Utility of a physiologically–based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug–drug–disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice

Abstract

Background: Rivaroxaban is an oral Factor Xa inhibitor. The primary objective of this communication was to quantitatively predict changes in rivaroxaban exposure when individuals with varying degrees of renal impairment are co-administered with another drug that is both a P-gp and a moderate CYP3A4 inhibitor. Methods: A physiologically based pharmacokinetic (PBPK) model was developed to simulate rivaroxaban pharmacokinetics in young (20-45 years) or older (55-65 years) subjects with normal renal function, mild, moderate and severe renal impairment, with or without concomitant use of the combined P-gp and moderate CYP3A4 inhibitor, erythromycin. Results: The simulations indicate that combined factors (i.e., renal impairment and the use of erythromycin) have a greater impact on rivaroxaban exposure than expected when the impact of these factors are considered individually. Compared with normal young subjects taking rivaroxaban, concurrent mild, moderate or severe renal impairment plus erythromycin resulted in 1.9-, 2.4-or 2.6-fold increase in exposure, respectively in young subjects; and 2.5-, 2.9-or 3.0-fold increase in exposure in older subjects. Conclusions: These simulations suggest that a drug-drug-disease interaction is possible, which may significantly increase rivaroxaban exposure and increase bleeding risk. These simulations render more mechanistic insights as to the possible outcomes and allow one to reach a decision to add cautionary language to the approved product labeling for rivaroxaban. Copyright © 2012 John Wiley & Sons, Ltd.

Comparisons between Novel Oral Anticoagulants and Vitamin K Antagonists in Patients with CKD

“…AKI would cause a sudden decrease in kidney function and hence the rapid accumulation of the NOACs, particularly for those for which the kidneys are the major route of excretion, such as dabigatran. 36,37 Our findings need to be interpreted within the limitations of the study design. We pooled the results of a group of studies that were not intended to explore bleeding outcomes in a subgroup of patients with CKD.…”

From preclinical to human – prediction of oral absorption and drug–drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: a workflow by using case example

“…With the advancement of science and shrinking budgets for performing expensive clinical studies, more and more pharmaceutical companies are relying on modeling and simulation before embarking upon any clinical study. There has been an increasing support and encouragement from regulatory authorities on this front as well [25][26][27]. Modeling and simulation can be a very promising and effective approach, however, one should recognize the fact that without proper mechanistic insight into any model considerations, it can act as a double-edged sword.…”

Dose Selection Based on Physiologically Based Pharmacokinetic (PBPK) Approaches

“…In this context, between July 2008 and June 2010, the Office of Clinical Pharmacology at CDER, FDA, reviewed several IND and NDA submissions that incorporated PBPK simulations (18). Examples where PBPK approaches have been used to simulate PK in different populations and/or to justify dose adjustments/selection, include simulation of paediatric PK (78-82) and patient PK (83-85) from healthy volunteer data and prediction of the effects of organ impairment on PK (75)(76)(77)86,87).…”

“…In later drug development stages, the model can be updated with animal data specific to the therapeutic mAb to enable translation from preclinical species to human populations. We will illustrate fit- Prospective prediction of first-in-human PK in healthy volunteers (72)(73)(74) Retrospective prediction of first-in-human PK in healthy volunteers (8-11, 16,71) Prediction of PK in paediatric populations (78)(79)(80)(81)(82) Prediction of PK in patients with hepatic impairment/ liver cirrhosis (75,76) Prediction of PK in patients with renal impairment (77,86,87) Predictions of PK in other patient populations (83)(84)(85) Predictions of PK in polymorphic populations (88) for-purpose PBPK models using one example from literature and one in-house example. Adalimumab is an anti-TNF-alpha mAb approved for the treatment of rheumatoid arthritis (RA), an autoimmune disease which leads to inflammation of joints.…”