used for mental disorders in children and adolescents, medications in the pipeline, opportunities in the development of novel medications, crucial priorities for the conduct of future clinical studies, challenges and opportunities in terms of the regulatory and legislative framework, and innovations in the way research is conducted, reported, and promoted.

The prevalence of off-label use and supratherapeutic blood levels of outpatient psychotropic medication in suicidal adolescents

Introduction

Adolescents with mental disorders show an increased risk of suicidal phenomena. Vice versa, suicidality is a serious adverse event of psychotropic drug therapy in adolescents. There are only a few new psychotropic agents approved for this young age group. We evaluated the (pre-pandemic) prevalence of off-label use as well as detailed blood concentrations of outpatient psychotropic medication and sex differences in a clinical population of suicidal adolescents.

Methods

The urine presence and serum levels of psychotropic substances of adolescents hospitalized due to their acute suicidality but without a known actual suicide attempt (i.e., no acute intoxication or serious self-injuries) were investigated routinely between 01.03.2017 and 31.01.2018. Urine (N = 205) and blood samples (N = 193) were taken at the beginning of closed inpatient admission, i.e., the results of the laboratory analysis reflect outpatient drug intake. The serum levels of psychopharmacological medication and OTC medication were measured.

Results

Our sample consists of 231 cases (boys: N = 54; girls: N = 177, ratio: 1:3.3), aged 12-17 years (average age: 15,4 years). The most prevalent psychiatric diagnoses were depressive episodes (54%) and adjustment disorders (25%), and girls were more often diagnosed with depressive disorders than boys (boy/girl ratio: 1:9.5, p < 0.0001). More than half of adolescents (56%) used at least one prescribed psychotropic drug at admission (24.8% ≥ two psychotropic drugs). Off-label use of second-generation antipsychotics was significantly more frequent than off-label use of antidepressants (85% vs. 31%, p < 0.01). Adolescents suffering from depressive disorders were significantly more often on-label treated than adolescents with neurotic or stress-related disorders (56% vs. 10%). Female cases with prescribed psychotropic drug use showed significantly more frequent supratherapeutic drug levels than male cases (5% vs. 27%, p < 0.05).

Conclusion

Female adolescents may have an increased risk of supratherapeutic blood levels, especially when outpatient prescribed psychotropic drugs are off-label used. Measurement of blood levels of outpatient-prescribed psychotropic drugs could be used to enhance the safety and efficacy of the individual psychopharmacological treatment of adolescent suicidal patients. There is an urgent need for more real-world evidence on the effective treatment of adolescents with psychotropic drugs.

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.

[Pharmacogenomic and pharmacometabolomic biomarkers of the efficacy and safety of antidepressants: focus on selective serotonin reuptake inhibitors]

The efficacy and safety of psychopharmacotherapy with antidepressants is of great medical importance. The search for clinical and biological predictors for choosing the optimal psychopharmacotherapy with antidepressants is actively underway all over the world. Research is mainly devoted to searching for associations of polymorphic gene variants with the efficacy and safety of therapy. However, information about a patient's genetic polymorphism is often insufficient to predict the efficacy and safety of a drug. Modern research on the personalization of pharmacotherapy should include, in addition to genetic, phenotypic biomarkers. This is important because genotyping, for example, cannot accurately predict the actual metabolic activity of an isoenzyme. To personalize therapy, a combination of methods is required to obtain the most complete profile of the efficacy and safety of the drug. Successful treatment of depression remains a challenge, and inter-individual differences in response to antidepressants are common. About half of patients with depressive disorders do not respond to the first attempt at antidepressant therapy. Serious side-effects of antidepressant pharmacotherapy and discontinuation of treatment due to their intolerance are associated with ineffective therapy. This review presents the results of the latest studies of «omics» biomarkers of the efficacy and safety of antidepressants.

Therapeutic Drug Monitoring in Children and Adolescents: Findings on Fluoxetine from the TDM-VIGIL Trial

Fluoxetine is the recommended first-line antidepressant in many therapeutic guidelines for children and adolescents. However, little is known about the relationships between drug dose and serum level as well as the therapeutic serum reference range in this age group. Within a large naturalistic observational prospective multicenter clinical trial ("TDM-VIGIL"), a transdiagnostic sample of children and adolescents (n = 138; mean age, 15; range, 7-18 years; 24.6% males) was treated with fluoxetine (10-40 mg/day). Analyses of both the last timepoint and all timepoints (n = 292 observations), utilizing (multiple) linear regressions, linear mixed-effect models, and cumulative link (mixed) models, were used to test the associations between dose, serum concentration, outcome, and potential predictors. The receiver operating curve and first to third interquartile methods, respectively, were used to examine concentration cutoff and reference values for responders. A strong positive relationship was found between dose and serum concentration of fluoxetine and its metabolite. Higher body weight was associated with lower serum concentrations, and female sex was associated with lower therapeutic response. The preliminary reference ranges for the active moiety (fluoxetine+norfluoxetine) were 208-328 ng/mL (transdiagnostically) and 201.5-306 ng/mL (depression). Most patients showed marked (45.6%) or minimal (43.5%) improvements and reported no adverse effects (64.9%). This study demonstrated a clear linear dose-serum level relationship for fluoxetine in youth, with the identified reference range being within that established for adults.

Treatment in Children and Adolescents With Obsessive-Compulsive Disorder: Review for Practitioners

Obsessive-compulsive disorder (OCD) is one of the most debilitating mental health conditions, interrupting functioning at school and social well-being in children and adolescents. Youth tend to delay interventions and when sought, response to treatment might not be optimal. The current article discusses treatment guidelines for youth with OCD and pediatric autoimmune neuropsychiatric symptoms. [Journal of Psychosocial Nursing and Mental Health Services, 61(8), 11-15.].

Toward therapeutic drug monitoring of citalopram in depression? Insights from a systematic review

Background

Within the framework of individualized psychopharmacotherapy, therapeutic drug monitoring (TDM) has gained increasing relevance. In the absence of high-quality evidence, the TDM of citalopram (CIT) and the recommended therapeutic ranges of the plasma concentrations have been proposed by guidelines. However, the correlation between the plasma concentration of CIT and treatment outcomes has not been well established. Therefore, the aim of this systematic review was to evaluate the relationship between plasma CIT concentration and treatment outcomes in depression.

Research design and methods

PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and Chinese databases (CNKI, Wanfang Data and Sinomed) were searched up to August 6, 2022. We included clinical studies evaluating the correlation between the plasma CIT concentration and treatment outcomes in patients with depression receiving CIT treatment. Outcomes measured included efficacy, safety, medication adherence, and cost-related outcomes. A narrative synthesis was performed to summarize findings from individual studies. This study was performed according to the Preferred Reporting Items for Systematic Reviews, Meta-Analysis (PRISMA) and the reporting guideline for Synthesis without meta-analysis (SWiM).

Results

Eleven studies involving 538 patients were included in total. The reported outcomes were mainly efficacy (n = 11) and safety (n = 3); one study reported the duration of hospitalization, and no study reported medication adherence. Regarding the efficacy outcomes, three studies revealed the plasma CIT concentration-response relationship and proposed a lower limit of 50 or 53 ng/mL, whereas this was not found in the rest of the studies. Regarding adverse drug events (ADEs), one study reported more ADEs in the low-concentration group (<50 ng/mL vs. >50 ng/mL), which is not convincing from the perspective of pharmacokinetics/pharmacodynamics. Regarding the cost-related outcomes, only one study reported that the high CIT concentration group (≥50 ng/mL) contributed to shortening the hospitalization duration, but it did not provide detailed information, including direct medical expenses and multiple potential factors contributing to longer hospital stays.

Conclusions

A definite correlation between plasma concentration and clinical or cost-related outcomes of CIT cannot be drawn, whereas a tendency toward improved efficacy in patients with plasma concentration above 50 or 53 ng/mL was suggestive from limited evidence.

Current Status of Therapeutic Drug Monitoring in Mental Health Treatment: A Review

Therapeutic drug monitoring (TDM) receives growing interest in different psychiatric clinical settings (emergency, inpatient, and outpatient services). Despite its usefulness, TDM remains underemployed in mental health. This is partly due to the need for evidence about the relationship between drug serum concentration and efficacy and tolerability, both in the general population and even more in subpopulations with atypical pharmacokinetics. This work aims at reviewing the scientific literature published after 2017, when the most recent guidelines about the use of TDM in mental health were written. We found 164 pertinent records that we included in the review. Some promising studies highlighted the possibility of correlating early drug serum concentration and clinical efficacy and safety, especially for antipsychotics, potentially enabling clinicians to make decisions on early laboratory findings and not proceeding by trial and error. About populations with pharmacokinetic peculiarities, the latest studies confirmed very common alterations in drug blood levels in pregnant women, generally with a progressive decrease over pregnancy and a very relevant dose-adjusted concentration increase in the elderly. For adolescents also, several drugs result in having different dose-related concentration values compared to adults. These findings stress the recommendation to use TDM in these populations to ensure a safe and effective treatment. Moreover, the integration of TDM with pharmacogenetic analyses may allow clinicians to adopt precise treatments, addressing therapy on an individual pharmacometabolic basis. Mini-invasive TDM procedures that may be easily performed at home or in a point-of-care are very promising and may represent a turning point toward an extensive real-world TDM application. Although the highlighted recent evidence, research efforts have to be carried on: further studies, especially prospective and fixed-dose, are needed to replicate present findings and provide clearer knowledge on relationships between dose, serum concentration, and efficacy/safety.