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Population pharmacokinetic analysis of dexmedetomidine in children using real-world data from electronic health records and remnant specimens.
James, Nathan T; Breeyear, Joseph H; Caprioli, Richard; Edwards, Todd; Hachey, Brian; Kannankeril, Prince J; Keaton, Jacob M; Marshall, Matthew D; Van Driest, Sara L; Choi, Leena.
Affiliation
  • James NT; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Breeyear JH; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Caprioli R; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Edwards T; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Hachey B; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Kannankeril PJ; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Keaton JM; Center for Pediatric Precision Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Marshall MD; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Van Driest SL; Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
  • Choi L; Department of Pharmaceutical Services, Vanderbilt University Medical Center, Nashville, TN, USA.
Br J Clin Pharmacol ; 88(6): 2885-2898, 2022 06.
Article in En | MEDLINE | ID: mdl-34957589
ABSTRACT

AIMS:

Our objectives were to perform a population pharmacokinetic analysis of dexmedetomidine in children using remnant specimens and electronic health records (EHRs) and explore the impact of patient's characteristics and pharmacogenetics on dexmedetomidine clearance.

METHODS:

Dexmedetomidine dosing and patient data were gathered from EHRs and combined with opportunistically sampled remnant specimens. Population pharmacokinetic models were developed using nonlinear mixed-effects modelling. Stage 1 developed a model without genotype variables; Stage 2 added pharmacogenetic effects.

RESULTS:

Our final study population included 354 post-cardiac surgery patients aged 0-22 years (median 16 mo). The data were best described with a 2-compartment model with allometric scaling for weight and Hill maturation function for age. Population parameter estimates and 95% confidence intervals were 27.3 L/h (24.0-31.1 L/h) for total clearance, 161 L (139-187 L) for central compartment volume of distribution, 26.0 L/h (22.5-30.0 L/h) for intercompartmental clearance and 7903 L (5617-11 119 L) for peripheral compartment volume of distribution. The estimate for postmenstrual age when 50% of adult clearance is achieved was 42.0 weeks (41.5-42.5 weeks) and the Hill coefficient estimate was 7.04 (6.99-7.08). Genotype was not statistically or clinically significant.

CONCLUSION:

Our study demonstrates the use of real-world EHR data and remnant specimens to perform a population pharmacokinetic analysis and investigate covariate effects in a large paediatric population. Weight and age were important predictors of clearance. We did not find evidence for pharmacogenetic effects of UGT1A4 or UGT2B10 genotype or CYP2A6 risk score.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Dexmedetomidine / Cardiac Surgical Procedures Type of study: Prognostic_studies Limits: Adult / Child / Humans Language: En Journal: Br J Clin Pharmacol Year: 2022 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Dexmedetomidine / Cardiac Surgical Procedures Type of study: Prognostic_studies Limits: Adult / Child / Humans Language: En Journal: Br J Clin Pharmacol Year: 2022 Document type: Article Affiliation country: United States