Application of a PBPK Model of Rivaroxaban to Prospective Simulations of Drug-Drug-Disease Interactions with Protein Kinase Inhibitors in CA-VTE

Investigating the association between CYP2J2 inhibitors and QT prolongation: a literature review

Drug withdrawal post-marketing due to cardiotoxicity is a major concern for drug developers, regulatory agencies, and patients. One common mechanism of cardiotoxicity is through inhibition of cardiac ion channels, leading to prolongation of the QT interval and sometimes fatal arrythmias. Recently, oxylipin signaling compounds have been shown to bind to and alter ion channel function, and disruption in their cardiac levels may contribute to QT prolongation. Cytochrome P450 2J2 (CYP2J2) is the predominant CYP isoform expressed in cardiomyocytes, where it oxidizes arachidonic acid to cardioprotective epoxyeicosatrienoic acids (EETs). In addition to roles in vasodilation and angiogenesis, EETs bind to and activate various ion channels. CYP2J2 inhibition can lower EET levels and decrease their ability to preserve cardiac rhythm. In this review, we investigated the ability of known CYP inhibitors to cause QT prolongation using Certara's Drug Interaction Database. We discovered that among the multiple CYP isozymes, CYP2J2 inhibitors were more likely to also be QT-prolonging drugs (by approximately 2-fold). We explored potential binding interactions between these inhibitors and CYP2J2 using molecular docking and identified four amino acid residues (Phe61, Ala223, Asn231, and Leu402) predicted to interact with QT-prolonging drugs. The four residues are located near the opening of egress channel 2, highlighting the potential importance of this channel in CYP2J2 binding and inhibition. These findings suggest that if a drug inhibits CYP2J2 and interacts with one of these four residues, then it may have a higher risk of QT prolongation and more preclinical studies are warranted to assess cardiovascular safety.

Mathematical modeling of cardio-oncology: Modeling the systemic effects of cancer therapeutics on the cardiovascular system

Cardiotoxicity is a common side-effect of many cancer therapeutics; however, to-date there has been very little push to understand the mechanisms underlying this group of pathologies. This has led to the emergence of cardio-oncology, a field of medicine focused on understanding the effects of cancer and its treatment on the human heart. Here, we describe how mechanistic modeling approaches have been applied to study open questions in the cardiovascular system and how these approaches are being increasingly applied to advance knowledge of the underlying effects of cancer treatments on the human heart. A variety of mechanistic, mathematical modeling techniques have been applied to explore the link between common cancer treatments, such as chemotherapy, radiation, targeted therapy, and immunotherapy, and cardiotoxicity, nevertheless there is limited coverage in the different types of cardiac dysfunction that may be associated with these treatments. Moreover, cardiac modeling has a rich heritage of mathematical modeling and is well suited for the further development of novel approaches for understanding the cardiotoxicities associated with cancer therapeutics. There are many opportunities to combine mechanistic, bottom-up approaches with data-driven, top-down approaches to improve personalized, precision oncology to better understand, and ultimately mitigate, cardiac dysfunction in cancer patients.

Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs

INTRODUCTION

Physiologically based pharmacokinetics (PBPK) models are increasingly used in the drug research and development, especially in anti-cancer drugs. Between 2001 and 2020, a total of 89 small-molecule targeted antitumor drugs were approved in China and the United States, some of which already included PBPK modeling in their application or approval packages. This article intended to review the prevalence and application of PBPK model in these drugs.

METHOD

Article search was performed in the PubMed to collect English research articles on small-molecule targeted anti-cancer drugs using PBPK modeling. The selected articles were classified into nine categorizes according to the application areas and further analyzed.

RESULT

From 2001 to 2020, more than 60% of small-molecule targeted anti-cancer drugs (54/89) were studied using PBPK model with a wide range of application. Ninety research articles were included, of which 48 involved enzyme-mediated drug-drug interaction (DDI). Of these retrieved articles, Simcyp, GastroPlus, and PK-Sim were the most widely model building platforms, which account for 63.8%, 15.2%, and 8.6%, respectively.

CONCLUSION

PBPK modeling is commonly and widely used to research small-molecule targeted anti-cancer drugs.

Effect of Clarithromycin, a Strong CYP3A and P-glycoprotein Inhibitor, on the Pharmacokinetics of Edoxaban in Healthy Volunteers and the Evaluation of the Drug Interaction with Other Oral Factor Xa Inhibitors by a Microdose Cocktail Approach

PURPOSE

We assessed the differential effect of clarithromycin, a strong inhibitor of cytochrome P450 (CYP) 3A4 and P-glycoprotein, on the pharmacokinetics of a regular dose of edoxaban and on a microdose cocktail of factor Xa inhibitors (FXaI). Concurrently, CYP3A activity was determined with a midazolam microdose.

METHODS

In an open-label fixed-sequence trial in 12 healthy volunteers, the pharmacokinetics of a microdosed FXaI cocktail (μ-FXaI; 25 μg apixaban, 50 μg edoxaban, and 25 μg rivaroxaban) and of 60 mg edoxaban before and during clarithromycin (2 x 500 mg/d) dosed to steady-state was evaluated. Plasma concentrations of study drugs were quantified using validated ultra-performance liquid chromatography-tandem mass spectrometry methods.

RESULTS

Therapeutic clarithromycin doses increased the exposure of a therapeutic 60 mg dose of edoxaban with a geometric mean ratio (GMR) of the area under the plasma concentration-time curve (AUC) of 1.53 (90 % CI: 1.37-1.70; p < 0.0001). Clarithromycin also increased the GMR (90% CI) of the exposure of microdosed FXaI apixaban to 1.38 (1.26-1.51), edoxaban to 2.03 (1.84-2.24), and rivaroxaban to 1.44 (1.27-1.63). AUC changes observed for the therapeutic edoxaban dose were significantly smaller than those observed with the microdose (p < 0.001).

CONCLUSION

Clarithromycin increases FXaI exposure. However, the magnitude of this drug interaction is not expected to be clinically relevant. The edoxaban microdose overestimates the extent of the drug interaction with the therapeutic dose, whereas AUC ratios for apixaban and rivaroxaban were comparable to the interaction with therapeutic doses as reported in the literature.

TRIAL REGISTRATION

EudraCT Number: 2018-002490-22.

Development and verification of a physiologically based pharmacokinetic model of dronedarone and its active metabolite N-desbutyldronedarone: Application to prospective simulation of complex drug-drug interaction with rivaroxaban

AIMS

Despite potential enzyme- and transporter-mediated drug-drug interactions (DDIs) between dronedarone and rivaroxaban in atrial fibrillation (AF) patients, pharmacokinetic/pharmacodynamic data remain limited to guide clinical practice. We aimed to develop, verify and validate a physiologically based pharmacokinetic (PBPK) model of dronedarone and its major metabolite, N-desbutyldronedarone (NDBD), to prospectively interrogate this clinically relevant DDI in healthy and mild renal impairment populations.

METHODS

The middle-out development of our PBPK model combined literature-derived or in-house in vitro data, predicted in silico data and in vivo clinical data. Model verification was performed for intravenous and oral (single and multiple) dosing regimens. Model validation for the accurate prediction of cytochrome P450 (CYP)3A4- and P-glycoprotein-mediated DDI utilized simvastatin and digoxin as respective victim drugs. Rivaroxaban-specific inhibitory parameters of dronedarone and/or NDBD against CYP3A4, CYP2J2, OAT3 and P-glycoprotein were incorporated into the PBPK-DDI model for prospective dronedarone-rivaroxaban DDI simulation.

RESULTS

Dronedarone and NDBD PK following clinically relevant doses of 400 mg dronedarone across single and multiple oral dosing were accurately simulated by incorporating effect of auto-inactivation on dose nonlinearities. Following successful model validation, nondose-adjusted rivaroxaban-dronedarone DDI in healthy and mild renal impairment populations revealed simulated rivaroxaban area under the plasma concentration-time curve up to 24 h fold change greater than dose exposure equivalence (0.70-1.43) at 1.65 and 1.84, respectively. Correspondingly, respective major bleeding risk was 4.24 and 4.70% compared with threshold of 4.5% representing contraindicated rivaroxaban-ketoconazole DDI.

CONCLUSION

Our PBPK-DDI model predicted clinically significant dronedarone-rivaroxaban DDI in both healthy and mild renal impairment subjects. Greater benefit vs. risk could be achieved with rivaroxaban dose reductions to at least 15 mg in mild renal impairment subjects on concomitant dronedarone and rivaroxaban.