Population pharmacokinetic analysis of dexmedetomidine in children using real‐world data from electronic health records and remnant specimens

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.

Variant‐based heritability assessment of dexmedetomidine and fentanyl clearance in pediatric patients

“…In this study, we sought to explore the genomic contribution to variation among pediatric patients in clearance of two drugs commonly used after cardiac surgery: dexmedetomidine and fentanyl. Our data verify the clinical observation of large inter‐individual variability in drug clearance, as our pharmacokinetic analyses demonstrated an approximately two‐fold difference between the first and third quartile of estimated dexmedetomidine clearance and an almost four‐fold difference for fentanyl clearance, consistent with previous estimates 16,17 . We used the clearance data, coupled with genome‐wide genotype data, to employ a Bayesian hierarchical modeling methodology.…”

“…Characteristics of the dexmedetomidine and fentanyl cohorts are shown in Table 1 and summaries of surgical procedures incurred by participants in each cohort are shown in Table S1. The dexmedetomidine cohort included 354 participants, as described previously 16 . Due to sensitivity of the BayesR method to population substructure, 70 individuals were removed due to genotypes consistent with non‐European ancestry, classified as individuals with PCs outside three standard deviations of the mean PC1 or PC2 of self‐reported white participants (Figure S1) and one individual was removed due to high relatedness.…”

“…The primary outcomes used for analysis were the clearances estimated per individual for each of the two target drugs, as previously described 16,17 . Briefly, dexmedetomidine and fentanyl dosing data were extracted from the EHR and drug concentrations were measured in remnant plasma specimens collected for research purposes using high‐throughput tandem mass spectrometry.…”

“…For dexmedetomidine, the following covariates were considered in the population pharmacokinetic modeling based on previous research and biologic plausibility: UGT1A4 , UGT2B10 , and CYP2A6 variants, body weight, postnatal age, postmenstrual age, sex, Society of Thoracic Surgeons‐European Association for Cardio‐Thoracic Surgery (STAT) Congenital Heart Surgery Mortality score, cardiac bypass time, length of ICU stay, and serum creatinine. Of these, only body weight and postmenstrual age significantly improved model fit and remained in the final covariate model 16 …”

“…We previously demonstrated validity of using one Bayesian method to explore pharmacologic outcomes, even when applied to small sample sizes, 23 and GWAS approaches have successfully detected pharmacogenomic signals despite small cohorts in a few examples where effect size of the variant(s) is very large, 24,25 including the NUDT15 association to thiopurine toxicity, as described above. 1,4 In this study, we used the results of previously performed population pharmacokinetic analyses 16,17 and deployed polygenic approaches to assess genomic contribution to dexmedetomidine and fentanyl clearance in children after cardiac surgery. We used BayesR, [26][27][28] an established Bayesian hierarchical analysis method, to estimate h 2 SNP of dexmedetomidine and fentanyl clearance and assess the relative contribution of small-, moderate-, and large-effect variants.…”