Simultaneous determination of 11 related impurities in propofol by gas chromatography/tandem mass spectrometry coupled with pulsed splitless injection technique

A fast, fully validated GC-MS method using a simplified pretreatment for the quantification of short and branched chain fatty acids in human stool

The study of short (SCFAs) and branched chain fatty acids (BCFAs) in human stool related to gastrointestinal diseases, gut microbiota, metabolism, and diet has dramatically increased. As a result, a fast, reliable method with minimal pretreatment is needed for quantification of these metabolites (acetic, propionic, isobutyric, butyric, isovaleric, valeric, and caproic acid) in stool. Therefore, a GC-MS method meeting this criterion was developed. A bias sampling study showed no statistical difference (p > 0.05) in analyte means when comparing 100 mg subsamples of homogenized to non-homogenized samples (n = 6, p values 0.153-0.910). Stool samples were homogenized, diluted with 80:20 water:methanol (v/v), and adjusted to a pH of 1.5-2.5. Samples were vortexed, centrifuged, and directly injected into the GC-MS using pulsed splitless injection offering twofold-to-threefold signal enhancement over a 10:1 split injection. DB-FATWAX Ultra Inert Polyethylene Glycol (PEG) Column showed no peak tailing, reduced responses, or retention time shifts after 1,476 stool injections, while other columns failed before 361 injections. Intra- and inter-day accuracy for stool supernatant samples ranged from -10.21% to 8.88% and -13.25% to 9.91%, while intra- and inter-day precision ranged from 0.21% to 1.21% and 0.89% to 2.84% coefficient of variation (CV), respectively. This method demonstrates excellent linearity (0.9999-1.0000) and low limits of quantification (1.50-8.01 μM). Stool samples proved stable stored at -20°C up to 28 days, and recoveries ranged from 85.04% to 106.59%. Matrix effects in stool are non-significant determined by comparing standard and stool supernatant calibration curve slopes (p > 0.05).

Continuous Flow Synthesis of Propofol

Herein, we report a continuous flow process for the synthesis of 2,6-diisopropylphenol—also known as Propofol—a short-acting intravenous anesthesia, widely used in intensive care medicine to provide sedation and hypnosis. The synthesis is based on a two-step procedure: a double Friedel–Crafts alkylation followed by a decarboxylation step, both under continuous flow.

The use of PBPK modeling across the pediatric age range using propofol as a case

The project SAFEPEDRUG aims to provide guidelines for drug research in children, based on bottom-up and top-down approaches. Propofol, one of the studied model compounds, was selected because it is extensively metabolized in liver and kidney, with an important role for the glucuronidation pathway. Besides, being a lipophilic molecule, it is distributed into fat tissues, from where it redistributes into the systemic circulation. In the past, both bottom-up (Physiologically based pharmacokinetic, PBPK) and top-down approaches (population pharmacokinetic, popPK) were applied to describe its pharmacokinetics (PK). In this work, a combination of the two was used to check their performance to describe PK in children and neonates (both term and preterm) using propofol as a case compound. First, in vitro data was generated in human liver microsomes and recombinant enzymes and used to develop an adult PBPK model in Simcyp^®. Activity adjustment factors (AAFs) were calculated to account for differences between in vitro and in vivo enzyme activity. Clinical data were analyzed using a 3-compartment model in NONMEM. These data were used to construct a retrograde PBPK model and for qualification of the PBPK models. Once an accurate in vivo clearance was obtained accounting for the contribution of the different metabolic pathways, the resulting PBPK models were challenged with new data for qualification. After that, the constructed adult PPBK model for propofol was extrapolated to the pediatric population. Both the default built-in and in vivo derived ontogeny functions were used to do so. The models were qualified by comparing their predicted PK parameters to published values, and by comparison of predicted concentration-time profiles to available clinical data. Clearance values were predicted well, especially when compared with values obtained from trials where long-term sampling was applied, whereas volume of distribution was lower compared to the most common popPK model predictions. Concentration-time profiles were predicted well up until and including the preterm neonatal population. In this work, it was thus shown that PBPK can be used to predict the PK up to and including the preterm neonatal population without the use of pediatric in vivo data. This work adds weight to the need for further development of PBPK models, especially regarding distribution modeling and the use of in vivo derived ontogeny functions.