Population Pharmacokinetic Analysis of Dexmedetomidine in Children using Real World Data from Electronic Health Records and Remnant Specimens

Abstract

Dexmedetomidine is commonly used as part of intraoperative anesthetic management and for sedation and pain control after surgery in children. Dexmedetomidine infusion dose is typically given on a fixed weight basis with titration to achieve sedation goals while avoiding potential toxicities. Pharmacokinetic (PK) studies are useful for accurate prediction of the individual dose required to achieve sedation and analgesia goals without toxicity, but lack of PK data is a challenge in precision dosing for pediatric populations. In this study, population PK models were developed using a nonlinear mixed-effects modeling approach and used to explore the relationship between PK profile and clinical, demographic, and genotype covariates. A simulation study was used to demonstrate the impact of important covariates on concentration using a fixed weight dosing scheme. Our final study population included data from 354 patients age 0 to 22 years (median age 16 months). In the final two-compartment model with fixed allometric weight scaling we found significant effects of both age and UGT2B10 genotype. The population PK parameter estimates (95% confidence interval) for a standard 70 kg weight were clearance 22.3 (18.3 - 27.3) L/hr, central compartment volume of distribution 133 (112 - 157) L, intercompartmental clearance 24.1 (19.4 - 29.9) L/hr, peripheral compartment volume of distribution 5230 (3310 - 8260) L. Our study provides support for the feasibility of using real-world data obtained from EHRs and remnant samples to perform population PK analysis for groups of patients where traditional PK studies are challenging to perform. Inclusion of UGT2B10 genotype in the model significantly improved the model fit, but the effects were not large enough to impact clinical dosing.

Dexmedetomidine in Children on Extracorporeal Membrane Oxygenation: Pharmacokinetic Data Exploration Using Previously Published Models

“… Observed concentrations vs. population predicted concentrations using previously published pharmacokinetics models in children. Observed concentrations vs. population predicted concentrations using population pharmacokinetics models developed by (1) Potts et al ( 12 ), (2) van Dijkman et al ( 13 ), (3) Greenberg et al ( 14 ), (4) Su et al ( 15 ), (5) Damian et al ( 16 ), and (6) James et al ( 17 ). …”

“…We therefore decided to determine whether models that were published in the literature in children who were not treated with ECMO were able to predict the data that we derived from our study. To this end, we identified six population PK models of intravenous dexmedetomidine (Table 1) (12)(13)(14)(15)(16)(17) and used external validation methods to compare our observed concentrations in children on ECMO with predicted concentrations using each model and the dosing information for each subject. The models were selected because they were developed in a population including infants <1 year old, which represent most of our population, and were described in sufficient detail in the original manuscript to be reproducible.…”

CYP2A6 and GABRA2 Gene Polymorphisms are Associated With Dexmedetomidine Drug Response

“…Nineteen UGT proteins have been identified in humans, and they are divided into two families, UGT1 and UGT2, and UGT2 is subdivided into two subfamilies UGT2A and UGT2B ( Nakamura et al, 2008 ). James et al indicated that the UGT1A4 or UGT2B10 genotype does not affect the pharmacokinetics of dexmedetomidine in children ( James et al, 2021 ). Similarly, SNPs in the UGT candidate genes screened in our study were not associated with pharmacokinetic parameters or drug effects of dexmedetomidine in adult female patients.…”

Patient-Specific Factors Associated with Dexmedetomidine Dose Requirements in Critically Ill Children

“…Higher dose needs in younger patients (<1 year) have been reported 15 and pediatric pharmacokinetic models of dexmedetomidine have demonstrated age-related changes. 9,[56][57][58] Though the understanding of the effect of age on relevant pharmacokinetic and pharmacodynamic pathways remains incomplete, maturation of metabolic pathways, receptor expression, and receptor functionality have been postulated to contribute. 56 In contrast to age, few pharmacokinetic studies have evaluated albumin as a covariate.…”