Aims: Rivaroxaban is a viable anticoagulant for the management of cancer-associated venous thromboembolism (CA-VTE). A previously verified physiologically-based pharmacokinetic (PBPK) model of rivaroxaban established how its multiple pathways of elimination via both CYP3A4/2J2-mediated hepatic metabolism and organic anion transporter 3 (OAT3)/P-glycoprotein-mediated renal secretion predisposes rivaroxaban to drug-drug-disease interactions (DDDIs) with clinically relevant protein kinase inhibitors (PKIs). We proposed the application of PBPK modelling to prospectively interrogate clinically significant DDIs between rivaroxaban and PKIs (erlotinib and nilotinib) for dose adjustments in CA-VTE.
Methods:The inhibitory potencies of the PKIs 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 transport inhibitory constants (K i ). Untested DDDIs between rivaroxaban and erlotinib or nilotinib were simulated.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 K i values of ketoconazole and nilotinib for the accurate prediction of interactions was illustrated. When simulated rivaroxaban exposure variations with concomitant erlotinib and nilotinib therapy were evaluated using published doseexposure equivalence metrics and bleeding risk analyses, dose reductions from 20 to 15 and 10 mg in normal and mild renal dysfunction, respectively, were warranted.
Perfluorooctanoic acid (PFOA) is an environmental toxicant exhibiting a years-long biological half-life (t 1/2 ) in humans and is linked with adverse health effects. However, limited understanding of its toxicokinetics (TK) has obstructed the necessary risk assessment. Here, we constructed the first middleout physiologically based toxicokinetic (PBTK) model to mechanistically explain the persistence of PFOA in humans. In vitro transporter kinetics were thoroughly characterized and scaled up to in vivo clearances using quantitative proteomics-based in vitro-to-in vivo extrapolation. These data and physicochemical parameters of PFOA were used to parameterize our model. We uncovered a novel uptake transporter for PFOA, highly likely to be monocarboxylate transporter 1 which is ubiquitously expressed in body tissues and may mediate broad tissue penetration. Our model was able to recapitulate clinical data from a phase I doseescalation trial and divergent half-lives from clinical trial and biomonitoring studies. Simulations and sensitivity analyses confirmed the importance of renal transporters in driving extensive PFOA reabsorption, reducing its clearance and augmenting its t 1/2 . Crucially, the inclusion of a hypothetical, saturable renal basolateral efflux transporter provided the first unified explanation for the divergent t 1/2 of PFOA reported in clinical (116 days) versus biomonitoring studies (1.3−3.9 years). Efforts are underway to build PBTK models for other perfluoroalkyl substances using similar workflows to assess their TK profiles and facilitate risk assessments.
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 (K). 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 K 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.
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