Stereoselective separation of omeprazole and 5-hydroxy-omeprazole using dried plasma spots and a heart-cutting 2D-LC approach for accurate CYP2C19 phenotyping.

Omeprazole (OME) is a widely used gastric proton pump inhibitor, marketed as a racemic mixture comprising (S)- and (R)-enantiomers, with distinct pharmacokinetic profiles. OME is primarily metabolized by the cytochrome P450 enzymes 2C19 (CYP2C19) and 3A4 (CYP3A4). OME is a conventional probe for CYP2C19 phenotyping. Accurate measurement of these enantiomers and their metabolites is essential for pharmacokinetic studies. This article presents a sensitive and accurate two-dimensional liquid chromatography-mass spectrometry (LC-MS/MS) method for the simultaneous quantification of OME enantiomers and its hydroxylated metabolite (5-hydroxyomeprazole) in human plasma. The method involves an online extraction using an achiral Discovery HS C18 trapping column for purification (20 × 2.1 mm ID, 5µm particle size, Supelco) and subsequent forward flush elution onto a chlorinated phenylcarbamate cellulose-based chiral column (150x2mm ID, 3 µm particle size, Lux Cellulose-4, Phenomenex). The assay was fully validated and met international validation criteria for accuracy, precision, and stability and ensured high selectivity and sensitivity within a short runtime (<8 min). Application of this method to clinical samples demonstrated its utility in studying OME enantiomer pharmacokinetics, particularly its potential for phenotyping the activity of the CYP2C19 isoenzyme. This robust analytical approach offers a valuable tool for clinicians and researchers studying OME's pharmacokinetics, providing insights into its metabolism and potential implications for personalized medicine.

Dexamethasone exposure in normal-weight and obese hospitalized COVID-19 patients: An observational exploratory trial.

During the latest pandemic, the RECOVERY study showed the benefits of dexamethasone (DEX) use in COVID-19 patients. Obesity has been proven to be an independent risk factor for severe forms of infection, but little information is available in the literature regarding DEX dose adjustment according to body weight. We conducted a prospective, observational, exploratory study at Geneva University Hospitals to assess the impact of weight on DEX pharmacokinetics (PK) in normal-weight versus obese COVID-19 hospitalized patients. Two groups of patients were enrolled: normal-weight and obese (body mass index [BMI] 18.5-25 and >30 kg/m2 , respectively). All patients received the standard of care therapy of 6 mg DEX orally. Blood samples were collected, and DEX concentrations were measured. The mean DEX AUC0-8 and Cmax were lower in the obese compared to the normal-weight group (572.02 ± 258.96 vs. 926.92 ± 552.12 ng h/ml and 138.67 ± 68.03 vs. 203.44 ± 126.30 ng/ml, respectively). A decrease in DEX AUC0-8 of 4% per additional BMI unit was observed, defining a significant relationship between weight and DEX AUC0-8 (p = 0.004, 95% CI 2-7%). In women, irrespective of the BMI, DEX AUC0-8 increased by 214% in comparison to men (p < 0.001, 95% CI 154-298%). Similarly, the mean Cmax increased by 205% in women (p < 0.001, 95% CI 141-297%). Conversely, no significant difference between the obese and normal-weight groups was observed for exploratory treatment outcomes, such as the length of hospitalization. BMI, weight, and gender significantly affected DEX AUC. We conclude that dose adjustment would be needed if the aim is to achieve the same exposures in normal-weight and obese patients.

Reviewing Data Integrated for PBPK Model Development to Predict Metabolic Drug-Drug Interactions: Shifting Perspectives and Emerging Trends.

Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.