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Abstract When used in real‐world conditions, substantial interindividual variations in direct oral anticoagulant (DOAC) plasma concentrations are observed for a given dose, leading to a risk of over‐ or under‐exposure and clinically significant adverse events. Physiologically‐based pharmacokinetic (PBPK) models could help physicians to tailor DOAC prescriptions in vulnerable patient populations, such as those in the hospital setting. The present study aims to validate prospectively PBPK models for rivaroxaban and apixaban in a large cohort of elderly, polymorbid, and hospitalized patients. In using a model of geriatric population integrating appropriate physiological parameters into models first optimized with healthy volunteer data, observed plasma concentration collected in hospitalized patients on apixaban (n = 100) and rivaroxaban (n = 100) were adequately predicted (ratio predicted/observed area under the concentration curve for a dosing interval [AUCtau] = 0.97 [0.96–0.99] geometric mean, 90% confidence interval, ratio predicted/observed AUCtau = 1.03 [1.02–1.05]) for apixaban and rivaroxaban, respectively. Validation of the present PBPK models for rivaroxaban and apixaban in in‐patients represent an additional step toward the feasibility of bedside use.

Abstract This study aimed to characterize apixaban pharmacokinetics (PKs) and its variability in a real‐world clinical setting of hospitalized patients using a population PK (PopPK) approach. Model‐based simulations helped to identify factors that affect apixaban exposure and their clinical significance. A classic stepwise strategy was applied to determine the best PopPK model for describing typical apixaban PKs in hospitalized patients from the OptimAT study (n = 100) and evaluating the associated variability and influencing factors. Apixaban exposure under specific conditions was assessed using the final model. A two‐compartment model with first‐order absorption and elimination best described the data. The developed PopPK model revealed a major role of renal function and a minor role of P‐glycoprotein phenotypic (P‐gp) activity in explaining apixaban variability. The final model indicated that a patient with stage 4 chronic kidney disease (creatinine clearance [CLcr] = 15–29 mL/min) would have a 45% higher drug exposure than a patient with normal renal function (CLcr >90 mL/min), with a further 12% increase if the patient was also a poor metabolizer of P‐gp. A high interindividual variability in apixaban PKs was observed in a real‐life setting, which was partially explained by renal function and by P‐gp phenotypic activity. Target apixaban concentrations are reached under standard dosage regimens, but overexposure can rapidly occur in the presence of cumulative factors warranting the development of a predictive tool for tailoring apixaban exposure and its clinical utility in at‐risk patients.

Abstract Xenobiotics can interact with cytochromes P450 (CYPs), resulting in drug–drug interactions, but CYPs can also contribute to drug–disease interactions, especially in the case of inflammation, which downregulates CYP activities through pretranscriptional and posttranscriptional mechanisms. Interleukin‐6 (IL‐6), a key proinflammatory cytokine, is mainly responsible for this effect. The aim of our study was to develop a physiologically based pharmacokinetic (PBPK) model to foresee the impact of elevated IL‐6 levels in combination with drug interactions with esomeprazole on CYP3A and CYP2C19. Data from a cohort of elective hip surgery patients whose CYP3A and CYP2C19 activities were measured before and after surgery were used to validate the accurate prediction of the developed models. Successive steps were to fit models for IL‐6, esomeprazole, and omeprazole and its metabolite from the literature and to validate them. The models for midazolam and its metabolite were obtained from the literature. When appropriate, a correction factor was applied to convert drug concentrations from whole blood to plasma. Mean ratios between simulated and observed areas under the curve for omeprazole/5‐hydroxy omeprazole, esomeprazole, and IL‐6 were 1.53, 1.06, and 0.69, respectively, indicating an accurate prediction of the developed models. The impact of IL‐6 and esomeprazole on the exposure to CYP3A and CYP2C19 probe substrates and respective metabolites were correctly predicted. Indeed, the ratio between predicted and observed mean concentrations were

Abstract Busulfan (Bu) is a common component of conditioning regimens before hematopoietic stem cell transplantation (HSCT) and is known for high interpatient pharmacokinetic (PK) variability. This study aimed to develop and externally validate a multicentric, population PK (PopPK) model for intravenous Bu in pediatric patients before HSCT to first study the influence of glutathione‐s‐transferase A1 (GSTA1) polymorphisms on Bu's PK in a large multicentric pediatric population while accounting for fludarabine (Flu) coadministration and, second, to establish an individualized, model‐based, first‐dose recommendation for intravenous Bu that can be widely used in pediatric patients. The model was built using data from 302 patients from five transplantation centers who received a Bu‐based conditioning regimen. External model validation used data from 100 patients. The relationship between body weight and Bu clearance (CL) was best described by an age‐dependent allometric scaling of a body weight model. A stepwise covariate analysis identified Day 1 of Bu conditioning, GSTA1 metabolic groups based on GSTA1 polymorphisms, and Flu coadministration as significant covariates influencing Bu CL. The final model adequately predicted Bu first‐dose CL in the external cohort, with 81% of predicted area under the curves within the therapeutic window. The final model showed minimal bias (mean prediction error, −0.5%; 95% confidence interval [CI], −3.1% to 2.0%) and acceptable precision (mean absolute prediction error percentage, 18.7%; 95% CI, 17.0%–20.5%) in Bu CL prediction for dosing. This multicentric PopPK study confirmed the influence of GSTA1 polymorphisms and Flu coadministration on Bu CL. The developed model accurately predicted Bu CL and first doses in an external cohort of pediatric patients.

The aim of this work was to predict the extent of Cytochrome P450 2D6 (CYP2D6)‐mediated drug–drug interactions (DDIs) in different CYP2D6 genotypes using physiologically‐based pharmacokinetic (PBPK) modeling. Following the development of a new duloxetine model and optimization of a paroxetine model, the effect of genetic polymorphisms on CYP2D6‐mediated intrinsic clearances of dextromethorphan, duloxetine, and paroxetine was estimated from rich pharmacokinetic profiles in activity score (AS)1 and AS2 subjects. We obtained good predictions for the dextromethorphan–duloxetine interaction (Ratio of predicted over observed area under the curve (AUC) ratio (Rpred/obs) 1.38–1.43). Similarly, the effect of genotype was well predicted, with an increase of area under the curve ratio of 28% in AS2 subjects when compared with AS1 (observed, 33%). Despite an approximately twofold underprediction of the dextromethorphan–paroxetine interaction, an Rpred/obs of 0.71 was obtained for the effect of genotype on the area under the curve ratio. Therefore, PBPK modeling can be successfully used to predict gene–drug–drug interactions (GDDIs). Based on these promising results, a workflow is suggested for the generic evaluation of GDDIs and DDIs that can be applied in other situations.