Physiologically-Based Pharmacokinetic Modeling for the Prediction of a Drug-Drug Interaction of Combined Effects on P-glycoprotein and Cytochrome P450 3A

Effectiveness and Safety of the Coadministration of Rifampin and Warfarin versus Direct Oral Anticoagulants: A Cohort Study

Introduction

Pharmacokinetic studies have shown that rifampin reduces the levels of oral anticoagulants during the initiation of coadministration, raising concerns about an increased thrombotic risk, but there are limited comparative clinical outcomes between rifampin and warfarin compared with direct oral anticoagulants (DOACs). This study aimed to evaluate the effectiveness and safety of concurrent use of rifampin and warfarin versus DOACs, with assessments of outcome-associated factors and oral anticoagulant (OAC) management quality.

Methods

A total of 142 patients given rifampin plus warfarin (n = 56) or DOACs (n = 86) for over 7 days were included, and their clinical data and outcomes were compared.

Results

The median Charlson Comorbidity Index and HAS-BLED (hypertension, abnormal renal/liver function, stroke, bleeding history or predisposition, labile INR, elderly, drugs/alcohol concomitantly) score of the two groups were 2 and 3, respectively. The incidence rate of composite ischemic or thromboembolic events was 2.16 and 1.44 per 10,000 patient-days in the warfarin and DOAC groups, respectively, with an adjusted hazard ratio (HR) of 0.41 (95% confidence interval [CI] 0.02-7.34). The incidence rate of composite major bleeding or clinically relevant nonmajor bleeding events was 1.58 and 1.52 per 10,000 patient-days in the warfarin and DOAC groups, respectively, with an adjusted HR of 1.12 (95% CI 0.32-4.45). The risk of composite bleeding events increased with a higher HAS-BLED score (HR: 1.62, 95% CI: 1.02-2.63). Moreover, 34.3% of warfarin users maintained a percent time in therapeutic range of above 50%. Furthermore, 77.9% of DOAC users received appropriate dosing.

Conclusion

No significant differences were observed in terms of the incidence of thrombotic or bleeding events between the two groups during coadministration. In addition, a higher HAS-BLED score was associated with a greater risk of bleeding events regardless of the class of OACs used. Finally, close monitoring of bleeding events should be considered.

Physiologically based pharmacokinetic modeling of apixaban to predict exposure in populations with hepatic and renal impairment and elderly populations

BACKGROUND

Apixaban is a factor Xa inhibitor with a limited therapeutic index that belongs to the family of oral direct anticoagulants. The pharmacokinetic (PK) behavior of apixaban may be altered in elderly populations and populations with renal or hepatic impairment, necessitating dosage adjustments.

METHODS

This study was conducted to examine how the physiologically based pharmacokinetic (PBPK) model describes the PKs of apixaban in adult and elderly populations and to determine the PKs of apixaban in elderly populations with renal and hepatic impairment. After PBPK models were constructed using the reported physicochemical properties of apixaban and clinical data, they were validated using data from clinical studies involving various dose ranges. Comparing predicted and observed blood concentration data and PK parameters was utilized to evaluate the model's fit performance.

RESULTS

Doses should be reduced to approximately 70% of the healthy adult population for the healthy elderly population to achieve the same PK exposure; approximately 88%, 71%, and 89% of that for the elderly populations with mild, moderate, and severe renal impairment, respectively; and approximately 96%, 81%, and 58% of that for the Child Pugh-A, Child Pugh-B, and Child Pugh-C hepatic impairment elderly populations, respectively to achieve the same PK exposure.

CONCLUSION

The findings indicate that the renal and hepatic function might be considered for apixaban therapy in Chinese elderly patients and the PBPK model can be used to optimize dosage regimens for specific populations.

Physiologically based pharmacokinetic modeling of candesartan to predict the exposure in hepatic and renal impairment and elderly populations

Background

Candesartan cilexetil is a widely used angiotensin II receptor blocker with minimal adverse effects and high tolerability for the treatment of hypertension. Candesartan is administered orally as the prodrug candesartan cilexetil, which is wholly and swiftly converted to the active metabolite candesartan by carboxylesterase during absorption in the intestinal tract. In populations with renal or hepatic impairment, candesartan's pharmacokinetic (PK) behavior may be altered, necessitating dosage adjustments.

Objectives

This study was conducted to examine how the physiologically based PK (PBPK) model characterizes the PKs of candesartan in adult and geriatric populations and to predict the PKs of candesartan in elderly populations with renal and hepatic impairment.

Design

After developing PBPK models using the reported physicochemical properties of candesartan and clinical data, these models were validated using data from clinical investigations involving various dose ranges.

Methods

Comparing predicted and observed blood concentration data and PK parameters was used to assess the fit performance of the models.

Results

Doses should be reduced to approximately 94% of Chinese healthy adults for the Chinese healthy elderly population; approximately 92%, 68%, and 64% of that of the Chinese healthy adult dose in elderly populations with mild, moderate, and severe renal impairment, respectively; and approximately 72%, 71%, and 52% of that of the Chinese healthy adult dose in elderly populations with Child-Pugh-A, Child-Pugh-B, and Child-Pugh-C hepatic impairment, respectively.

Conclusion

The results suggest that the PBPK model of candesartan can be utilized to optimize dosage regimens for special populations.

Apixaban and rivaroxaban's physiologically-based pharmacokinetic model validation in hospitalized patients: A first step for larger use of a priori modeling approach at bed side

When used in real-world conditions, substantial interindividual variations in direct oral anticoagulant (DOAC) plasma concentrations are observed for a given dose, leading to a risk of over- or under-exposure and clinically significant adverse events. Physiologically-based pharmacokinetic (PBPK) models could help physicians to tailor DOAC prescriptions in vulnerable patient populations, such as those in the hospital setting. The present study aims to validate prospectively PBPK models for rivaroxaban and apixaban in a large cohort of elderly, polymorbid, and hospitalized patients. In using a model of geriatric population integrating appropriate physiological parameters into models first optimized with healthy volunteer data, observed plasma concentration collected in hospitalized patients on apixaban (n = 100) and rivaroxaban (n = 100) were adequately predicted (ratio predicted/observed area under the concentration curve for a dosing interval [AUCtau ] = 0.97 [0.96-0.99] geometric mean, 90% confidence interval, ratio predicted/observed AUCtau  = 1.03 [1.02-1.05]) for apixaban and rivaroxaban, respectively. Validation of the present PBPK models for rivaroxaban and apixaban in in-patients represent an additional step toward the feasibility of bedside use.

Physiologically-based pharmacokinetic modeling to predict drug-drug interaction of enzalutamide with combined P-gp and CYP3A substrates

Enzalutamide is known to strongly induce cytochrome P450 3A4 (CYP3A4). Furthermore, enzalutamide showed induction and inhibition of P-glycoprotein (P-gp) in in vitro studies. A clinical drug-drug interaction (DDI) study between enzalutamide and digoxin, a typical P-gp substrate, suggested enzalutamide has weak inhibitory effect on P-gp substrates. Direct oral anticoagulants (DOACs), such as apixaban and rivaroxaban, are dual substrates of CYP3A4 and P-gp, and hence it is recommended to avoid co-administration of these DOACs with combined P-gp and strong CYP3A inducers. Enzalutamide's net effect on P-gp and CYP3A for apixaban and rivaroxaban plasma exposures is of interest to physicians who treat patients for venous thromboembolism with prostate cancer. Accordingly, a physiologically-based pharmacokinetic (PBPK) analysis was performed to predict the magnitude of DDI on apixaban and rivaroxaban exposures in the presence of 160 mg once-daily dosing of enzalutamide. The PBPK models of enzalutamide and M2, a major metabolite of enzalutamide which also has potential to induce CYP3A and P-gp and inhibit P-gp, were developed and verified as perpetrators of CYP3A-and P-gp-mediated interaction. Simulation results predicted a 31% decrease in AUC and no change in Cmax for apixaban and a 45% decrease in AUC and a 25% decrease in Cmax for rivaroxaban when 160 mg multiple doses of enzalutamide were co-administered. In summary, enzalutamide is considered to decrease apixaban and rivaroxaban exposure through the combined effects of CYP3A induction and net P-gp inhibition. Concurrent use of these drugs warrants careful monitoring for efficacy and safety.

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.

Current Practices, Gap Analysis, and Proposed Workflows for PBPK Modeling of Cytochrome P450 Induction: An Industry Perspective

The International Consortium for Innovation and Quality (IQ) Physiologically Based Pharmacokinetic (PBPK) Modeling Induction Working Group (IWG) conducted a survey across participating companies around general strategies for PBPK modeling of induction, including experience with its utility to address various questions, regulatory interactions, and regulatory acceptance. The results highlight areas where PBPK modeling is used with high confidence and identifies opportunities where confidence is lower and further evaluation is needed. To enhance the survey results, the PBPK-IWG also collected case studies and analyzed recent literature examples where PBPK models were applied to predict CYP3A induction-mediated drug-drug interactions. PBPK modeling of induction has evolved and progressed significantly, proving to have great potential to accelerate drug discovery and development. With the aim of enabling optimal use for new molecular entities that are either substrates and/or inducers of CYP3A, the PBPK-IWG proposes initial workflows for PBPK application, discusses future trends, and identifies gaps that need to be addressed.

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

BACKGROUND AND PURPOSE

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.

EXPERIMENTAL APPROACH

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.

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_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 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 a PBPK-DDDI model to prospectively evaluate clinically relevant interactions between rivaroxaban and PKIs for the safe and efficacious management of CA-VTE.

Physiologically-based pharmacokinetic modeling to evaluate in vitro-to-in vivo extrapolation for intestinal P-glycoprotein inhibition

As one of the key components in model‐informed drug discovery and development, physiologically‐based pharmacokinetic (PBPK) modeling linked with in vitro‐to‐in vivo extrapolation (IVIVE) is widely applied to quantitatively predict drug–drug interactions (DDIs) on drug‐metabolizing enzymes and transporters. This study aimed to investigate an IVIVE for intestinal P‐glycoprotein (Pgp, ABCB1)‐mediated DDIs among three Pgp substrates, digoxin, dabigatran etexilate, and quinidine, and two Pgp inhibitors, itraconazole and verapamil, via PBPK modeling. For Pgp substrates, assuming unbound Michaelis‐Menten constant (K_m) to be intrinsic, in vitro‐to‐in vivo scaling factors for maximal Pgp‐mediated efflux rate (J_max) were optimized based on the clinically observed results without co‐administration of Pgp inhibitors. For Pgp inhibitors, PBPK models utilized the reported in vitro values of Pgp inhibition constants (K_i), 1.0 μM for itraconazole and 2.0 μM for verapamil. Overall, the PBPK modeling sufficiently described Pgp‐mediated DDIs between these substrates and inhibitors with the prediction errors of less than or equal to ±25% in most cases, suggesting a reasonable IVIVE for Pgp kinetics in the clinical DDI results. The modeling results also suggest that Pgp kinetic parameters of both the substrates (K_m and J_max) and the inhibitors (K_i) are sensitive to Pgp‐mediated DDIs, thus being key for successful DDI prediction. It would also be critical to incorporate appropriate unbound inhibitor concentrations at the site of action into PBPK models. The present results support a quantitative prediction of Pgp‐mediated DDIs using in vitro parameters, which will significantly increase the value of in vitro studies to design and run clinical DDI studies safely and effectively.

Recent Advances in Drug Transporter Sciences: Highlights From the Year 2020

Drug Metabolism Reviews has an impressive track record of providing scientific reviews in the area of xenobiotic biotransformation over 47 years. It has consistently proved to be resourceful to many scientists from pharmaceutical industry, academia, regulatory agencies working in diverse areas including enzymology, pharmacology, pharmacokinetics and toxicology. Over the last 5 years Drug metabolism Reviews has annually published an industry commentary aimed to highlight novel insights and approaches that have made significant impacts on the field of biotransformation (led by Cyrus Khojasteh). We hope to continue this tradition by providing an overview of advances made in the field of drug transporters during 2020. The field of drug transporters is rapidly evolving as they play an essential role in drug absorption, distribution, clearance and elimination. In this review we have selected outstanding drug transporter articles that have significantly contributed to moving forward the field of transporter science with respect to translation and improved understanding of diverse aspects including uptake clearance, clinical biomarkers, induction, proteomics, emerging transporters and tissue targeting.The theme of this review consists of synopsis that summarizes each article followed by our commentary. The objective of this work is not to provide a comprehensive review but rather exemplify novel insights and state-of-the-art highlights of recent research that have advanced our understanding of drug transporters in drug disposition. We are hopeful that this effort will prove useful to the scientific community and as such request feedback, and further extend an invitation to anyone interested in contributing to future reviews.

Evaluation of Pharmacokinetic Drug-Drug Interactions: A Review of the Mechanisms, In Vitro and In Silico Approaches

Pharmacokinetic drug–drug interactions (DDIs) occur when a drug alters the absorption, transport, distribution, metabolism or excretion of a co-administered agent. The occurrence of pharmacokinetic DDIs may result in the increase or the decrease of drug concentrations, which can significantly affect the drug efficacy and safety in patients. Enzyme-mediated DDIs are of primary concern, while the transporter-mediated DDIs are less understood but also important. In this review, we presented an overview of the different mechanisms leading to DDIs, the in vitro experimental tools for capturing the factors affecting DDIs, and in silico methods for quantitative predictions of DDIs. We also emphasized the power and strategy of physiologically based pharmacokinetic (PBPK) models for the assessment of DDIs, which can integrate relevant in vitro data to simulate potential drug interaction in vivo. Lastly, we pointed out the future directions and challenges for the evaluation of pharmacokinetic DDIs.

Oral peptide delivery: challenges and the way ahead

Peptides and proteins have emerged as potential therapeutic agents and, in the search for the best treatment regimen, the oral route has been extensively evaluated because of its non-invasive and safe nature. The physicochemical properties of peptides and proteins along with the hurdles in the gastrointestinal tract (GIT), such as degrading enzymes and permeation barriers, are challenges to their delivery. To address these challenges, several conventional and novel approaches, such as nanocarriers, site-specific and stimuli specific delivery, are being used. In this review, we discuss the challenges to the oral delivery of peptides and the approaches used to tackle these challenges.