Static Versus Dynamic Model Predictions of Competitive Inhibitory Metabolic Drug-Drug Interactions via Cytochromes P450: One Step Forward and Two Steps Backwards

BACKGROUND

Predicting metabolic drug-drug interactions (DDIs) via cytochrome P450 enzymes (CYP) is essential in drug development, but controversy has reemerged recently about whether in vitro-in vivo extrapolation (IVIVE) using static models can replace dynamic models for some regulatory filings and label recommendations.

OBJECTIVE

The aim of this study was to determine if static and dynamic models are equivalent for the quantitative prediction of metabolic DDIs arising from competitive CYP inhibition.

METHODS

Drug parameter spaces were varied to simulate 30,000 DDIs between hypothetical substrates and inhibitors of CYP3A4. Predicted area under the plasma concentration-time profile ratios for substrates (AUCr = AUC(presence of precipitant)/AUC(absence of precipitant)) were compared between dynamic simulations (Simcyp® V21) and corresponding static calculations, giving an inter-model discrepancy ratio (IMDR = AUCrdynamic/AUCrstatic). Dynamic simulations were conducted using a 'population' representative and a 'vulnerable patient' representative with maximal concentration (Cmax) or average steady-state concentration (Cavg,ss) as the inhibitor driver concentrations. IMDRs outside the interval 0.8-1.25 were defined as discrepancy between models.

RESULTS

The highest rate of IMDR <0.8 and IMDR >1.25 discrepancies in the 'population' representative was 85.9% and 3.1%, respectively, when using Cavg,ss as the inhibitor driver concentration. Using the 'vulnerable patient' representative showed the highest rate of IMDR >1.25 discrepancies at 37.8%.

CONCLUSION

Static models are not equivalent to dynamic models for predicting metabolic DDIs via competitive CYP inhibition across diverse drug parameter spaces, particularly for vulnerable patients. Caution is warranted in drug development if static IVIVE approaches are used alone to evaluate metabolic DDI risks.

In vitro assessment of inhibitory effects of kinase inhibitors on CYP2C9, 3A and 1A2: Prediction of drug-drug interaction risk with warfarin and direct oral anticoagulants

OBJECTIVE

This study aimed to evaluate the cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) potential of kinase inhibitors with warfarin and direct oral anticoagulants (DOACs).

METHODS

An in vitro CYP probe substrate cocktail assay was used to study the inhibitory effects of fifteen kinase inhibitors on CYP2C9, 3A, and 1A2. Then, DDI predictions were performed using both mechanistic static and physiologically-based pharmacokinetic (PBPK) models.

RESULTS

Linsitinib, masitinib, regorafenib, tozasertib, trametinib, and vatalanib were identified as competitive CYP2C9 inhibitors (Ki = 1.4, 1.0, 1.1, 3.8, 0.5, and 0.1 μM, respectively). Masitinib and vatalanib were competitive CYP3A inhibitors (Ki = 1.3 and 0.2 μM), and vatalanib noncompetitively inhibited CYP1A2 (Ki = 2.0 μM). Moreover, linsitinib and tozasertib were CYP3A time-dependent inhibitors (KI = 26.5 and 400.3 μM, kinact = 0.060 and 0.026 min-1, respectively). Only linsitinib showed time-dependent inhibition of CYP1A2 (KI = 13.9 μM, kinact = 0.018 min-1). Mechanistic static models identified possible DDI risks for linsitinib and vatalanib with (S)-/(R)-warfarin, and for masitinib with (S)-warfarin. PBPK simulations further confirmed that vatalanib may increase (S)- and (R)-warfarin exposure by 4.37- and 1.80-fold, respectively, and that linsitinib may increase (R)-warfarin exposure by 3.10-fold. Mechanistic static models predicted a smaller risk of DDIs between kinase inhibitors and apixaban or rivaroxaban. The greatest AUC increases (1.50-1.74) were predicted for erlotinib in combination with apixaban and rivaroxaban. Linsitinib, masitinib, and vatalanib were predicted to have a smaller effect on apixaban and rivaroxaban AUCs (AUCR 1.22-1.53). No kinase inhibitor was predicted to increase edoxaban exposure.

CONCLUSIONS

Our results suggest that several kinase inhibitors, including vatalanib and linsitinib, can cause CYP-mediated drug-drug interactions with warfarin and, to a lesser extent, with apixaban and rivaroxaban. The work provides mechanistic insights into the risk of DDIs between kinase inhibitors and anticoagulants, which can be used to avoid preventable DDIs in the clinic.

Simulating Healthy Participant Pharmacokinetics for Renal and Hepatic Impairment Studies: Retrospective Assessment of the Approach

The recruitment of a parallel, healthy participants (HPs) arm in renal and hepatic impairment (RI and HI) studies is a common strategy to assess differences in pharmacokinetics. Limitations in this approach include the underpowered estimate of exposure differences and the use of the drug in a population for which there is no benefit. Recently, a method was published by Purohit et. al. (2023) that leveraged prior population pharmacokinetic (PopPK) modeling-based simulation to infer the distribution of exposure ratios between the RI/HI arms and HPs. The approach was successful, but it was a single example with a robust model having several iterations of development and fitting to extensive HP data. To test in more studies and models at different stages of development, our catalogue of RI/HI studies was searched, and those with suitable properties and from programs with available models were analyzed with the simulation approach. There were 9 studies included in the analysis. Most studies were associated with models that would have been available at the time (ATT) of the study, and all had a current, final model. For 3 studies, the HP PK was not predicted well by the ATT (2) or final (1) models. In comparison to conventional analysis of variance (ANOVA), the simulation approach provided similar point estimates and confidence intervals of exposure ratios. This PopPK based approach can be considered as a method of choice in situations where the simulation of HP data would not be an extrapolation, and when no other complicating factors are present.

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.

Understanding Statin-Roxadustat Drug-Drug-Disease Interaction Using Physiologically-Based Pharmacokinetic Modeling

A different drug-drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HVs), depending on the interplay between drug-drug and disease (drug-drug-disease interaction (DDDI)). Physiologically-based pharmacokinetic (PBPK) modeling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modeling in the severe CKD population is still low when nonrenal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: (i) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) pharmacokinetics (PK) and DDI; and (ii) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and nonrenal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PKs in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HVs within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in patients with severe CKD was predicted to be similar to that in HVs. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat.

Coming full circle: The potential utility of real-world evidence to discern predictions from a physiologically based pharmacokinetic model

Today real word data (RWD) are playing a greater role in informing health care decisions. A physiologically based pharmacokinetic model (PBPK) and observed exposure-risk relationship predicted an increased bleeding risk induced by rivaroxaban (RXB) in patients with mild to moderate chronic kidney disease (CKD) taking concomitant medications that are combined Pgp-CYP3A inhibitors. In this commentary, we explore the potential use of RWD to assess the clinical consequence of this complex drug-drug interaction predicted from PBPK. This is a retrospective, case control, pilot study using a RWD dataset of 896,728 patients with mild to moderate chronic kidney disease and rivaroxaban use that was refined based upon combined Pgp-CYP3A inhibitor exposure and report of drug-induced bleeding (DIB). The odds ratio of patients with mild to moderate chronic kidney disease taking rivaroxaban with or without concurrent Pgp-CYP3A inhibitor use having a DIB was calculated. The odds ratio for DIB was 2.04 (CI95 1.82, 2.3; p < 0.001) suggesting an approximate doubling of bleeding risk which is consistent with the rivaroxaban exposure changes predicted by the published PBPK model and observed exposure-risk relationship. This exploratory analysis demonstrated the potential utility of RWD to assess model-based predictions as part of a drugs life cycle management.

Physiologically-based pharmacokinetic modeling to predict drug-drug interactions of dabigatran etexilate and rivaroxaban in the Chinese older adults

INTRODUCTION

Drug-drug interaction (DDI) is one of the major concerns for the clinical use of NOACs in the older adults considering that coexistence of multiple diseases and comorbidity were common. Current guidelines on the DDI management were established based on clinical studies conducted in healthy adults and mainly focus on the Caucasians, whereas systemic and ethnic differences may lead to distinct management in the Chinese older adults.

OBJECTIVES

To investigate the impact of aging on the DDI magnitude between P-gp and/or CYP3A4 inhibitors with dabigatran etexilate and rivaroxaban in older adults, providing additional information for the use in clinical practice.

RESULTS

Compared with the simulated adult, the AUC of the simulated older adults increased by 42-88% (DABE) and 21-60% (rivaroxaban), respectively, during NOACs monotherapy. Simulation on DDIs predicted that verapamil and clarithromycin further increase the exposure of dabigatran by 29-72% and 40-47%, whereas clarithromycin, fluconazole, and ketoconazole increase the exposure of rivaroxaban by 21-30%, 16-24%, and 194-247% in the older adults. Overall, our simulation result demonstrated that aging and DDIs both increased the exposure of NOACs. However, aging does not have a drastic impact on the extent of DDIs. The DDI ratios of young and old older adults were similar to the adults and were also similar between Caucasians and Chinese.

DISCUSSION

We further simulated the interactions under steady-state based on the EHRA guideline (2021). Our simulation results revealed that recommended reduced dosing regimen of dabigatran etexilate during comedication with verapamil and clarithromycin (110 and 75 mg BID for Chinese young and old older adults) will result in exposure (trough concentration) that was either slightly higher or similar to the trough concentration of patients with any bleeding events. Routine monitoring of bleeding risk is encouraged. Further studies on the use of rivaroxaban in Chinese older adults are warranted.

CONCLUSION

Aging and DDI increases exposure of drug in Chinese older adults. However, aging does not have a drastic impact on the extent of DDIs. Clinical management of DDIs in Chinese older adults in the absence of complex polypharmacy can a priori be similar to the EHRA guideline but routine monitoring of bleeding risk is encouraged when dabigatran etexilate given with verapamil and clarithromycin.

Delineating gene-environment effects using virtual twins of patients treated with clozapine

Studies that focus on individual covariates, while ignoring their interactions, may not be adequate for model-informed precision dosing (MIPD) in any given patient. Genetic variations that influence protein synthesis should be studied in conjunction with environmental covariates, such as cigarette smoking. The aim of this study was to build virtual twins (VTs) of real patients receiving clozapine with interacting covariates related to genetics and environment and to delineate the impact of interacting covariates on predicted clozapine plasma concentrations. Clozapine-treated patients with schizophrenia (N = 42) with observed clozapine plasma concentrations, demographic, environmental, and genotype data were used to construct VTs in Simcyp. The effect of increased covariate virtualization was assessed by performing simulations under three conditions: "low" (demographic), "medium" (demographic and environmental interaction), and "high" (demographic and environmental/genotype interaction) covariate virtualization. Increasing covariate virtualization with interaction improved the coefficient of variation (R² ) from 0.07 in the low model to 0.391 and 0.368 in the medium and high models, respectively. Whereas R² was similar between the medium and high models, the high covariate virtualization model had improved accuracy, with systematic bias of predicted clozapine plasma concentration improving from -138.48 ng/ml to -74.65 ng/ml. A high level of covariate virtualization (demographic, environmental, and genotype) may be required for MIPD using VTs.

Use of Antibiotics in Preterm Newborns

Due to complex maturational and physiological changes that characterize neonates and affect their response to pharmacological treatments, neonatal pharmacology is different from children and adults and deserves particular attention. Although preterms are usually considered part of the neonatal population, they have physiological and pharmacological hallmarks different from full-terms and, therefore, need specific considerations. Antibiotics are widely used among preterms. In fact, during their stay in neonatal intensive care units (NICUs), invasive procedures, including central catheters for parental nutrition and ventilators for respiratory support, are often sources of microbes and require antimicrobial treatments. Unfortunately, the majority of drugs administered to neonates are off-label due to the lack of clinical studies conducted on this special population. In fact, physiological and ethical concerns represent a huge limit in performing pharmacokinetic (PK) studies on these subjects, since they limit the number and volume of blood sampling. Therapeutic drug monitoring (TDM) is a useful tool that allows dose adjustments aiming to fit plasma concentrations within the therapeutic range and to reach specific drug target attainment. In this review of the last ten years’ literature, we performed Pubmed research aiming to summarize the PK aspects for the most used antibiotics in preterms.

Identification of Infigratinib as a Potent Reversible Inhibitor and Mechanism-Based Inactivator of CYP2J2: Nascent Evidence for a Potential In Vivo Metabolic Drug-Drug Interaction with Rivaroxaban

Infigratinib (INF) is a fibroblast growth factor receptor inhibitor that was recently United States Food and Drug Administration-approved for the treatment of advanced or metastatic cholangiocarcinoma. We previously established that INF inhibited and inactivated cytochrome P450 3A4 (CYP3A4). Here, in a follow up to our previous study, we identified for the first time that INF also elicited potent competitive inhibition and mechanism-based inactivation of CYP2J2 with kinetic parameters K i, K I, k inact, and a partition ratio of 1.94 µM, 0.10 µM, 0.026 minute-1, and ∼3, respectively, when rivaroxaban was harnessed as the probe substrate. Inactivation was revealed to exhibit cofactor-dependency and was attenuated by an alternative substrate (astemizole) and direct inhibitor (nilotinib) of CYP2J2. Additionally, the nature of inactivation was unlikely to be pseudo-irreversible and instead arose from covalent modification due to the lack of substantial enzyme activity recovery after dialysis and chemical oxidation, as well as the lack of a resolvable Soret band in spectral scans. Glutathione trapping confirmed that the identity of the putative reactive intermediate implicated in the covalent inactivation of both CYP2J2 and CYP3A4 was identical and likely attributable to an electrophilic p-benzoquinonediimine intermediate of INF. Finally, mechanistic static modeling revealed that by integrating the previously arcane inhibition and inactivation kinetic parameters of CYP2J2-mediated rivaroxaban hydroxylation by INF illuminated in this work, together with those previously documented for CYP3A4, a 49% increase in the systemic exposure of rivaroxaban was projected. Our modeling results predicted a potential risk of metabolic drug-drug interactions between the clinically relevant combination of rivaroxaban and INF in the setting of cancer. SIGNIFICANCE STATEMENT: This study reported that INF elicits potent reversible inhibition and mechanism-based inactivation of CYP2J2. Furthermore, static modelling predicted that its coadministration with the direct oral anticoagulant rivaroxaban may potentially culminate in a metabolic drug-drug interaction (DDI) leading to an increased risk of major bleeding. As rivaroxaban is steadily gaining prominence as the anticoagulant of choice in the treatment of cancer-associated venous thromboembolism, the DDI projections reported here are clinically relevant and warrant further investigation via physiologically based pharmacokinetic modelling and simulation.

Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective

Several regulatory guidances on the use of physiologically based pharmacokinetic (PBPK) analyses and physiologically based biopharmaceutics model(s) (PBBM(s)) have been issued. Workshops are routinely held, demonstrating substantial interest in applying these modeling approaches to address scientific questions in drug development. PBPK models and PBBMs have remarkably contributed to model-informed drug development (MIDD) such as anticipating clinical PK outcomes affected by extrinsic and intrinsic factors in general and specific populations. In this review, we proposed practical considerations for a "base" PBPK model construction and development, summarized current status, challenges including model validation and gaps in system models, and future perspectives in PBPK evaluation to assess a) drug metabolizing enzyme(s)- or drug transporter(s)- mediated drug-drug interactions b) dosing regimen prediction, sampling timepoint selection and dose validation in pediatric patients from newborns to adolescents, c) drug exposure in patients with renal and/or and hepatic organ impairment, d) maternal-fetal drug disposition during pregnancy, and e) pH-mediated drug-drug interactions in patients treated with proton pump inhibitors/acid-reducing agents (PPIs/ARAs) intended for gastric protection. Since PBPK can simulate outcomes in clinical studies with enrollment challenges or ethical issues, the impact of PBPK models on waivers and how to strengthen study waiver is discussed.

Evaluating pharmacokinetic drug-drug interactions of direct oral anticoagulants in patients with renal dysfunction

INTRODUCTION

Drug transporters, metabolic enzymes, and renal clearance play significant roles in the pharmacokinetics of direct oral anticoagulants (DOACs). Recommendations for DOAC drug-drug interactions (DDIs) by the product labeling are limited to selected CYP3A4 and P-glycoprotein inhibitors and lack considerations for concomitant renal dysfunction.

AREAS COVERED

This review focuses on: 1) current recommendations for the management of pharmacokinetic DOAC DDIs and the evidence used to support them; 2) alterations in DOAC exposure in the setting of concomitant DDIs and mild, moderate, and severe renal impairment; 3) clinical outcomes associated with this combination; and 4) expert recommendations for the management of pharmacokinetic DOAC DDIs. English-language, full-text articles on apixaban, dabigatran, rivaroxaban, and edoxaban with a publication date up to 30 September 2021 were retrieved from PubMed.

EXPERT OPINION

Given the lack of supporting clinical data, empiric dose adjustments based on pharmacokinetic data alone should be avoided. When a considerable increase in a DOAC exposure is anticipated, it may be advisable to use an alternative DOAC or anticoagulant from a different class. Future research on identification of DOAC therapeutic ranges and target patient populations is needed to inform clinical utility of DOAC level monitoring to guide the management of DDIs.

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.

Factors Affecting Time-Varying Clearance of Cyclosporine in Adult Renal Transplant Recipients: A Population Pharmacokinetic Perspective

AIM

The pharmacokinetic (PK) properties of cyclosporine (CsA) in renal transplant recipients are patient- and time-dependent. Knowledge of this time-related variability is necessary to maintain or achieve CsA target exposure. Here, we aimed to identify factors explaining variabilities in CsA PK properties and characterize time-varying clearance (CL/F) by performing a comprehensive analysis of CsA PK factors using population PK (popPK) modeling of long-term follow-up data from our institution.

METHODS

In total, 3674 whole-blood CsA concentrations from 183 patients who underwent initial renal transplantation were analyzed using nonlinear mixed-effects modeling. The effects of potential covariates were selected according to a previous study and well-accepted theoretical mechanisms. Model-informed individualized therapeutic regimens were also evaluated.

RESULTS

A two-compartment model adequately described the data and the estimated mean CsA CL/F was 32.6 L h^-1 (relative standard error: 5%). Allometrically scaled body size, hematocrit (HCT) level, CGC haplotype carrier status, and postoperative time may contribute to CsA PK variability. The CsA bioavailability in patients receiving a prednisolone dose (PD) of 80 mg was 20.6% lower than that in patients receiving 20 mg. A significant decrease (52.6%) in CL/F was observed as the HCT increased from 10.5% to 60.5%. The CL/F of the non-CGC haplotype carrier was 14.4% lower than that of the CGC haplotype carrier at 3 months post operation.

CONCLUSIONS

By monitoring body size, HCT, PD, and CGC haplotype, changes in CsA CL/F over time could be predicted. Such information could be used to optimize CsA therapy. CsA dose adjustments should be considered in different postoperative periods.

Safety and efficacy of direct oral anticoagulants under long-term immunosuppressive therapy after liver, kidney and pancreas transplantation

The safety of direct oral anticoagulants (DOACs) in patients after solid organ transplantation (SOT) is not well defined. This study aimed at describing the safety and efficacy of DOACs in patients after SOT. Patients after kidney and/or liver transplantation under maintenance immunosuppression treated with rivaroxaban (n = 26), apixaban (n = 20) and edoxaban (n = 1) were included. Clinical data were collected retrospectively and using a questionnaire. DOAC plasma levels and thrombin generation (TG) were measured in patients after SOT and compared with nontransplanted controls receiving DOACs. DOACs were administered for 84.6 patient-years. Mean immunosuppressive trough levels after DOAC initiation increased from baseline by 18.8 ± 29.6% compared to 3.0 ± 16.5% in matched controls (P = 0.004), without significant differences in dose adjustments. No transplant rejection or significant change in liver or renal function was observed. There was one major bleeding after the observation period but no thromboembolic complication. DOAC plasma levels reached the expected range in all patients. The intrinsic hemostatic activity in transplanted patients was higher compared to nontransplant controls. Treatment with DOACs after SOT is safe and effective. Immunosuppressive trough levels should be monitored after DOAC initiation, particularly in the early phase after SOT. These data should be confirmed in a prospective study.

Design and conduct considerations for studies in patients with impaired renal function

An impaired renal function, including acute and chronic kidney disease and end‐stage renal disease, can be the result of aging, certain disease conditions, the use of some medications, or as a result of smoking. In patients with renal impairment (RI), the pharmacokinetics (PKs) of drugs or drug metabolites may change and result in increased safety risks or decreased efficacy. In order to make specific dose recommendations in the label of drugs for patients with RI, a clinical trial may have to be conducted or, when not feasible, modeling and simulations approaches, such as population PK modeling or physiologically‐based PK modelling may be applied. This tutorial aims to provide an overview of the global regulatory landscape and a practical guidance for successfully designing and conducting clinical RI trials or, alternatively, on applying modeling and simulation tools to come to a dose recommendation for patients with RI in the most efficient manner.

Physiological-based pharmacokinetic modeling trends in pharmaceutical drug development over the last 20-years; in-depth analysis of applications, organizations, and platforms

We assess the advancement of physiologically based pharmacokinetic (PBPK) modeling and simulation (M&S) over the last 20 years (start of 2000 to end of 2019) focusing on the trends in each decade with the relative contributions from different organizations, areas of applications, and software tools used. Unlike many of the previous publications which focused on regulatory applications, our analysis is based on PBPK publications in peer-reviewed journals based on a large sample (>700 original articles). We estimated a rate of growth for PBPK (>40 fold/20 years) that was much steeper than the general pharmacokinetic modeling (<3 fold/20 years) or overall scientific publications (∼3 fold/20 years). The analyses demonstrated that contrary to commonly held belief, commercial specialized PBPK platforms with graphical-user interface were a much more popular choice than open-source alternatives even within academic organizations. These platforms constituted 81% of the whole set of the sample we assessed. The major PBPK applications (top 3) were associated with the study design, predicting formulation effects, and metabolic drug-drug interactions, while studying the fate of drugs in special populations, predicting kinetics in early drug development, and investigating transporter drug interactions have increased proportionally over the last decade. The proportions of application areas based on published research were distinctively different from those shown previously for the regulatory submissions and impact on labels. This may demonstrate the lag time between the research applications versus verified usage within the regulatory framework. The report showed the trend of overall PBPK publications in pharmacology drug development from the past 2 decades stratified by the organizations involved, software used, and area of applications. The analysis showed a more rapid increase in PBPK than that of the pharmacokinetic space itself with an equal contribution from academia and industry. By establishing and recording the journey of PBPK modeling in the past and looking at its current status, the analysis can be used for devising plans based on the anticipated trajectory of future regulatory applications.

Applications of Physiologically Based Pharmacokinetic Modeling of Rivaroxaban-Renal and Hepatic Impairment and Drug-Drug Interaction Potential

The non–vitamin K antagonist oral anticoagulant rivaroxaban is used in several thromboembolic disorders. Rivaroxaban is eliminated via both metabolic degradation and renal elimination as unchanged drug. Therefore, renal and hepatic impairment may reduce rivaroxaban clearance, and medications inhibiting these clearance pathways could lead to drug‐drug interactions. This physiologically based pharmacokinetic (PBPK) study investigated the pharmacokinetic behavior of rivaroxaban in clinical situations where drug clearance is impaired. A PBPK model was developed using mass balance and bioavailability data from adults and qualified using clinically observed data. Renal and hepatic impairment were simulated by adjusting disease‐specific parameters, and concomitant drug use was simulated by varying enzyme activity in virtual populations (n = 1000) and compared with pharmacokinetic predictions in virtual healthy populations and clinical observations. Rivaroxaban doses of 10 mg or 20 mg were used. Mild to moderate renal impairment had a minor effect on area under the concentration‐time curve and maximum plasma concentration of rivaroxaban, whereas severe renal impairment caused a more pronounced increase in these parameters vs normal renal function. Area under the concentration‐time curve and maximum plasma concentration increased with severity of hepatic impairment. These effects were smaller in the simulations compared with clinical observations. AUC and C_max increased with the strength of cytochrome P450 3A4 and P‐glycoprotein inhibitors in simulations and clinical observations. This PBPK model can be useful for estimating the effects of impaired drug clearance on rivaroxaban pharmacokinetics. Identifying other factors that affect the pharmacokinetics of rivaroxaban could facilitate the development of models that approximate real‐world pharmacokinetics more accurately.

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

Direct oral anticoagulants, such as apixaban and rivaroxaban, are important for the treatment and prophylaxis of venous thromboembolism and to reduce the risk of stroke and systemic embolism in patients with nonvalvular atrial fibrillation. Because apixaban and rivaroxaban are predominantly eliminated by cytochrome P450 (CYP) 3A and P‐glycoprotein (P‐gp), concomitant use of combined P‐gp and strong CYP3A4 inhibitors and inducers should be avoided. Physiologically‐based pharmacokinetic models for apixaban and rivaroxaban were developed to estimate the net effect of CYP3A induction, P‐gp inhibition, and P‐gp induction by rifampicin. The disposition of rivaroxaban is more complex compared with apixaban because both hepatic and renal P‐gp is considered to contribute to rivaroxaban elimination. Furthermore, organic anion transporter‐3, a renal uptake transporter, may also contribute the elimination of rivaroxaban from systemic circulation. The models were verified with observed clinical drug–drug interactions with CYP3A and P‐gp inhibitors. With the developed models, the predicted area under the concentration time curve and maximum concentration ratios were 0.43 and 0.48, respectively, for apixaban, and 0.50–0.52 and 0.72–0.73, respectively, for rivaroxaban when coadministered with 600 mg multiple doses of rifampicin and that were very close to observed data. The impact of each of the elimination pathways was assessed for rivaroxaban, and inhibition of CYP3A led to a larger impact over intestinal and hepatic P‐gp. Inhibition of renal organic anion transporter‐3 or P‐gp led to an overall modest interaction. The developed apixaban and rivaroxaban models can be further applied to the investigation of interactions with other P‐gp and/or CYP3A4 inhibitors and inducers.

Novel Mechanistic PBPK Model to Predict Renal Clearance in Varying Stages of CKD by Incorporating Tubular Adaptation and Dynamic Passive Reabsorption

Chronic kidney disease (CKD) has significant effects on renal clearance (CLr ) of drugs. Physiologically-based pharmacokinetic (PBPK) models have been used to predict CKD effects on transporter-mediated renal active secretion and CLr for hydrophilic nonpermeable compounds. However, no studies have shown systematic PBPK modeling of renal passive reabsorption or CLr for hydrophobic permeable drugs in CKD. The goal of this study was to expand our previously developed and verified mechanistic kidney model to develop a universal model to predict changes in CLr in CKD for permeable and nonpermeable drugs that accounts for the dramatic nonlinear effect of CKD on renal passive reabsorption of permeable drugs. The developed model incorporates physiologically-based tubular changes of reduced water reabsorption/increased tubular flow rate per remaining functional nephron in CKD. The final adaptive kidney model successfully (absolute fold error (AFE) all < 2) predicted renal passive reabsorption and CLr for 20 permeable and nonpermeable test compounds across the stages of CKD. In contrast, use of proportional glomerular filtration rate reduction approach without addressing tubular adaptation processes in CKD to predict CLr generated unacceptable CLr predictions (AFE = 2.61-7.35) for permeable compounds in severe CKD. Finally, the adaptive kidney model accurately predicted CLr of para-amino-hippuric acid and memantine, two secreted compounds, in CKD, suggesting successful integration of active secretion into the model, along with passive reabsorption. In conclusion, the developed adaptive kidney model enables mechanistic predictions of in vivo CLr through CKD progression without any empirical scaling factors and can be used for CLr predictions prior to assessment of drug disposition in renal impairment.

Incorporating nonlinear kinetics to improve predictive performance of population pharmacokinetic models for ciclosporin in adult renal transplant recipients: A comparison of modelling strategies

BACKGROUND

Ciclosporin has been shown to follow nonlinear pharmacokinetics (PK) in renal transplant recipients who received ciclosporin (Neoral^Ⓡ, Novartis)-based triple immunosuppressive therapy. Some of these nonlinear properties have not been fully considered in population PK (popPK) analysis. Therefore, the aim of this study was to determine the potential influence of nonlinearity and the functional forms of covariates on model predictability as well as to analyse multiple nonlinear factors in the in vivo process.

METHODS

A total of 2969 ciclosporin whole-blood measurements, including 1328 pre-dose and 1641 2-h post-dose concentrations, were collected from 173 patients who underwent their first renal transplantation. Four popPK models based on different modelling strategies were developed to investigate the discrepancy between empirical and theory-based, linear and nonlinear compartmental kinetic models and empirical formulae on model predictability. Prediction and simulation-based diagnostics (prediction-corrected visual predictive checks) were performed to determine the stability and predictive performance of these four models.

RESULTS

Model predictability improved when nonlinearity was considered. The theory-based nonlinear model which incorporated nonlinear property based on known theoretical relationships performed better than the other two compartmental models. The nonlinear Michaelis-Menten model showed a remarkable improvement in predictive performance compared to the other three compartmental models. The saturated binding of ciclosporin to erythrocytes, auto-inhibition induced by the inhibitory effects of ciclosporin on cytochrome P450 3A4/P-glycoprotein may have contributed to the nonlinearity. Ciclosporin-prednisolone drug interaction should be given serious consideration in clinical settings.

CONCLUSIONS

Incorporation of nonlinear properties is likely to be a promising approach for improving ciclosporin model predictability. Theory-based modelling is helpful to improve model predictability. However, ciclosporin nonlinear kinetics resources need further investigation.

Assessing Potential Drug-Drug Interactions Between Dabigatran Etexilate and a P-Glycoprotein Inhibitor in Renal Impairment Populations Using Physiologically Based Pharmacokinetic Modeling

Plasma concentrations of dabigatran, an active principle of prodrug dabigatran etexilate (DABE), are increased by renal impairment (RI) or coadministration of a P‐glycoprotein inhibitor. Because the combined effects of drug–drug interactions and RI have not been evaluated by means of clinical studies, the decision of DABE dosing for RI patients receiving P‐glycoprotein inhibitors is empirical at its best. We conducted virtual drug–drug interactions studies between DABE and the P‐glycoprotein inhibitor verapamil in RI populations using physiologically based pharmacokinetic modeling. The developed physiologically based pharmacokinetic model for DABE and dabigatran was used to predict trough dabigatran concentrations in the presence and absence of verapamil in virtual RI populations. The population‐based physiologically based pharmacokinetic model provided the most appropriate dosing regimen of DABE for likely clinical scenarios, such as drug–drug interactions in this RI population based on available knowledge of the systems changes and in the absence of actual clinical studies.

Systematic Development and Verification of a Physiologically Based Pharmacokinetic Model of Rivaroxaban

Rivaroxaban is indicated for stroke prevention in nonvalvular atrial fibrillation (AF). Its elimination is mediated by both hepatic metabolism and renal excretion. Consequently, its clearance is susceptible to both intrinsic (pathophysiological) and extrinsic (concomitant drugs) variabilities that in turn implicate bleeding risks. Upon systematic model verification, physiologically based pharmacokinetic (PBPK) models are qualified for the quantitative rationalization of complex drug-drug-disease interactions (DDDIs). Hence, this study aimed to develop and verify a PBPK model of rivaroxaban systematically. Key parameters required to define rivaroxaban's disposition were either obtained from in vivo data or generated via in vitro metabolism and transport kinetic assays. Our developed PBPK model successfully predicted rivaroxaban's clinical pharmacokinetic parameters within predefined success metrics. Consideration of basolateral organic anion transporter 3 (OAT3)-mediated proximal tubular uptake in tandem with apical P-glycoprotein (P-gp)-mediated efflux facilitated mechanistic characterization of the renal elimination of rivaroxaban in both healthy and renal impaired patients. Retrospective drug-drug interaction (DDI) simulations, incorporating in vitro metabolic inhibitory parameters, accurately recapitulated clinically observed attenuation of rivaroxaban's hepatic clearance due to enzyme-mediated DDIs with CYP3A4/2J2 inhibitors (verapamil and ketoconazole). Notably, transporter-mediated DDI simulations between rivaroxaban and the P-gp inhibitor ketoconazole yielded minimal increases in rivaroxaban's systemic exposure when P-gp-mediated efflux was solely inhibited, but were successfully characterized when concomitant basolateral uptake inhibition was incorporated in the simulation. In conclusion, our developed PBPK model of rivaroxaban is systematically verified for prospective interrogation and management of untested yet clinically relevant DDDIs pertinent to AF management using rivaroxaban. SIGNIFICANCE STATEMENT: Rivaroxaban is susceptible to DDDIs comprising renal impairment and P-gp and CYP3A4/2J2 inhibition. Here, systematic construction and verification of a PBPK model of rivaroxaban, with the inclusion of a mechanistic kidney component, provided insight into the previously arcane role of OAT3-mediated basolateral uptake in influencing both clinically observed renal elimination of rivaroxaban and differential extents of transporter-mediated DDIs. The verified model holds potential for investigating clinically relevant DDDIs involving rivaroxaban and designing dosing adjustments to optimize its pharmacotherapy in atrial fibrillation.

Simultaneous Assessment of Hepatic Transport and Metabolism Pathways with a Single Probe Using Individualized PBPK Modeling of 14CO2 Production Rate Data

Erythromycin is a substrate of cytochrome P4503A4 (CYP3A4) and multiple drug transporters. Although clinical evidence suggests that uptake transport is likely to play a dominant role in erythromycin's disposition, the relative contributions of individual pathways are unclear. Phenotypic evaluation of multiple pathways generally requires a probe drug cocktail. This approach can result in ambiguous conclusions due to imprecision stemming from overlapping specificity of multiple drugs. We hypothesized that an individualized physiologically based pharmacokinetic modeling approach incorporating 14CO2 production rates (iPBPK-R) of the erythromycin breath test (ERMBT) would enable us to differentiate the contribution of metabolic and transporter pathways to erythromycin disposition. A seven-compartmental physiologically based pharmacokinetic (PBPK) model was built for 14C-erythromycin administered intravenously. Transporter clearance and CYP3A4 clearance were embedded in hepatic compartments. 14CO2 production rates were simulated taking the first derivative of by-product 14CO2 concentrations. Parameters related to nonrenal elimination pathways were estimated by model fitting the ERMBT data of 12 healthy subjects individually. Optimized iPBPK-R models fit the individual rate data well. Using one probe, nine PBPK parameters were simultaneously estimated per individual. Maximum velocity of uptake transport, CYP3A4 clearance, total passive diffusion, and others were found to collectively control 14CO2 production rates. The median CYP3A4 clearance was 12.2% of the input clearance. Male subjects had lower CYP3A4 activity than female subjects by 11.3%. We applied iPBPK-R to ERMBT data to distinguish and simultaneously estimate the activity of multiple nonrenal elimination pathways in healthy subjects. The iPBPK-R framework is a novel tool for delineating rate-limiting and non-rate-limiting elimination pathways using a single probe. SIGNIFICANCE STATEMENT: Our developed individualized physiologically based pharmacokinetic modeling approach incorporating rate data (iPBPK-R) enabled us to distinguish and simultaneously estimate the activity of multiple nonrenal elimination pathways of erythromycin in healthy subjects. A new interpretation of erythromycin breath test (ERMBT) data was also obtained via iPBPK-R. We found that rate data have rich information allowing estimation of per-person PBPK parameters. This study serves as proof of principle that the iPBPK-R framework is a novel tool for delineating rate-limiting and non-rate-limiting elimination pathways using a single probe. iPBPK-R can be applied to other rate-derived data beyond ERMBT. Potential areas of application include drug-drug interaction, pathophysiological effects on drug disposition, and the role of biomarkers on hemodialysis efficiency utilizing estimated adjustment factors with correlation analysis.

Drug interactions between direct-acting oral anticoagulants and calcineurin inhibitors during solid organ transplantation: considerations for therapy

Introduction: There is a high incidence of venous thromboembolism (VTE) in solid organ transplant recipients. The safety and efficacy of direct-acting oral anticoagulants (DOAC) have been well established in clinical practice for the prevention and treatment of VTE in broad populations. However, the management of VTE in the setting of solid organ transplantation remains a challenge to clinicians due to limited evidence of DOAC usage with calcineurin inhibitors. Areas covered: The current literature available on the pharmacokinetic-pharmacodynamic interaction between DOACs and calcineurin inhibitors is presented. A comprehensive review was undertaken using PubMed, Embase, drug product labeling, and drug product review conducted by the US Food and Drug Administration using Drugs@FDA. The potential for mitigation strategies and clinical management using extant knowledge is explored. Expert opinion: Immunosuppression therapy is necessary to prevent graft rejection by the host. The sparsity of data together with the lack of well-designed prospective studies of DOAC use in solid organ transplant recipients presents a unique challenge to clinicians in determining the clinical relevance of possible drug interactions. Existing evidence suggests that with attention to concomitant drug use and renal function, the co-administration of DOACs and calcineurin inhibitors is safe and effective.

Effects of medicines used to treat gastrointestinal diseases on the pharmacokinetics of coadministered drugs: a PEARRL Review

OBJECTIVES

Drugs used to treat gastrointestinal diseases (GI drugs) are widely used either as prescription or over-the-counter (OTC) medications and belong to both the 10 most prescribed and 10 most sold OTC medications worldwide. The objective of this review article is to discuss the most frequent interactions between GI and other drugs, including identification of the mechanisms behind these interactions, where possible.

KEY FINDINGS

Current clinical practice shows that in many cases, these drugs are administered concomitantly with other drug products. Due to their metabolic properties and mechanisms of action, the drugs used to treat gastrointestinal diseases can change the pharmacokinetics of some coadministered drugs. In certain cases, these interactions can lead to failure of treatment or to the occurrence of serious adverse events. The mechanism of interaction depends highly on drug properties and differs among therapeutic categories. Understanding these interactions is essential to providing recommendations for optimal drug therapy.

SUMMARY

Interactions with GI drugs are numerous and can be highly significant clinically in some cases. While alterations in bioavailability due to changes in solubility, dissolution rate, GI transit and metabolic interactions can be (for the most part) easily identified, interactions that are mediated through other mechanisms, such as permeability or microbiota, are less well-understood. Future work should focus on characterising these aspects.

Application of physiologically based pharmacokinetic modeling to the prediction of drug-drug and drug-disease interactions for rivaroxaban

PURPOSE

Rivaroxaban is a direct oral anticoagulant with a large inter-individual variability. The present study is to develop a physiologically based pharmacokinetic (PBPK) model to predict several scenarios in clinical practice.

METHODS

A whole-body PBPK model for rivaroxaban, which is metabolized by the cytochrome P450 (CYP) 3A4/5, 2J2 pathways and excreted via kidneys, was developed to predict the pharmacokinetics at different doses in healthy subjects and patients with hepatic or renal dysfunction. Hepatic clearance and drug-drug interactions (DDI) were estimated by in vitro in vivo extrapolation (IVIVE) based on parameters obtained from in vitro experiments. To validate the model, observed concentrations were compared with predicted concentrations, and the impact of special scenarios was investigated.

RESULTS

The PBPK model successfully predicted the pharmacokinetics for healthy subjects and patients as well as DDIs. Sensitivity analysis shows that age, renal, and hepatic clearance are important factors affecting rivaroxaban pharmacokinetics. The predicted fold increase of rivaroxaban AUC values when combined administered with the inhibitors such as ketoconazole, ritonavir, and clarithromycin were 2.3, 2.2, and 1.3, respectively. When DDIs and hepatic dysfunction coexist, the fold increase of rivaroxaban exposure would increase significantly compared with one factor alone.

CONCLUSIONS

Our study using PBPK modeling provided a reasonable approach to evaluate exposure levels in special patients under special scenarios. Although further clinical study or real-life experience would certainly merit the current work, the modeling work so far would at least suggest caution of using rivaroxaban in complicated clinical settings.

Minimal Physiologically Based Pharmacokinetic and Drug-Drug-Disease Interaction Model of Rivaroxaban and Verapamil in Healthy and Renally Impaired Subjects

Current dosing recommendations for rivaroxaban advocate dosage reduction in patients with moderate to severe renal impairment and avoidance of concomitant strong inhibitors of CYP3A or P-glycoprotein. However, rivaroxaban dosing in patients with mild renal impairment taking concomitant moderate inhibitors of CYP3A and P-glycoprotein is not addressed. To quantify the impacts of concomitant verapamil administration and renal impairment on rivaroxaban pharmacokinetics, a minimal physiologically based pharmacokinetic model system was developed and used to evaluate potential increases in rivaroxaban exposure and the consequent increase in risk of major bleeding. Data from a phase 1, drug-drug interaction study were used to qualify the minimal physiologically based pharmacokinetic model system. Model-based simulations indicate that coadministration of rivaroxaban with verapamil substantially increases rivaroxaban exposure across all renal function categories, resulting in an exponential increase in bleeding risk. Reduction of the daily rivaroxaban dose to 10 to 15 mg reduces the major bleeding risk below the designated 4.5% threshold in the majority of patients with normal or mildly impaired renal function. A reduction to 10 mg daily in patients with moderate to severe renal impairment provides additional risk reduction so that 90% of those patients fall below the 4.5% threshold. A risk threshold of 4.5% was selected because it is the median predicted risk in patients treated concomitantly with ketoconazole, which is contraindicated for use with rivaroxaban. Patients taking both rivaroxaban and verapamil should take a reduced daily dose of rivaroxaban to minimize bleeding risk.

Efficacy and Safety of Rivaroxaban Versus Warfarin in Patients Taking Nondihydropyridine Calcium Channel Blockers for Atrial Fibrillation (from the ROCKET AF Trial)

Non-dihydropyridine calcium channel blockers (non-DHP CCBs) possess combined P-glycoprotein and moderate CYP3A4 inhibition, which may lead to increased exposure of medications that are substrates for these metabolic pathways, such as rivaroxaban. We evaluated the use and outcomes of non-DHP CCBs in patients with atrial fibrillation (AF) in Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation (ROCKET AF). We assessed clinical outcomes in patients who received non-DHP CCBs and the impact on the efficacy and safety of rivaroxaban compared with warfarin. Stroke or noncentral nervous system (CNS) systemic embolism (SE), major or nonmajor clinically relevant (NMCR) bleeding, all-cause death, and major bleeding were compared according to non-DHP CCB use. At randomization, 1,308 patients (9.2%) were taking a non-DHP CCB. They were more likely to be women, have diabetes and COPD, and less likely to have heart failure and had a lower mean CHADS₂ score (3.3 vs 3.5). Non-DHP CCB use was not associated with an increased risk of stroke/non-CNS SE (p = 0.11) or the composite outcome of NMCR or major bleeding (p = 0.087). Non-DHP CCB use was associated with an increased risk of major bleeding (adjusted hazard ratio 1.50, 95% CI 1.11 to 2.04) and intracranial hemorrhage (adjusted hazard ratio 2.84, 95% CI 1.53 to 5.29). No significant difference was observed in the primary efficacy (stroke or non-CNS SE; adjusted interaction p value = 0.38) or safety outcome (NMCR or major bleeding; adjusted interaction p value = 0.14) between rivaroxaban and warfarin with non-DHP CCB use. In conclusion, although the overall use of non-DHP CCBs was associated with an increased risk of major bleeding and intracranial hemorrhage, the use was not associated with a significant change in the safety or efficacy of rivaroxaban compared with warfarin observed in ROCKET AF.

The Application of Physiologically Based Pharmacokinetic Modeling to Predict the Role of Drug Transporters: Scientific and Regulatory Perspectives

Transporters play an important role in drug absorption, disposition, and drug action. The evaluation of drug transporters requires a comprehensive understanding of transporter biology and pharmacology. Physiologically based pharmacokinetic (PBPK) models may offer an integrative platform to quantitatively evaluate the role of drug transporters and its interplay with other drug disposition processes such as passive drug diffusion and elimination by metabolizing enzymes. To date, PBPK modeling and simulations integrating drug transporters lag behind that for drug-metabolizing enzymes. In addition, predictive performance of PBPK has not been well established for predicting the role of drug transporters in the pharmacokinetics of a drug. To enhance overall predictive performance of transporter-based PBPK models, it is necessary to have a detailed understanding of transporter biology for proper representation in the models and to have a quantitative understanding of the contribution of transporters in the absorption and metabolism of a drug. This article summarizes PBPK-based submissions evaluating the role of drug transporters to the Office of Clinical Pharmacology of the US Food and Drug Administration.

Cell cultures in drug discovery and development: The need of reliable in vitro-in vivo extrapolation for pharmacodynamics and pharmacokinetics assessment

For ethical and cost-related reasons, use of animals for the assessment of mode of action, metabolism and/or toxicity of new drug candidates has been increasingly scrutinized in research and industrial applications. Implementation of the 3 "Rs"¹; rule (Reduction, Replacement, Refinement) through development of in silico or in vitro assays has become an essential element of risk assessment. Physiologically based pharmacokinetic (PBPK²) modeling is the most potent in silico tool used for extrapolation of pharmacokinetic parameters to animal or human models from results obtained in vitro. Although, many types of in vitro assays are conducted during drug development, use of cell cultures is the most reliable one. Two-dimensional (2D) cell cultures have been a part of drug development for many years. Nowadays, their role is decreasing in favor of three-dimensional (3D) cell cultures and co-cultures. 3D cultures exhibit protein expression patterns and intercellular junctions that are closer to in vivo states in comparison to classical monolayer cultures. Co-cultures allow for examinations of the mutual influence of different cell lines. However, the complexity and high costs of co-cultures and 3D equipment exclude such methods from high-throughput screening (HTS).³In vitro absorption, distribution, metabolism, and excretion assessment, as well as drug-drug interaction (DDI), are usually performed with the use of various cell culture based assays. Progress in in silico and in vitro methods can lead to better in vitro-in vivo extrapolation (IVIVE⁴) outcomes and have a potential to contribute towards a significant reduction in the number of laboratory animals needed for drug research. As such, concentrated efforts need to be spent towards the development of an HTS in vitro platform with satisfactory IVIVE features.

Physiologically Based Pharmacokinetic Modeling of Drug Transporters to Facilitate Individualized Dose Prediction

Physiologically based pharmacokinetic modeling is a commonly used strategy in the drug development and regulatory submissions. This commentary provides a critical overview of the current status of physiologically based pharmacokinetic methodologies to predict transporter-mediated pharmacokinetics, in addition to the impact of disease and genetics with respect to local and systemic concentration.

Delineating the Role of Various Factors in Renal Disposition of Digoxin through Application of Physiologically Based Kidney Model to Renal Impairment Populations

Development of submodels of organs within physiologically-based pharmacokinetic (PBPK) principles and beyond simple perfusion limitations may be challenging because of underdeveloped in vitro-in vivo extrapolation approaches or lack of suitable clinical data for model refinement. However, advantage of such models in predicting clinical observations in divergent patient groups is now commonly acknowledged. Mechanistic understanding of altered renal secretion in renal impairment is one area that may benefit from such models, despite knowledge gaps in renal pathophysiology. In the current study, a PBPK kidney model was developed for digoxin, accounting for the roles of organic anion transporting peptide 4C1 (OATP4C1) and P-glycoprotein (P-gp) in its tubular secretion, with the aim to investigate the impact of age and renal impairment (moderate to severe) on renal drug disposition. Initial PBPK simulations based on changes in glomerular filtration rate (GFR) underestimated the observed reduction in digoxin renal excretion clearance (CLR) in subjects with moderately impaired renal function relative to healthy. Reduction in either proximal tubule cell number or the OATP4C1 abundance in the mechanistic kidney model successfully predicted 59% decrease in digoxin CLR, in particular when these changes were proportional to reduction in GFR. In contrast, predicted proximal tubule concentration of digoxin was only sensitive to changes in the transporter expression/ million proximal tubule cells. Based on the mechanistic modeling, reduced proximal tubule cellularity and OATP4C1 abundance, and inhibition of OATP4C1-mediated transport, are proposed as possible causes of reduced digoxin renal secretion in renally impaired patients.

Application of Static Modeling --in the Prediction of In Vivo Drug-Drug Interactions between Rivaroxaban and Antiarrhythmic Agents Based on In Vitro Inhibition Studies

Rivaroxaban, a direct Factor Xa inhibitor, is indicated for stroke prevention in nonvalvular atrial fibrillation (AF). Studies have revealed that the clearance of rivaroxaban is largely attributed to CYP3A4, CYP2J2 metabolism, and P-glycoprotein (P-gp) efflux pathways. Amiodarone and dronedarone are antiarrhythmic agents employed in AF management. Amiodarone, dronedarone, and their major metabolites, N-desethylamiodarone (NDEA) and N-desbutyldronedarone (NDBD), demonstrate inhibitory effects on CYP3A4 and CYP2J2 with U.S. Food and Drug Administration-recommended probe substrates. In addition, both amiodarone and dronedarone are known P-gp inhibitors. Hence, the concomitant administration of these antiarrhythmic agents has the potential to augment the systemic exposure of rivaroxaban through simultaneous impairment of its clearance pathways. Currently, however, clinical data on the extent of these postulated drug-drug interactions are lacking. In this study, in vitro inhibition assays using rivaroxaban as the probe substrate demonstrated that both dronedarone and NDBD produced reversible inhibition as well as irreversible mechanism-based inactivation of CYP3A4- and CYP2J2-mediated metabolism of rivaroxaban. However, amiodarone and NDEA were observed to cause reversible inhibition as well as mechanism-based inactivation of CYP3A4 but not CYP2J2. In addition, amiodarone, NDEA, and dronedarone, but not NDBD, were determined to inhibit P-gp-mediated rivaroxaban transport. The in vitro inhibition parameters were fitted into a mechanistic static model, which predicted a 37% and 31% increase in rivaroxaban exposure due to the inhibition of hepatic and gut metabolism by amiodarone and dronedarone, respectively. A separate model quantifying the inhibition of P-gp-mediated efflux by amiodarone or dronedarone projected a 9% increase in rivaroxaban exposure.

Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities

PURPOSE OF REVIEW

This review gives a perspective on the current "state of the art" in metabolic drug-drug interaction (DDI) prediction. We highlight areas of successful prediction and illustrate progress in areas where limits in scientific knowledge or technologies prevent us from having full confidence.

RECENT FINDINGS

Several examples of success are highlighted. Work done for bitopertin shows how in vitro and clinical data can be integrated to give a model-based understanding of pharmacokinetics and drug interactions. The use of interpolative predictions to derive explicit dosage recommendations for untested DDIs is discussed using the example of ibrutinib, and the use of DDI predictions in lieu of clinical studies in new drug application packages is exemplified with eliglustat and alectinib. Alectinib is also an interesting case where dose adjustment is unnecessary as the activity of a major metabolite compensates sufficiently for changes in parent drug exposure. Examples where "unusual" cytochrome P450 (CYP) and non-CYP enzymes are responsible for metabolic clearance have shown the importance of continuing to develop our repertoire of in vitro regents and techniques. The time-dependent inhibition assay using human hepatocytes suspended in full plasma allowed improved DDI predictions, illustrating the importance of continued in vitro assay development and refinement.

SUMMARY

During the past 10 years, a highly mechanistic understanding has been developed in the area of CYP-mediated metabolic DDIs enabling the prediction of clinical outcome based on preclinical studies. The combination of good quality in vitro data and physiologically based pharmacokinetic modeling may now be used to evaluate DDI risk prospectively and are increasingly accepted in lieu of dedicated clinical studies.

Evaluation of Drug-Drug Interaction Potential Between Sacubitril/Valsartan (LCZ696) and Statins Using a Physiologically Based Pharmacokinetic Model

Sacubitril/valsartan (LCZ696) has been approved for the treatment of heart failure. Sacubitril is an in vitro inhibitor of organic anion-transporting polypeptides (OATPs). In clinical studies, LCZ696 increased atorvastatin C_max by 1.7-fold and area under the plasma concentration-time curve by 1.3-fold, but had little or no effect on simvastatin or simvastatin acid exposure. A physiologically based pharmacokinetics modeling approach was applied to explore the underlying mechanisms behind the statin-specific LCZ696 drug interaction observations. The model incorporated OATP-mediated clearance (CL_int,T) for simvastatin and simvastatin acid to successfully describe the pharmacokinetic profiles of either analyte in the absence or presence of LCZ696. Moreover, the model successfully described the clinically observed drug effect with atorvastatin. The simulations clarified the critical parameters responsible for the observation of a low, yet clinically relevant, drug-drug interaction DDI between sacubitril and atorvastatin and the lack of effect with simvastatin acid. Atorvastatin is administered in its active form and rapidly achieves C_max that coincide with the low C_max of sacubitril. In contrast, simvastatin requires a hydrolysis step to the acid form and therefore is not present at the site of interactions at sacubitril concentrations that are inhibitory. Similar models were used to evaluate the drug-drug interaction risk for additional OATP-transported statins which predicted to maximally result in a 1.5-fold exposure increase.

Rivaroxaban-Induced Hemorrhage Associated with ABCB1 Genetic Defect

We report a patient who presented a non-ST segment elevation myocardial infarction in the context of severe normocytic hypochromic anemia related to gastrointestinal bleeding, 3 months after switching anticoagulant from the vitamin K antagonist acenocoumarol to the direct oral anticoagulant rivaroxaban. High levels of both anti-Xa activity and rivaroxaban plasma concentrations were measured despite rivaroxaban withdrawal, suggesting reduced elimination/drug clearance. Estimated half-life was 2–3 times longer than usually reported. The patient is a homozygous carrier of ABCB1 variant alleles, which could have participated to reduced elimination of rivaroxaban. Furthermore, CYP3A4/5 phenotyping showed moderately reduced enzyme activity. Drug-drug interaction with simvastatin may have contributed to decreased rivaroxaban elimination. Although in the present case moderate acute renal failure probably played a role, more clinical data are required to elucidate the impact of ABCB1 polymorphism on rivaroxaban pharmacokinetics and bleeding complications.

Unequivocal evidence supporting the segregated flow intestinal model that discriminates intestine versus liver first-pass removal with PBPK modeling

Merits of the segregated flow model (SFM), highlighting the intestine as inert serosa and active enterocyte regions, with a smaller fractional (f_Q < 0.3) intestinal flow (Q_I ) perfusing the enterocyte region, are described. Less drug in the circulation reaches the enterocytes due to the lower flow (f_Q Q_I ) in comparison with drug administered into the gut lumen, fostering the idea of route-dependent intestinal removal. The SFM has been found superior to the traditional model (TM), which views the serosa and enterocytes totally as a well-mixed tissue perfused by 100% of the intestinal flow, Q_I . The SFM model is able to explain the lower extents of intestinal metabolism of enalapril, morphine and midazolam with i.v. vs. p.o. dosing. For morphine, the urine/bile ratio of the metabolite, morphine glucuronide MGurineMGbile for p.o. was 2.6× that of i.v. This was due to the higher proportion of intestinally formed morphine glucuronide, appearing more in urine than in bile due to its low permeability and greater extent of intestinal formation with p.o. administration. By contrast, the TM predicted the same MGurineMGbile for p.o. vs. i.v. The TM predicted that the contributions of the intestine:liver to first-pass removal were 46%:54% for both p.o. and i.v. The SFM predicted same 46%:54% (intestine:liver) for p.o., but 9%:91% for i.v. By contrast, the kinetics of codeine, the precursor of morphine, was described equally well by the SFM- and TM-PBPK models, a trend suggesting that intestinal metabolism of codeine is negligible. Fits to these PBPK models further provide insightful information towards metabolite formation: available fractions and the fractions of hepatic and total clearances that form the metabolite in question. The SFM-PBPK model is useful to identify not only the presence of intestinal metabolism but the contributions of the intestine and liver for metabolite formation. Copyright © 2016 John Wiley & Sons, Ltd.

Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine

Personalized medicine strives to deliver the 'right drug at the right dose' by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the quality by design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.

Perspectives in regulatory science: translational and clinical pharmacology

This paper focuses on the role of clinical and translational pharmacology in the drug development and the regulatory process. Contemporary regulatory issues faced by FDA's Office of Clinical Pharmacology (OCP) in fulfilling its mission to advance the science of drug response and translate patient diversity into optimal drug therapy are discussed. Specifically current focus of the following key aspects of the drug development and regulatory science processes are discussed: the OCP vision and mission, two key OCP initiatives (i.e. guidance modernization, labeling and health communications), and translational and clinical pharmacology related regulatory science issues in (i.e. uncertainty, breakthrough therapies, individualization).

Characterizing the Developmental Trajectory of Sirolimus Clearance in Neonates and Infants

Sirolimus is increasingly being used in neonates and infants, but the mechanistic basis of age‐dependent changes in sirolimus disposition has not been fully addressed yet. In order to characterize the age‐dependent changes, serial sirolimus clearance (CL) estimates in individual young pediatric patients were collected and analyzed by population modeling analysis. In addition, sirolimus metabolite formation was also investigated to further substantiate the corresponding age‐dependent change in CYP3A activity. The increasing pattern over time of allometrically size‐normalized sirolimus CL estimates vs. age was well described by a sigmoidal E_max model. This age‐dependent increase was also observed within each individual patient over a 4‐year study period. CYP3A‐dependent sirolimus metabolite formation changed in a similar fashion. This study clearly demonstrates the rapid increase of sirolimus CL over time in neonates and infants, indicating the developmental change. This developmental pattern can be explained by a parallel increase in CYP3A metabolic activity.

Modeling and simulation in pediatric drug therapy: Application of pharmacometrics to define the right dose for children

During the past decades significant progress has been made in our understanding of the importance of age-appropriate development of new drug therapies in children. Importantly, several regulatory initiatives in Europe and the US have provided a framework for a rationale. In the US, most notably the enactment of the Best Pharmaceuticals for Children Act (BPCA) and Product Research and Equity Act (PREA) has facilitated the studying of on-patent and off-patent drugs in children. The biggest challenge in pediatric studies is defining a safe and effective dose or dose range in a patient population that can span from premature neonates to adolescents. From a mechanism-based perspective, advances in the science of quantitative pharmacology and pharmacometrics have resulted in the development of model-based approaches to better describe and understand important age-related factors influencing drug disposition and response in pediatric patients. The application of modeling and simulation has been shown to result in better estimates of pediatric doses as evidenced by several studies, although the optimal approach is still being debated. The extrapolation of efficacy findings from adults to the pediatric population has streamlined the development process especially for studies in older children. However, a focus on developmental changes in neonates and infants as well as further developing a paradigm for conducting pharmacodynamic studies in neonates, infants, and children remain important unmet needs. In this overview we will review current approaches for age-appropriate dose selection and highlight ongoing efforts to define exposure-response and clinical outcome relationships across the pediatric age spectrum.