Prediction of Drug-Drug Interactions Arising from CYP3A induction Using a Physiologically Based Dynamic Model

Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective

Physiologically-based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug-drug interactions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made with sufficient confidence. In the current work, the ability to predict the effect of rifampin, a well-characterized strong CYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow with optimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, fm,CYP3A4, and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of fm,CYP3A4 (0.086-1.0), Fg (0.11-1.0) and hepatic availability (0.09-0.96) were included. Predictions were most often accurate for compounds that are not P-gp substrates or that are P-gp substrates but that have high permeability. Case studies for three challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parameter sensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures should be considered, for example, a mechanistic absorption model instead of a first-order absorption model might be more appropriate for a P-gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate that rifampin might impact (e.g., induction of other enzymes or P-gp) should be considered for inclusion in the model. PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates with limited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused on rifampin, the learnings may apply to other inducers.

Advancements in physiologically based pharmacokinetic modeling for fedratinib: updating dose guidance in the presence of a dual inhibitor of CYP3A4 and CYP2C19

PURPOSE

A physiologically based pharmacokinetic (PBPK) model for fedratinib was updated and revalidated to bridge a gap between the observed drug-drug interaction (DDI) of a single sub-efficacious dose in healthy participants and the potential DDI in patients with cancer at steady state. The study aimed to establish an appropriate dose for fedratinib in patients coadministered with dual CYP3A4 and CYP2C19 inhibitors, providing quantitative evidence to inform dosing guidance.

METHODS

The original minimal PBPK model was developed using Simcyp® Simulator v17. The model was updated by substituting a single distribution rate (Qsac) with 2 separate rates (CLin/CLout) and transitioning to v20. Model parameter updates were further informed with 3 clinical studies, and 3 more studies served as independent validation data. The validated model was applied to simulate potential DDIs between fedratinib and a known dual inhibitor of CYP3A4 and CYP2C19 (fluconazole).

RESULTS

Coadministration of fedratinib with fluconazole in patients was predicted to increase fedratinib exposure by < 2-fold in all simulated scenarios. For patients with cancer receiving the approved dose of fedratinib 400 mg once daily along with fluconazole 200 mg daily, the model predicted an approximate 50% increase in fedratinib exposure at steady state.

CONCLUSIONS

The updated PBPK model improved description of the observed pharmacokinetics and predicted a low risk of clinically significant DDIs between fedratinib and fluconazole. The quantitative evidence serves as a primary foundation for providing dose guidance in clinical practice for the coadministration of fedratinib with dual CYP3A4 and CYP2C19 inhibitors.

Applications of the Cholesterol Metabolite, 4β-Hydroxycholesterol, as a Sensitive Endogenous Biomarker for Hepatic CYP3A Activity Evaluated within a PBPK Framework

Background/Objectives: Plasma levels of 4β-hydroxycholesterol (4β-OHC), a CYP3A-specific metabolite of cholesterol, are elevated after administration of CYP3A inducers like rifampicin and carbamazepine. To simulate such plasma 4β-OHC increase, we developed a physiologically based pharmacokinetic (PBPK) model of cholesterol and 4β-OHC in the Simcyp PBPK Simulator (Version 23, Certara UK Ltd.) using a middle-out approach. Methods: Relevant physicochemical properties and metabolic pathway data for CYP3A and CYP27A1 was incorporated in the model. Results: The PBPK model recovered the observed baseline plasma 4β-OHC levels in Caucasian, Japanese, and Korean populations. The model also captured the higher baseline 4β-OHC levels in females compared to males, indicative of sex-specific differences in CYP3A abundance. More importantly, the model recapitulated the increased 4β-OHC plasma levels after multiple-dose rifampicin treatment in six independent studies, indicative of hepatic CYP3A induction. The verified model also captured the altered 4β-OHC levels in CYP3A4/5 polymorphic populations and with other CYP3A inducers. The model is limited by scant data on relative contributions of CYP3A and CYP27A1 pathways and does not account for regulatory mechanisms that control plasma cholesterol and 4β-OHC levels. Conclusion: This study provides a quantitative fit-for-purpose and framed-for-future modelling framework for an endogenous biomarker to evaluate the DDI risk with hepatic CYP3A induction.

Physiologically based pharmacokinetic models for predicting lamotrigine exposure and dose optimization in pediatric patients receiving combination therapy with carbamazepine or valproic acid

INTRODUCTION

Lamotrigine (LTG) is an antiepileptic drug that has been used in pediatric epilepsy as a combination therapy or monotherapy after stabilization in recent years. However, there are significant drug-drug interactions (DDI) between LTG and combined drugs such as carbamazepine (CBZ) and valproic acid (VPA). It is particularly important to consider the risk of DDI in combination therapy for intractable epilepsy in pediatric patients. Therefore, it is necessary to adjust the dosage of LTG accordingly. The aim of this study was to establish and validate a pediatric physiologically based pharmacokinetic (PBPK) model for predicting LTG exposure. The model is designed to explore the potential for quantifying pharmacokinetic (PK) DDI of LTG when administered concurrently with CBZ or VPA in pediatric patients.

METHOD

Adult and pediatric PBPK models for LTG and VPA were developed using PK-Sim® software in combination with physiological information and drug-specific parameters, and a DDI model was developed in combination with the published CBZ model. The models were validated against available PK data.

RESULTS

Predictive and observational results in adults, children, and the DDI model were in good agreement. The recommended doses of LTG for preschool children (2-6 years) and school-aged children (6-12 years) in the absence of drug interactions were 1.47 and 1.2 times higher than those for adults, respectively; 3.1 and 2.6 times higher than those for adults in combination with CBZ; and 0.67 and 0.57 times lower than those for adults in combination with VPA. In addition, plasma exposures in adolescents (12-18 years) were similar to those in adults at the same doses.

CONCLUSION

We have successfully developed PBPK models and DDI models for LTG in adults and children, which provide a reference for rational drug use in the pediatric population.

A Physiologically-Based Pharmacokinetic Simulation to Evaluate Approaches to Mitigate Efavirenz-Induced Decrease in Levonorgestrel Exposure with a Contraceptive Implant

Background: Levonorgestrel implant is a highly effective hormonal contraceptive, but its efficacy may be compromised when used with cytochrome enzyme inducers such as efavirenz. The primary aim of this study was to evaluate methods of mitigating the drug interaction. Methods: Using a physiologically-based pharmacokinetic (PBPK) model for levonorgestrel that we developed within the Simcyp® program, we evaluated a higher dose of levonorgestrel implant, a lower dose of efavirenz, and the combination of both, as possible methods to mitigate the interaction. In addition, we investigated the impact on levonorgestrel total and unbound concentrations of other events likely to be associated with efavirenz coadministration: changes in plasma protein binding of levonorgestrel (as with displacement) and high variability of efavirenz exposure (as with genetic polymorphism of its metabolism). The range of fraction unbound tested was 0.6% to 2.6%, and the range of efavirenz exposure ranged from the equivalent of 200 mg to 4800 mg doses. Results: Levonorgestrel plasma concentrations at any given time with a standard 150 mg implant dose are predicted to be approximately 68% of those of control when given with efavirenz 600 mg and 72% of control with efavirenz 400 mg. With double-dose levonorgestrel, the predictions are 136% and 145% of control, respectively. A decrease in levonorgestrel plasma protein binding is predicted to primarily decrease total levonorgestrel plasma concentrations, whereas higher efavirenz exposure is predicted to decrease total and unbound concentrations. Conclusions: Simulations suggest that doubling the dose of levonorgestrel, particularly in combination with 400 mg daily efavirenz, may mitigate the drug interaction. Changes in levonorgestrel plasma protein binding and efavirenz genetic polymorphism may help explain differences between model predictions and clinical data but need to be studied further.

Quantitative prediction of CYP3A induction-mediated drug-drug interactions in clinical practice

There have been no reports on the quantitative prediction of CYP3A induction-mediated decreases in AUC and Cmax for drug candidates identified as a "victims" of CYP3A induction. Our previous study separately evaluated the fold-induction of hepatic and intestinal CYP3A by known inducers using clinical induction data and revealed that we were able to quantitatively predict the AUC ratio (AUCR) of a few CYP3A substrates in the presence and absence of CYP3A inducers. In the present study, we investigate the predictability of AUCR and also Cmax ratio (CmaxR) in additional 54 clinical studies. The fraction metabolized by CYP3A (fm), the intestinal bioavailability (Fg), and the hepatic intrinsic clearance (CLint) of substrates were determined by the in vitro experiments as well as clinical data used for calculating AUCR and CmaxR. The result showed that 65-69% and 65-67% of predictions were within 2-fold of observed AUCR and CmaxR, respectively. A simulation using multiple parameter combinations suggested that the variability of fm and Fg within a certain range might have a minimal impact on the calculation output. These findings suggest that clinical AUCR and CmaxR of CYP3A substrates can be quantitatively predicted from the preclinical stage.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Human Pharmacokinetic and CYP3A Drug-Drug Interaction Prediction of GDC-2394 Using Physiologically Based Pharmacokinetic Modeling and Biomarker Assessment

Physiologically based pharmacokinetic (PBPK) modeling was used to predict the human pharmacokinetics and drug-drug interaction (DDI) of GDC-2394. PBPK models were developed using in vitro and in vivo data to reflect the oral and intravenous PK profiles of mouse, rat, dog, and monkey. The learnings from preclinical PBPK models were applied to a human PBPK model for prospective human PK predictions. The prospective human PK predictions were within 3-fold of the clinical data from the first-in-human study, which was used to optimize and validate the PBPK model and subsequently used for DDI prediction. Based on the majority of PBPK modeling scenarios using the in vitro CYP3A induction data (mRNA and activity), GDC-2394 was predicted to have no-to-weak induction potential at 900 mg twice daily (BID). Calibration of the induction mRNA and activity data allowed for the convergence of DDI predictions to a narrower range. The plasma concentrations of the 4β-hydroxycholesterol (4β-HC) were measured in the multiple ascending dose study to assess the hepatic CYP3A induction risk. There was no change in plasma 4β-HC concentrations after 7 days of GDC-2394 at 900 mg BID. A dedicated DDI study found that GDC-2394 has no induction effect on midazolam in humans, which was reflected by the totality of predicted DDI scenarios. This work demonstrates the prospective utilization of PBPK for human PK and DDI prediction in early drug development of GDC-2394. PBPK modeling accompanied with CYP3A biomarkers can serve as a strategy to support clinical pharmacology development plans. SIGNIFICANCE STATEMENT: This work presents the application of physiologically based pharmacokinetic modeling for prospective human pharmacokinetic (PK) and drug-drug interaction (DDI) prediction in early drug development. The strategy taken in this report represents a framework to incorporate various approaches including calibration of in vitro induction data and consideration of CYP3A biomarkers to inform on the overall CYP3A-related DDI risk of GDC-2394.

Hepatic OATP1B zonal distribution: Implications for rifampicin-mediated drug-drug interactions explored within a PBPK framework

OATP1B facilitates the uptake of xenobiotics into hepatocytes and is a prominent target for drug-drug interactions (DDIs). Reduced systemic exposure of OATP1B substrates has been reported following multiple-dose rifampicin; one explanation for this observation is OATP1B induction. Non-uniform hepatic distribution of OATP1B may impact local rifampicin tissue concentrations and rifampicin-mediated protein induction, which may affect the accuracy of transporter- and/or metabolizing enzyme-mediated DDI predictions. We incorporated quantitative zonal OATP1B distribution data from immunofluorescence imaging into a PBPK modeling framework to explore rifampicin interactions with OATP1B and CYP substrates. PBPK models were developed for rifampicin, two OATP1B substrates, pravastatin and repaglinide (also metabolized by CYP2C8/CYP3A4), and the CYP3A probe, midazolam. Simulated hepatic uptake of pravastatin and repaglinide increased from the periportal to the pericentral region (approximately 2.1-fold), consistent with OATP1B distribution data. Simulated rifampicin unbound intracellular concentrations increased in the pericentral region (1.64-fold) compared to simulations with uniformly distributed OATP1B. The absolute average fold error of the rifampicin PBPK model for predicting substrate maximal concentration (Cmax) and area under the plasma concentration-time curve (AUC) ratios was 1.41 and 1.54, respectively (nine studies). In conclusion, hepatic OATP1B distribution has a considerable impact on simulated zonal substrate uptake clearance values and simulated intracellular perpetrator concentrations, which regulate transporter and metabolic DDIs. Additionally, accounting for rifampicin-mediated OATP1B induction in parallel with inhibition improved model predictions. This study provides novel insight into the effect of hepatic OATP1B distribution on site-specific DDI predictions and the impact of accounting for zonal transporter distributions within PBPK models.

Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine's Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes

Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ's nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent-metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus®. The model was utilized for Drug-Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ's nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUClast ratio was 0.49 (slightly outside of the two-fold range). CBZ's nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

Assessment of the CYP3A4 Induction Potential by Carbamazepine: Insights from Two Clinical DDI Studies and PBPK Modeling

In the past, rifampicin was well-established as strong index CYP3A inducer in clinical drug-drug interaction (DDI) studies. However, due to identified potentially genotoxic nitrosamine impurities, it should not any longer be used in healthy volunteer studies. Available clinical data suggest carbamazepine as an alternative to rifampicin as strong index CYP3A4 inducer in clinical DDI studies. Further, physiologically-based pharmacokinetic (PBPK) modeling is a tool with increasing importance to support the DDI risk assessment of drugs during drug development. CYP3A4 induction properties and the safety profile of carbamazepine were investigated in two open-label, fixed sequence, crossover clinical pharmacology studies in healthy volunteers using midazolam as a sensitive index CYP3A4 substrate. Carbamazepine was up-titrated from 100 mg twice daily (b.i.d.) to 200 mg b.i.d., and to a final dose of 300 mg b.i.d. for 10 consecutive days. Mean area under plasma concentration-time curve from zero to infinity (AUC(0-∞)) of midazolam consistently decreased by 71.8% (ratio: 0.282, 90% confidence interval (CI): 0.235-0.340) and 67.7% (ratio: 0.323, 90% CI: 0.256-0.407) in study 1 and study 2, respectively. The effect was adequately described by an internally developed PBPK model for carbamazepine which has been made freely available to the scientific community. Further, carbamazepine was safe and well-tolerated in the investigated dosing regimen in healthy participants. The results demonstrated that the presented design is appropriate for the use of carbamazepine as alternative inducer to rifampicin in DDI studies acknowledging its CYP3A4 inductive potency and safety profile.

Evaluation of the drug-drug interaction potential of brigatinib using a physiologically-based pharmacokinetic modeling approach

Brigatinib is an oral anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK-positive metastatic non-small cell lung cancer. In vitro studies indicated that brigatinib is primarily metabolized by CYP2C8 and CYP3A4 and inhibits P-gp, BCRP, OCT1, MATE1, and MATE2K. Clinical drug-drug interaction (DDI) studies with the strong CYP3A inhibitor itraconazole or the strong CYP3A inducer rifampin demonstrated that CYP3A-mediated metabolism was the primary contributor to overall brigatinib clearance in humans. A physiologically-based pharmacokinetic (PBPK) model for brigatinib was developed to predict potential DDIs, including the effect of moderate CYP3A inhibitors or inducers on brigatinib pharmacokinetics (PK) and the effect of brigatinib on the PK of transporter substrates. The developed model was able to predict clinical DDIs with itraconazole (area under the plasma concentration-time curve from time 0 to infinity [AUC∞] ratio [with/without itraconazole]: predicted 1.86; observed 2.01) and rifampin (AUC∞ ratio [with/without rifampin]: predicted 0.16; observed 0.20). Simulations using the developed model predicted that moderate CYP3A inhibitors (e.g., verapamil and diltiazem) may increase brigatinib AUC∞ by ~40%, whereas moderate CYP3A inducers (e.g., efavirenz) may decrease brigatinib AUC∞ by ~50%. Simulations of potential transporter-mediated DDIs predicted that brigatinib may increase systemic exposures (AUC∞) of P-gp substrates (e.g., digoxin and dabigatran) by 15%-43% and MATE1 substrates (e.g., metformin) by up to 29%; however, negligible effects were predicted on BCRP-mediated efflux and OCT1-mediated uptake. The PBPK analysis results informed dosing recommendations for patients receiving moderate CYP3A inhibitors (40% brigatinib dose reduction) or inducers (up to 100% increase in brigatinib dose) during treatment, as reflected in the brigatinib prescribing information.

Physiologically based pharmacokinetic modeling of ritonavir-oxycodone drug interactions and its implication for dosing strategy

The concomitant administration of ritonavir and oxycodone may significantly increase the plasma concentrations of oxycodone. This study was aimed to simulate DDI between ritonavir and oxycodone, a widely used opioid, and to formulate dosing protocols for oxycodone by using physiologically based pharmacokinetic (PBPK) modeling. We developed a ritonavir PBPK model incorporating induction and competitive and time-dependent inhibition of CYP3A4 and competitive inhibition of CYP2D6. The ritonavir model was evaluated with observed clinical pharmacokinetic data and validated for its CYP3A4 inhibition potency. We then used the model to simulate drug interactions between oxycodone and ritonavir under various dosing scenarios. The developed model captured the pharmacokinetic characteristics of ritonavir from clinical studies. The model also accurately predicts exposure changes of midazolam, triazolam, and oxycodone in the presence of ritonavir. According to model simulations, the steady-state maximum, minimum and average concentrations of oxycodone increased by up to 166% after co-administration with ritonavir, and the total exposure increased by approximately 120%. To achieve similar steady-state concentrations, halving the dose with an unchanged dosing interval or doubling the dosing interval with an unaltered single dose should be practical for oxycodone, whether formulated in uncoated or controlled-release tablets during long-term co-medication with ritonavir. The results revealed exposure-related risks of oxycodone-ritonavir interactions that have not been studied clinically and emphasized PBPK as a workable method to direct judicious dosage.

Study of the ketohexokinase inhibitor PF-06835919 as a clinical cytochrome P450 3A inducer: Integrated use of oral midazolam and liquid biopsy

PF-06835919, a ketohexokinase inhibitor, presented as an inducer of cytochrome P450 3A4 (CYP3A4) in vitro (human primary hepatocytes), and static mechanistic modeling exercises predicted significant induction in vivo (oral midazolam area under the plasma concentration-time curve [AUC] ratio [AUCR] = 0.23-0.79). Therefore, a drug-drug interaction study was conducted to evaluate the effect of multiple doses of PF-06835919 (300 mg once daily × 10 days; N = 10 healthy participants) on the pharmacokinetics of a single oral midazolam 7.5 mg dose. The adjusted geometric means for midazolam AUC and its maximal plasma concentration were similar following co-administration with PF-06835919 (vs. midazolam administration alone), with ratios of the adjusted geometric means (90% confidence interval [CI]) of 97.6% (90% CI: 79.9%-119%) and 98.9% (90% CI: 76.4%-128%), respectively, suggesting there was minimal effect of PF-06835919 on midazolam pharmacokinetics. Lack of CYP3A4 induction was confirmed after the preparation of subject plasma-derived small extracellular vesicles (sEVs) and conducting proteomic and activity (midazolam 1'-hydroxylase) analysis. Consistent with the midazolam AUCR observed, the CYP3A4 protein expression fold-induction (geometric mean, 90% CI) was low in liver (0.9, 90% CI: 0.7-1.2) and non-liver (0.9, 90% CI: 0.7-1.2) sEVs (predicted AUCR = 1.0, 90% CI: 0.9-1.2). Likewise, minimal induction of CYP3A4 activity (geometric mean, 90% CI) in both liver (1.1, 90% CI: 0.9-1.3) and non-liver (0.9, 90% CI: 0.5-1.5) sEVs was evident (predicted AUCR = 0.9, 90% CI: 0.6-1.4). The results showcase the integrated use of an oral CYP3A probe (midazolam) and plasma-derived sEVs to assess a drug candidate as inducer.

Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug-Drug Interactions of Phenytoin

Regulatory agencies worldwide expect that clinical pharmacokinetic drug-drug interactions (DDIs) between an investigational new drug and other drugs should be conducted during drug development as part of an adequate assessment of the drug's safety and efficacy. However, it is neither time nor cost efficient to test all possible DDI scenarios clinically. Phenytoin is classified by the Food and Drug Administration as a strong clinical index inducer of CYP3A4, and a moderate sensitive substrate of CYP2C9. A physiologically based pharmacokinetic (PBPK) platform model was developed using GastroPlus® to assess DDIs with phenytoin acting as the victim (CYP2C9, CYP2C19) or perpetrator (CYP3A4). Pharmacokinetic data were obtained from 15 different studies in healthy subjects. The PBPK model of phenytoin explains the contribution of CYP2C9 and CYP2C19 to the formation of 5-(4'-hydroxyphenyl)-5-phenylhydantoin. Furthermore, it accurately recapitulated phenytoin exposure after single and multiple intravenous and oral doses/formulations ranging from 248 to 900 mg, the dose-dependent nonlinearity and the magnitude of the effect of food on phenytoin pharmacokinetics. Once developed and verified, the model was used to characterize and predict phenytoin DDIs with fluconazole, omeprazole and itraconazole, i.e., simulated/observed DDI AUC ratio ranging from 0.89 to 1.25. This study supports the utility of the PBPK approach in informing drug development.

Impact of Obesity on the Drug-Drug Interaction Between Dolutegravir and Rifampicin or Any Other Strong Inducers

Background

Obesity is increasingly prevalent among people with HIV. Obesity can impact drug pharmacokinetics and consequently the magnitude of drug-drug interactions (DDIs) and, thus, the related recommendations for dose adjustment. Virtual clinical DDI studies were conducted using physiologically based pharmacokinetic (PBPK) modeling to compare the magnitude of the DDI between dolutegravir and rifampicin in nonobese, obese, and morbidly obese individuals.

Methods

Each DDI scenario included a cohort of virtual individuals (50% female) between 20 and 50 years of age. Drug models for dolutegravir and rifampicin were verified against clinical observed data. The verified models were used to simulate the concurrent administration of rifampicin (600 mg) at steady state with dolutegravir (50 mg) administered twice daily in normal-weight (BMI 18.5-30 kg/m²), obese (BMI 30-40 kg/m²), and morbidly obese (BMI 40-50 kg/m²) individuals.

Results

Rifampicin was predicted to decrease dolutegravir area under the curve (AUC) by 72% in obese and 77% in morbidly obese vs 68% in nonobese individuals; however, dolutegravir trough concentrations were reduced to a similar extent (83% and 85% vs 85%). Twice-daily dolutegravir with rifampicin resulted in trough concentrations always above the protein-adjusted 90% inhibitory concentration for all BMI groups and above the 300 ng/mL threshold in a similar proportion for all BMI groups.

Conclusions

The combined effect of obesity and induction by rifampicin was predicted to further decrease dolutegravir exposure but not the minimal concentration at the end of the dosing interval. Thus, dolutegravir 50 mg twice daily with rifampicin can be used in individuals with a high BMI up to 50 kg/m².

Management of Drug Interactions with Inducers: Onset and Disappearance of Induction on Cytochrome P450 3A4 and Uridine Diphosphate Glucuronosyltransferase 1A1 Substrates

BACKGROUND

People living with HIV may present co-morbidities requiring the initiation and subsequently the discontinuation of medications with inducing properties. The time to reach maximal enzyme induction and to return to baseline enzyme levels has not been thoroughly characterized.

OBJECTIVE

The aim of this study was to evaluate the onset and disappearance of dolutegravir [uridine diphosphate glucuronosyltransferase (UGT) 1A1 and cytochrome P450 (CYP) 3A4 substrate] and raltegravir (UGT1A1 substrate) induction with strong and moderate inducers using physiologically based pharmacokinetic (PBPK) modeling.

METHODS

The predictive performance of the PBPK model to simulate dolutegravir and raltegravir pharmacokinetics and to reproduce the strength of induction was verified using clinical drug-drug interaction studies (steady-state induction) and switch studies (residual induction). The model was considered verified when the predictions were within 2-fold of the observed data. One hundred virtual individuals (50% female) were generated to simulate the unstudied scenarios. The results were used to calculate the fold-change in CYP3A4 and UGT1A1 enzyme levels upon initiation and discontinuation of strong (rifampicin) or moderate (efavirenz or rifabutin) inducers.

RESULTS

The time for reaching maximal induction and subsequent disappearance of CYP3A4 induction was 14 days for rifampicin and efavirenz but 7 days for rifabutin. The distinct timelines for the moderate inducers relate to their different half-lives and plasma concentrations. The induction and de-induction processes were more rapid for UGT1A1.

CONCLUSIONS

Our simulations support the common practice of maintaining the adjusted dosage of a drug for another 2 weeks after stopping an inducer. Furthermore, our simulations suggest that an inducer should be administered for at least 14 days before conducting interaction studies to reach maximal induction.

Mechanistic Modeling of the Drug-Drug Interaction Between Efavirenz and Dolutegravir: Is This Interaction Clinically Relevant When Switching From Efavirenz to Dolutegravir During Pregnancy?

Following the 2021 World Health Organization's updated recommendations on the management of HIV infection, millions of people living with HIV are currently switched from efavirenz-based antiretroviral therapy to dolutegravir-based antiretroviral therapy. Pregnant individuals transitioning from efavirenz to dolutegravir might be at increased risk of insufficient viral suppression in the immediate postswitch period because both efavirenz- and pregnancy-related increases in hormone levels induce enzymes involved in dolutegravir metabolism, namely, cytochrome P450 3A4 and uridine 5'-diphospho-glucuronosyltransferase 1A1. This study aimed at developing physiologically based pharmacokinetic models to simulate the switch from efavirenz to dolutegravir in the late second and third trimester. To this end, the drug-drug interaction between efavirenz and the uridine 5'-diphospho-glucuronosyltransferase 1A1 substrates dolutegravir and raltegravir was first simulated in nonpregnant subjects. After successful validation, the physiologically based pharmacokinetic models were translated to pregnancy and dolutegravir pharmacokinetics following efavirenz discontinuation were predicted. Modeling results indicated that, at the end of the second trimester, both efavirenz concentrations and dolutegravir trough concentrations fell below respective pharmacokinetic target thresholds (defined as reported thresholds producing 90%-95% of the maximum effect) during the time interval from 9.75 to 11 days after dolutegravir initiation. At the end of the third trimester, this time interval spanned from 10.3 days to >4 weeks after dolutegravir initiation. These findings suggest that dolutegravir exposure in the immediate post-efavirenz switch period during pregnancy may be suboptimal, leading to HIV viremia and, potentially, resistance. The clinical implications of these findings remain to be substantiated by future studies.

The MPRINT Hub Data, Model, Knowledge and Research Coordination Center: Bridging the gap in maternal-pediatric therapeutics research through data integration and pharmacometrics

Maternal and pediatric populations have historically been considered "therapeutic orphans" due to their limited inclusion in clinical trials. Physiologic changes during pregnancy and lactation and growth and maturation of children alter pharmacokinetics (PK) and pharmacodynamics (PD) of drugs. Precision therapy in these populations requires knowledge of these effects. Efforts to enhance maternal and pediatric participation in clinical studies have increased over the past few decades. However, studies supporting precision therapeutics in these populations are often small and, in isolation, may have limited impact. Integration of data from various studies, for example through physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling or bioinformatics approaches, can augment the value of data from these studies, and help identify gaps in understanding. To catalyze research in maternal and pediatric precision therapeutics, the Obstetric and Pediatric Pharmacology and Therapeutics Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) established the Maternal and Pediatric Precision in Therapeutics (MPRINT) Hub. Herein, we provide an overview of the status of maternal-pediatric therapeutics research and introduce the Indiana University-Ohio State University MPRINT Hub Data, Model, Knowledge and Research Coordination Center (DMKRCC), which aims to facilitate research in maternal and pediatric precision therapeutics through the integration and assessment of existing knowledge, supporting pharmacometrics and clinical trials design, development of new real-world evidence resources, educational initiatives, and building collaborations among public and private partners, including other NICHD-funded networks. By fostering use of existing data and resources, the DMKRCC will identify critical gaps in knowledge and support efforts to overcome these gaps to enhance maternal-pediatric precision therapeutics.

Rifampin Drug-Drug-Interaction Studies: Reflections on the Nitrosamine Impurities Issue

Clinical development of new drugs may require dedicated drug-drug interaction (DDI) studies, such as to evaluate the effect of cytochrome P450 3A induction on the pharmacokinetics of investigational drugs. However, as a result of N-nitrosamine impurity findings in marketed rifampin formulations, the application of rifampin in DDI studies has been halted. This mini-review considers the root cause and impact of the nitrosamine impurity as well as alternative options for the continued conduct of DDIs.

The Impact of N-nitrosamine Impurities on Clinical Drug Development

Over the past few years, an increasing number of commercially available drugs have been reported to contain N-nitrosamine impurities above acceptable intake limits. Consequent interruption or discontinuation of the manufacturing and distribution of several marketed drugs has culminated into shortages of marketed drugs, including the antidiabetic drug metformin and the potentially life-saving drug rifampin for the treatment of tuberculosis. Alarmingly, the clinical development of new investigational products has been complicated as well by the presence of N-nitrosamine impurities in batches of marketed drug. In particular, rifampin is a key clinical index drug employed in drug-drug interaction (DDI) studies, and as a result of nitrosamine impurities regulatory bodies no longer accept the administration of rifampin in DDI studies involving healthy subjects. Drug developers are now forced to look at alternative approaches for commonly employed perpetrators, which will be discussed in this review.

Does the choice of applied physiologically‐based pharmacokinetics platform matter? A case study on simvastatin disposition and drug–drug interaction

Physiologically‐based pharmacokinetic (PBPK) models have an important role in drug discovery/development and decision making in regulatory submissions. This is facilitated by predefined PBPK platforms with user‐friendly graphical interface, such as Simcyp and PK‐Sim. However, evaluations of platform differences and the potential implications for disposition‐related applications are still lacking. The aim of this study was to assess how PBPK model development, input parameters, and model output are affected by the selection of PBPK platform. This is exemplified via the establishment of simvastatin PBPK models (workflow, final models, and output) in PK‐Sim and Simcyp as representatives of established whole‐body PBPK platforms. The major finding was that the choice of PBPK platform influenced the model development strategy and the final model input parameters, however, the predictive performance of the simvastatin models was still comparable between the platforms. The main differences between the structure and implementation of Simcyp and PK‐Sim were found in the absorption and distribution models. Both platforms predicted equally well the observed simvastatin (lactone and acid) pharmacokinetics (20–80 mg), BCRP and OATP1B1 drug–gene interactions (DGIs), and drug–drug interactions (DDIs) when co‐administered with CYP3A4 and OATP1B1 inhibitors/inducers. This study illustrates that in‐depth knowledge of established PBPK platforms is needed to enable an assessment of the consequences of PBPK platform selection. Specifically, this work provides insights on software differences and potential implications when bridging PBPK knowledge between Simcyp and PK‐Sim users. Finally, it provides a simvastatin model implemented in both platforms for risk assessment of metabolism‐ and transporter‐mediated DGIs and DDIs.

Investigation of CYP3A induction by PF-05251749 in early clinical development: comparison of linear slope physiologically based pharmacokinetic prediction and biomarker response

PF-05251749 is a dual inhibitor of casein kinase 1 δ/ε under clinical development to treat disruption of circadian rhythm in Alzheimer's and Parkinson's diseases. In vitro, PF-05251749 (0.3-100 μM) induced CYP3A in cryopreserved human hepatocytes, demonstrating non-saturable, dose-dependent CYP3A mRNA increases, with induction slopes in the range 0.036-0.39 μM^-1 . In a multiple-dose study (B8001002) in healthy participants, CYP3A activity was explored by measuring changes in 4β-hydroxycholesterol/cholesterol ratio. Following repeated oral administration of PF-05251749, up to 400 mg q.d., no significant changes were observed in 4β-hydroxycholesterol/cholesterol ratio; this ratio increased significantly (~1.5-fold) following administration of PF-05251749 at 750 mg q.d., suggesting potential CYP3A induction at this dose. Physiologically based pharmacokinetic (PBPK) models were developed to characterize the observed clinical pharmacokinetics (PK) of PF-05251749 at 400 and 750 mg q.d.; the PBPK induction model was calibrated using the in vitro linear fit induction slope, with rifampin as reference compound (Ind_max  = 8, EC₅₀  = 0.32 μM). Clinical trial simulation following co-administration of PF-05251749, 400 mg q.d. with oral midazolam 2 mg, predicted no significant drug interaction risk. PBPK model predicted weak drug interaction following co-administration of PF-05251749, 750 mg q.d. with midazolam 2 mg. In conclusion, good agreement was obtained between CYP3A drug interaction risk predicted using linear-slope PBPK model and exploratory biomarker trends. This agreement between two orthogonal approaches enabled assessment of drug interaction risks of PF-05251749 in early clinical development, in the absence of a clinical drug-drug interaction study.

Guide to development of compound files for PBPK modeling in the Simcyp population-based simulator

The Simcyp Simulator is a software platform for population physiologically‐based pharmacokinetic (PBPK) modeling and simulation. It links in vitro data to in vivo absorption, distribution, metabolism, excretion and pharmacokinetic/pharmacodynamic outcomes to explore clinical scenarios and support drug development decisions, including regulatory submissions and drug labels. This tutorial describes the different input parameters required, as well as the considerations needed when developing a PBPK model within the Simulator, for a small molecule intended for oral administration. A case study showing the development and application of a PBPK model for ondansetron is herein used to aid the understanding of different PBPK model development concepts.

Evaluation of drug–drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling

Pemigatinib is a potent inhibitor of fibroblast growth factor receptor being developed for oncology indications. It is primarily metabolized by cytochrome P450 (CYP) 3A4, and the ratio of estimated concentration over concentration required for 50% inhibition ratio for pemigatinib as an inhibitor of P‐glycoprotein (P‐gp), organic cation transporter‐2 (OCT2), and multidrug and toxin extrusion protein‐1 (MATE1) exceeds the cutoff values established in regulatory guidance. A Simcyp minimal physiologically based pharmacokinetic (PBPK) with advanced dissolution, absorption, and metabolism absorption model for pemigatinib was developed and validated using observed clinical pharmacokinetic (PK) data and itraconazole/rifampin drug–drug interaction (DDI) data. The model accurately predicted itraconazole DDI (approximate 90% area under the plasma drug concentration–time curve [AUC] and approximate 20% maximum plasma drug concentration [C_max] increase). The model underpredicted rifampin induction by 100% (approximate 6.7‐fold decrease in AUC and approximate 2.6‐fold decrease in C_max in the DDI study), presumably reflecting non‐CYP3A4 mechanisms being impacted. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors/inducers on pemigatinib PK and pemigatinib as an inhibitor of P‐gp or OCT2/MATE1 substrates. The worst‐case scenario DDI simulation for pemigatinib as an inhibitor of P‐gp or OCT2/MATE1 substrates showed only a modest DDI effect. The recommendation based on this simulation and clinical data is to reduce pemigatinib dose for coadministration with strong and moderate CYP3A4 inhibitors. No dose adjustment is required for weak CYP3A4 inhibitors. The coadministration of strong and moderate CYP3A4 inducers with pemigatinib should be avoided. PBPK modeling suggested no dose adjustment with P‐gp or OCT2/MATE1 substrates.

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.

Regulation of Drug Transport Proteins-From Mechanisms to Clinical Impact: A White Paper on Behalf of the International Transporter Consortium

Membrane transport proteins are involved in the absorption, disposition, efficacy, and/or toxicity of many drugs. Numerous mechanisms (e.g., nuclear receptors, epigenetic gene regulation, microRNAs, alternative splicing, post‐translational modifications, and trafficking) regulate transport protein levels, localization, and function. Various factors associated with disease, medications, and dietary constituents, for example, may alter the regulation and activity of transport proteins in the intestine, liver, kidneys, brain, lungs, placenta, and other important sites, such as tumor tissue. This white paper reviews key mechanisms and regulatory factors that alter the function of clinically relevant transport proteins involved in drug disposition. Current considerations with in vitro and in vivo models that are used to investigate transporter regulation are discussed, including strengths, limitations, and the inherent challenges in predicting the impact of changes due to regulation of one transporter on compensatory pathways and overall drug disposition. In addition, translation and scaling of in vitro observations to in vivo outcomes are considered. The importance of incorporating altered transporter regulation in modeling and simulation approaches to predict the clinical impact on drug disposition is also discussed. Regulation of transporters is highly complex and, therefore, identification of knowledge gaps will aid in directing future research to expand our understanding of clinically relevant molecular mechanisms of transporter regulation. This information is critical to the development of tools and approaches to improve therapeutic outcomes by predicting more accurately the impact of regulation‐mediated changes in transporter function on drug disposition and response.

Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models

Background and Objective

Induction of drug-metabolizing enzymes can lead to drug-drug interactions (DDIs); therefore, early assessment is often conducted. Previous reports focused on true positive cytochrome P450 3A (CYP3A) inducers leaving a gap in translation for in vitro inducers which do not manifest in clinical induction. The goal herein was to expand the in vitro induction dataset by including true negative clinical inducers to identify a correction factor to basic DDI models, which reduces false positives without impacting false negatives.

Methods

True negative clinical inducers were identified through a literature search, in vitro induction parameters were generated in three human hepatocyte donors, and the performance of basic induction models proposed by regulatory agencies, concentration producing twofold induction (F2), basic static model (R3) and relative induction score (RIS), was used to characterize clinical induction risk.

Results

The data demonstrated the importance of correcting for in vitro binding and metabolism to derive induction parameters. The aggregate analysis indicates that the RIS with a positive cut-off of < 0.7-fold area under the curve ratio (AUCR) provides the best quantitative prediction. Additionally, correction factors of ten and two times the unbound peak plasma concentration at steady state (C_max,ss,u) can be confidently used to identify true positive inducers when referenced against the concentration resulting in twofold increase in messenger ribonucleic acid (mRNA) or using the R3 equation, respectively.

Conclusions

These iterative improvements, which reduce the number of false positives, could aid regulatory recommendations and limit unnecessary clinical explorations into CYP3A induction.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13318-022-00763-y.

Prediction of metabolizing enzyme-mediated clinical drug interactions using in vitro information

Evaluation of drug interactions is an essential step in the new drug development process. Regulatory agencies, including U.S. Food and Drug Administrations and European Medicines Agency, have been published documents containing guidelines to evaluate potential drug interactions. Here, we have streamlined in vitro experiments to assess metabolizing enzyme-mediated drug interactions and provided an overview of the overall process to evaluate potential clinical drug interactions using in vitro data. An experimental approach is presented when an investigational drug (ID) is either a victim or a perpetrator, respectively, and the general procedure to obtain in vitro drug interaction parameters is also described. With the in vitro inhibitory and/or inductive parameters of the ID, basic, static, and/or dynamic models were used to evaluate potential clinical drug interactions. In addition to basic and static models which assume the most conservative conditions, such as the concentration of perpetrators as C_max, dynamic models including physiologically-based pharmacokinetic models take into account changes in in vivo concentrations and metabolizing enzyme levels over time.

Effect of CYP3A4 Inhibitors and Inducers on Pharmacokinetics and Pharmacodynamics of Saxagliptin and Active Metabolite M2 in Humans Using Physiological-Based Pharmacokinetic Combined DPP-4 Occupancy

We aimed to develop a physiological-based pharmacokinetic and dipepidyl peptidase 4 (DPP-4) occupancy model (PBPK-DO) characterized by two simultaneous simulations to predict pharmacokinetic (PK) and pharmacodynamic changes of saxagliptin and metabolite M2 in humans when coadministered with CYP3A4 inhibitors or inducers. Ketoconazole, delavirdine, and rifampicin were selected as a CYP3A4 competitive inhibitor, a time-dependent inhibitor, and an inducer, respectively. Here, we have successfully simulated PK profiles and DPP-4 occupancy profiles of saxagliptin in humans using the PBPK-DO model. Additionally, under the circumstance of actually measured values, predicted results were good and in line with observations, and all fold errors were below 2. The prediction results demonstrated that the oral dose of saxagliptin should be reduced to 2.5 mg when coadministrated with ketoconazole. The predictions also showed that although PK profiles of saxagliptin showed significant changes with delavirdine (AUC 1.5-fold increase) or rifampicin (AUC: a decrease to 0.19-fold) compared to those without inhibitors or inducers, occupancies of DPP-4 by saxagliptin were nearly unchanged, that is, the administration dose of saxagliptin need not adjust when there is coadministration with delavirdine or rifampicin.

Physiologically based pharmacokinetic modeling for dose optimization of quinine-phenobarbital coadministration in patients with cerebral malaria

Patients with cerebral malaria with polymorphic Cytochrome P450 2C19 (CYP2C19) genotypes who receive concurrent treatment with quinine are at risk of inadequate or toxic therapeutic drug concentrations due to metabolic drug interactions. The study aimed to predict the potential dose regimens of quinine when coadministered with phenobarbital in adult patients with cerebral malaria and complications (e.g., lactic acidosis and acute renal failure) and concurrent with seizures and acute renal failure who carry wild‐type and polymorphic CYP2C19. The whole‐body physiologically based pharmacokinetic (PBPK) models for quinine, phenobarbital, and quinine–phenobarbital coadministration were constructed based on the previously published information using Simbiology®. Four published articles were used for model validation. A total of 100 virtual patients were simulated based on the 14‐day and 3‐day courses of treatment. using the drug–drug interaction approach. The predicted results were within 15% of the observed values. Standard phenobarbital dose, when administered with quinine, is suitable for all groups with single or continuous seizures regardless of CYP2C19 genotype, renal failure, and lactic acidosis. Dose adjustment based on area under the curve ratio provided inappropriate quinine concentrations. The recommended dose of quinine when coadministered with phenobarbital based on the PBPK model for all groups is a loading dose of 2000 mg intravenous (i.v.) infusion rate 250 mg/h followed by 1200 mg i.v. rate 150 mg/h. The developed PBPK models are credible for further simulations. Because the predicted quinine doses in all groups were similar regardless of the CYP2C19 genotype, genotyping may not be required.

Unraveling pleiotropic effects of rifampicin by using physiologically based pharmacokinetic modeling: Assessing the induction magnitude of P-glycoprotein-cytochrome P450 3A4 dual substrates

Rifampicin induces both P-glycoprotein (P-gp) and cytochrome P450 3A4 (CYP3A4) through regulating common nuclear receptors (e.g., pregnane X receptor). The interplay of P-gp and CYP3A4 has emerged to be an important factor in clinical drug-drug interactions (DDIs) with P-gp-CYP3A4 dual substrates and requires qualitative and quantitative understanding. Although physiologically based pharmacokinetic (PBPK) modeling has become a widely accepted approach to assess DDIs and is able to reasonably predict DDIs caused by CYP3A4 induction and P-gp induction individually, the predictability of PBPK models for the effect of simultaneous P-gp and CYP3A4 induction on P-gp-CYP3A4 dual substrates remains to be systematically evaluated. In this study, we used a PBPK modeling approach for the assessment of DDIs between rifampicin and 12 drugs: three sensitive P-gp substrates, seven P-gp-CYP3A4 dual substrates, and two P-gp-CYP3A4 dual substrates and inhibitors. A 3.5-fold increase of intestinal P-gp abundance was incorporated in the PBPK models to account for rifampicin-mediated P-gp induction at steady state. The simulation results showed that accounting for P-gp induction in addition to CYP3A4 induction improved the prediction accuracy of the area under the concentration-time curve and maximum (peak) plasma drug concentration ratios compared with considering CYP3A4 induction alone. Furthermore, the interplay of relevant drug-specific parameters and its impact on the magnitude of DDIs were evaluated using sensitivity analysis. The PBPK approach described herein, in conjunction with robust in vitro and clinical data, can help in the prospective assessment of DDIs involving other P-gp and CYP3A4 dual substrates. The database reported in the present study provides a valuable aid in understanding the combined effect of P-gp and CYP3A4 induction during drug development.

Development of physiologically-based pharmacokinetic models for standard of care and newer tuberculosis drugs

Tuberculosis (TB) remains a global health problem and there is an ongoing effort to develop more effective therapies and new combination regimes that can reduce duration of treatment. The purpose of this study was to demonstrate utility of a physiologically‐based pharmacokinetic modeling approach to predict plasma and lung concentrations of 11 compounds used or under development as TB therapies (bedaquiline [and N‐desmethyl bedaquiline], clofazimine, cycloserine, ethambutol, ethionamide, isoniazid, kanamycin, linezolid, pyrazinamide, rifampicin, and rifapentine). Model accuracy was assessed by comparison of simulated plasma pharmacokinetic parameters with healthy volunteer data for compounds administered alone or in combination. Eighty‐four percent (area under the curve [AUC]) and 91% (maximum concentration [C_max]) of simulated mean values were within 1.5‐fold of the observed data and the simulated drug‐drug interaction ratios were within 1.5‐fold (AUC) and twofold (C_max) of the observed data for nine (AUC) and eight (C_max) of the 10 cases. Following satisfactory recovery of plasma concentrations in healthy volunteers, model accuracy was assessed further (where patients’ with TB data were available) by comparing clinical data with simulated lung concentrations (9 compounds) and simulated lung: plasma concentration ratios (7 compounds). The 5th–95th percentiles for the simulated lung concentration data recovered between 13% (isoniazid and pyrazinamide) and 88% (pyrazinamide) of the observed data points (Am J Respir Crit Care Med, 198, 2018, 1208; Nat Med, 21, 2015, 1223; PLoS Med, 16, 2019, e1002773). The impact of uncertain model parameters, such as the fraction of drug unbound in lung tissue mass (fu_mass), is discussed. Additionally, the variability associated with the patient lung concentration data, which was sparse and included extensive within‐subject, interlaboratory, and experimental variability (as well interindividual variability) is reviewed. All presented models are transparently documented and are available as open‐source to aid further research.

Pharmacokinetic Profile of Gilteritinib: A Novel FLT-3 Tyrosine Kinase Inhibitor

BACKGROUND AND OBJECTIVE

Gilteritinib is a novel, highly selective tyrosine kinase inhibitor approved in the USA, Canada, Europe, Brazil, Korea, and Japan for the treatment of FLT3 mutation-positive acute myeloid leukemia. This article describes the clinical pharmacokinetic profile of gilteritinib.

METHODS

The pharmacokinetic profile of gilteritinib was assessed from five clinical studies.

RESULTS

Dose-proportional pharmacokinetics was observed following once-daily gilteritinib administration (dose range 20-450 mg). Median maximum concentration was reached 2-6 h following single and repeat dosing of gilteritinib; mean elimination half-life was 113 h. Elimination was primarily via feces. Exposure to gilteritinib was comparable under fasted and fed conditions. Gilteritinib is primarily metabolized via cytochrome P450 (CYP) 3A4; coadministration of gilteritinib with itraconazole (a strong P-glycoprotein inhibitor and CYP3A4 inhibitor) or rifampicin (a strong P-glycoprotein inducer and CYP3A inducer) significantly affected the gilteritinib pharmacokinetic profile. No clinically relevant interactions were observed when gilteritinib was coadministered with midazolam (a CYP3A4 substrate) or cephalexin (a multidrug and toxin extrusion 1 substrate). Unbound gilteritinib exposure was similar between subjects with hepatic impairment and normal hepatic function.

CONCLUSIONS

Gilteritinib exhibits a dose-proportional pharmacokinetic profile in healthy subjects and in patients with relapsed/refractory acute myeloid leukemia. Gilteritinib exposure is not significantly affected by food. Moderate-to-strong CYP3A inhibitors demonstrated a significant effect on gilteritinib exposure. Coadministration of gilteritinib with CYP3A4 or multidrug and toxin extrusion 1 substrates did not impact substrate concentrations. Unbound gilteritinib was comparable between subjects with hepatic impairment and normal hepatic function; dose adjustment is not warranted for patients with hepatic impairment.

CLINICAL TRIAL REGISTRATION

NCT02014558, NCT02456883, NCT02571816.

Brexpiprazole Pharmacokinetics in CYP2D6 Poor Metabolizers: Using Physiologically Based Pharmacokinetic Modeling to Optimize Time to Effective Concentrations

Brexpiprazole is an oral antipsychotic agent indicated for use in patients with schizophrenia, or as adjunctive treatment for major depressive disorder. As cytochrome P450 (CYP) 2D6 contributes significantly to brexpiprazole metabolism, there is a label-recommended 50% reduction in dose among patients with the CYP2D6 poor metabolizer phenotype. This study uses a whole-body physiologically based pharmacokinetic (PBPK) model to compare the pharmacokinetics of brexpiprazole in patients known to be extensive metabolizers (EMs) and poor metabolizers (PMs). A PBPK model was constructed, verified, and validated against brexpiprazole clinical data, and simulations of 500 subjects were performed to establish the median time to effective concentrations in EMs and PMs. The PBPK simulations captured brexpiprazole PK well and demonstrated significant differences in the time to effective concentrations between EMs and PMs according to the label-recommended titration. Additionally, these simulations suggest that CYP2D6 PMs consistently achieve lower minimum concentrations during the dosing interval than CYP2D6 EMs. Simulations using an alternative dosing strategy of twice-daily dosing (as opposed to once daily) in PMs during the first week of brexpiprazole dosing yielded more consistent plasma concentrations between EMs and PMs, without exceeding the area under the plasma concentration-time curve observed in the EMs. Taken together, the results of these PBPK simulations suggest that product labeling for brexpiprazole titration in CYP2D6 PMs likely overcompensates for the decreased clearance seen in this population. We propose an alternative dosing strategy that decreases the time to effective concentrations and recommend a reevaluation of steady-state PK in this population to potentially allow for higher daily doses in CYP2D6 PMs.

Drug-drug interaction comparison between tacrolimus and phenobarbital in different formulations for paediatrics and adults

To compare drug-drug interaction (DDI) between tacrolimus and different formulations of phenobarbital in paediatrics and adults.Physiologically based pharmacokinetics (PBPK) models were used to evaluate DDI between phenobarbital (oral (p.o.) and intravenous (i.v.) formulations) and tacrolimus in paediatrics and adults. All dosing regimens were maintained for 7 days.Compared to i.v. phenobarbital, p.o. phenobarbital could decrease pharmacokinetic (PK) parameters of tacrolimus much more in both paediatrics and adults. On day 7, the results showed that the ratio of C_max for tacrolimus in the presence and absence of phenobarbital were 0.13 (p.o.) and 0.48 (i.v.), respectively, in paediatrics, while 0.54 (p.o.) and 0.73 (i.v.) in adults, respectively. The ratios of the area under the concentration-time curve (AUC) were 0.06 (p.o.) and 0.18 (i.v.) in paediatrics, while 0.46 (p.o.) and 0.53 (i.v.) in adults, respectively. PK parameters of tacrolimus decreased more significantly in paediatrics compared to adults.In paediatric, phenobarbital had a greater impact on PK of tacrolimus than that in adults. P.o. phenobarbital reduced PK parameters of tacrolimus even more than i.v. administration. In clinical practice, the concentration monitoring and dosage adjustment of tacrolimus should be emphasised when co-administrated with phenobarbital, especially in paediatric or in p.o. formulation.Key pointsThe results indicated that p.o. and i.v. phenobarbital both had a significant DDI with tacrolimus in paediatrics and adults.Phenobarbital had a greater impact on the PK of tacrolimus over time in paediatrics.P.o. administration of phenobarbital can reduce the PK parameters of tacrolimus more.

Physiologically Based Pharmacokinetic Modeling and Allometric Scaling in Pediatric Drug Development: Where Do We Draw the Line?

Developing medicines for children is now established in legislation in both the United States and Europe; new drugs require pediatric study or investigation plans as part of their development. Particularly in early age groups, many developmental processes are not reflected by simple scalars such as body weight or body surface area, and even projecting doses based on simple allometric scaling can lead to significant overdoses in certain age groups. Modeling and simulation methodology, including physiologically based modeling, has evolved as part of the drug development toolkit and is being increasingly applied to various aspects of pediatric drug development. Pediatric physiologically based pharmacokinetic (PBPK) models account for the development of organs and the ontogeny of specific enzymes and transporters that determine the age-related pharmacokinetic profiles. However, when should this approach be used, and when will simpler methods such as allometric scaling suffice in answering specific problems? The aim of this review article is to illustrate the application of allometric scaling and PBPK in pediatric drug development and explore the optimal application of the latter approach with reference to case examples. In reality, allometric scaling included as part of population pharmacokinetic and PBPK approaches are all part of a model-informed drug development toolkit helping with decision making during the process of drug discovery and development; to that end, they should be viewed as complementary.

Physiologically Based Pharmacokinetic Modelling to Identify Pharmacokinetic Parameters Driving Drug Exposure Changes in the Elderly

BACKGROUND

Medication use is highly prevalent with advanced age, but clinical studies are rarely conducted in the elderly, leading to limited knowledge regarding age-related pharmacokinetic changes.

OBJECTIVE

The objective of this study was to investigate which pharmacokinetic parameters determine drug exposure changes in the elderly by conducting virtual clinical trials for ten drugs (midazolam, metoprolol, lisinopril, amlodipine, rivaroxaban, repaglinide, atorvastatin, rosuvastatin, clarithromycin and rifampicin) using our physiologically based pharmacokinetic (PBPK) framework.

METHODS

PBPK models for all ten drugs were developed in young adults (20-50 years) following the best practice approach, before predicting pharmacokinetics in the elderly (≥ 65 years) without any modification of drug parameters. A descriptive relationship between age and each investigated pharmacokinetic parameter (peak concentration [C_max], time to C_max [t_max], area under the curve [AUC], clearance, volume of distribution, elimination-half-life) was derived using the final PBPK models, and verified with independent clinically observed data from 52 drugs.

RESULTS

The age-related changes in drug exposure were successfully simulated for all ten drugs. Pharmacokinetic parameters were predicted within 1.25-fold (70%), 1.5-fold (86%) and 2-fold (100%) of clinical data. AUC increased progressively by 0.9% per year throughout adulthood from the age of 20 years, which was explained by decreased clearance, while C_max, t_max and volume of distribution were not affected by human aging. Additional clinical data of 52 drugs were contained within the estimated variability of the established age-dependent correlations for each pharmacokinetic parameter.

CONCLUSION

The progressive decrease in hepatic and renal blood flow, as well as glomerular filtration, rate led to a reduced clearance driving exposure changes in the healthy elderly, independent of the drug.

Concomitant use of isavuconazole and CYP3A4/5 inducers: Where pharmacogenetics meets pharmacokinetics

BACKGROUND

Isavuconazole is a triazole antifungal drug, approved for the treatment of invasive aspergillosis and mucormycosis. Isavuconazole is metabolised by CYP3A4 and CYP3A5, and it has been shown that the CYP3A inducer rifampin reduces isavuconazole exposure. By extrapolation, the concomitant use of isavuconazole with moderate and strong CYP450 inducers is contraindicated, although it is known that some CYP450 inducers are less potent in comparison with rifampin.

OBJECTIVES

We aim to document exposure to isavuconazole in patients concomitantly treated with a CYP450 inducer that is less potent compared to rifampin. Moreover, although it is well known that CYP3A enzymes are important for the metabolism of isavuconazole, this induction effect has never been studied in combination with the patient's CYP3A genotype.

PATIENTS

We report three patients treated with both isavuconazole and a CYP3A inducer that is less potent compared to rifampin (rifabutin or phenobarbital), in whom we determined isavuconazole concentrations.

RESULTS

These cases suggest that the CYP3A4/5 genotype is an important determinant for isavuconazole exposure and that it might also influence the CYP450 induction interaction.

CONCLUSIONS

CYP3A inducers that are less potent compared to rifampin, may be combined with isavuconazole in patients with loss of CYP3A5 activity (CYP3A5*3/*3). Therapeutic drug monitoring is recommended during this combination. However, low-isavuconazole exposure was observed in the extensive metaboliser with CYP3A4*1/*1 and CYP3A5*1/*3 alleles.

PBPK modeling to predict drug-drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction

Ivosidenib is a potent, targeted, orally active, small-molecule inhibitor of mutant isocitrate dehydrogenase 1 (IDH1) that has been approved in the United States for the treatment of adults with newly diagnosed acute myeloid leukemia (AML) who are greater than or equal to 75 years of age or ineligible for intensive chemotherapy, and those with relapsed or refractory AML, with a susceptible IDH1 mutation. Ivosidenib is an inducer of the CYP2B6, CYP2C8, CYP2C9, and CYP3A4 and an inhibitor of P-glycoprotein (P-gp), organic anion transporting polypeptide-1B1/1B3 (OATP1B1/1B3), and organic anion transporter-3 (OAT3) in vitro. A physiologically-based pharmacokinetic (PK) model was developed to predict drug-drug interactions (DDIs) of ivosidenib in patients with AML. The in vivo CYP3A4 induction effect of ivosidenib was quantified using 4β-hydroxycholesterol and was subsequently verified with the PK data from an ivosidenib and venetoclax combination study. The verified model was prospectively applied to assess the effect of multiple doses of ivosidenib on a sensitive CYP3A4 substrate, midazolam. The simulated midazolam geometric mean area under the curve (AUC) and maximum plasma concentration (C_max ) ratios were 0.18 and 0.27, respectively, suggesting ivosidenib is a strong inducer. The model was also used to predict the DDIs of ivosidenib with CYP2B6, CYP2C8, CYP2C9, P-gp, OATP1B1/1B3, and OAT3 substrates. The AUC ratios following multiple doses of ivosidenib and a single dose of CYP2B6 (bupropion), CYP2C8 (repaglinide), CYP2C9 (warfarin), P-gp (digoxin), OATP1B1/1B3 (rosuvastatin), and OAT3 (methotrexate) substrates were 0.90, 0.52, 0.84, 1.01, 1.02, and 1.27, respectively. Finally, in accordance with regulatory guidelines, the Simcyp modeling platform was qualified to predict CYP3A4 induction using known inducers and sensitive substrates.

Physiologically-based pharmacokinetic modeling to predict drug interactions of lemborexant with CYP3A inhibitors

Lemborexant, a recently approved dual orexin receptor antagonist for treatment of adults with insomnia, is eliminated primarily by cytochrome P450 (CYP)3A metabolism. The recommended dose of lemborexant is 5 mg once per night, with a maximum recommended dose of 10 mg once daily. A physiologically-based pharmacokinetic (PBPK) model for lemborexant was developed and applied to integrate data obtained from in vivo drug-drug interaction (DDI) assessments, and to further explore lemborexant interaction with CYP3A inhibitors and inducers. The model predictions were in good agreement with observed pharmacokinetic data and with DDI results from clinical studies with CYP3A inhibitors, itraconazole and fluconazole. The model further predicted that DDI effects of weak CYP3A inhibitors (fluoxetine and ranitidine) are weak, and effects of moderate inhibitors (erythromycin and verapamil) are moderate. Based on the PBPK simulations and clinical efficacy and safety data, the maximum daily recommended lemborexant dose when administered with weak CYP3A inhibitors is 5 mg; co-administration of moderate and strong inhibitors should be avoided except in countries where 2.5 mg has been approved.

Comprehensive PBPK model to predict drug interaction potential of Zanubrutinib as a victim or perpetrator

A physiologically based pharmacokinetic (PBPK) model was developed to evaluate and predict (1) the effect of concomitant cytochrome P450 3A (CYP3A) inhibitors or inducers on the exposures of zanubrutinib, (2) the effect of zanubrutinib on the exposures of CYP3A4, CYP2C8, and CYP2B6 substrates, and (3) the impact of gastric pH changes on the pharmacokinetics of zanubrutinib. The model was developed based on physicochemical and in vitro parameters, as well as clinical data, including pharmacokinetic data in patients with B-cell malignancies and in healthy volunteers from two clinical drug-drug interaction (DDI) studies of zanubrutinib as a victim of CYP modulators (itraconazole, rifampicin) or a perpetrator (midazolam). This PBPK model was successfully validated to describe the observed plasma concentrations and clinical DDIs of zanubrutinib. Model predictions were generally within 1.5-fold of the observed clinical data. The PBPK model was used to predict untested clinical scenarios; these simulations indicated that strong, moderate, and mild CYP3A inhibitors may increase zanubrutinib exposures by approximately four-fold, two- to three-fold, and <1.5-fold, respectively. Strong and moderate CYP3A inducers may decrease zanubrutinib exposures by two- to three-fold or greater. The PBPK simulations showed that clinically relevant concentrations of zanubrutinib, as a DDI perpetrator, would have no or limited impact on the enzyme activity of CYP2B6 and CYP2C8. Simulations indicated that zanubrutinib exposures are not impacted by acid-reducing agents. Development of a PBPK model for zanubrutinib as a DDI victim and perpetrator in parallel can increase confidence in PBPK models supporting zanubrutinib label dose recommendations.

Physiologically-Based Pharmacokinetic Modeling to Support the Clinical Management of Drug-Drug Interactions With Bictegravir

Bictegravir is equally metabolized by cytochrome P450 (CYP)3A and uridine diphosphate‐glucuronosyltransferase (UGT)1A1. Drug–drug interaction (DDI) studies were only conducted for strong inhibitors and inducers, leading to some uncertainty whether moderate perpetrators or multiple drug associations can be safely coadministered with bictegravir. We used physiologically‐based pharmacokinetic (PBPK) modeling to simulate DDI magnitudes of various scenarios to guide the clinical DDI management of bictegravir. Clinically observed DDI data for bictegravir coadministered with voriconazole, darunavir/cobicistat, atazanavir/cobicistat, and rifampicin were predicted within the 95% confidence interval of the PBPK model simulations. The area under the curve (AUC) ratio of the DDI divided by the control scenario was always predicted within 1.25‐fold of the clinically observed data, demonstrating the predictive capability of the used modeling approach. After the successful verification, various DDI scenarios with drug pairs and multiple concomitant drugs were simulated to analyze their effect on bictegravir exposure. Generally, our simulation results suggest that bictegravir should not be coadministered with strong CYP3A and UGT1A1 inhibitors and inducers (e.g., atazanavir, nilotinib, and rifampicin), but based on the present modeling results, bictegravir could be administered with moderate dual perpetrators (e.g., efavirenz). Importantly, the inducing effect of rifampicin on bictegravir was predicted to be reversed with the concomitant administration of a strong inhibitor such as ritonavir, resulting in a DDI magnitude within the efficacy and safety margin for bictegravir (0.5–2.4‐fold). In conclusion, the PBPK modeling strategy can effectively be used to guide the clinical management of DDIs for novel drugs with limited clinical experience, such as bictegravir.

Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug-Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach

The anticonvulsant carbamazepine is frequently used in the long-term therapy of epilepsy and is a known substrate and inducer of cytochrome P450 (CYP) 3A4 and CYP2B6. Carbamazepine induces the metabolism of various drugs (including its own); on the other hand, its metabolism can be affected by various CYP inhibitors and inducers. The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) parent-metabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drug-drug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentration-time profiles (dosing range 50-800 mg), as well as fractions excreted unchanged in urine measurements. The carbamazepine model applies metabolism by CYP3A4 and CYP2C8 to produce carbamazepine-10,11-epoxide, metabolism by CYP2B6 and UDP-glucuronosyltransferase (UGT) 2B7 and glomerular filtration. The carbamazepine-10,11-epoxide model applies metabolism by epoxide hydroxylase 1 (EPHX1) and glomerular filtration. Good DDI performance was demonstrated by the prediction of carbamazepine DDIs with alprazolam, bupropion, erythromycin, efavirenz and simvastatin, where 14/15 DDI AUClast ratios and 11/15 DDI Cmax ratios were within the prediction success limits proposed by Guest et al. The thoroughly evaluated model will be freely available in the Open Systems Pharmacology model repository.

Pharmacokinetic Estimation Models-based Approach to Predict Clinical Implications for CYP Induction by Calcitriol in Human Cryopreserved Hepatocytes and HepaRG Cells

Calcitriol, a vitamin D₃ metabolite, is approved for various indications because it is the bioactive form of vitamin D in the body. The purpose of this study was to predict the clinical significance of cytochrome P450 (CYP) induction by calcitriol using in vitro human cryopreserved hepatocytes, HepaRG experimental systems, and various pharmacokinetic estimation models. CYP2B6, 3A4, 2C8, and 2C9 mRNA levels increased in a concentration-dependent manner in the presence of calcitriol in human cryopreserved hepatocytes and HepaRG cells. Using the half maximal effective concentration (EC₅₀) and maximum induction effect (E_max) obtained from the in vitro study, a basic kinetic model was applied, suggesting clinical relevance. In addition, a static mechanistic model showed the improbability of a clinically significant effect; however, the calculated area under the plasma concentration-time curve ratio (AUCR) was marginal for CYP3A4 in HepaRG cells. To clarify the effect of CYP3A4 in vivo, physiologically based pharmacokinetic (PBPK) modeling was applied as a dynamic mechanistic model, revealing a low clinically significant effect of CYP3A4 induction by calcitriol. Therefore, we conclude that CYP induction by calcitriol treatment would not be clinically significant under typical clinical conditions.

Physiologically based pharmacokinetic modeling and simulation to predict drug-drug interactions of ivosidenib with CYP3A perpetrators in patients with acute myeloid leukemia

PURPOSE

Develop a physiologically based pharmacokinetic (PBPK) model of ivosidenib using in vitro and clinical PK data from healthy participants (HPs), refine it with clinical data on ivosidenib co-administered with itraconazole, and develop a model for patients with acute myeloid leukemia (AML) and apply it to predict ivosidenib drug-drug interactions (DDI).

METHODS

An HP PBPK model was developed in Simcyp Population-Based Simulator (version 15.1), with the CYP3A4 component refined based on a clinical DDI study. A separate model accounting for the reduced apparent oral clearance in patients with AML was used to assess the DDI potential of ivosidenib as the victim of CYP3A perpetrators.

RESULTS

For a single 250 mg ivosidenib dose, the HP model predicted geometric mean ratios of 2.14 (plasma area under concentration-time curve, to infinity [AUC_0-∞]) and 1.04 (maximum plasma concentration [C_max]) with the strong CYP3A4 inhibitor, itraconazole, within 1.26-fold of the observed values (2.69 and 1.0, respectively). The AML model reasonably predicted the observed ivosidenib concentration-time profiles across all dose levels in patients. Predicted ivosidenib geometric mean steady-state AUC_0-∞ and C_max ratios were 3.23 and 2.26 with ketoconazole, and 1.90 and 1.52 with fluconazole, respectively. Co-administration of the strong CYP3A4 inducer, rifampin, predicted a greater DDI effect on a single dose of ivosidenib than on multiple doses (AUC ratios 0.35 and 0.67, C_max ratios 0.91 and 0.81, respectively).

CONCLUSION

Potentially clinically relevant DDI effects with CYP3A4 inducers and moderate and strong inhibitors co-administered with ivosidenib were predicted. Considering the challenges of conducting clinical DDI studies in patients, this PBPK approach is valuable in ivosidenib DDI risk assessment and management.

An Adaptive Physiologically Based Pharmacokinetic-Driven Design to Investigate the Effect of Itraconazole and Rifampicin on the Pharmacokinetics of Molibresib (GSK525762) in Healthy Female Volunteers

Molibresib (GSK525762), an orally bioavailable small molecule with 2 major equipotent active metabolites, is being developed for the treatment of cancers. Molibresib is a substrate of cytochrome P450 (CYP) 3A4 and P‐glycoprotein (P‐gp). To enable administering safe doses of molibresib to healthy volunteers, this 2‐part randomized, open‐label, crossover drug‐drug interaction trial was conducted as an adaptive design study using physiologically based pharmacokinetic (PBPK) modeling and simulation to predict the lowest doses of molibresib that could be safely administered alone (10 mg) or with itraconazole and rifampicin (strong inhibitors and inducers of CYP3A and P‐gp, respectively). PBPK simulation guided the molibresib dose (5 mg) to be administered along with itraconazole in part 1. Itraconazole increased total exposure (AUC) of molibresib by 4.15‐fold with a 66% increase in C_max, whereas the total AUC and C_max for the 2 major active metabolites of molibresib decreased by about 70% and 87%, respectively. A second PBPK simulation was conducted with part 1 data to also include the active metabolites to update the recommendation for the molibresib dose (20 mg) with rifampicin. With rifampicin, the AUC and C_max of molibresib decreased by approximately 91% and 80%, respectively, whereas the AUC of the 2 active metabolites decreased to a lesser extent (8%), with a 2‐fold increase in C_max. The results of this study confirmed the in vitro data that molibresib is a substrate for CYP3A4. The adaptive design, including Simcyp simulations, allowed evaluation of 2 drug interactions of an oncology drug in a single trial, thus minimizing time and exposures administered to healthy subjects.

Cardiovascular Risk Management and Hepatitis C: Combining Drugs

Direct-acting antivirals (DAAs) are known victims (substrate) and perpetrators (cause) of drug–drug interactions (DDIs). These DAAs are used for the treatment of hepatitis C virus (HCV) infections and are highly effective drugs. Drugs used for cardiovascular risk management are frequently used by HCV-infected patients, whom also are treated with DAAs. Therefore, the aim of this review was to describe DDIs between cardiovascular drugs (CVDs) and DAAs. An extensive literature search was performed containing search terms for the marketed DAAs and CVDs (β-blocking agents, ACE inhibitors, angiotensin II antagonists, renin inhibitors, diuretics, calcium channel blockers, statins/ezetimibe, fibrates, platelet aggregation inhibitors, vitamin K antagonists, heparins, direct Xa inhibitors, nitrates, amiodarone, and digoxin). In particular, the drug labels from the European Medicines Agency and the US Food and Drug Administration were used. A main finding of this review is that CVDs are mostly victims of DDIs with DAAs. Therefore, when possible, monitoring of pharmacodynamics is recommended when coadministering these drugs with DAAs. Nevertheless, it is sometimes better to discontinue a drug on a temporary basis (statins, ezetimide). The DAAs are victims of DDIs in combination with bisoprolol, carvedilol, labetalol, verapamil, and gemfibrozil. Despite there are many DDIs predicted in this review, most of these DDIs can be managed by monitoring the efficacy and toxicity of the victim drug or by switching to another CVD/DAA.