Assessing circadian rhythms in propofol PK and PD during prolonged infusion in ICU patients

This study evaluates possible circadian rhythms during prolonged propofol infusion in patients in the intensive care unit. Eleven patients were sedated with a constant propofol infusion. The blood samples for the propofol assay were collected every hour during the second day, the third day, and after the termination of the propofol infusion. Values of electroencephalographic bispectral index (BIS), arterial blood pressure, heart rate, blood oxygen saturation and body temperature were recorded every hour at the blood collection time points. A two-compartment model was used to describe propofol pharmacokinetics. Typical values of the central and peripheral volume of distribution and inter-compartmental clearance were VC = 27.7 l, VT = 801 l, and CLD = 2.73 l/min. The systolic blood pressure (SBP) was found to influence the propofol metabolic clearance according to Cl (l/min) = 2.65·(1 − 0.00714·(SBP − 135)). There was no significant circadian rhythm detected with respect to propofol pharmacokinetics. The BIS score was assessed as a direct effect model with EC50 equal 1.98 mg/l. There was no significant circadian rhythm detected within the BIS scores. We concluded that the light–dark cycle did not influence propofol pharmacokinetics and pharmacodynamics in intensive care units patients. The lack of night–day differences was also noted for systolic blood pressure, diastolic blood pressure and blood oxygenation. Circadian rhythms were detected for heart rate and body temperature, however they were severely disturbed from the pattern of healthy patients

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, p<i>K</i>_a, 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