Target-controlled Infusion of Remimazolam in Healthy Volunteers Shows Some Acute Tolerance.

BACKGROUND: Remimazolam exhibits sedative properties by binding to γ-aminobutyric acid type A receptors. Remimazolam is administered as a bolus dose or continuous infusion, but has not been studied using target-controlled infusion (TCI). The study quantified the relationship between the remimazolam concentration, Modified Observer's Assessment of Alertness and Sedation (MOAAS) score, and bispectral index (BIS) using TCI. METHODS: The authors performed a three-period, crossover, dose-ranging clinical trial in 24 healthy volunteers using age and sex stratification. Data collected in the first period, where remimazolam was administered alone using a step-up and step-down TCI protocol, were used for this analysis. Remimazolam concentrations, MOAAS scores, and BIS values were collected at each step at steady state. Data were analyzed using nonlinear mixed-effects modeling methodology. RESULTS: The relationship between remimazolam, BIS, and MOAAS differed between step-up and step-down infusions at similar remimazolam target concentrations. Tolerance, driven by remimazolam or CNS7054, significantly improved overall model fit (P < 0.01) for both BIS and MOAAS models. After 30 min of repeated bolus dosing, mimicking the regimen in the label for procedural sedation, the BIS and probability of MOAAS 2/3 were predicted to be 54 (95% prediction interval, 44 to 67) and 2% (95% prediction interval, 0 to 32%) versus 58 (95% prediction interval, 48 to 70) and 8% (95% prediction interval, 0 to 36%) in a model without and with tolerance, respectively. After 60 min of continuous infusion, mimicking the regimen in the label for general anesthesia, the BIS and probability of MOAAS 0 were predicted to be 40 (95% prediction interval, 33 to 50) and 87% (95% prediction interval, 18 to 100%) versus 50 (95% prediction interval, 41 to 60) and 59% (95% prediction interval, 6 to 99%) in a model without and with tolerance, respectively. CONCLUSIONS: In this study, it was shown that remimazolam-induced sedation is prone to tolerance development, which is potentially mediated by the CNS7054 concentration. The clinical consequences are, however, limited in situations where remimazolam is titrated to effect.

A Joint Pharmacokinetic Model for the Simultaneous Description of Plasma and Whole Blood Tacrolimus Concentrations in Kidney and Lung Transplant Recipients.

BACKGROUND AND OBJECTIVE: Historically, dosing of tacrolimus is guided by therapeutic drug monitoring (TDM) of the whole blood concentration, which is strongly influenced by haematocrit. The therapeutic and adverse effects are however expected to be driven by the unbound exposure, which could be better represented by measuring plasma concentrations. OBJECTIVE: We aimed to establish plasma concentration ranges reflecting whole blood concentrations within currently used target ranges. METHODS: Plasma and whole blood tacrolimus concentrations were determined in samples of transplant recipients included in the TransplantLines Biobank and Cohort Study. Targeted whole blood trough concentrations are 4-6 ng/mL and 7-10 ng/mL for kidney and lung transplant recipients, respectively. A population pharmacokinetic model was developed using non-linear mixed-effects modelling. Simulations were performed to infer plasma concentration ranges corresponding to whole blood target ranges. RESULTS: Plasma (n = 1973) and whole blood (n = 1961) tacrolimus concentrations were determined in 1060 transplant recipients. A one-compartment model with fixed first-order absorption and estimated first-order elimination characterised observed plasma concentrations. Plasma was linked to whole blood using a saturable binding equation (maximum binding 35.7 ng/mL, 95% confidence interval (CI) 31.0-40.4 ng/mL; dissociation constant 0.24 ng/mL, 95% CI 0.19-0.29 ng/mL). Model simulations indicate that patients within the whole blood target range are expected to have plasma concentrations (95% prediction interval) of 0.06-0.26 ng/mL and 0.10-0.93 ng/mL for kidney and lung transplant recipients, respectively. CONCLUSION: Whole blood tacrolimus target ranges, currently used to guide TDM, were translated to plasma concentration ranges of 0.06-0.26 ng/mL and 0.10-0.93 ng/mL for kidney and lung transplant recipients, respectively.

Pharmacodynamic mechanism-based interaction model for the haemodynamic effects of remifentanil and propofol in healthy volunteers.

BACKGROUND: Propofol and remifentanil are frequently combined for the induction and maintenance of general anaesthesia. Both propofol and remifentanil cause vasodilation and potentially reduce arterial BP. We aimed to develop a mechanism-based model that characterises the haemodynamic interactions between remifentanil and propofol. METHODS: Data from two clinical trials in healthy volunteers were analysed using remifentanil-alone, propofol-alone, and combination groups. We evaluated remifentanil effects on haemodynamics using a previously developed mechanism-based haemodynamic model of propofol. The interaction between propofol and remifentanil was explored using the principles of the general pharmacodynamic interaction (GPDI) model. RESULTS: Remifentanil alone increased the dissipation rate of total peripheral resistance by 50% at 3.0 ng ml-1. Additionally, the dissipation rates of HR and stroke volume were attenuated by 4.8% and 4.9% per 1 ng ml-1 increase in remifentanil concentration, respectively. The maximal effect of propofol alone in decreasing the production rate of total peripheral resistance was 78%, which decreased to 32% when combined with remifentanil 4 ng ml-1. The effects of remifentanil on HR and stroke volume were attenuated by propofol with maximum decreases of 11.9% and 21.2%, respectively. Goodness-of-fit plots and prediction-corrected visual predictive check plots showed good predictive performance of the models. CONCLUSIONS: The structure of the previous mechanism-based haemodynamic model for propofol was able to describe the effects of remifentanil alone on haemodynamic variables. The GPDI model provided a good framework for characterising the pharmacodynamic interaction between remifentanil and propofol on haemodynamic properties. CLINICAL TRIAL REGISTRATION: NCT02043938; NCT03143972.

Gentamicin Administration in Dialysis Patients: Before or After Hemodialysis?

BACKGROUND: Gentamicin is used to treat severe infections and has a small therapeutic window. This study aimed to optimize the dosing strategy of gentamicin in intermittently hemodialyzed patients by simulating concentration-time profiles during pre- and postdialysis dosing, based on a published pharmacokinetic model. METHODS: Pharmacokinetic simulations were performed with virtual patients, including septic patients, who were treated with gentamicin and received weekly hemodialysis with an interval of 48 h-48 h-72 h. The following dosing regimens were simulated: for nonseptic patients, 5 mg/kg gentamicin was given 1 h or 2 h before dialysis or a starting dose of 2.5 mg/kg and a maintenance dose of 1.5 mg/kg immediately after dialysis were given; for septic patients, 6 mg/kg gentamicin was given 1 h or 2 h before dialysis or a starting dose of 3 mg/kg and a maintenance dose of 1.8 mg/kg immediately were given after dialysis. The mean maximum concentration (C max ), area under the curve (AUC) 24 h , and target attainment (TA) of pharmacodynamic targets were calculated and compared. The following targets were adopted from the literature: C max >8 mg/L and <20 mg/L and AUC 24 h >70 mg·h/L and <120 mg·h/L. RESULTS: In nonseptic patients, postdialysis dosing resulted in a TA of 35% for C max of >8 mg/L, 100% for <20 mg/L and AUC 24 h >70 mg·h/L, and 45% for <120 mg·h/L. Dosing 2 h before dialysis resulted in a TA of 100% for C max of >8 mg/L, 40% for <20 mg/L, 65% for AUC 24 h >70 mg·h/L, and 77% for <120 mg·h/L. Simulations of septic patients resulted in comparable outcomes with higher TAs for C max <20 mg/L (96%), AUC 24 h >70 mg·h/L (90%), and AUC 24 h <120 mg·h/L (53%) for dosing 1 h before dialysis. CONCLUSIONS: Postdialysis dosing resulted in a low TA of C max >8 mg/L; however, predialysis dosing ensured a high TA of C max >8 mg/L and acceptable TA of C max <20 mg/L, AUC 24 h >70 mg·h/L, and AUC 24 h <120 mg·h/L, which could increase the efficacy of gentamicin. Therefore, clinicians should consider predialysis dosing of gentamicin in patients undergoing intermittent hemodialysis.

Dose assessment of melatonin in sepsis (DAMSEL2) study: Pharmacokinetics of two doses of oral melatonin in patients with sepsis.

Sepsis is defined as a dysregulated host response to infection, and high-dose melatonin has been proposed as a treatment due to its antioxidant and anti-inflammatory properties. However, there are no data describing the pharmacokinetics of high-dose oral melatonin in critically ill patients. We undertook an open-label trial to determine the tolerance of melatonin administration in these patients and pharmacokinetic analysis, to inform a planned randomised controlled trial. Two cohorts of critically ill patients with sepsis due to community-acquired pneumonia received either 20 or 50 mg oral melatonin liquid as a single dose. Blood samples and clinical measures were analysed over the next 24 h. Melatonin was well tolerated and there were no adverse events. Pharmacokinetic modelling showed that a semiphysiological model, which incorporates saturable first-pass hepatic extraction, was a good fit for our data. Maximum levels of melatonin were extremely high in patients receiving the 50 mg dose and levels of the major metabolite were much lower than expected and not different from those seen after 20 mg, suggesting saturation at the higher dose. We conclude that 20 mg seems a suitable dose of liquid melatonin in patients with sepsis.

Mechanism-based pharmacodynamic model for propofol haemodynamic effects in healthy volunteers☆.

BACKGROUND: The adverse haemodynamic effects of the intravenous anaesthetic propofol are well known, yet few empirical models have explored the dose-response relationship. Evidence suggests that hypotension during general anaesthesia is associated with postoperative mortality. We developed a mechanism-based model that quantitatively characterises the magnitude of propofol-induced haemodynamic effects during general anaesthesia. METHODS: Mean arterial pressure (MAP), heart rate (HR) and pulse pressure (PP) measurements were available from 36 healthy volunteers who received propofol in a step-up and step-down fashion by target-controlled infusion using the Schnider pharmacokinetic model. A mechanistic pharmacodynamic model was explored based on the Snelder model. To benchmark the performance of this model, we developed empirical models for MAP, HR, and PP. RESULTS: The mechanistic model consisted of three turnover equations representing total peripheral resistance (TPR), stroke volume (SV), and HR. Propofol-induced changes were implemented by Emax models on the zero-order production rates of the turnover equations for TPR and SV. The estimated 50% effective concentrations for propofol-induced changes in TPR and SV were 2.96 and 0.34 µg ml-1, respectively. The goodness-of-fit for the mechanism-based model was indistinguishable from the empirical models. Simulations showed that predictions from the mechanism-based model were similar to previously published MAP and HR observations. CONCLUSIONS: We developed a mechanism-based pharmacodynamic model for propofol-induced changes in MAP, TPR, SV, and HR as a potential approach for predicting haemodynamic alterations. CLINICAL TRIAL REGISTRATION: NCT02043938.

Dexmedetomidine Clearance Decreases with Increasing Drug Exposure: Implications for Current Dosing Regimens and Target-controlled Infusion Models Assuming Linear Pharmacokinetics.

BACKGROUND: Numerous pharmacokinetic models have been published aiming at more accurate and safer dosing of dexmedetomidine. The vast majority of the developed models underpredict the measured plasma concentrations with respect to the target concentration, especially at plasma concentrations higher than those used in the original studies. The aim of this article was to develop a dexmedetomidine pharmacokinetic model in healthy adults emphasizing linear versus nonlinear kinetics. METHODS: The data of two previously published clinical trials with stepwise increasing dexmedetomidine target-controlled infusion were pooled to build a pharmacokinetic model using the NONMEM software package (ICON Development Solutions, USA). Data from 48 healthy subjects, included in a stratified manner, were utilized to build the model. RESULTS: A three-compartment mamillary model with nonlinear elimination from the central compartment was superior to a model assuming linear pharmacokinetics. Covariates included in the final model were age, sex, and total body weight. Cardiac output did not explain between-subject or within-subject variability in dexmedetomidine clearance. The results of a simulation study based on the final model showed that at concentrations up to 2 ng · ml-1, the predicted dexmedetomidine plasma concentrations were similar between the currently available Hannivoort model assuming linear pharmacokinetics and the nonlinear model developed in this study. At higher simulated plasma concentrations, exposure increased nonlinearly with target concentration due to the decreasing dexmedetomidine clearance with increasing plasma concentrations. Simulations also show that currently approved dosing regimens in the intensive care unit may potentially lead to higher-than-expected dexmedetomidine plasma concentrations. CONCLUSIONS: This study developed a nonlinear three-compartment pharmacokinetic model that accurately described dexmedetomidine plasma concentrations. Dexmedetomidine may be safely administered up to target-controlled infusion targets under 2 ng · ml-1 using the Hannivoort model, which assumed linear pharmacokinetics. Consideration should be taken during long-term administration and during an initial loading dose when following the dosing strategies of the current guidelines.

Pharmacokinetic properties of remimazolam in subjects with hepatic or renal impairment.

BACKGROUND: Remimazolam is a new benzodiazepine for procedural sedation and general anaesthesia. The aim of this study was to characterise its pharmacokinetic properties and safety in renally and hepatically impaired subjects. METHODS: Two separate trials were conducted in patients with hepatic (n=11) or renal impairment (n=11) compared with matched healthy subjects (n=9 and n=12, respectively). The hepatic impairment trial was an open-label adaptive 'Reduced Design' trial, using a single bolus of remimazolam 0.1 mg kg-1 i.v., whereas the renal impairment trial was an open-label trial of a single bolus dose of remimazolam 1.5 mg i.v. Remimazolam plasma concentrations over time were analysed by population pharmacokinetic modelling. RESULTS: Remimazolam pharmacokinetic properties were adequately described by a three-compartment, recirculatory model. Exposure in subjects with severe hepatic impairment was 38.1% higher (i.e. clearance was 38.1% lower) compared with healthy volunteers. This increase caused a slightly delayed recovery (8.0 min for healthy, 12.1 min for moderate, and 16.7 min for severe hepatic impairment). With renal impairment, plasma clearance was comparable with that measured in healthy subjects. Simulations of Cmax after a bolus dose of 10 mg showed no relevant impact of hepatic or renal impairment. The overall incidence of adverse events was low, and all adverse events were mild. CONCLUSIONS: As Cmax after a remimazolam bolus i.v. was not affected by hepatic or renal impairment, no dose adjustments are required. No unexpected adverse events related to remimazolam were seen in subjects with renal or hepatic impairment. CLINICAL TRIAL REGISTRATION: Hepatic impairment trial: ClinicalTrials.gov, NCT01790607 (https://clinicaltrials.gov/ct2/show/NCT01790607). Renal impairment trial: EudraCT Number: 2014-004575-23.

Comment on Morse et al. A Universal Pharmacokinetic Model for Dexmedetomidine in Children and Adults. J. Clin. Med. 2020, 9, 3480.

We read with interest the recently published manuscript [...].

Do Vancomycin Pharmacokinetics Differ Between Obese and Non-obese Patients? Comparison of a General-Purpose and Four Obesity-Specific Pharmacokinetic Models.

BACKGROUND: Over the past decade, numerous obesity-specific pharmacokinetic (PK) models and dosage regimens have been developed. However, it is unclear whether vancomycin PKs differ between obese and other patients after accounting for weight, age, and kidney function. In this study, the authors investigated whether using obesity-specific population PK models for vancomycin offers any advantage in accuracy and precision over using a recently developed general-purpose model. METHODS: Vancomycin plasma concentrations in a cohort of 49 obese patients (body mass index [BMI] >30 kg/m2), not previously used in the development of any of the evaluated models, were used to validate the performance of 4 obesity-specific models and a general model. Bias and imprecision were calculated for the a priori and a posteriori predictive performance. RESULTS: The bias of the a priori prediction was lowest for one of the obesity-specific models (-1.40%) and that of the general model was a close second (-7.0%). The imprecision was lowest for the general model (4.34 mg/L). The predictive performance for the a posteriori predictions was best for the general model, both for bias (1.96%) and imprecision (2.75 mg/L). CONCLUSIONS: The results of the external validation of vancomycin PK in obese patients showed that currently available obesity-specific models do not necessarily outperform a broadly supported general-purpose model. Based on these results, the authors conclude that there is no advantage in using vancomycin PK models specifically tailored to obese patients over the general-purpose model reported by Colin et al.

Influence of an "Electroencephalogram-Based" Monitor Choice on the Delay Between the Predicted Propofol Effect-Site Concentration and the Measured Drug Effect.

BACKGROUND: Clinicians can optimize propofol titration by using 2 sources of pharmacodynamic (PD) information: the predicted effect-site concentration for propofol (Ceprop) and the electroencephalographically (EEG) measured drug effect. Relation between these sources should be time independent, that is, perfectly synchronized. In reality, various issues corrupt time independency, leading to asynchrony or, in other words, hysteresis. This asynchrony can lead to conflicting information, making effective drug dosing challenging. In this study, we tried to quantify and minimize the hysteresis between the Ceprop (calculated using the Schnider model for propofol) and EEG measured drug effect, using nonlinear mixed-effects modeling (NONMEM). Further, we measured the influence of EEG-based monitor choice, namely Bispectral index (BIS) versus qCON index (qCON) monitor, on propofol PD hysteresis. METHODS: We analyzed the PD data from 165 patients undergoing propofol-remifentanil anesthesia for outpatient surgery. Drugs were administered using target-controlled infusion (TCI) pumps. Pumps were programmed with Schnider model for propofol and Minto model for remifentanil. We constructed 2 PD models (direct models) relating the Schnider Ceprop to the measured BIS and qCON monitor values. We quantified the models' misspecification due to hysteresis, on an individual level, using the root mean squared errors (RMSEs). Subsequently, we optimized the PD models' predictions by adding a lag term to both models (lag-time PD models) and quantified the optimization using the RMSE. RESULTS: There is a counterclockwise hysteresis between Ceprop and BIS/qCON values. Not accounting for this hysteresis results in a direct PD model with an effect-site concentration which produces 50% of the maximal drug effect (Ce50) of 6.24 and 8.62 µg/mL and RMSE (median and interquartile range [IQR]) of 9.38 (7.92-11.23) and 8.41(7.04-10.2) for BIS and qCON, respectively. Adding a modeled lag factor of 49 seconds to the BIS model and 53 seconds to the qCON model improved both models' prediction, resulting in similar Ce50 (3.66 and 3.62 µg/mL for BIS and qCON) and lower RMSE (median (IQR) of 7.87 (6.49-9.90) and 6.56 (5.28-8.57) for BIS and qCON. CONCLUSIONS: There is a significant "Ceprop versus EEG measured drug effect" hysteresis. Not accounting for it leads to conflicting PD information and false high Ce50 for propofol in both monitors. Adding a lag term improved the PD model performance, improved the "pump-monitor" synchrony, and made the estimates of Ce50 for propofol more realistic and less monitor dependent.

Genetic Algorithms as a Tool for Dosing Guideline Optimization: Application to Intermittent Infusion Dosing for Vancomycin in Adults.

This paper demonstrates the use of a genetic algorithm (GA) for the optimization of a dosing guideline. GAs are well-suited to derive combinations of doses and dosing intervals that go into a dosing guideline when the number of possible combinations rule out the calculation of all possible outcomes. GAs also allow for different constraints to be imposed on the optimization process to safeguard the clinical feasibility of the dosing guideline. In this work, we demonstrate the use of a GA for the optimization of intermittent vancomycin administration in adult patients. Constraints were placed on the dose strengths, the length of the dosing intervals, and the maximum infusion rate. In addition, flexibility with respect to the timing of the first maintenance dose was included in the optimization process. The GA-based optimal solution is compared with the Scottish Antimicrobial Prescribing Group vancomycin guideline.

Target-Controlled Infusion of Cefepime in Critically Ill Patients.

Attainment of appropriate pharmacokinetic-pharmacodynamic (PK-PD) targets for antimicrobial treatment is challenging in critically ill patients, particularly for cefepime, which exhibits a relative narrow therapeutic-toxic window compared to other beta-lactam antibiotics. Target-controlled infusion (TCI) systems, which deliver drugs to achieve specific target drug concentrations, have successfully been implemented for improved dosing of sedatives and analgesics in anesthesia. We conducted a clinical trial in an intensive care unit (ICU) to investigate the performance of TCI for adequate target attainment of cefepime. Twenty-one patients treated with cefepime according to the standard of care were included. Cefepime was administered through continuous infusion using TCI for a median duration of 4.5 days. TCI was based on a previously developed population PK model incorporating the estimated creatinine clearance based on the Cockcroft-Gault formula as the input variable to calculate cefepime clearance. A cefepime blood concentration of 16 mg/liter was targeted. To evaluate the measured versus predicted plasma concentrations, blood samples were taken (median of 10 samples per patient), and total cefepime concentrations were measured using ultraperformance liquid chromatography-tandem mass spectrometry. The performance of the TCI system was evaluated using Varvel criteria. Half (50.3%) of the measured cefepime concentrations were within ±30% around the target value of 16 mg liter-1 The wobble was 11.4%, the median performance error (MdPE) was 21.1%, the median absolute performance error (MdAPE) was 32.0%, and the divergence was -3.72% h-1 Based on these results, we conclude that TCI is useful for dose optimization of cefepime in ICU patients. (This study has been registered at ClinicalTrials.gov under identifier NCT02688582.).

Population Pharmacodynamics of Propofol and Sevoflurane in Healthy Volunteers Using a Clinical Score and the Patient State Index: A Crossover Study.

BACKGROUND: The population pharmacodynamics of propofol and sevoflurane with or without opioids were compared using the endpoints no response to calling the person by name, tolerance to shake and shout, tolerance to tetanic stimulus, and two versions of a processed electroencephalographic measure, the Patient State Index (Patient State Index-1 and Patient State Index-2). METHODS: This is a reanalysis of previously published data. Volunteers received four anesthesia sessions, each with different drug combinations of propofol or sevoflurane, with or without remifentanil. Nonlinear mixed effects modeling was used to study the relationship between drug concentrations, clinical endpoints, and Patient State Index-1 and Patient State Index-2. RESULTS: The C50 values for no response to calling the person by name, tolerance to shake and shout, and tolerance to tetanic stimulation for propofol (µg · ml) and sevoflurane (vol %; relative standard error [%]) were 1.62 (7.00)/0.64 (4.20), 1.85 (6.20)/0.90 (5.00), and 2.82 (15.5)/0.91 (10.0), respectively. The C50 values for Patient State Index-1 and Patient State Index-2 were 1.63 µg · ml (3.7) and 1.22 vol % (3.1) for propofol and sevoflurane. Only for sevoflurane was a significant difference found in the pharmacodynamic model for Patient State Index-2 compared with Patient State Index-1. The pharmacodynamic models for Patient State Index-1 and Patient State Index-2 as a predictor for no response to calling the person by name, tolerance to shake and shout, and tetanic stimulation were indistinguishable, with Patient State Index50 values for propofol and sevoflurane of 46.7 (5.1)/68 (3.0), 41.5 (4.1)/59.2 (3.6), and 29.5 (12.9)/61.1 (8.1), respectively. Post hoc C50 values for propofol and sevoflurane were perfectly correlated (correlation coefficient = 1) for no response to calling the person by name and tolerance to shake and shout. Post hoc C50 and Patient State Index50 values for propofol and sevoflurane for tolerance to tetanic stimulation were independent within an individual (correlation coefficient = 0). CONCLUSIONS: The pharmacodynamics of propofol and sevoflurane were described on both population and individual levels using a clinical score and the Patient State Index. Patient State Index-2 has an improved performance at higher sevoflurane concentrations, and the relationship to probability of responsiveness depends on the drug used but is unaffected for Patient State Index-1 and Patient State Index-2.

Pharmacodynamic Interaction of Remifentanil and Dexmedetomidine on Depth of Sedation and Tolerance of Laryngoscopy.

BACKGROUND: Dexmedetomidine is a sedative with modest analgesic efficacy, whereas remifentanil is an opioid analgesic with modest sedative potency. Synergy is often observed when sedative-hypnotics are combined with opioid analgesics in anesthetic practice. A three-phase crossover trial was conducted to study the pharmacodynamic interaction between remifentanil and dexmedetomidine. METHODS: After institutional review board approval, 30 age- and sex- stratified healthy volunteers were studied. The subjects received consecutive stepwise increasing target-controlled infusions of dexmedetomidine, remifentanil, and remifentanil with a fixed dexmedetomidine background concentration. Drug effects were measured using binary (yes or no) endpoints: no response to calling the subject by name, tolerance of shaking the patient while shouting the name ("shake and shout"), tolerance of deep trapezius squeeze, and tolerance of laryngoscopy. The drug effect was measured using the electroencephalogram-derived "Patient State Index." Pharmacokinetic-pharmacodynamic modeling related the administered dexmedetomidine and remifentanil concentration to these observed effects. RESULTS: The binary endpoints were correlated with dexmedetomidine concentrations, with increasing concentrations required for increasing stimulus intensity. Estimated model parameters for the dexmedetomidine EC50 were 2.1 [90% CI, 1.6 to 2.8], 9.2 [6.8 to 13], 24 [16 to 35], and 35 [23 to 56] ng/ml, respectively. Age was inversely correlated with dexmedetomidine EC50 for all four stimuli. Adding remifentanil did not increase the probability of tolerance of any of the stimuli. The cerebral drug effect as measured by the Patient State Index was best described by the Hierarchical interaction model with an estimated dexmedetomidine EC50 of 0.49 [0.20 to 0.99] ng/ml and remifentanil EC50 of 1.6 [0.87 to 2.7] ng/ml. CONCLUSIONS: Low dexmedetomidine concentrations (EC50 of 0.49 ng/ml) are required to induce sedation as measured by the Patient State Index. Sensitivity to dexmedetomidine increases with age. Despite falling asleep, the majority of subjects remained arousable by calling the subject's name, "shake and shout," or a trapezius squeeze, even when reaching supraclinical concentrations. Adding remifentanil does not alter the likelihood of response to graded stimuli.

Vancomycin Pharmacokinetics Throughout Life: Results from a Pooled Population Analysis and Evaluation of Current Dosing Recommendations.

BACKGROUND AND OBJECTIVES: Uncertainty exists regarding the optimal dosing regimen for vancomycin in different patient populations, leading to a plethora of subgroup-specific pharmacokinetic models and derived dosing regimens. We aimed to investigate whether a single model for vancomycin could be developed based on a broad dataset covering the extremes of patient characteristics. Furthermore, as a benchmark for current dosing recommendations, we evaluated and optimised the expected vancomycin exposure throughout life and for specific patient subgroups. METHODS: A pooled population-pharmacokinetic model was built in NONMEM based on data from 14 different studies in different patient populations. Steady-state exposure was simulated and compared across patient subgroups for two US Food and Drug Administration/European Medicines Agency-approved drug labels and optimised doses were derived. RESULTS: The final model uses postmenstrual age, weight and serum creatinine as covariates. A 35-year-old, 70-kg patient with a serum creatinine level of 0.83 mg dL-1 (73.4 µmol L-1) has a V1, V2, CL and Q2 of 42.9 L, 41.7 L, 4.10 L h-1 and 3.22 L h-1. Clearance matures with age, reaching 50% of the maximal value (5.31 L h-1 70 kg-1) at 46.4 weeks postmenstrual age then declines with age to 50% at 61.6 years. Current dosing guidelines failed to achieve satisfactory steady-state exposure across patient subgroups. After optimisation, increased doses for the Food and Drug Administration label achieve consistent target attainment with minimal (± 20%) risk of under- and over-dosing across patient subgroups. CONCLUSIONS: A population model was developed that is useful for further development of age and kidney function-stratified dosing regimens of vancomycin and for individualisation of treatment through therapeutic drug monitoring and Bayesian forecasting.

Target-Controlled Continuous Infusion for Antibiotic Dosing: Proof-of-Principle in an In-silico Vancomycin Trial in Intensive Care Unit Patients.

OBJECTIVES: In this in-silico study, we investigate the clinical utility of target-controlled infusion for antibiotic dosing in an intensive care unit setting using vancomycin as a model compound. We compared target-controlled infusion and adaptive target-controlled infusion, which combines target-controlled infusion with data from therapeutic drug monitoring, with conventional (therapeutic drug monitoring-based) vancomycin dosing strategies. METHODS: A clinical trial simulation was conducted. This simulation was based on a comprehensive database of clinical records of intensive care unit patients and a systematic review of currently available population-pharmacokinetic models for vancomycin in intensive care unit patients. Dosing strategies were compared in terms of the probability of achieving efficacious concentrations as well as the potential for inducing toxicity. RESULTS: Adaptive target-controlled infusion outperforms rule-based dosing guidelines for vancomycin. In the first 48 h of treatment, the probability of target attainment is significantly higher for adaptive target-controlled infusion than for the second-best method (Cristallini). Probability of target attainments of 54 and 72% and 47 and 59% for both methods after 24 and 48 h, respectively. Compared to the Cristallini method, which is characterized by a probability of attaining concentrations above 30 mg.L-1 > 65% in the first few hours of treatment, adaptive target-controlled infusion shows negligible time at risk and a probability of attaining concentrations above 30 mg.L-1 not exceeding 25%. Finally, in contrast to the other methods, the performance of target-controlled infusion is consistent across subgroups within the population. CONCLUSIONS: Our study shows that adaptive target-controlled infusion has the potential to become a practical tool for patient-tailored antibiotic dosing in the intensive care unit.