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

Tini, E.; Smigielski, Lukasz; Romanos, Marcel; Wewetzer, Christoph; Karwautz, Andreas; Reitzle, Karl; Correll, Christoph U.; Plener, Paul L.; Malzahn, Uwe; Heuschmann, Peter U.; Unterecker, Stefan; Scherf‐Clavel, Maike; Rock, Hans; Antony, Gisela; Briegel, Wolfgang; Fleischhaker, Christian; Banaschewski, Tobias; Hellenschmidt, Tobias; Imgart, Hartmut; Kaess, Michael; Kölch, Michael; Renner, Tobias; Reuter-Dang, Su-Yin; Rexroth, Christian A.; Schulte‐Körne, Gerd; Theisen, Frank; Fekete, Stefanie; Taurines, Regina; Gerlach, Manfred; Egberts, Karin; Walitza, Susanne

doi:10.1016/j.comppsych.2022.152301

Cited by 21 publications

(18 citation statements)

References 94 publications

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“…16 For instance, therapeutic drug monitoring and model-informed precision dosing can help optimize dosing to balance the efficacy-tolerability tradeoff, and these strategies could benefit SSRI-treated patients. [17][18][19][20] Recent studies have suggested that using artificial intelligence in tandem with pharmacometric approaches can improve patient outcomes. 21 Indeed, machine learning methodologies can process high dimensional data and have demonstrated good performance in predicting treatment outcomes, including antidepressant response.…”

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confidence: 99%

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

Poweleit,

Vaughn,

Desta

et al. 2024

Clin Pharma and Therapeutics

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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.

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confidence: 99%

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

Poweleit,

Vaughn,

Desta

et al. 2024

Clin Pharma and Therapeutics

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“…Os fatores fisiológicos são o metabolismo acelerado desses indivíduos quando comparado a outras faixas etárias, a maior reabsorção intestinal, maior filtração glomerular, mudanças importantes no peso devido ao crescimento constante e as mudanças hormonais presentes na puberdade. Portanto, é crucial para o tratamento do Transtorno de Humor Depressivo Maior em crianças, que o profissional tenha claro que esses fatores podem alterar a eficácia da medicação e, devido a isso, levar em considerações tais diferenças fisiológicas (TINI et al, 2022).…”

Section: Fatores Que Podem Alterar a Efetividade Dos Antidepressivosunclassified

“…Outra comorbidade tratada com antidepressivos seria o Transtorno Obsessivo Compulsivo (TOC), que tem como droga de primeira escolha em crianças a Sertralina, um ISRS's, que também pode ser usado efetivamente para a depressão e ansiedade, quando comparado com o placebo. A diferença para o tratamento nesses casos está na dose da medicação, sendo uma dose relativamente maior para o tratamento do TOC (TINI et al, 2022).…”

Section: Efetividade Clínicaunclassified

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A Eficácia E a Segurança Dos Antidepressivos Em Crianças E Adolescentes Na Prática Clínica: Uma Revisão De Literatura

MARTINS¹,

FERREIRA²,

MORESI³

et al. 2022

Ciências Farmacêuticas - Edição II

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“…Therapeutic drug monitoring (TDM) is the clinical practice of measuring drug exposure at designated intervals to tailor drug doses, thereby optimizing outcomes in individual patients ( 4 ). The past years have witnessed great progress in TDM in the field of psychotropic drugs ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

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

Song

Jiang

et al. 2023

Front. Psychiatry

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BackgroundWithin 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 methodsPubMed, 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).ResultsEleven 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.ConclusionsA 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.

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

Cited by 21 publications

References 94 publications

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

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

A Eficácia E a Segurança Dos Antidepressivos Em Crianças E Adolescentes Na Prática Clínica: Uma Revisão De Literatura

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

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