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Background. Dexmedetomidine (DEX) is a widely used sedative agent for treating post-surgery patients. It also acts on hemodynamic parameters like heart rate or cardiac output. This study aimed to develop a pharmacokinetic-pharmacodynamic (PK/PD) model of DEX using bispectral index (BIS) and cardiac output (CO) as a response. Methodology and results. 21 mechanically ventilated elderly cardiac patients undergoing abdominal aortic surgery were enrolled in the study. DEX was given to maintain moderate or deep sedation. Genotypes of ADR2A*55 were identified using real-time PCR-HRM. Data were analyzed using nonlinear mixed-effect modelling. A two-compartment model described DEX pharmacokinetics. The sigmoid Emax and linear models were used to describe BIS and CO measurements. The typical value of EC50 for DEX effects on BIS was 3.62 ng/ml, and the slope between CO and DEX concentrations was 0.819 (L/min)/(ng/ml). We were unable to show the effects of considered covariates on DEX pharmacodynamics. Conclusions. We proposed the PK/PD model of DEX to understand better the BIS and CO changes observed after surgery. The measured CI values were in the reference range showing that the used doses of DEX ensured stable cardiac function in the studied patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 highperformance 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we acquaint the readers with the fundamentals of gradient separation, followed by the latest innovations in this field. We describe the principles of organic modifier- and pH-gradient elution emphasizing the differences and similarities with isocratic separation. The double organic modifier-/pH-gradient is also thoroughly reviewed as a useful method for the simultaneous determination of logkw (substitute of logP) and the pKa of analytes present in complex mixtures. [ABSTRACT FROM AUTHOR]