Application of a PBPK Model of Rivaroxaban to Prospective Simulations of
Drug-Drug-Disease Interactions with Protein Kinase Inhibitors in CA-VTE
Abstract
Background and Purpose Rivaroxaban is emerging as a viable
anticoagulant for the pharmacological management of cancer associated
venous thromboembolism (CA-VTE). Being eliminated via
CYP3A4/2J2-mediated metabolism and organic anion transporter 3
(OAT3)/P-glycoprotein-mediated renal secretion, rivaroxaban is
susceptible to drug-drug interactions (DDIs) with protein kinase
inhibitors (PKIs), erlotinib and nilotinib. Physiologically based
pharmacokinetic (PBPK) modelling was applied to interrogate the DDIs for
dose adjustment of rivaroxaban in CA-VTE. Experimental Approach
The inhibitory potencies of erlotinib and nilotinib on
CYP3A4/2J2-mediated metabolism of rivaroxaban were characterized. Using
prototypical OAT3 inhibitor ketoconazole, in vitro OAT3
inhibition assays were optimized to ascertain the in vivo relevance of
derived inhibitory constants (Ki). DDIs between
rivaroxaban and erlotinib or nilotinib were investigated using
iteratively verified PBPK model. Key Results Mechanism-based
inactivation (MBI) of CYP3A4-mediated rivaroxaban metabolism by both
PKIs and MBI of CYP2J2 by erlotinib were established. The importance of
substrate specificity and nonspecific binding to derive OAT3-inhibitory
Ki values of ketoconazole and nilotinib for the
accurate prediction of DDIs was illustrated. When simulated rivaroxaban
exposure variations with concomitant erlotinib and nilotinib therapy
were evaluated using published dose-exposure equivalence metrics and
bleeding risk analyses, dose reductions from 20 mg to 15 mg and 10 mg in
normal and mild renal dysfunction, respectively, were warranted.
Conclusion and Implications We established the PBPK-DDI
platform to prospectively interrogate and manage clinically relevant
interactions between rivaroxaban and PKIs in patients with underlying
renal impairment. Rational dose adjustments were proposed, attesting to
the capacity of PBPK modelling in facilitating precision medicine.