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Dexamethasone (DEX) is currently the treatment of choice for patients with oxygen-dependent COVID-19. It has been observed, primarily in vitro, that dexamethasone induces the expression of CYP3A and the ABCB1 gene, which encodes P-glycoprotein (P-gp). This has raised concerns about potential interactions between DEX and substrates of CYP3A and P-gp, such as direct oral anticoagulants (DOAC). Currently, there is limited robust evidence to support a clinically significant interaction between DEX and DOAC. Using physiologically based pharmacokinetic modeling (PBPK), we investigated the impact of DEX administered in the context of SARS-CoV-2 infection on the pharmacokinetics of apixaban (APX) and rivaroxaban (RVX). After validating the induction effect of the DEX compound on two CYP3A4 substrates using the limited available studies, we optimized the compound in a COVID-19 patient population, where significantly higher DEX plasma concentrations were observed compared to healthy volunteers. Our PBPK-based PK simulations showed a 20% decrease in the AUC of APX and RVX in a worst-case scenario and when DEX was administered at 6 mg PO for 10 days. This finding confirms the limited clinical data currently available and supports the use of APX and RVX with DEX in COVID-19 patients at low-risk for thrombo-embolism. In addition, our results suggest that prednisone (PRED), when used at an equipotent dose, could serve as a viable alternative to DEX, given its less pronounced induction effect on APX and RVX. Further research is needed to validate these findings and to explore the clinical implications of using PRED in place of DEX in such scenarios., (© 2024 The Author(s). Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Omeprazole (OME) is a CYP2C19 phenotyping probe, marketed as a racemic (S)/(R) mixture or as an S-enantiomer. Both CYP2C19 and CYP3A4 enzymes mediate (R)-OME hydroxylation to (R)-5-hydroxyomeprazole, while (S)-OME is exclusively hydroxylated via CYP2C19. This study investigates OME and its 5-hydroxymetabolite enantiomers' pharmacokinetics using data from two studies involving healthy volunteers. In Study A, volunteers received OME alone in Session 1, OME combined with voriconazole and fluvoxamine in Session 2 and finally OME with rifampicin in Session 3. In Study B, volunteers received OME alone in Session 1, OME combined with voriconazole in Session 2 and finally OME with fluvoxamine in Session 3. Despite low metabolic ratio values of (S)-OME, detectable modulation of CYP2C19 activity suggests both (R)- and (S)-OME isomers could effectively assess CYP2C19 activity. Further research is needed for precise cut-offs in different phenotype groups., (© 2024 The Author(s). Basic & Clinical Pharmacology & Toxicology published by John Wiley & Sons Ltd on behalf of Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society).)

To compensate for drug response variability, drug metabolism phenotypes are determined based on the results of genetic testing, and if necessary, drug dosages are adjusted. In some cases, discrepancies between predicted and observed phenotypes (phenoconversion) may occur due to drug-drug interactions caused by concomitant medications. We conducted a prospective, exploratory study to evaluate the risk of CYP2C19 phenoconversion in genotyped healthy volunteers exposed to CYP2C19 inhibitors. Three groups of volunteers were enrolled: CYP2C19 g-RM, g-NM, and g-IM (g- for genetically predicted). All volunteers received as CYP2C19 phenotyping substrate 10 mg omeprazole (OME) alone at the control session and in co-administration with CYP2C19 inhibitors: voriconazole 400 mg and fluvoxamine 50 mg in second and third study sessions, respectively. Phenoconversion occurred in over 80% of healthy volunteers, with variations among genotypic groups, revealing distinct proportions in response to fluvoxamine and voriconazole. Statistically significant differences were observed in mean metabolic ratios between CYP2C19 intermediate metabolizers (g-IMs) with *1/*2 and *2/*17 genotypes, with the *2/*17 group exhibiting lower ratios, and distinctions were noted between genotypic groups, emphasizing the impact of genetic variations on drug metabolism. When reclassified according to CYP2C19 baseline-measured phenotype into p-RM, p-NM, and p-IM (p- for measured phenotype), we observed 100% phenoconversion of p-RMs and a significant phenotype switch in p-NMs, p-IMs, and p-PMs after fluvoxamine and voriconazole, and complete phenoconversion of p-IMs to p-PMs on both inhibitors, emphasizing the impact of genetic variations on the vulnerability to CYP2C19 phenoconversion and the importance of considering both genotyping and phenotyping in predicting drug response., (© 2024 The Author(s). Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

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 C 18 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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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/m 2 , 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 AUC 0-8 and C max 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 AUC 0-8 of 4% per additional BMI unit was observed, defining a significant relationship between weight and DEX AUC 0-8 (p = 0.004, 95% CI 2-7%). In women, irrespective of the BMI, DEX AUC 0-8 increased by 214% in comparison to men (p < 0.001, 95% CI 154-298%). Similarly, the mean C max 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., (© 2022 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Abouir, Samer, Gloor, Desmeules and Daali.)