Predicting drug-drug interactions with physiologically based pharmacokinetic/pharmacodynamic modelling and optimal dosing of apixaban and rivaroxaban with dronedarone co-administration.

BACKGROUND: The concurrent administration of dronedarone and oral anti-coagulants is common because both are used in managing atrial fibrillation (AF). Dronedarone is a moderate inhibitor of the cytochrome P450 3A4 (CYP3A4) enzyme and P-glycoprotein (P-gp). Apixaban and rivaroxaban are P-gp and CYP3A4 substrates. This study aims to investigate the impact of exposure and bleeding risk of apixaban or rivaroxaban when co-administered with dronedarone using physiologically based pharmacokinetic/pharmacodynamic analysis. METHODS: Modeling and simulation were conducted using Simcyp® Simulator. The parameters required for dronedarone modeling were collected from the literature. The developed dronedarone physiologically based pharmacokinetic (PBPK) model was verified using reported drug-drug interactions (DDIs) between dronedarone and CYP3A4 and P-gp substrates. The model was applied to evaluate the DDI potential of dronedarone on the exposure of apixaban 5 mg every 12 h or rivaroxaban 20 mg every 24 h in geriatric and renally impaired populations. DDIs precipitating major bleeding risks were assessed using exposure-response analyses derived from literature. RESULTS: The model accurately described the pharmacokinetics of orally administered dronedarone in healthy subjects and accurately predicted DDIs between dronedarone and four CYP3A4 and P-gp substrates with fold errors <1.5. Dronedarone co-administration led to a 1.29 (90 % confidence interval (CI): 1.14-1.50) to 1.31 (90 % CI: 1.12-1.46)-fold increase in the area under concentration-time curve for rivaroxaban and 1.33 (90 % CI: 1.15-1.68) to 1.46 (90 % CI: 1.24-1.92)-fold increase for apixaban. The PD model indicated that dronedarone co-administration might potentiate the mean major bleeding risk of apixaban with a 1.45 to 1.95-fold increase. However, the mean major bleeding risk of rivaroxaban was increased by <1.5-fold in patients with normal or impaired renal function. CONCLUSIONS: Dronedarone co-administration increased the exposure of rivaroxaban and apixaban and might potentiate major bleeding risks. Reduced apixaban and rivaroxaban dosing regimens are recommended when dronedarone is co-administered to patients with AF.

Population pharmacokinetic modeling of pyrotinib in patients with HER2-positive advanced or metastatic breast cancer.

OBJECTIVE: Pyrotinib, a new oral irreversible pan-ErbB tyrosine kinase inhibitor (TKI), has been approved in China for the treatment of HER2-positive advanced or metastatic breast cancer. This study aimed to conduct a population pharmacokinetics (PK) analysis of pyrotinib and to evaluate the impact of patient characteristics on pyrotinib's PK. METHOD: A total of 1152 samples, provided by 59 adult female patients from two phase I clinical trials, were analyzed by nonlinear mixed-effects modeling. Monte Carlo simulation was conducted to assess impact of covariates on the exposure to pyrotinib. RESULTS: The PK of pyrotinib was adequately described by a one-compartment model with first-order absorption and elimination. Patient's age and total protein levels could affect pyrotinib's apparent volume of distribution, and concomitant use of montmorillonite could decrease the bioavailability of pyrotinib by 50.3%. No PK interactions were observed between capecitabine and pyrotinib. CONCLUSION: In this study, a population PK model of pyrotinib was developed to determine the influence of patient characteristics on the PK of pyrotinib. While patient age and total protein levels can significantly affect the apparent distribution volume of pyrotinib, the magnitude of the impact was limited, thus no dosage adjustment was recommended. Furthermore, concomitant use of montmorillonite for diarrhea needs to be taken with precaution.

Precision Cardio-Oncology: Use of Mechanistic Pharmacokinetic and Pharmacodynamic Modeling to Predict Cardiotoxicities of Anti-Cancer Drugs.

The cardiotoxicity of anti-cancer drugs presents as a challenge to both clinicians and patients. Significant advances in cancer treatments have improved patient survival rates, but have also led to the chronic effects of anti-cancer therapies becoming more prominent. Additionally, it is difficult to clinically predict the occurrence of cardiovascular toxicities given that they can be transient or irreversible, with large between-subject variabilities. Further, cardiotoxicities present a range of different symptoms and pathophysiological mechanisms. These notwithstanding, mechanistic pharmacokinetic (PK) and pharmacodynamic (PD) modeling offers an important approach to predict cardiotoxicities and offering precise cardio-oncological care. Efforts have been made to integrate the structures of physiological and pharmacological networks into PK-PD modeling to the end of predicting cardiotoxicities based on clinical evaluation as well as individual variabilities, such as protein expression, and physiological changes under different disease states. Thus, this review aims to report recent progress in the use of PK-PD modeling to predict cardiovascular toxicities, as well as its application in anti-cancer therapies.