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.

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.

Development of a pharmacokinetic and pharmacodynamic model for intranasal administration of midazolam in older adults: a single-site two-period crossover study.

BACKGROUND: Intranasal midazolam can produce procedural sedation in frail older patients with dementia who are unable to tolerate necessary medical or dental procedures during domiciliary medical care. Little is known about the pharmacokinetics and pharmacodynamics of intranasal midazolam in older (>65 yr old) people. The aim of this study was to understand the pharmacokinetic/pharmacodynamic properties of intranasal midazolam in older people with the primary goal of developing a pharmacokinetic/pharmacodynamic model to facilitate safer domiciliary sedation care. METHODS: We recruited 12 volunteers: ASA physical status 1-2, aged 65-80 yr, and received midazolam 5 mg intravenously and 5 mg intranasally on two study days separated by a 6 day washout period. Concentrations of venous midazolam and 1'-OH-midazolam, Modified Observer's Assessment of Alertness/Sedation (MOAA/S) score, bispectral index (BIS), arterial pressure, ECG, and respiratory parameters were measured for 10 h. RESULTS: Time to peak effect of intranasal midazolam for BIS, MAP, and SpO2 were 31.9 (6.2), 41.0 (7.6), and 23.1 (3.0) min, respectively. Intranasal bioavailability was lower compared with intravenous administration (Fabs 95%; 95% confidence interval: 89-100%). A three-compartment model best described midazolam pharmacokinetics following intranasal administration. A separate effect compartment linked to the dose compartment best described an observed time-varying drug-effect difference between intranasal and intravenous midazolam, suggesting direct nose-to-brain transport. CONCLUSIONS: Intranasal bioavailability was high and sedation onset was rapid, with maximum sedative effects after 32 min. We developed a pharmacokinetic/pharmacodynamic model for intranasal midazolam for older persons and an online tool to simulate changes in MOAA/S, BIS, MAP, and SpO2 after single and additional intranasal boluses. CLINICAL TRIAL REGISTRATION: EudraCT (2019-004806-90).

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.

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.

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.

Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients.

BACKGROUND: Beta-lactam antimicrobial concentrations are frequently suboptimal in critically ill patients. Population pharmacokinetic (PopPK) modeling is the golden standard to predict drug concentrations. However, currently available PopPK models often lack predictive accuracy, making them less suited to guide dosing regimen adaptations. Furthermore, many currently developed models for clinical applications often lack uncertainty quantification. We, therefore, aimed to develop machine learning (ML) models for the prediction of piperacillin plasma concentrations while also providing uncertainty quantification with the aim of clinical practice. METHODS: Blood samples for piperacillin analysis were prospectively collected from critically ill patients receiving continuous infusion of piperacillin/tazobactam. Interpretable ML models for the prediction of piperacillin concentrations were designed using CatBoost and Gaussian processes. Distribution-based Uncertainty Quantification was added to the CatBoost model using a proposed Quantile Ensemble method, useable for any model optimizing a quantile function. These models are subsequently evaluated using the distribution coverage error, a proposed interpretable uncertainty quantification calibration metric. Development and internal evaluation of the ML models were performed on the Ghent University Hospital database (752 piperacillin concentrations from 282 patients). Ensuing, ML models were compared with a published PopPK model on a database from the University Medical Centre of Groningen where a different dosing regimen is used (46 piperacillin concentrations from 15 patients.). RESULTS: The best performing model was the Catboost model with an RMSE and [Formula: see text] of 31.94-0.64 and 33.53-0.60 for internal evaluation with and without previous concentration. Furthermore, the results prove the added value of the proposed Quantile Ensemble model in providing clinically useful individualized uncertainty predictions and show the limits of homoscedastic methods like Gaussian Processes in clinical applications. CONCLUSIONS: Our results show that ML models can consistently estimate piperacillin concentrations with acceptable and high predictive accuracy when identical dosing regimens as in the training data are used while providing highly relevant uncertainty predictions. However, generalization capabilities to other dosing schemes are limited. Notwithstanding, incorporating ML models in therapeutic drug monitoring programs seems definitely promising and the current work provides a basis for validating the model in clinical practice.

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.

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.

Target-controlled-infusion models for remifentanil dosing consistent with approved recommendations.

BACKGROUND: Target-controlled infusion (TCI) systems use pharmacokinetic (PK) models to predict the drug infusion rates necessary to achieve a desired target plasma or effect-site concentration. As new PK models are developed and implemented in TCI systems, there can be uncertainty as to which target concentrations are appropriate. Existing dose recommendations can serve as a point of reference to identify target concentrations suitable for clinical applications. METHODS: Simulations of remifentanil TCI were performed using three PK models (Minto, Eleveld, and Kim). We sought to identify models and target concentrations for remifentanil administration in children, adult, older people, and severely obese individuals, consistent with the remifentanil product label. In a typical adult this is an induction dose of 0.5-1 µg kg-1 and starting maintenance infusion rate of 0.25 µg kg-1 min-1. RESULTS: For the Minto, Eleveld, and Kim remifentanil models, a plasma target concentration of ∼ 4 ng ml-1 achieves drug administration consistent with product label recommended initial doses for all groups with minor exceptions. With effect-site targeting in older individuals, a target concentration of ∼2 ng ml-1 is required for induction and ∼4 ng ml-1 for starting maintenance to achieve drug dosages close to product label recommendations. CONCLUSIONS: We identified remifentanil TCI target concentrations that resulted in drug administration similar to product label dosing recommendations. This approach did not necessarily identify target concentrations that achieve desired clinical effect, only those that are consistent with the product label recommended doses. We estimate that plasma target concentrations of 3.1-5.3 ng ml-1 are suitable for initial dosing.

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.

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.).

Pharmacokinetic evaluation of linezolid administered intravenously in obese patients with pneumonia.

OBJECTIVES: Altered linezolid pharmacokinetics (PK) in obese individuals has been hypothesized in previous studies. However, specific dosing recommendations for this population are still lacking. The main goal of this study was to evaluate PK/pharmacodynamic (PKPD) target attainment when using a 600 mg intravenous q12h linezolid dose against MRSA in obese patients with pneumonia. METHODS: Fifteen obese pneumonia patients with a confirmed or suspected MRSA involvement treated with 600 mg of intravenous linezolid q12h were studied for 3 days. Population PK modelling was used to characterize the PK variability and to screen for influential patient characteristics. Monte Carlo simulations were carried out to investigate the PTA and time to target attainment for linezolid dosing against MRSA. RESULTS: A two-compartment model with linear elimination adequately described the data. Body weight and age both have a significant effect on linezolid clearance. Simulations demonstrate that the probability of attaining PKPD targets is low. Moreover, the PTA decreases with weight, and increases with age. Standard linezolid dosing in obese pneumonia patients with MRSA (MICs of 1-4 mg/L) leads to unacceptably low (near zero to 60%) PTA for patients <65 years old. CONCLUSIONS: Standard linezolid dosing is likely to provide insufficient target attainment against MRSA in obese patients. Body weight and especially age are important characteristics to be considered when administering linezolid to treat MRSA infections.

Population pharmacokinetics and evaluation of the predictive performance of pharmacokinetic models in critically ill patients receiving continuous infusion meropenem: a comparison of eight pharmacokinetic models.

Background: Several population pharmacokinetic (PopPK) models for meropenem dosing in ICU patients are available. It is not known to what extent these models can predict meropenem concentrations in an independent validation dataset when meropenem is infused continuously. Patients and methods: A PopPK model was developed with concentration-time data collected from routine care of 21 ICU patients (38 samples) receiving continuous infusion meropenem. The predictability of this model and seven other published PopPK models was studied using an independent dataset that consisted of 47 ICU patients (161 samples) receiving continuous infusion meropenem. A statistical comparison of imprecision (mean square prediction error) and bias (mean prediction error) was conducted. Results: A one-compartment model with linear elimination and creatinine clearance as a covariate of clearance best described our data. The mean ± SD parameter estimate for CL was 9.89 ±âŸ3.71 L/h. The estimated volume of distribution was 48.1 L. The different PopPK models showed a bias in predicting serum concentrations from the validation dataset that ranged from -8.76 to 7.06 mg/L. Imprecision ranged from 9.90 to 42.1 mg/L. Conclusions: Published PopPK models for meropenem vary considerably in their predictive performance when validated in an external dataset of ICU patients receiving continuous infusion meropenem. It is necessary to validate PopPK models in a target population before implementing them in a therapeutic drug monitoring program aimed at optimizing meropenem dosing.

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.

A semiphysiological population pharmacokinetic model of agomelatine and its metabolites in Chinese healthy volunteers.

AIMS: Agomelatine is an antidepressant for major depressive disorders. It undergoes extensive first-pass hepatic metabolism and displays irregular absorption profiles and large interindividual variability (IIV) and interoccasion variability of pharmacokinetics. The objective of this study was to characterize the complex pharmacokinetics of agomelatine and its metabolites in healthy subjects. METHODS: Plasma concentration-time data of agomelatine and its metabolites were collected from a 4-period, cross-over bioequivalence study, in which 44 healthy subjects received 25 mg agomelatine tablets orally. Nonlinear mixed effects modelling was used to characterize the pharmacokinetics and variability of agomelatine and its metabolites. Deterministic simulations were carried out to investigate the influence of pathological changes due to liver disease on agomelatine pharmacokinetics. RESULTS: A semiphysiological pharmacokinetic model with parallel first-order absorption and a well-stirred liver compartment adequately described the data. The estimated IIV and interoccasion variability of the intrinsic clearance of agomelatine were 130.8% and 28.5%, respectively. The IIV of the intrinsic clearance turned out to be the main cause of the variability of area under the curve-based agomelatine exposure. Simulations demonstrated that a reduction in intrinsic clearance or liver blood flow, and an increase in free drug fraction had a rather modest influence on agomelatine exposures (range: -50 to 200%). Portosystemic shunting, however, substantially elevated agomelatine exposure by 12.6-109.1-fold. CONCLUSIONS: A semiphysiological pharmacokinetic model incorporating first-pass hepatic extraction was developed for agomelatine and its main metabolites. The portosystemic shunting associated with liver disease might lead to significant alterations of agomelatine pharmacokinetics, and lead to substantially increased exposure.

Model-based analysis of treatment effects of paclitaxel microspheres in a microscopic peritoneal carcinomatosis model in mice.

PURPOSE: Paclitaxel (PTX)-loaded genipin-crosslinked gelatin microspheres (GP-MS) are a prolonged IP delivery system under development for the treatment of peritoneal minimal residual disease (pMRD). Here, we show the use of a pharmacokinetic-pharmacodynamic (PKPD) modelling approach to inform the formulation development of PTX-GP-MS in a mice pMRD model. METHODS: PTX blood concentrations and survival data were obtained in Balb/c Nu mice receiving different single IP doses (7.5 and/or 35 mg/kg) of PTX-ethanolic loaded GP-MS (PTXEtOH-GP-MS), PTX-nanosuspension loaded GP-MS (PTXnano-GP-MS), and immediate release formulation Abraxane®. A population PK model was developed to characterize the PTX blood concentration pattern and to predict PTX concentrations in peritoneum. Afterwards, PKPD relationships between the predicted peritoneal or blood concentrations and survival were explored using time-to-event modelling. RESULTS: A PKPD model was developed that simultaneously describes the competing effects of treatment efficacy (driven by peritoneal concentration) and toxicity (driven by blood concentration) of PTX on survival. Clear survival advantages of PTXnano-GP-MS over PTXEtOH-GP-MS and Abraxane® were found. Simulations of different doses of PTXnano-GP-MS demonstrated that drug-induced toxicity is high at doses between 20 and 35 mg/kg. CONCLUSIONS: The model predicts that the dose range of 7.5-15 mg/kg of PTXnano-GP-MS provides an optimal balance between efficacy and safety.

Community pharmacists' evaluation of potentially inappropriate prescribing in older community-dwelling patients with polypharmacy: observational research based on the GheOP³S tool.

Background: In this study, we aimed to (i) determine the prevalence of potentially inappropriate prescribing (PIP) in community-dwelling older polypharmacy patients using the Ghent Older People's Prescriptions community-Pharmacy Screening (GheOP³S) tool, (ii) identify the items that account for the highest proportion of PIP and (iii) identify the patient variables that may influence the occurrence of PIP. Additionally, pharmacist-physician contacts emerging from PIP screening with the GheOP³S tool and feasibility of the GheOP³S tool in daily practice were evaluated. Methods: A prospective observational study was carried out between December 2013 and July 2014 in 204 community pharmacies in Belgium. Patients were eligible if they were (i) ≥70 years, (ii) community-dwelling, (iii) using ≥5 chronic drugs, (iv) a regular visitor of the pharmacy and (v) understanding Dutch or French. Community pharmacists used a structured interview to obtain demographic data and medication use and subsequently screened for PIP using the GheOP³S tool. A Poisson regression was used to investigate the association between different covariates and the number of PIP. Results: In 987 (97%) of 1016 included patients, 3721 PIP items were detected (median of 3 per patient; inter quartile range: 2-5). Most frequently involved with PIP are drugs for the central nervous system such as hypnosedatives, antipsychotics and antidepressants. Risk factors for a higher PIP prevalence appeared to be a higher number of drugs (30% extra PIPs per 5 extra drugs), female gender (20% extra PIPs), higher body mass index (BMI, 20% extra PIPs per 10-unit increase in BMI) and poorer functional status (30% extra PIPs with 6-point increase). The feasibility of the GheOP³S tool was acceptable although digitalization of the tool would improve implementation. Despite detecting at least one PIP in 987 patients, only 39 physicians were contacted by the community pharmacists to discuss the items. Conclusion: A high prevalence of PIP in community-dwelling older polypharmacy patients in Belgium was detected which urges for interventions to reduce PIP.