| Literature DB >> 28105598 |
Maud A S Weerink1, Michel M R F Struys2,3, Laura N Hannivoort1, Clemens R M Barends1, Anthony R Absalom1, Pieter Colin1,4.
Abstract
Dexmedetomidine is an α2-adrenoceptor agonist with sedative, anxiolytic, sympatholytic, and analgesic-sparing effects, and minimal depression of respiratory function. It is potent and highly selective for α2-receptors with an α2:α1 ratio of 1620:1. Hemodynamic effects, which include transient hypertension, bradycardia, and hypotension, result from the drug's peripheral vasoconstrictive and sympatholytic properties. Dexmedetomidine exerts its hypnotic action through activation of central pre- and postsynaptic α2-receptors in the locus coeruleus, thereby inducting a state of unconsciousness similar to natural sleep, with the unique aspect that patients remain easily rousable and cooperative. Dexmedetomidine is rapidly distributed and is mainly hepatically metabolized into inactive metabolites by glucuronidation and hydroxylation. A high inter-individual variability in dexmedetomidine pharmacokinetics has been described, especially in the intensive care unit population. In recent years, multiple pharmacokinetic non-compartmental analyses as well as population pharmacokinetic studies have been performed. Body size, hepatic impairment, and presumably plasma albumin and cardiac output have a significant impact on dexmedetomidine pharmacokinetics. Results regarding other covariates remain inconclusive and warrant further research. Although initially approved for intravenous use for up to 24 h in the adult intensive care unit population only, applications of dexmedetomidine in clinical practice have been widened over the past few years. Procedural sedation with dexmedetomidine was additionally approved by the US Food and Drug Administration in 2003 and dexmedetomidine has appeared useful in multiple off-label applications such as pediatric sedation, intranasal or buccal administration, and use as an adjuvant to local analgesia techniques.Entities:
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Year: 2017 PMID: 28105598 PMCID: PMC5511603 DOI: 10.1007/s40262-017-0507-7
Source DB: PubMed Journal: Clin Pharmacokinet ISSN: 0312-5963 Impact factor: 6.447
Overview of published population pharmacokinetic dexmedetomidine (DMED) models in the adult population
| Study (year) | Population |
| Blood PK samples | Patient characteristics | Drug administration | Tested covariates | Covariate models | Remarks | |
|---|---|---|---|---|---|---|---|---|---|
| No. of samples | Last sample (time after termination of infusion) | Age/WGT/HGT average (range) | |||||||
| Dyck (1993) [ | Male HV | 10 + 6 | 14 a samples after different target plasma concentrations | 120 min | 31.5 years (27–40) | TCI (based on PK parameters from first 10 subjects) targeting 0.49, 0.65, 0.81, and 0.97 ng/mL | Age, WGT, HGT | 3-compartment model with HGT as a covariate on CL | Data were pooled and fitted using ELS non-linear regression; the authors suggest DMED-induced changes in SVR and CO, leading to a non-linearity in DMED PK (with higher CL at lower DMED targets) |
| Talke (1997) [ | Female postoperative patients | 8 | 14 a samples during ( | 180 min | 36 years (23–44) | TCI (based on a combination of previously published PK data) targeting 0.60 ng/mL for 60 min | Age, WGT, HGT | 2-compartment model with no significant influence of tested covariates | A general overshoot of the DMED target. This is likely owing to the concomitant intra-operative use of other anesthetics |
| Dutta (2000) [ | Male HV | 10 | 22 v samples during ( | 240 min | 24 years (20–27) | CCIP (based on an unpublished two-compartment PK model) targeting 7 different plasma DMED target concentrations, resulting in measured DMED concentrations from 0.7 to 14.7 ng/mL | CO | 2-compartment model with CO as covariate on CL | CO and DMED clearance were found to decrease with increasing DMED concentrations. An estimated reduction in CO and DMED CL of 19 and 12% was found at 1.2 vs. 0.3 ng/mL. The |
| Venn (2002) [ | Postoperative ICU patients | 10 | 25 a samples during ( | 720 min | 68 years (35–80) | 2.5 µg/kg/h for 10 min followed by a 0.7 µg/kg/h for 660 min (median) Average measured | 2-compartment model with no tested covariates reported | ||
| Lin (2011) [ | Chinese postoperative patients | 22 | 24 v samples during ( | 720 min | 46 years (22–69) | 6 µg/kg/h loading dose for 10 min followed by 0.4 µg/kg/h maintenance dose for 350 min. Highest measured DMED concentration is approximately 1.7 ng/mL | Age, WGT, HGT, sex, BSA, BMI, LBM | 3-compartment model with HGT as a covariate on CL | The authors hypothesize that the difference in |
| Iirola (2012) [ | ICU patients | 21 | a samples during loading dose ( | 0 min | 60 years (22–85) | 3–6 µg/kg/h for 10 min followed by 0.1–2.5 µg/kg/h for 96 h (median in study; range: 20–571). Highest measured DMED concentration is approximately 7 ng/mL | Age, WGT, HGT, sex, BMI, LBM | 2-compartment model with age as a covariate on CL and ALB on | Lack of identification of “third” compartment likely owing to limited availability of samples after termination of the DMED infusion Authors warn for potential confounding by the large number of concomitant drugs that were used throughout the study |
| Lee (2012) [ | Korean HV | 24 | 13 a/v samples during ( | 720 min | 27 years (median) | 3 µg/kg/h for 10 min followed by 0.17 µg/kg/h for 50 min | Age, WGT, HGT, serum creatinine, AST, ALT, ALB | 2-compartment model with ALB as a covariate on clearance and age on | Very similar to other HV data. Authors suggest that there is little evidence to support an ethnic difference in pharmacokinetics for DMED |
| Välitalo (2013) [ | Critically ill patients (3 phase III trials) | 527 | a/v samples taken during (every 24 h) and after ( | 48 h | 62 years | 0.7 µg/kg/h infusion for 1 h; afterwards titration to RASS 0 to −3 (dose levels ranging from 0.2 to 1.4 µg/kg/h) Average treatment duration: 2 days 14 h. Most measured DMED concentrations <5 ng/mL | Age, WGT, creatinine clearance, bilirubin, AST, ALT, ALB | 1-compartment model with weight as a covariate on clearance and ALB on | Most/all patients were mechanically ventilated. The analysis found no relationship between |
| Cortínez (2015) [ | Obese and non-obese laparoscopic surgery patients | 20 obese/20 non-obese | 21 v samples during ( | 360 min | 34/40 years | 0.5 µg/kg/h for 10 minutes followed by 0.25 µg/kg/h or 0.5 µg/kg/h | Age, WGT, FFM, normal fat mass, intra-operative | 2-compartment model with FFM as a covariate on clearance, | DMED was administered at the same time as propofol and remifentanil. According to the authors, TBW-based dosing is responsible for an overshoot in the obese. This is because of a lack of an effect of TBW on |
| Hannivoort (2015) [ | HV | 18 × 2 sessions | 14 a samples during ( | 300 min | 20–70 years (range) | TCI (based on the model from Dyck et al.) targeting 1, 2, 3, 4, 6, and 8 ng/mL 10 min after a short (20 s) bolus infusion at 6 µg/kg/h | Age, WGT, HGT, BMI, sex | 3-compartment model with weight as a covariate on clearance, | The authors found no systematic difference in |
| Kuang (2016) [ | Chinese patients under spinal anesthesia | 19 young/16 elderly | 15 a/v during ( | 600 min | 33 vs. 69 years | 3.0 µg/kg/h for 10 min followed by 0.5 µg/kg/h for 50 min Maximum measured DMED concentration is approximately 1.7 ng/mL | Age, WGT, HGT, sex, BMI, AST, ALT, creatinine clearance | 3-compartment model with ALT as a covariate on clearance, age on | |
ALB albumin, ALT alanine transaminase, AST aspartate transaminase, BMI body mass index, BSA body surface area, CL clearance, C maximum plasma concentration, CO cardiac output, C plasma concentration at steady state, ELS extended least squares, FAT fat mass, FFM fat-free mass, HGT height, HV healthy volunteers, IC half maximal inhibitory concentration, IIV inter-individual variability, I maximal inhibition, IOV inter-occasion variability, LBM lean body mass, N number of subjects, PK pharmacokinetic, Q inter-compartmental clearance, SVR systemic vascular resistance, TBW total body weight, TCI target-controlled infusion, V apparent volume of distribution, WGT weight
Fig. 1Simulated concentration time profiles according to the different reported adult population pharmacokinetic models. A 35-µg loading dose infused over 10 min (i.e. at an infusion rate of 210 µg/h), followed by a 35-µg/h maintenance dose was simulated to illustrate the impact of the different covariates on the concentration time profile in the first 2 h after dosing. In addition, on the top of each graph the predicted dexmedetomidine (DMED) plasma concentration at steady state is shown for the typical patient with, between parentheses, the expected fold-difference in the C ss for patients with a covariate at opposite sides of the studied covariate range. ALB albumin, ALT alanine aminotransferase, FFM fat free mass, TBW total body weight. Created with R® (R foundation for statistical computing, Vienna, Austria)
Overview of published population pharmacokinetic dexmedetomidine (DMED) models in the pediatric population
| Population |
| Blood PK samples | Patient characteristics | Drug administration | Tested covariates | Covariate models | Remarks | ||
|---|---|---|---|---|---|---|---|---|---|
| No. of samples | Last sample (time after termination of infusion) | Age/WGT/HGT | |||||||
| Potts (2009) [ | Pediatric ICU patients | 95a | a (1 trial) and v (3 trials) samples during and after DMED infusion | 8 h | 3.83 years (0.01–14.4) | 1–6 µg/kg over 5 or 10 min or 0.2-µg/kg/h infusion | Age, WGT, cardiac surgery, arterial/venous sampling, study site | 2-compartment model with age, WGT (allometry) and post-cardiac surgery state as covariates on CL and WGT (allometry) as a covariate on | IIV is almost twofold higher than the effect of maturation (30.9% vs. approximately 20%). Clearance in post-operative cardiac pediatric patients was approximately 27% reduced compared with other pediatric patients |
| Su (2010) [ | Pediatric cardiac post-operative patients | 36 | a/v samples obtained during ( | 24 h | 7.8 months (2.6–20.4) | 0.35–1 µg/kg over 10 min followed by 0.25–0.75 µg/kg/h for 2–24 hours | Age, WGT, total cardiopulmonary bypass time, ventricular physiology | 2-compartment model with age and ventricular physiology as covariates on CL | A full covariate model was reported. Nevertheless, only the covariate for ventricular physiology on CL had acceptable precision (i.e., RSE <50%). BSV is higher than the effect of maturation |
| Liu (2016) [ | Chinese pediatric general surgery patients | 39 | v samples obtained during ( | 8 h | 3.0 years (1–9) | 1.0–2.0 µg/kg over 10 min | Age, WGT, BMI, sex, lean body mass | 2-compartment model with WGT (allometry) as a covariate on CL, | During surgery patients were maintained under anesthesia with sevoflurane, which might have caused a shift in plasma protein binding of DMED resulting in a higher distribution volume |
| Su (2016) [ | Neonatal and pediatric post-operative patients | 23 + 36 | a/v samples obtained during and after DMED infusionb | 18 hb | 4.3 months (0.03–20.4) | 0.25–1 µg/kg over 10 min followed by 0.20–0.75 µg/kg/h for 2–24 h | Age, WGT, total cardiopulmonary bypass time, ventricular physiology | 2-compartment model with age, WGT (allometry), total bypass time, and ventricular physiology as covariates on CL and WGT (allometry) as a covariate on | WGT-corrected CL increases with age until approximately 1 month. A linearly scaled version of the model performs slightly better, probably owing to the limited WGT range of the included subjects |
| Wiczling (2016) [ | Critically ill pediatric patients | 38 | a samples obtained during ( | 6 h | 5.8 years (0.12–15.7) | Initiation of 0.8 µg/kg/h with titration to effect for ventilated patients with maximum of 1.4 µg/kg/h | cfr. Potts | 2-compartment model with age, WGT (allometry), and fractional increase in the 2nd session as covariates on CL and with WGT (allometry) and fractional increase in the 2nd session as covariates on | Results might be confounded by concomitant use of sufentanil and midazolam. No population PK model was developed, the parameter estimates were obtained by a Bayesian fit of the Potts model to these data. The posterior distribution for the parameters closely resembles the prior distributions |
BMI body mass index, CL clearance, HGT height, ICU intensive care unit, IIV inter-individual variability, N number of subjects, PK pharmacokinetic, Q2 inter-compartmental clearance, RSE relative squared error, BSV between subject variability, V apparent volume of distribution, WGT weight
aCombination of four earlier published trials
bSampling schedules were adapted to the subjects WGT, samples were obtained up to 10, 12, 15, and 18 h after stopping the drug infusion
| Pharmacokinetic studies have shown that body size and hepatic function have a significant influence on the pharmacokinetic profile of dexmedetomidine. Plasma albumin and cardiac output are suggested to have an impact on the apparent volume of distribution and clearance. Studies of the influence of other patient characteristics have produced inconclusive results. |
| Unlike sedative drugs such as propofol and the benzodiazepines, dexmedetomidine does not act at the gamma-aminobutyric acid (GABA) receptors. It induces sedation through activation of α2-receptors in the locus coeruleus and induces a state mimicking natural sleep. Whilst sedated, respiration is minimally affected and patients remain rousable. Side effects are mainly hemodynamic and include hypertension, hypotension, and bradycardia as a result of vasoconstriction, sympatholysis, and baroreflex-mediated parasympathetic activation. |
| Further research is needed to investigate the clinical feasibility of different promising off-label indications, such as use in the pediatric and geriatric population, intranasal dexmedetomidine administration, its use as an adjuvant to prolong peripheral or spinal nerve blocks, and the potential of dexmedetomidine to reduce opioid consumption. |