Using Real‐World Data to Externally Evaluate Population Pharmacokinetic Models of Dexmedetomidine in Children and Infants

Author:

McCann Sean1,Helfer Victória E.1ORCID,Balevic Stephen J.23ORCID,Hornik Chi D.23,Goldstein Stuart L.4,Autmizguine Julie5,Meyer Marisa6,Al‐Uzri Amira7,Anderson Sarah G.8,Payne Elizabeth H.8,Turdalieva Sitora8,Gonzalez Daniel39,

Affiliation:

1. Division of Pharmacotherapy and Experimental Therapeutics UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill Chapel Hill NC USA

2. Department of Pediatrics Duke University Medical Center Durham NC USA

3. Duke Clinical Research Institute Durham NC USA

4. Cincinnati Children's Hospital Medical Center Cincinnati OH USA

5. Department of Pediatrics Center Hospitalier Universitaire Sainte‐Justine Montreal Quebec Canada

6. Critical Care Medicine Nemours Children's Hospital, Delaware Wilmington DE USA

7. Oregon Health and Science University Portland OR USA

8. The Emmes Company, LLC Rockville MD USA

9. Division of Clinical Pharmacology Department of Medicine Duke University School of Medicine Durham NC USA

Abstract

AbstractDexmedetomidine is a sedative used in both adults and off‐label in children with considerable reported pharmacokinetic (PK) interindividual variability affecting drug exposure across populations. Several published models describe the population PKs of dexmedetomidine in neonates, infants, children, and adolescents, though very few have been externally evaluated. A prospective PK dataset of dexmedetomidine plasma concentrations in children and young adults aged 0.01‐19.9 years was collected as part of a multicenter opportunistic PK study. A PubMed search of studies reporting dexmedetomidine PK identified five population PK models developed with data from demographically similar children that were selected for external validation. A total of 168 plasma concentrations from 102 children were compared with both population (PRED) and individualized (IPRED) predicted values from each of the five published models by quantitative and visual analyses using NONMEM (v7.3) and R (v4.1.3). Mean percent prediction errors from observed values ranged from −1% to 120% for PRED, and −24% to 60% for IPRED. The model by James et al, which was developed using similar “real‐world” data, nearly met the generalizability criteria from IPRED predictions. Other models developed using clinical trial data may have been limited by inclusion/exclusion criteria and a less racially diverse population than this study's opportunistic dataset. The James model may represent a useful, but limited tool for model‐informed dosing of hospitalized children.

Funder

National Institute of Child Health and Human Development

University of North Carolina

GlaxoSmithKline

Publisher

Wiley

Reference47 articles.

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