Comparison of Chromatographic Stationary Phases Using a Bayesian-Based Multilevel Model.

Herein, we used a Bayesian multilevel model of chromatographic retention to compare five reversed-phase high-performance liquid chromatography stationary phases: XBridge Shield RP18, XTerra MS C18, XBridge Phenyl, XBridge C8, and Xterra MS C8. For this, we used a large data set of retention times collected using chromatographic techniques coupled with mass spectrometry. The experiments were conducted in gradient mode for an initial mixture of 300 small analytes for a wide range of pH values in methanol and acetonitrile at two temperatures and for three gradient durations. Our analysis was based on a mechanistic model derived from the principles and fundamentals of liquid chromatography and utilized previously reported chromatographic parameters. The data and model were used to characterize the between-column differences in the chromatographic parameters of the neutral, acidic, and basic analytes. The analysis provides an interpretable summary of stationary-phase properties that can be used in decision-making, i.e., finding the best chromatographic conditions using limited experimental data. The proposed approach is an interesting alternative to the existing approaches used to compare chromatographic stationary phases.

Targeted quantitative metabolomics with a linear mixed-effect model for analysis of urinary nucleosides and deoxynucleosides from bladder cancer patients before and after tumor resection.

In the present study, we developed and validated a fast, simple, and sensitive quantitative method for the simultaneous determination of eleven nucleosides and deoxynucleosides from urine samples. The analyses were performed with the use of liquid chromatography coupled with triple quadrupole mass spectrometry. The sample pretreatment procedure was limited to centrifugation, vortex mixing of urine samples with a methanol/water solution (1:1, v/v), evaporation and dissolution steps. The analysis lasted 20 min and was performed in dynamic multiple reaction monitoring mode (dMRM) in positive polarity. Process validation was conducted to determine the linearity, precision, accuracy, limit of quantification, stability, recovery and matrix effect. All validation procedures were carried out in accordance with current FDA and EMA regulations. The validated method was applied for the analysis of 133 urine samples derived from bladder cancer patients before tumor resection and 24 h, 2 weeks, and 3, 6, 9, and 12 months after the surgery. The obtained data sets were analyzed using a linear mixed-effect model. The analysis revealed that concentration level of 2-methylthioadenosine was decreased, while for inosine, it was increased 24 h after tumor resection in comparison to the preoperative state. The presented quantitative longitudinal study of urine nucleosides and deoxynucleosides before and up to 12 months after bladder tumor resection brings additional prospective insight into the metabolite excretion pattern in bladder cancer disease. Moreover, incurred sample reanalysis was performed proving the robustness and repeatability of the developed targeted method.

Toward the General Mechanistic Model of Liquid Chromatographic Retention.

Large datasets of chromatographic retention times are relatively easy to collect. This statement is particularly true when mixtures of compounds are analyzed under a series of gradient conditions using chromatographic techniques coupled with mass spectrometry detection. Such datasets carry much information about chromatographic retention that, if extracted, can provide useful predictive information. In this work, we proposed a mechanistic model that jointly explains the relationship between pH, organic modifier type, temperature, gradient duration, and analyte retention based on liquid chromatography retention data collected for 187 small molecules. The model was built utilizing a Bayesian multilevel framework. The model assumes (i) a deterministic Neue equation that describes the relationship between retention time and analyte-specific and instrument-specific parameters, (ii) the relationship between analyte-specific descriptors (log P, pKa, and functional groups) and analyte-specific chromatographic parameters, and (iii) stochastic components of between-analyte and residual variability. The model utilizes prior knowledge about model parameters to regularize predictions which is important as there is ample information about the retention behavior of analytes in various stationary phases in the literature. The usefulness of the proposed model in providing interpretable summaries of complex data and in decision making is discussed.

Statistical analysis of isocratic chromatographic data using Bayesian modeling.

Chromatographic retention times are usually modeled considering only one analyte at a time. However, it has certain limitations as no information is shared between the analytes, and consequently the model predictions poorly generalize to out-of-sample analytes. In this work, a publicly available dataset was used to illustrate the benefits of pooling the individual data and analyzing them simultaneously utilizing Bayesian hierarchical approach. Statistical analysis was carried out using the Stan program coupled with R, which enables full Bayesian inference with Markov chain Monte Carlo sampling. This methodology allows (i) incorporating prior knowledge about the likely values of model parameters, (ii) considering the between-analyte variability and the correlation between the model parameters, (iii) explaining the between-analyte variability by available predictors, and (iv) sharing information across the analytes. The latter is especially valuable when only limited information is available in the data about certain model parameters. The results are obtained in the form of posterior probability distribution, which quantifies uncertainty about the model parameters and predictions. Posterior probability is also directly relevant for decision-making. In this work, we used the Neue model to describe the relationship between retention factor and acetonitrile content in the mobile phase for 1026 analytes. The model was parametrized in terms of retention factor in 100% water, retention factor in 100% acetonitrile, and curvature coefficient, and considered log P and pKa as predictors. From this analysis, we discovered that the analytes formed two clusters with different retention depending on the degree of analyte dissociation. The final model turned out to be well calibrated with the data. It gives insight into the behavior of analytes in the chromatographic column and can be used to make predictions for a structurally diverse set of analytes if their log P and pKa values are known.

Application of Bayesian Multilevel Modeling in the Quantitative Structure-Retention Relationship Studies of Heterogeneous Compounds.

Quantitative structure-retention relationships (QSRRs) are used in the field of chromatography to model the relationship between an analyte structure and chromatographic retention. Such models are typically difficult to build and validate for heterogeneous compounds because of their many descriptors and relatively limited analyte-specific data. In this study, a Bayesian multilevel model is proposed to characterize the isocratic retention time data collected for 1026 heterogeneous analytes. The QSRR considers the effects of the molecular mass and 100 functional groups (substituents) on analyte-specific chromatographic parameters of the Neue model (i.e., the retention factor in water, the retention factor in acetonitrile, and the curvature coefficient). A Bayesian multilevel regression model was used to smooth noisy parameter estimates with too few data and to consider the uncertainties in the model parameters. We discuss the benefits of the Bayesian multilevel model (i) to understand chromatographic data, (ii) to quantify the effect of functional groups on chromatographic retention, and (iii) to predict analyte retention based on various types of preliminary data. The uncertainty of isocratic and gradient predictions was visualized using uncertainty chromatograms and discussed in terms of usefulness in decision making. We think that this method will provide the most benefit in providing a unified scheme for analyzing large chromatographic databases and assessing the impact of functional groups and other descriptors on analyte retention.

Nonstationary Pharmacokinetics of Caspofungin in ICU Patients.

Standard dosing of caspofungin in critically ill patients has been reported to result in lower drug exposure, which can lead to subtherapeutic 24-h area under the curve to MIC (AUC0-24/MIC) ratios. The aim of the study was to investigate the population pharmacokinetics of caspofungin in a cohort of 30 intensive care unit patients with a suspected invasive fungal infection, with a large proportion of patients requiring extracorporeal therapies, including extracorporeal membrane oxygenation (ECMO) and continuous renal replacement therapy (CRRT). Caspofungin was administered as empirical 70 mg antifungal therapy administered intravenously (i.v.) on the first day and at 50 mg i.v. on the consecutive days once daily, and the concentrations were measured after three subsequent doses. Population pharmacokinetic data were analyzed by nonlinear mixed-effects modeling. The pharmacokinetics of caspofungin was described by two-compartment model. A particular drift of the individual clearance (CL) and the volume of distribution of the central compartment (V1) with time was discovered and described by including three separate typical values of CL and V1 in the final model. The typical CL values at days 1, 2, and 3 were 0.563 liters/h (6.7% relative standard error [6.7%RSE]), 0.737 liters/h (6.1%RSE), and 1.01 liters/h (9.1%RSE), respectively. The change in parameters with time was not explained by any of the recorded covariates. Increasing clearance with subsequent doses was associated with a clinically relevant decrease in caspofungin exposure (>20%). The use of ECMO, CRRT, albumin concentration, and other covariates did not significantly affect caspofungin pharmacokinetics. Additional pharmacokinetic studies are urgently required to assess the possible lack of acquiring steady-state and suboptimal concentrations of the drug in critically ill patients. (This study has been registered at ClinicalTrials.gov under identifier NCT03399032.).

The influence of cardiac output on propofol and fentanyl pharmacokinetics and pharmacodynamics in patients undergoing abdominal aortic surgery.

Cardiac output (CO) is expected to affect elimination and distribution of highly extracted and perfusion rate-limited drugs. This work was undertaken to quantify the effect of CO measured by the pulse pressure method on pharmacokinetics and pharmacodynamics of propofol and fentanyl administrated during total intravenous anesthesia (TIVA). The data were obtained from 22 ASA III patients undergoing abdominal aortic surgery. Propofol was administered via target-controlled infusion system (Diprifusor) and fentanyl was administered at a dose of 2-3 µg/kg each time analgesia appeared to be inadequate. Hemodynamic measurements as well as bispectral index were monitored and recorded throughout the surgery. Data analysis was performed by using a non-linear mixed-effect population modeling (NONMEM 7.4 software). Three compartment models that incorporated blood flows as parameters were used to describe propofol and fentanyl pharmacokinetics. The delay of the anesthetic effect, with respect to plasma concentrations, was described using a biophase (effect) compartment. The bispectral index was linked to the propofol and fentanyl effect site concentrations through a synergistic Emax model. An empirical linear model was used to describe CO changes observed during the surgery. Cardiac output was identified as an important predictor of propofol and fentanyl pharmacokinetics. Consequently, it affected the depth of anesthesia and the recovery time after propofol-fentanyl TIVA infusion cessation. The model predicted (not observed) CO values correlated best with measured responses. Patients' age was identified as a covariate affecting the rate of CO changes during the anesthesia leading to age-related difference in individual patient's responses to both drugs.

Population analysis to assess the influence of age and body weight on pharmacokinetics and pharmacodynamics of dexmedetomidine in New Zealand White rabbits.

The purpose of this work was i) to develop a population pharmacokinetic (PK) and pharmacodynamic (PD) model of dexmedetomidine (DEX) in New Zealand White rabbits, ii) to investigate the influence of the age and weight of the animals on the model parameters, and iii) to assess the linearity of DEX PKs in the examined dose range. This was a prospective, crossover study, using a total of 18 New Zealand White rabbits. DEX was administered as a single intravenous bolus injection in the doses from 25 to 300 µg kg-1 . Each New Zealand White rabbit was given the same dose of drug in its three developmental stages. To determine the DEX PK, seven blood samples were taken from each animal. The pedal withdrawal reflex was the PD response used to assess the degree of sedation. Nonlinear mixed effects modelling was used for the population PK/PD analysis. The typical value of elimination clearance was 0.061 L min-1 and was 35% higher in younger New Zealand White rabbits compared with older animals. The PK of DEX was linear in the examined concentration range. Age-related changes in sensitivity to DEX were not detected. The results suggest that due to the pharmacokinetics, younger animals will have lower DEX concentrations and a shorter duration of sedation than older animals given the same doses of DEX per kg of body weight.

Population Pharmacokinetics of High-Dose Tigecycline in Patients with Sepsis or Septic Shock.

Tigecycline is a glycylcycline often used in critically ill patients as the antibiotic of last resort. The pharmacokinetics (PK) of tigecycline in intensive care unit (ICU) patients can be affected by severe pathophysiological changes so that standard dosing might not be adequate. The aim of this study was to describe population PK of high-dose tigecycline in patients with sepsis or septic shock and evaluate the relationship between individual PK parameters and patient covariates. The study population consisted of 37 adult ICU patients receiving a 200-mg loading dose of tigecycline followed by multiple doses of 100 mg every 12 h. Blood samples were collected at 0.5, 2, 4, 8, and 12 h after dose administration. A two-compartment model with interindividual (IIV) and interoccasion (IOV) variability in PK parameters was used to describe the concentration-time course of tigecycline. The estimated values of mean population PK parameters were 22.1 liters/h and 69.4 liters/h for elimination and intercompartmental clearance, respectively, and 162 liters and 87.9 liters for volume of the central and peripheral compartment, respectively. The IIV and IOV in clearance were less than 20%. The estimated values of distribution volumes were different from previously published values, which might be due to pathophysiological changes in ICU patients. No systematic relationship between individual PK parameters and patient covariates was found. The developed model does not show evidence that individual tigecycline dosing adjustment based on patient covariates is necessary to obtain the same target concentration in patients with sepsis or septic shock. Dosing adjustments should be based on the pathogens, their susceptibility, and PK targets.

Analysis of Isocratic-Chromatographic-Retention Data using Bayesian Multilevel Modeling.

The objective of this work was to develop a multilevel (hierarchical) model based on isocratic-reversed-phase-high-performance-chromatographic data collected in methanol and acetonitrile for 58 chemical compounds. Such a multilevel model is a regression model of the analyte-specific chromatographic measurements, in which all the regression parameters are given a probability model. It is a fundamentally different approach from the most common approach, where parameters are separately estimated for each analyte (without sharing information across analytes and different organic modifiers). The statistical analysis was done with Stan software implementing the Bayesian-statistics inference with Markov-chain Monte Carlo sampling. During the model-building process, a series of multilevel models of different complexity were obtained, such as (1) a model with no pooling (separate models were fitted for each analyte), (2) a model with partial pooling (a common distribution was used for analyte-specific parameters), and (3) a model with partial pooling as well as a regression model relating analyte-specific parameters and analyte-specific properties (QSRR equations). All the models were compared with each other using 10-fold cross-validation. The benefits of multilevel models in inference and predictions were shown. In particular the obtained models allowed us to (i) better understand the data and (ii) solve many routine analytical problems, such as obtaining well-calibrated predictions of retention factors for an analyte in acetonitrile-containing mobile phases given zero, one, or several measurements in methanol-containing mobile phases and vice versa.

Hydroxycarbamide in children with sickle cell anaemia after first-dose vs. chronic therapy: pharmacokinetics and predictive models for drug exposure.

AIMS: The purposes of this work were to: (1) compare pharmacokinetic (PK) parameters for hydroxycarbamide in children receiving their first dose (HCnew ) vs. those receiving chronic therapy (HCchronic ), (2) assess the external validity of a published PK dosing strategy, and (3) explore the accuracy of dosing strategies based on a limited number of HC measurements. METHODS: Utilizing data from two prospective, multicenter trials of hydroxycarbamide (Pharmacokinetics of Liquid Hydroxyurea in Pediatric Patients with Sickle Cell Anemia; NCT01506544 and Single-Dose (SD) and Steady-State (SS) Pharmacokinetics of Hydroxyurea in Children and Adolescents with Sickle Cell Disease), plasma drug concentration vs. time profiles were evaluated with a model independent approach in the HCnew and HCchronic groups. Various predictive scenarios were analysed to evaluate whether systemic exposure with hydroxycarbamide could be accurately predicted. RESULTS: Absorption of hydroxycarbamide was rapid, variable and dose independent. Dose-normalized peak plasma concentrations and drug exposure (AUC) were higher, and weight-normalized apparent oral clearance was lower in the HCnew group. We assessed a PK-guided dosing strategy along with other predictive scenarios and found that inclusion of plasma samples only slightly improved the accuracy of AUC predictions when compared to a population-based method. CONCLUSIONS: Children naïve to hydroxycarbamide exhibit a different PK profile compared to children receiving chronic therapy. Accuracy of population-based dosing is sufficient to target AUCs in individual patients. Further clearance/bioavailability studies are needed to address the factors responsible for variability in the disposition of hydroxycarbamide.

Ionization of tocopherols and tocotrienols in atmospheric pressure chemical ionization.

RATIONALE: Tocopherols and tocotrienols are chemical compounds insusceptible to the ionization process under atmospheric pressure conditions. Therefore, the selection of the optimal ion source settings for their quantification requires special attention. The aim of this study was to analyse the influence of the APCI source parameters on the response of tocochromanols and two related compounds. METHODS: Standard solutions of target compounds were injected on the high-performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry (HPLC/APCI-MS/MS) system separately and analysed with 30 randomly selected ion source settings. The obtained responses were modelled by multivariate linear regression with least absolute shrinkage and selection operator. The developed models were used to choose the best APCI conditions. RESULTS: Multivariate linear models were built for eight tocochromanols, trolox and BHT. The APCI settings derived from the models did not increase the peak areas obtained for T and T3 during the ionization process. Ionization conditions based on models for trolox and BHT improved analytical responses by 12-36% and 4-32%, respectively. The application of the ion source settings optimal for trolox and BHT to tocochromanols did not result in better analytical responses. CONCLUSIONS: The ionization pattern of tocochromanols in the APCI source is problematic and should be further investigated. Modelling methodology for response improvement presented in this study can be applied in similar studies.

Analyzing chromatographic data using multilevel modeling.

It is relatively easy to collect chromatographic measurements for a large number of analytes, especially with gradient chromatographic methods coupled with mass spectrometry detection. Such data often have a hierarchical or clustered structure. For example, analytes with similar hydrophobicity and dissociation constant tend to be more alike in their retention than a randomly chosen set of analytes. Multilevel models recognize the existence of such data structures by assigning a model for each parameter, with its parameters also estimated from data. In this work, a multilevel model is proposed to describe retention time data obtained from a series of wide linear organic modifier gradients of different gradient duration and different mobile phase pH for a large set of acids and bases. The multilevel model consists of (1) the same deterministic equation describing the relationship between retention time and analyte-specific and instrument-specific parameters, (2) covariance relationships relating various physicochemical properties of the analyte to chromatographically specific parameters through quantitative structure-retention relationship based equations, and (3) stochastic components of intra-analyte and interanalyte variability. The model was implemented in Stan, which provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods. Graphical abstract Relationships between log k and MeOH content for acidic, basic, and neutral compounds with different log P. CI credible interval, PSA polar surface area.

Pharmacokinetics of dexmedetomidine during analgosedation in ICU patients.

Dexmedetomidine (DEX) is a fairly new alfa2-agonist which has been increasingly used in recent years for analgosedation, mostly because it offers a unique ability of providing both moderate level of sedation and analgesia without respiratory depression. Despite of many papers published, there are still only a few concerning the PK of the drug given as long-term infusion in ICU patients. The aim of this work was to characterize the population pharmacokinetics of dexmedetomidine and to investigate the potential benefits of individualization of drug dosing based on patient characteristics in the heterogeneous group of medical and surgical patients staying in intensive care unit. This study was performed in the group of 17 males and 10 females patients with a median age of 59.5 years and median body weight of 75 kg. Blood samples for dexmedetomidine assay were collected from arterial catheter, during and after discontinuation of a standard infusion, that ranged from 24 to 102 h. The following covariates were examined to influence dexmedetomidine PK: age, sex, body weight, patients' health status described by Sequential Organ Failure Assessment Score (SOFA), inotropes usage, and infusion duration. The dexmedetomidine PK was best described by a two-compartment model. The typical values of PK parameters were estimated as 27 L for the volume of the central compartment, 87.6 L for the volume of the peripheral compartment, 38.5 L/h (9.2 mL/min/kg for a 70 kg patient) for systemic clearance and 46.4 L/h for the distribution clearance. Those values are consistent with literature findings. We were unable to show any significant relationship between collected covariates and dexmedetomidine PK. This study does not provide sufficient evidence to support the individualization of dexmedetomidine dosing based on age, sex, body weight, SOFA, and infusion duration.

How to model temporal changes in nontargeted metabolomics study? A Bayesian multilevel perspective.

Analysis of time series data addresses the question on mechanisms underlying normal physiology and its alteration under pathological conditions. However, adding time variable to high-dimension, collinear, noisy data is a challenge in terms of mining and analysis. Here, we used Bayesian multilevel modeling for time series metabolomics in vivo study to model different levels of random effects occurring as a consequence of hierarchical data structure. A multilevel linear model assuming different treatment effects with double exponential prior, considering major sources of variability and robustness to outliers was proposed and tested in terms of performance. The treatment effect for each metabolite was close to zero suggesting small if any effect of cancer on metabolomics profile change. The average difference in 964 signals for all metabolites varied by a factor ranging from 0.8 to 1.25. The inter-rat variability (expressed as a coefficient of variation) ranged from 3-30% across all metabolites with median around 10%, whereas the inter-occasion variability ranged from 0-30% with a median around 5%. Approximately 36% of metabolites contained outlying data points. The complex correlation structure between metabolite signals was revealed. We conclude that kinetics of metabolites can be modeled using tools accepted in pharmacokinetics type of studies.

Age-structured population model of cell survival.

Age-structured cell population model was introduced to describe cell survival. The impact of the environment on the cell population is represented by drug plasma concentration. A key model variable is the hazard of cell removal that is a subject to the environment effect. The model is capable of describing cohort and random labeling cell survival data. In addition, it accounts for cell loss due to labeling of cell sample, but it lacks ability to describe the effect of label elution on the survival data. The model was applied to red blood cell (RBC) survival data in two groups of Wistar rats obtained by two techniques: cohort labeling using 14C-glycine (N = 4) and random labeling using biotin (N = 8). The Weibull probability density function was selected for the RBC lifespan distribution. The data were simultaneously fitted by the mixed effects model implemented in Monolix 4.3.3. The estimated typical values of RBC lifespan and age were 53.7 and 27.8 days, respectively. A noticeable effect of biotinylation on RBC survival was observed that resulted in a significant difference between the means of individual RBC lifespan for two groups. The model provides a mechanistic framework flexible enough to account for various experimental designs to generate the cell survival data. Despite model qualification using animal data, the model has the same potential to be applied to cell survival data analysis in humans.

How Much Can We Learn from a Single Chromatographic Experiment? A Bayesian Perspective.

In this work, we proposed and investigated a Bayesian inference procedure to find the desired chromatographic conditions based on known analyte properties (lipophilicity, pKa, and polar surface area) using one preliminary experiment. A previously developed nonlinear mixed effect model was used to specify the prior information about a new analyte with known physicochemical properties. Further, the prior (no preliminary data) and posterior predictive distribution (prior + one experiment) were determined sequentially to search towards the desired separation. The following isocratic high-performance reversed-phase liquid chromatographic conditions were sought: (1) retention time of a single analyte within the range of 4-6 min and (2) baseline separation of two analytes with retention times within the range of 4-10 min. The empirical posterior Bayesian distribution of parameters was estimated using the "slice sampling" Markov Chain Monte Carlo (MCMC) algorithm implemented in Matlab. The simulations with artificial analytes and experimental data of ketoprofen and papaverine were used to test the proposed methodology. The simulation experiment showed that for a single and two randomly selected analytes, there is 97% and 74% probability of obtaining a successful chromatogram using none or one preliminary experiment. The desired separation for ketoprofen and papaverine was established based on a single experiment. It was confirmed that the search for a desired separation rarely requires a large number of chromatographic analyses at least for a simple optimization problem. The proposed Bayesian-based optimization scheme is a powerful method of finding a desired chromatographic separation based on a small number of preliminary experiments.

The pharmacokinetics of dexmedetomidine during long-term infusion in critically ill pediatric patients. A Bayesian approach with informative priors.

The purpose of this study was to assess the pharmacokinetics of dexmedetomidine in the ICU settings during the prolonged infusion and to compare it with the existing literature data using the Bayesian population modeling with literature-based informative priors. Thirty-eight patients were included in the analysis with concentration measurements obtained at two occasions: first from 0 to 24 h after infusion initiation and second from 0 to 8 h after infusion end. Data analysis was conducted using WinBUGS software. The prior information on dexmedetomidine pharmacokinetics was elicited from the literature study pooling results from a relatively large group of 95 children. A two compartment PK model, with allometrically scaled parameters, maturation of clearance and t-student residual distribution on a log-scale was used to describe the data. The incorporation of time-dependent (different between two occasions) PK parameters improved the model. It was observed that volume of distribution is 1.5-fold higher during the second occasion. There was also an evidence of increased (1.3-fold) clearance for the second occasion with posterior probability equal to 62 %. This work demonstrated the usefulness of Bayesian modeling with informative priors in analyzing pharmacokinetic data and comparing it with existing literature knowledge.

Pharmacokinetics and pharmacodynamics of propofol and fentanyl in patients undergoing abdominal aortic surgery - a study of pharmacodynamic drug-drug interactions.

Propofol is routinely combined with opioid analgesics to ensure adequate anesthesia during surgery. The aim of the study was to assess the effect of fentanyl on the hypnotic effect of propofol and the possible clinical implications of this interaction. The pharmacokinetic/pharmacodynamic (PK/PD) data were obtained from 11 patients undergoing abdominal aortic surgery, classified as ASA III. Propofol was administered by a target-controlled infusion system. Fentanyl 2-3 µg/kg was given whenever insufficient analgesia occurred. The bispectral index (BIS) was used to monitor the depth of anesthesia. A population PK/PD analysis with a non-linear mixed-effect model (NONMEM 7.2 software) was conducted. Two-compartment models satisfactorily described the PK of propofol and fentanyl. The delay of the anesthetic effect in relation to PK was described by the effect compartment. The BIS was linked to propofol and fentanyl effect-site concentrations through an additive Emax model. Context-sensitive decrement times (CSDT) determined from the final model were used to assess the influence of fentanyl on the recovery after anesthesia. The population PK/PD model was successfully developed to describe simultaneously the time course and variability of propofol and fentanyl concentrations and BIS. Additive propofol-fentanyl interactions were observed and quantitated. The duration of the fentanyl infusion had minimal effect on CSDT when it was shorter than the duration of the propofol infusion. If the fentanyl infusion was longer than the propofol infusion, an almost two-fold increase in CSDT occurred. Additional doses of fentanyl administered after the cessation of the propofol infusion result in lower BIS values, and can prolong the time of recovery from anesthesia. Copyright © 2016 John Wiley & Sons, Ltd.

The pharmacokinetics of propofol in ICU patients undergoing long-term sedation.

The aim of this study was to characterize the pharmacokinetics (PK) of propofol in ICU patients undergoing long-term sedation and to assess the influence of routinely collected covariates on the PK parameters. Propofol concentration-time profiles were collected from 29 patients. Non-linear mixed-effects modelling in NONMEM 7.2 was used to analyse the observed data. The propofol pharmacokinetics was best described with a three-compartment disposition model. Non-parametric bootstrap and a visual predictive check were used to evaluate the adequacy of the developed model to describe the observations. The typical value of the propofol clearance (1.46 l/min) approximated the hepatic blood flow. The volume of distribution at steady state was high and was equal to 955.1 l, which is consistent with other studies involving propofol in ICU patients. There was no statistically significant covariate relationship between PK parameters and opioid type, SOFA score on the day of admission, APACHE II, predicted death rate, reason for ICU admission (sepsis, trauma or surgery), gender, body weight, age, infusion duration and C-reactive protein concentration. The population PK model was developed successfully to describe the time-course of propofol concentration in ICU patients undergoing prolonged sedation. Despite a very heterogeneous group of patients, consistent PK profiles were observed. Copyright © 2016 John Wiley & Sons, Ltd.