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84 Using Opportunistic Sampling and Remnant Blood Samples to Develop Pediatric Pharmacokinetic Models to Inform Antidepressant Dosing

Published online by Cambridge University Press:  03 April 2024

Jeffrey R. Strawn
Affiliation:
University of Cincinnati
Ethan A. Poweleit
Affiliation:
Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
Zachary L. Taylor
Affiliation:
Division of Clinical Pharmacology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
Tomoyuki Mizuno
Affiliation:
Division of Clinical Pharmacology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
Samuel Vaughn
Affiliation:
Division of Child and Adolescent Psychiatry, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
Zeruesenay Desta
Affiliation:
Division of Clinical Pharmacology, School of Medicine, Indiana University, Indianapolis, IN, USA
Stephani Stancil
Affiliation:
Division of Pediatric Clinical Pharmacology, Children’s Mercy Hospital & Clinics, USA
Laura B. Ramsey
Affiliation:
Division of Pediatric Clinical Pharmacology, Children’s Mercy Hospital & Clinics, USA
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Abstract

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OBJECTIVES/GOALS: Developing pharmacokinetic (PK) models to guide selective serotonin reuptake inhibitor (SSRI) dosing in youth is costly, time-intensive, and requires large numbers of participants. We evaluated the use of remnant blood samples from SSRI-treated youth and developed precision PK dosing strategies. METHODS/STUDY POPULATION: Following IRB approval, we used a clinical surveillance platform to identify patients with routine phlebotomy within 24 hours of escitalopram or sertraline dosing. Remnant blood samples were obtained from youth aged 5–18 years, escitalopram and sertraline concentrations were determined, and clinical characteristics (e.g., age, sex, weight, concomitant medications that inhibit sertraline or escitalopram metabolism) and phenotypes for CYP2C19, the predominant enzyme that metabolizes these SSRIs, were extracted from the electronic medical record (EMR). A population PK analysis of escitalopram and sertraline was performed using NONMEM. The influence of clinical variables, CYP2C19, and dosing was evaluated from simulated concentration-time curves. RESULTS/ANTICIPATED RESULTS: Over 21 months, we collected315 samples from escitalopram-treated patients (N=288) and 265 samples from sertraline-treated patients (N=255). In youth, escitalopram and sertraline exposure (concentrations over time) and specific pharmacokinetic parameters (e.g., clearance) were influenced by CYP2C19 phenotype, concomitant CYP2C19 inhibitors, and patient-specific characteristics. Escitalopram and sertraline concentrations from remnant blood samples were 3.98-fold higher and 3.23-fold higher, respectively, in poor metabolizers compared to normal metabolizers (escitalopram, p<0.001) and compared to normal, rapid, and ultrarapid metabolizers combined (sertraline, p<0.001). DISCUSSION/SIGNIFICANCE: Combining remnant blood sampling with pharmacogenetic-integrated EMR data can facilitate large-scale population PK analyses of escitalopram and sertraline in youth. This real-world approach can be used to rapidly develop precision SSRI dosing strategies, including slower titration and reduced target doses in CYP2C19 poor metabolizers.

Type
Contemporary Research Challenges
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2024. The Association for Clinical and Translational Science