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

Aim Our objectives were to perform a population pharmacokinetic analysis of dexmedetomidine in children using remnant specimens and data from 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 modeling. The first stage developed a model without genotype variables; the second stage added pharmacogenetic effects. Results Our final study population included 354 post-cardiac surgery patients age 0 to 22 years (median 16 months). The final two-compartment model included allometric weight scaling and age maturation. Population parameter estimates and 95% confidence intervals were 27.3 L/hr (24.0 – 31.1 L/hr) for total clearance (CL), 161 L (139 – 187 L) for central compartment volume of distribution (V 1 ), 26.0 L/hr (22.5 – 30.0 L/hr) for intercompartmental clearance (Q), and 7903 L (5617 – 11119 L) for peripheral compartment volume of distribution (V 2 ). 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 PK analysis and investigate covariate effects in a large pediatric 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. What is already known about this subject ∘ Previous dexmedetomidine pharmacokinetic (PK) studies in pediatric populations have limited sample size. ∘ Smaller studies present a challenge for identifying covariates that may impact individual PK profiles. What this study adds ∘ We performed a dexmedetomidine population PK study with a large pediatric cohort using data obtained from electronic health records and remnant plasma specimens to enable increased sample size. ∘ xsDifferences in PK due to UGT1A4 or UGT2B10 variants or CYP2A6 risk score are not clinically impactful for this population.