Pharmacogenetic Testing and Therapeutic Drug Monitoring Of Sertraline at a Residential Treatment Center for Children and Adolescents: A Pilot Study

France, Katelyn; Ammend, David; Brown, Jacob L.

doi:10.24926/iip.v13i4.5035

Cited by 4 publications

(2 citation statements)

References 24 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%

“…Data‐driven and computational modeling‐based approaches can assist clinicians in treating children and adolescents with antidepressant medications by facilitating better understanding of the mechanisms behind and predictions of treatment outcomes for individual patients 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 17–20 . Recent studies have suggested that using artificial intelligence in tandem with pharmacometric approaches can improve patient outcomes 21 .…”

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confidence: 99%

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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%