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.

Population Pharmacokinetics and Probability of Target Attainment Analysis of Nadroparin in Different Stages of COVID-19.

BACKGROUND AND OBJECTIVE: The risk of thrombotic complications in critical patients with COVID-19 remains extremely high, and multicenter trials failed to prove a survival benefit of escalated doses of low-molecular-weight heparins (nadroparin calcium) in this group. The aim of this study was to develop a pharmacokinetic model of nadroparin according to different stages of COVID-19 severity. METHODS: Blood samples were obtained from 43 patients with COVID-19 who received nadroparin and were treated with conventional oxygen therapy, mechanical ventilation, and extracorporeal membrane oxygenation. We recorded clinical, biochemical, and hemodynamic variables during 72 h of treatment. The analyzed data comprised 782 serum nadroparin concentrations and 219 anti-Xa levels. We conducted population nonlinear mixed-effects modeling (NONMEM) and performed Monte Carlo simulations of the probability of target attainment for reaching 0.2-0.5 IU/mL anti-Xa levels in study groups. RESULTS: We successfully developed a one-compartment model to describe the population pharmacokinetics of nadroparin in different stages of COVID-19. The absorption rate constant of nadroparin was 3.8 and 3.2 times lower, concentration clearance was 2.22 and 2.93 times higher, and anti-Xa clearance was 0.87 and 1.1 times higher in mechanically ventilated patients and the extracorporeal membrane oxygenation group compared with patients treated with conventional oxygen, respectively. The newly developed model indicated that 5.900 IU of nadroparin given subcutaneously twice daily in the mechanically ventilated patients led to a similar probability of target attainment of 90% as 5.900 IU of subcutaneous nadroparin given once daily in the group supplemented with conventional oxygen. CONCLUSIONS: Different nadroparin dosing is required for patients undergoing mechanical ventilation and extracorporeal membrane oxygenation to achieve the same targets as those for non-critically ill patients. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov identifier no. NCT05621915.

Population Pharmacokinetic-Pharmacodynamic Modeling and Probability of Target Attainment Analysis of Rocuronium and Sugammadex in Children Undergoing Surgery.

BACKGROUND AND OBJECTIVES: Probability of target attainment (PTA) curves are commonly used to support dose recommendations of antibiotics for different patient groups. In this study we propose PTA analysis to optimize sugammadex dosing in children. METHODS: This study involved data from an observational cohort study of 30 American Society of Anesthesiologists (ASA) Physical Status I and II children undergoing surgery requiring muscle relaxation. All patients received 0.6 mg/kg rocuronium, with sugammadex administered at the end of surgery in three different doses (0.5, 1.0, and 2.0 mg/kg) to reverse the neuromuscular blockade. RESULTS: The data were analyzed using a population Bayesian-based approach. The developed model was used to simulate pharmacokinetic-pharmacodynamic profiles for different patient groups and dosing regimens before the PTA analysis was performed to translate these simulations into a clinically useful measure. The target was defined as neuromuscular blockade reversal measured by Train-of-Four (TOF ratio > 90%) at 1.5, 3, and 5 min post sugammadex dose. The sugammadex doses leading to 90% PTA were determined for different patients' body weights, rocuronium doses, and time gaps between rocuronium and sugammadex administration assuming the model, priors, and gathered data. For comparison, PTA curves for a range of clinical scenarios are provided to illustrate the usefulness of PTA analysis in selecting the appropriate dose for a given patient. CONCLUSIONS: The proposed PTA analysis is useful to support the sugammadex dose selection in different clinical scenarios. TRIAL REGISTRATION: The study was registered by ClinicalTrials.gov under number NCT04851574 on 21 April 2021.

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.

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.

Proof-of-concept study on improved efficacy of rHuEPO administered as a long-term infusion in rats.

BACKGROUND: Human recombinant erythropoietin (rHuEPO) is often used in the treatment of diseases associated with a decreased production of red blood cells (RBC), such as chronic renal failure. rHuEPO is typically administered as an intravenous or subcutaneous (SC) injection every few days. The low minimum effective concentration (MEC) of rHuEPO, compared to the concentrations observed after standard doses, suggests that a low dose of the drug administered as a long-term infusion should be efficacious. This study aimed to compare the efficacy observed after a single subcutaneous administration of rHuEPO with that observed after a long-term infusion of rHuEPO via implanted osmotic pumps at a similar or lower dose. MATERIALS AND METHODS: In this study three rats received rHuEPO as a single SC injection at a dose of 1350 IU/kg, nine via osmotic pumps at a rate of 0.25, 0.5 and 1 IU/kg and at a total dose of 333 IU/kg, 667 IU/kg, 1333 IU/kg. Three rats served as a control group. The erythropoietin concentrations, RBC count and hemoglobin were measured. RESULTS: An increase in RBC count and hemoglobin was observed after SC infusion of rHuEPO. The baseline corrected area under the effect curve for hemoglobin and RBC count was more than 10-times higher for the SC infusion than for a single SC administration with a comparable dose. CONCLUSIONS: This study demonstrates that administration of rHuEPO as a long-term infusion at a rate ensuring MEC allows to achieve a high efficacy of therapy using relatively small doses of the drug.

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.).

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 Pharmacokinetic Model of Dexmedetomidine in a Heterogeneous Group of Patients.

Dexmedetomidine is a hepatically eliminated drug with sedative, anxiolytic, sympatholytic, and analgesic properties that has been increasingly used for various indications in the form of a short or continuous intravenous infusion. This study aimed to propose a population pharmacokinetic (PK) model of dexmedetomidine in a heterogeneous group of intensive care unit patients, incorporating 29 covariates potentially linked with dexmedetomidine PK. Data were collected from 70 patients aged between 0.25 and 88 years and treated with dexmedetomidine infusion for various durations at 1 of 4 medical centers. Statistical analysis was performed using a nonlinear mixed-effect model. Categorical and continuous covariates including demographic data, hemodynamic parameters, biochemical markers, and 11 single-nucleotide polymorphisms were tested. A 2-compartment model was used to describe dexmedetomidine PK. An allometric/isometric scaling was used to account for body weight difference in PK parameters, and the Hill equation was used to describe the maturation of clearance. Typical values of the central and peripheral volume of distribution and the systemic and distribution clearance for a theoretical adult patient were central volume of distribution = 22.50 L, peripheral volume of distribution = 86.1 L, systemic clearance = 34.7 L/h, and distribution clearance = 40.8 L/h. The CYP1A2 genetic polymorphism and noradrenaline administration were identified as significant covariates for clearance. A population PK model of dexmedetomidine was successfully developed. The proposed model is well calibrated to the observed data. The identified covariates account for <5% of interindividual variability and consequently are of low clinical significance for the purpose of dose adjustment.

Population pharmacokinetics of standard-dose meropenem in critically ill patients on continuous renal replacement therapy: a prospective observational trial.

BACKGROUND: The primary objective of this study was to develop a population pharmacokinetic model of meropenem, based on the population of critically ill adult patients undergoing CRRT. The secondary one was to examine the relationship between patient characteristics (covariates) and individual PK parameters. Finally, we aimed to perform Monte Carlo simulations to assess the probability of target attainment (PTA) of %T > MIC considering the uncertainty of PK parameters. MATERIALS AND METHODS: The study population included 19 adult critically ill patients on CRRT, receiving 1 g of meropenem in 1-h infusions every 8 h. Blood samples were collected prior to (time zero) and 15, 30, 45, 60, 75, 90, 120, 180, 240 and 480 min after the start of meropenem administration. Population nonlinear mixed-effects modeling was conducted using NONMEM software, Fortran, and Wings for NONMEM. RESULTS: A two-compartment model was used to describe the available data. Typical values of the central and peripheral volume of distribution, and the CRRT and inter-compartmental clearance for a theoretical patient with 24.6 g/l albumin concertation were V1 = 27.9 l, V2 = 33.7 l, ClCRRT = 15.1 l/h, and Q = 21.1 l/h. A significant covariate relationship between V1 and albumin concentration was observed in the data that was described by a power relationship with - 2.87 exponent. Subsequently performed Monte Carlo simulations of the model allowed us to assess the impact of albumin concentration on PTA. The 40%T > 2 mg/l target was reached in more than 90% of subjects after 1-h infusion of 1000 mg q8h and steady-state conditions. The more stringent 100%T > 2 mg/l target requires higher doses and/or longer infusion durations that depend on the albumin concentration. CONCLUSIONS: The population PK model was successfully developed to describe the time course of meropenem concentrations. The hypoalbuminemia was found to be associated with higher PTA in the CRRT patients after multiple short-term infusions.

Bayesian multilevel model of micro RNA levels in ovarian-cancer and healthy subjects.

In transcriptomics, micro RNAs (miRNAs) has gained much interest especially as potential disease indicators. However, apart from holding a great promise related to their clinical application, a lot of inconsistent results have been published. Our aim was to compare the miRNA expression levels in ovarian cancer and healthy subjects using the Bayesian multilevel model and to assess their potential usefulness in diagnosis. We have analyzed a case-control observational data on expression profiling of 49 preselected miRNA-based ovarian cancer indicators in 119 controls and 59 patients. A Bayesian multilevel model was used to characterize the effect of disease on miRNA levels controlling for differences in age and body weight. The difference between the miRNA level and health status of the patient on the scale of the data variability were discussed in the context of their potential usefulness in diagnosis. Additionally, the cross-validated area under the ROC curve (AUC) was used to assess the expected out-of-sample discrimination index of a different sets of miRNAs. The proposed model allowed us to describe the set of miRNA levels in patients and controls. Three highly correlated miRNAs: miR-101-3p, miR-142-5p, miR-148a-3p rank the highest with almost identical effect sizes that ranges from 0.45 to 1.0. For those miRNAs the credible interval for AUC ranged from 0.63 to 0.67 indicating their limited discrimination potential. A little benefit in adding information from other miRNAs was observed. There were several miRNAs in the dataset (miR-604, hsa-miR-221-5p) for which inferences were uncertain. For those miRNAs more experimental effort is needed to fully assess their effect in the context of new hits discovery and usefulness as disease indicators. The proposed multilevel Bayesian model can be used to characterize the panel of miRNA profile and to assess the difference in expression levels between healthy and cancer individuals.

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.

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.

Dynamics of Erythropoietic Biomarkers in Response to Treatment With Erythropoietin in Belgrade Rats.

Recombinant human erythropoietin (rHuEPO) is used effectively in the treatment of various anemic disorders. Belgrade rat is a useful animal model of anemia caused by defect in iron utilization. The objective of the present study was to investigate the dynamics of erythropoietic biomarkers in Belgrade rats receiving rHuEPO. Pharmacokinetics of rHuEPO was evaluated in Belgrade rats and normal rats after intravenous administration of single doses of the drug (100 and 1350 IU/kg). Pharmacodynamic biomarkers included levels of red blood cells, hemoglobin, and reticulocytes following administration of a single intravenous dose of rHuEPO (100 IU/kg). Red blood cell survival was assessed after treatment with rHuEPO (450 IU/kg), three times a week for 2 weeks. It was found that rHuEPO exhibited non-linear pharmacokinetics in both Belgrade and control rats. At the low dose, plasma concentrations and AUC (area under the curve) were significantly lower while clearance and volume of distribution were higher in Belgrade rats (p < 0.05). At the higher dose, there was no difference in pharmacokinetics between the two groups. Erythropoietic effect of rHuEPO was negligible in Belgrade rats at the dose of 100 IU/kg whereas all studied erythropoietic biomarkers were increased in normal rats. The levels of red blood cells, hemoglobin were significantly lower whereas the percentage of reticulocytes was higher in Belgrade rats compared to that in normal rats (p < 0.05). RHuEPO increased red blood cell survival in both animal groups. In conclusion, rHuEPO effect on erythropoietic biomarkers was stronger in normal rats than Belgrade rats at the studied doses. The findings from this study may provide further insights into understanding of anemic disorders resulting from mutations in the divalent metal transporter.

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.

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.

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.