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 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. Stage one developed a model without genotype variables; Stage two added pharmacogenetic effects.

RESULTS

Our final study population included 354 post-cardiac surgery patients age 0 to 22 years (median 16 months). The data were best described with a two-compartment model with allometric scaling for weight and Hill maturation function for age. 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 (V₁ ), 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 (V₂ ). 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.

Pragmatic pharmacology: population pharmacokinetic analysis of fentanyl using remnant samples from children after cardiac surgery

AIMS

One barrier contributing to the lack of pharmacokinetic (PK) data in paediatric populations is the need for serial sampling. Analysis of clinically obtained specimens and data may overcome this barrier. To add evidence for the feasibility of this approach, we sought to determine PK parameters for fentanyl in children after cardiac surgery using specimens and data generated in the course of clinical care, without collecting additional blood samples.

METHODS

We measured fentanyl concentrations in plasma from leftover clinically-obtained specimens in 130 paediatric cardiac surgery patients and successfully generated a PK dataset using drug dosing data extracted from electronic medical records. Using a population PK approach, we estimated PK parameters for this population, assessed model goodness-of-fit and internal model validation, and performed subset data analyses. Through simulation studies, we compared predicted fentanyl concentrations using model-driven weight-adjusted per kg vs. fixed per kg fentanyl dosing.

RESULTS

Fentanyl clearance for a 6.4 kg child, the median weight in our cohort, is 5.7 l h(-1) (2.2-9.2 l h(-1) ), similar to values found in prior formal PK studies. Model assessment and subset analyses indicated the model adequately fit the data. Of the covariates studied, only weight significantly impacted fentanyl kinetics, but substantial inter-individual variability remained. In simulation studies, model-driven weight-adjusted per kg fentanyl dosing led to more consistent therapeutic fentanyl concentrations than fixed per kg dosing.

CONCLUSIONS

We show here that population PK modelling using sparse remnant samples and electronic medical records data provides a powerful tool for assessment of drug kinetics and generation of individualized dosing regimens.

An LC-MS assay for the screening of cardiovascular medications in human samples

Cardiovascular drugs are the most commonly prescribed medications. Some prior assays successfully detect cardiovascular drugs among multiple classes using a single sample. Here, we develop an assay to detect a broad range of cardiovascular drug classes to include commonly used cardiovascular drugs and evaluate the assay's analytical and statistical properties in a clinical setting. We describe a protocol for drug detection that encompasses 34 commonly prescribed cardiovascular drugs or drug metabolites with a single LC-MS/MS method using 100μL of serum or plasma. Drug classes monitored by this assay include: anticoagulants, angiotensin converting enzyme inhibitors (ACEI), angiotensin II receptor blockers (ARB), beta blockers, calcium channel blockers, diuretics, statins, and vasodilators, as well as digoxin, fenofibrate, and niacin. Analytical accuracy and precision for each drug were evaluated by repeating the assay on spiked samples at low, medium, and high concentrations. In 294 clinical samples obtained from hospitalized patients for whom medication administration was recorded, we evaluated the assay's statistical sensitivity, specificity, and accuracy. For the 34 drugs or drug metabolites, the assay was statistically sensitive (>0.90) for all drugs except captopril (0.25), isosorbide (0.81), and niacin (0.89). The assay was statistically specific for all drugs, with a minimum specificity of 0.94 (aspirin). To our knowledge, this method is the first method of simultaneous analysis of 34 cardiovascular drugs or drug metabolites from nine drug classes evaluated using clinical samples from hospitalized patients.