Pharmacokinetic Evaluation of the CYP3A4 and CYP2C9 Drug-Drug Interaction of Avacopan in 2 Open-Label Studies in Healthy Participants.

Avacopan, a complement 5a receptor (C5aR) antagonist approved for treating severe active antineutrophil cytoplasmic autoantibody (ANCA)-associated vasculitis, was evaluated in 2 clinical drug-drug interaction studies. The studies assessed the impact of avacopan on the pharmacokinetics (PK) of CYP3A4 substrates midazolam and simvastatin and CYP2C9 substrate celecoxib, and the influence of CYP3A4 inhibitor itraconazole and inducer rifampin on the PKs of avacopan. The results indicated that twice-daily oral administration of 30 mg of avacopan increased the area under the curve (AUC) of midazolam by 1.81-fold and celecoxib by 1.15-fold when administered without food, and twice-daily oral administration of 30 or 60 mg of avacopan increased the AUC of simvastatin by approximately 2.6-3.5-fold and the AUC of the active metabolite ß-hydroxy-simvastatin acid by approximately 1.4-1.7-fold when co-administered with food. Furthermore, the AUC of avacopan increased by approximately 2.19-fold when co-administered with itraconazole and decreased by approximately 13.5-fold when co-administered with rifampin. These findings provide critical insights into the potential drug-drug interactions involving avacopan, which could have significant implications for patient care and treatment planning. (NCT06207682).

Prediction of drug-drug interactions between roflumilast and CYP3A4/1A2 perpetrators using a physiologically-based pharmacokinetic (PBPK) approach.

This study aimed to develop a physiologically-based pharmacokinetic (PBPK) model to predict changes in the pharmacokinetics (PK) and pharmacodynamics (PD, PDE4 inhibition) of roflumilast (ROF) and ROF N-oxide when co-administered with eight CYP3A4/1A2 perpetrators. The population PBPK model of ROF and ROF N-oxide has been successfully developed and validated based on the four clinical PK studies and five clinical drug-drug interactions (DDIs) studies. In PK simulations, every ratio of prediction to observation for PK parameters fell within the range 0.7 to 1.5. In DDI simulations, except for tow peak concentration ratios (Cmax) of ROF with rifampicin (prediction: 0.63 vs. observation: 0.19) and with cimetidine (prediction: 1.07 vs. observation: 1.85), the remaining predicted ratios closely matched the observed ratios. Additionally, the PBPK model suggested that co-administration with the three perpetrators (cimetidine, enoxacin, and fluconazole) may use with caution, with CYP3A4 strong inhibitor (ketoconazole and itraconazole) or with dual CYP3A41A2 inhibitor (fluvoxamine) may reduce to half-dosage or use with caution, while co-administration with CYP3A4 strong or moderate inducer (rifampicin, efavirenz) should avoid. Overall, the present PBPK model can provide recommendations for adjusting dosing regimens in the presence of DDIs.

Exploring the impact of CYP2D6 and UGT2B7 gene-drug interactions, and CYP-mediated DDI on oxycodone and oxymorphone pharmacokinetics using physiologically-based pharmacokinetic modeling and simulation.

Oxycodone is one of the most commonly used opioids to treat moderate to severe pain. It is metabolized mainly by CYP3A4 and CYP2D6, while only a small fraction of the dose is excreted unchanged into the urine. Oxymorphone, the metabolite primarily formed by CYP2D6, has a 40- to 60-fold higher mu-opioid receptor affinity than the parent compound. While CYP2D6-mediated gene-drug-interactions (GDIs) and drug-drug interactions (DDIs) are well-studied, they only account for a portion of the variability in oxycodone and oxymorphone exposure. The combined impact of CYP2D6-mediated GDIs and DDIs, CYP3A4-mediated DDIs, and UGT2B7 GDIs is not fully understood yet and hard to study in head-to-head clinical trials given the relatively large number of scenarios. Instead, we propose the use of a physiologically-based pharmacokinetic model that integrates available information on oxycodone's metabolism to characterize and predict the impact of DDIs and GDIs on the exposure of oxycodone and its major, pharmacologically-active metabolite oxymorphone. To this end, we first developed and verified a PBPK model for oxycodone and its metabolites using published clinical data. The verified model was then applied to determine the dose-exposure relationship of oxycodone and oxymorphone stratified by CYP2D6 and UGT2B7 phenotypes respectively, and administered perpetrators of CYP-based drug interactions. Our simulations demonstrate that the combination of CYP2D6 UM and a UGT2B7Y (268) mutation may lead to a 2.3-fold increase in oxymorphone exposure compared to individuals who are phenotyped as CYP2D6 NM / UGT2B7 NM. The extent of oxymorphone exposure increases up to 3.2-fold in individuals concurrently taking CYP3A4 inhibitors, such as ketoconazole. Inhibition of the CYP3A4 pathway results in a relative increase in the partial metabolic clearance of oxycodone to oxymorphone. Oxymorphone is impacted to a higher extent by GDIs and DDIs than oxycodone. We predict oxymorphone exposure to be highest in CYP2D6 UMs/UGT2B7 PMs in the presence of ketoconazole (strong CYP3A4 index inhibitor) and lowest in CYP2D6 PMs/UGT2B7 NMs in the presence of rifampicin (strong CYP3A4 index inducer) covering a 55-fold exposure range.

Assessment of CYP3A-mediated drug interaction via cytokine (IL-6) elevation for mosunetuzumab using physiologically-based pharmacokinetic modeling.

Mosunetuzumab is a CD3/CD20 bispecific antibody. As an on-target effect, transient elevation of interleukin-6 (IL-6) occurs in early treatment cycles. A physiologically-based pharmacokinetic (PBPK) model was developed to assess potential drug interaction caused by IL-6 enzyme suppression on cytochrome P450 3A (CYP3A) during mosunetuzumab treatment. The model's performance in predicting IL-6 CYP3A suppression and subsequent drug-drug interactions (DDIs) was verified using existing clinical data of DDIs caused by chronic and transient IL-6 elevation. Sensitivity analyses were performed for a complete DDI risk assessment. The IL-6 concentration- and time-dependent CYP3A suppression during mosunetuzumab treatment was simulated using PBPK model with incorporation of in vitro IL-6 inhibition data. At clinically approved doses/regimens, the DDI at maximum CYP3A suppression was predicted to be a midazolam maximum drug concentration in plasma (Cmax ) and area under the plasma drug concentration-time curve (AUC) ratio of 1.17 and 1.37, respectively. At the 95th percentile of IL-6 concentration level or when gut CYP3A suppression was considered, the predicted DDI risk for mosunetuzumab remained low (<2-fold). The PBPK-based DDI predictions informed the mosunetuzumab product label to monitor, in early cycles, the concentrations and toxicities for sensitive CYP3A substrates with narrow therapeutic windows.

Physiologically based pharmacokinetic modelling to predict drug-drug interactions for encorafenib. Part I. Model building, validation, and prospective predictions with enzyme inhibitors, inducers, and transporter inhibitors.

Encorafenib, a potent BRAF kinase inhibitor undergoes significant metabolism by CYP3A4 (83%) and CYP2C19 (16%) and also a substrate of P-glycoprotein (P-gp). Because of this, encorafenib possesses potential for enzyme-transporter related interactions. Clinically, its drug-drug interactions (DDIs) with CYP3A4 inhibitors (posaconazole, diltiazem) were reported and hence there is a necessity to study DDIs with multiple enzyme inhibitors, inducers, and P-gp inhibitors.USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors, CYP3A4 inducers were selected and prospective DDIs were simulated using physiologically based pharmacokinetic modelling (PBPK). Impact of dose (50 mg vs. 300 mg) and staggering of administrations (0-10 h) on the DDIs were predicted.PBPK models for encorafenib, perpetrators simulated PK parameters within twofold prediction error. Clinically reported DDIs with posaconazole and diltiazem were successfully predicted.CYP2C19 inhibitors did not result in significant DDI whereas strong CYP3A4 inhibitors resulted in DDI ratio up to 4.5. Combining CYP3A4, CYP2C19 inhibitors yielded DDI equivalent CYP3A4 alone. Strong CYP3A4 inducers yielded DDI ratio up to 0.3 and no impact of P-gp inhibitors on DDIs was observed. The DDIs were not impacted by dose and staggering of administration. Overall, this work indicated significance of PBPK modelling for evaluating clinical DDIs with enzymes, transporters and interplay.

Physiologically based pharmacokinetic modeling (PBPK) to predict drug-drug interactions for encorafenib. Part II. Prospective predictions in hepatic and renal impaired populations with clinical inhibitors and inducers.

Encorafenib, a potent BRAF kinase inhibitor gets significantly metabolised by CYP3A4 (83%) and CYP2C19 (16%) and is a substrate for P-glycoprotein (P-gp). Due to significant metabolism by CYP3A4, encorafenib exposure can increase in hepatic and renal impairment and may lead to altered magnitude of drug-drug interactions (DDI). Hence, it is necessary to assess the exposures & DDI's in impaired population.Physiologically based pharmacokinetic modelling (PBPK) was utilised to determine the exposures of encorafenib in hepatic and renal impairment along with altered DDI's. Prospective DDI's were predicted with USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors and CYP3A4 inducers.PBPK models for encorafenib, perpetrators simulated PK parameters within 2-folds error. Encorafenib exposures significantly increased in hepatic as compared to renal impairment because of reduced CYP3A4 levels.Hepatic impairment caused changes in inhibition and induction DDI's, when compared to healthy population. Renal impairment did not cause significant changes in DDIs except for itraconazole. P-gp, CYP2C19 inhibitors did not result in altered DDI's. The DDI's were found to have insignificant correlation with relative exposure increase of perpetrators in case of impairment. Overall, this work signifies use of PBPK modelling for DDI's evaluations in hepatic and renal impairment populations.

Evaluation of the Potential for Drug-Drug Interactions with Inhaled Itraconazole Using Physiologically Based Pharmacokinetic Modelling, Based on Phase 1 Clinical Data.

Itraconazole is a potent inhibitor of cytochrome P450 3A4 (CYP3A4), associated with numerous drug-drug interactions (DDI). PUR1900, a dry powder formulation of itraconazole for oral inhalation, results in high lung and low systemic exposure. This project used physiologically based pharmacokinetic (PBPK) modeling to assess the DDI potential of inhaled PUR1900, using midazolam as a "victim drug." The basic and mechanistic static models evaluated the DDI potential of PUR1900, assuming 5 mg of midazolam coadministration at steady-state itraconazole exposure. Subsequently, Simcyp® PBPK simulation software and pharmacokinetic data from a Phase 1 clinical trial with PUR1900 (NCT03479411) were used to optimize an existing itraconazole PBPK model. The model was applied to investigate the potential for CYP3A4 DDI when 5 mg of midazolam is co-administered with inhaled PUR1900 at a steady state in a virtual healthy population at PUR1900 doses up to 40 mg per day. The basic static and mechanistic static models suggested a strong likelihood for DDI with inhaled PUR1900. The PBPK model was consistent with PUR1900 Phase 1 trial data. The geometric mean Cmax and AUC ratios of midazolam at a maximum dose of 40 mg PUR1900 were 1.14 and 1.26, respectively, indicating a minimal likelihood of DDI with inhaled PUR1900. The low systemic exposure of itraconazole when administered as PUR1900 results in minimal to no CYP3A4 inhibition, reducing the concern of drug-drug interactions. As the risk of CYP3A4 DDI is predicted to be significantly lower when itraconazole is administered via oral inhalation as PUR1900, it is likely that PUR1900 can be safely used for the treatment of pulmonary fungal infections in patients taking pharmaceuticals currently contraindicated with oral itraconazole.

Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network.

The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P-glycoprotein (P-gp) and is therefore recommended for use in clinical drug-drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P-gp, it is susceptible to DDIs involving these proteins. Physiologically-based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug-drug(-gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P-gp perpetrators as well as CYP2D6 and P-gp victims. The quinidine parent-metabolite model including 3-hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1-600 mg). The model covers efflux transport via P-gp and metabolic transformation to either 3-hydroxyquinidine or unspecified metabolites via CYP3A4. The 3-hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two-fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two-fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.

Tiered approach to evaluate the CYP3A victim and perpetrator drug-drug interaction potential for vonoprazan using PBPK modeling and clinical data to inform labeling.

Vonoprazan is metabolized extensively through CYP3A and is an in vitro time-dependent inhibitor of CYP3A. A tiered approach was applied to understand the CYP3A victim and perpetrator drug-drug interaction (DDI) potential for vonoprazan. Mechanistic static modeling suggested vonoprazan is a potential clinically relevant CYP3A inhibitor. Thus, a clinical study was conducted to evaluate the impact of vonoprazan on the exposure of oral midazolam, an index substrate for CYP3A. A physiologically-based pharmacokinetic (PBPK) model for vonoprazan was also developed using in vitro data, drug- and system-specific parameters, and clinical data and observations from a [14 C] human absorption, distribution, metabolism, and excretion study. The PBPK model was refined and verified using data from a clinical DDI study with the strong CYP3A inhibitor, clarithromycin, to confirm the fraction metabolized by CYP3A, and the oral midazolam clinical DDI data assessing vonoprazan as a time-dependent inhibitor of CYP3A. The verified PBPK model was applied to simulate the anticipated changes in vonoprazan exposure due to moderate and strong CYP3A inducers (efavirenz and rifampin, respectively). The clinical midazolam DDI study indicated weak inhibition of CYP3A, with a less than twofold increase in midazolam exposure. PBPK simulations projected a 50% to 80% reduction in vonoprazan exposure when administered concomitantly with moderate or strong CYP3A inducers. Based on these results, the vonoprazan label was revised and states that lower doses of sensitive CYP3A substrates with a narrow therapeutic index should be used when administered concomitantly with vonoprazan, and co-administration with moderate and strong CYP3A inducers should be avoided.

Physiologically based pharmacokinetic modeling of ponatinib to describe drug-drug interactions in patients with cancer.

PURPOSE: This study aimed to investigate the drug-drug interactions of ponatinib with strong, moderate, or weak CYP3A4 inhibitors/inducers by developing physiologically based pharmacokinetic (PBPK) models. METHODS: Simcyp® Ver 20.1 (Certara Inc., Sheffield, UK) was used to construct a PBPK model for ponatinib and to predict its interaction with strong, moderate, or weak CYP3A4 inhibitors/inducers. The constructed model was validated by comparing predicted values with actual observed values. Inhibitors or inducers that increased or decreased the area under the plasma concentration curve of ponatinib by more than two-fold when used in combination were considered significant. RESULTS: The PBPK model of ponatinib accurately represented its oral pharmacokinetics. It also reasonably predicted its pharmacokinetics when combined with ketoconazole and rifampicin. No weak to strong CYP3A4 inhibitor combinations significantly increased the AUC of ponatinib. However, the strong CYP3A4 inducers rifampicin (oral, 600 mg QD) and phenytoin (oral, 100 mg TID) decreased AUC by 60-70% and 50%, respectively. CONCLUSIONS: The PBPK model predicted a significant drug interaction when ponatinib was combined with a strong CYP3A4 inducer. Conversely, the combination with weak-to-strong CYP3A4 inhibitors did not suggest a drug interaction with ponatinib.

[CYP3A inhibitors as a pharmacokinetic enhancer: pros and cons of drug interactions]. / CYP3A-remmers als farmacokinetische hulpstof.

Certain drugs inherently have unfavourable pharmacokinetic properties; for example, they are poorly absorbed or broken down too quickly in the liver. In some cases, the addition of a pharmacokinetic excipient, thus deliberately causing an interaction, may offer a solution. To date, this concept has been most widely applied in HIV treatment where addition of the CYP3A inhibitors ritonavir and cobicistat greatly increases plasma levels of other HIV medications. For the same reason, ritonavir has been added to the new oral antiviral drug against the SARS CoV-2 virus, nirmatrelvir. In addition to a better and/or longer effect, theoretically lower doses can also be used, resulting in cost savings. Deliberately inducing a pharmacokinetic interaction is not without risk: after all, interactions with other CYP3A substrates can also occur. Nevertheless, we believe that with good interaction management, CYP3A inhibitors can be used safely with benefits for patients and society.

Physiologically based pharmacokinetic combined BTK occupancy modeling for optimal dosing regimen prediction of acalabrutinib in patients alone, with different CYP3A4 variants, co-administered with CYP3A4 modulators and with hepatic impairment.

PURPOSE: To develop a mathematical model combined between physiologically based pharmacokinetic and BTK occupancy (PBPK-BO) to simultaneously predict pharmacokinetic (PK) and pharmacodynamic (PD) changes of acalabrutinib (ACA) and active metabolite ACP-5862 in healthy humans as well as PD in patients. Next, to use the PBPK-BO to determine the optimal dosing regimens in patients alone, with different CYP3A4 variants, when co-administration with four CYP3A4 modulators and in patients with hepatic impairment, respectively. METHODS: The PBPK-BO model was built using physicochemical and biochemical properties of ACA and ACP-5862 and then verified by observed PK and PD data from healthy humans and patients. Finally, the model was applied to determine optimal dosing regimens in various clinical situations. RESULTS: The simulations demonstrated that 100 mg ACA twice daily (BID) was the optimal dosing regimen in patients alone. Additionally, dosage regimens might be reduced to 50 mg BID in patients with five CYP3A4 variants. Moreover, the dosing regimen should be modified to 100 mg (even to 50 mg) once daily (QD) when co-administration with erythromycin or clarithromycin, and be increased to 200 mg BID with rifampicin, and but be avoided co-administration with itraconazole. Furthermore, dosage regimen simulations showed that optimal dosing might be decreased to 50 mg BID in patients with mild and moderate hepatic impairment, and be avoided taking ACA in severely hepatically impaired patients. CONCLUSION: This PBPK-BO model can predict PK and PD in healthy humans and patients and also predict the optimal dosing regimens in various clinical situations.

Development of Physiology Based Pharmacokinetic Model to Predict the Drug Interactions of Voriconazole and Venetoclax.

PURPOSE: Venetoclax (VEN), an anti-tumor drug that is a substrate of cytochrome P450 3A enzyme (CYP3A4), is used to treat leukemia. Voriconazole (VCZ) is an antifungal medication that inhibits CYP3A4. The goal of this study is to predict the effect of VCZ on VEN exposure. METHOD: Two physiological based pharmacokinetics (PBPK) models were developed for VCZ and VEN using the bottom-up and top-down method. VCZ model was also developed to describe the effect of CYP2C19 polymorphism on its pharmacokinetics (PK). The reversible inhibition constant (Ki) of VCZ for CYP3A4 was calibrated using drug-drug interaction (DDI) data of midazolam and VCZ. The clinical verified VCZ and VEN model were used to predict the DDI of VCZ and VEN at clinical dosing scenario. RESULT: VCZ model predicted VCZ exposure in the subjects of different CYP2C19 genotype and DDI related fold changes of sensitive CYP3A substrate with acceptable prediction error. VEN model can capture PK of VEN with acceptable prediction error. The DDI PBPK model predicted that VCZ increased the exposure of VEN by 4.5-9.6 fold. The increase in VEN exposure by VCZ was influenced by subject's CYP2C19 genotype. According to the therapeutic window, VEN dose should be reduced to 100 mg when co-administered with VCZ. CONCLUSION: The PBPK model developed here could support individual dose adjustment of VEN and DDI risk assessment. Predictions using the robust PBPK model confirmed that the 100 mg dose adjustment is still applicable in the presence of VCZ with high inter-individual viability.

Effects of itraconazole and rifampicin on the pharmacokinetics and safety of youkenafil, a novel phosphodiesterase type 5 inhibitor, in healthy Chinese subjects.

Youkenafil is a novel selective phosphodiesterase type 5 inhibitor to treat erectile dysfunction. In order to study the drug-drug interactions of youkenafil, in vitro experiments were conducted with human liver microsomes and recombinant isoenzymes to identify the effect of cytochrome P450 (CYP) enzymes on the metabolism of youkenafil. Then two clinical studies were performed to investigate the effects of itraconazole and rifampicin (potent CYP3A4/5 inhibitor and inducer, respectively) on the pharmacokinetics of youkenafil and its main metabolite, N-desethyl youkenafil (M1). Each study enrolled thirty healthy male subjects. In study 1, subjects were given a single dose of youkenafil (50 mg on Days 1 and 13) and multiple doses of itraconazole (200 mg once daily from Days 6 to 14). In study 2, subjects were given a single dose of youkenafil (100 mg on Days 1 and 20) and multiple doses of rifampicin (600 mg once daily from Days 6 to 20). The results showed that youkenafil was mainly metabolized through CYP3A4/5 in vitro. Itraconazole increased youkenafil AUC and Cmax by about 12- and 6-fold, respectively, and increased M1 AUC and Cmax by 5- and 1.3-fold, respectively. Conversely, rifampicin reduced youkenafil AUC and Cmax both by about 98%. It did not change the AUC of M1 significantly, but increased the Cmax by 30%. All treatments were well tolerated by subjects in both studies. Therefore, co-administration of youkenafil with potent inhibitors or inducers of CYP3A4/5 should be avoided or carefully monitored.

Application of physiologically based pharmacokinetic modelling for the prediction of drug-drug interactions involving anlotinib as a perpetrator of cytochrome P450 enzymes.

Anlotinib is a small molecule of novel tyrosine kinase inhibitor initially approved to treat non-small cell lung cancer in China. Drug-drug interaction (DDI) is an extrinsic factor important for the appropriate use of anlotinib in clinical practice. In vitro experiments demonstrated that anlotinib is a substrate of cytochrome P450 (CYP) enzymes and moderate inhibitor of several common ones; however, no clinical DDI studies have been performed to investigate inhibitory effects of anlotinib on these CYP enzymes. Thus, its drug label recommends avoiding co-administration with substrates of these enzymes, which have narrow therapeutic windows. In this study, we performed a CYP450 inhibition study, followed by gathering in vitro and clinical pharmacokinetic data to build the first physiologically based pharmacokinetic (PBPK) model of anlotinib. The verified model was subsequently used to predict the DDI mediated by anlotinib. As a result, the marginal plasma exposure changes of typical CYP3A and CYP2C9 substrates were less than the bioequivalence threshold, indicating that anlotinib has a very low potential of causing clinically meaningful DDI through the inhibition of several major CYP enzymes. According to the FDA's latest guideline on DDI, the established model with the simulation results may support the revision of anlotinib labelling without further clinical studies, lifting unnecessary restrictions on anlotinib regimens.

Use of Physiologically Based Pharmacokinetic Modeling to Evaluate the Impact of Chronic Kidney Disease on CYP3A4-Mediated Metabolism of Saxagliptin.

We characterized the impact of chronic kidney disease (CKD) on the cytochrome P450 (CYP) 3A4-mediated metabolism of saxagliptin to its metabolite, 5-hydroxysaxagliptin, using a physiologically based pharmacokinetic (PBPK) model. A PBPK model of saxagliptin and its CYP3A4 metabolite, 5-hydroxysaxagliptin, was constructed and validated for oral doses ranging from 5 to 100 mg. The observed ratios of area under the plasma concentration-time curve (AUC) and maximum plasma concentration (Cmax ) between healthy subjects and subjects with CKD were compared with those predicted using PBPK model simulations. Simulations were performed with virtual CKD populations having decreased CYP3A4 activity (ie, 64%-75% of the healthy subjects' CYP3A4 abundance) and preserved CYP3A4 activity (ie, 100% of the healthy subjects' CYP3A4 abundance). We found that simulations using decreased CYP3A4 activity generally overpredicted the ratios of saxagliptin AUC and Cmax in CKD compared with those using preserved CYP3A4 activity. Similarly, simulations using decreased CYP3A4 activity underpredicted the ratio of 5-hydroxysaxagliptin AUC in moderate and severe CKD compared with simulations using preserved CYP3A4 activity. These findings suggest that decreased CYP3A4 activity in CKD underpredicts saxagliptin clearance compared with that observed clinically. Preserving CYP3A4 activity in CKD more closely estimates saxagliptin clearance and 5-hydroxysaxagliptin exposure changes observed in vivo. Our findings suggest that there is no clinically meaningful impact of CKD on the metabolism of saxagliptin by CYP3A4. Since saxagliptin is not a highly sensitive substrate and validated probe for CYP3A4, this work represents a case study of a CYP3A4 substrate-metabolite pair and is not a generalization for all CYP3A4 substrates.

Accelerating Clinical Development of Idasanutlin through a Physiologically Based Pharmacokinetic Modeling Risk Assessment for CYP450 Isoenzyme-Related Drug-Drug Interactions.

Idasanutlin is a potent inhibitor of the p53-MDM2 interaction that enables reactivation of the p53 pathway, which induces cell cycle arrest and/or apoptosis in tumor cells expressing functional p53. It was investigated for the treatment of solid tumors and several hematologic indications such as relapsed/refractory acute myeloid leukemia, polycythemia vera, or non-Hodgkin lymphoma. For safety reasons, it cannot be given in healthy volunteers for drug-drug interaction (DDI) explorations. This triggered the need for in silico explorations on top of the one available CYP3A clinical DDI study with posaconazole in solid tumor patients. Idasanutlin's clearance is dependent on CYP3A4/2C8 forming its major circulating metabolite M4, with contributions from UGT1A3 and biliary excretion. Idasanutlin and M4 have low permeability, very low clearance, and extremely low unbound fraction in plasma (<0.001), which makes in vitro data showing inhibition on CYP3A4/2C8 enzymes challenging to translate to clinical relevance. Physiologically-based pharmacokinetic models of idasanutlin and M4 have been established to simulate perpetrator and victim DDI scenarios and to evaluate whether further DDI studies in oncology patients are necessary. Modeling indicated that idasanutlin and M4 would show no or weak clinical inhibition of selective CYP3A4/2C8 substrates. Co-administered strong CYP3A and CYP2C8 inhibitors might lead to weak or moderate idasanutlin exposure increases, and the strong inducer rifampicin might cause moderate exposure reduction. As the simulated idasanutlin systemic exposure changes would be within the range of observed intrinsic variability, the target population can take co-medications that are either CYP2C8/3A4 inhibitors or weak/moderate CYP2C8/3A4 inducers without dose adjustment. SIGNIFICANCE STATEMENT: Clinical trials for idasanutlin are restricted to cancer patients, which imposes practical, scientific, and ethical challenges on drug-drug interaction investigations. Furthermore, idasanutlin and its major circulating metabolite have very challenging profiles of absorption, distribution, metabolism and excretion including high protein binding, low permeability and a combination of different elimination pathways each with extremely low clearance. Nonetheless, physiologically-based pharmacokinetic models could be established and applied for drug-drug interaction risk assessment and were especially useful to provide guidance on concomitant medications in patients.

Effects of Cytochrome P450 3A4 Induction and Inhibition on the Pharmacokinetics of BI 425809, a Novel Glycine Transporter 1 Inhibitor.

BACKGROUND AND OBJECTIVE: Increased glycine availability at the synaptic cleft may enhance N-methyl-D-aspartate receptor signalling and provide a promising therapeutic strategy for cognitive impairment associated with schizophrenia. These studies aimed to assess the pharmacokinetics of BI 425809, a potent glycine-transporter-1 inhibitor, when co-administered with a strong cytochrome P450 3A4 (CYP3A4) inhibitor (itraconazole) and inducer (rifampicin). METHODS: In vitro studies using recombinant CYPs, human liver microsomes, and human hepatocytes were conducted to determine the CYP isoforms responsible for BI 425809 metabolism. In addition, two open-label, fixed-treatment period, phase I studies in healthy male volunteers are described. Period 1: participants received oral BI 425809 25 mg (single dose) on day 1; period 2: participants received multiple doses, across 10 days, of oral itraconazole or rifampicin combined with a single dose of oral BI 425809 25 mg on day 4/7 of the itraconazole/rifampicin treatment, respectively. Pharmacokinetic and safety endpoints were assessed in the absence/presence of itraconazole/rifampicin and included area under the concentration-time curve (AUC) over the time interval 0-167 h (AUC0‒167; itraconazole), 0-168 h (AUC0‒168; rifampicin), or 0-infinity (AUC0-∞; rifampicin and itraconazole), maximum measured concentration (Cmax) of BI 425809, and adverse events. RESULTS: In vitro results suggested that CYP3A4 accounted for ≥ 90% of the metabolism of BI 425809. BI 425809 exposure (adjusted geometric mean ratio [%]) was higher in the presence of itraconazole (AUC0‒167: 265.3; AUC0-∞: 597.0; Cmax: 116.1) and lower in the presence of rifampicin (AUC0‒168: 10.3; AUC0-∞: 9.8; Cmax: 37.4) compared with BI 425809 alone. Investigational treatments were well tolerated. CONCLUSIONS: Systemic exposure of BI 425809 was altered in the presence of strong CYP3A4 modulators, corroborating in vitro results that CYP3A4 mediates a major metabolic pathway for BI 425809. TRIAL REGISTRATION NUMBER: NCT02342717 (registered on 15 January 2015) and NCT03082183 (registered on 10 March 2017).

Effect of fluconazole on the pharmacokinetics of fuzuloparib: an open-label, crossover study in Chinese healthy male volunteers.

PURPOSE: Fuzuloparib (AiRuiYiTM, formerly fluzoparib, SHR3162) is a new orally active poly adenosine diphosphate ribose polymerase (PARP) inhibitor. It has multiple pharmacological activities in breast, ovarian, and prostatic cancer. Fuzuloparib is mainly metabolized through the enzyme CYP3A4 may slow fuzuloparib metabolism and increase its concentrations in blood. We evaluated the pharmacokinetics and tolerability of fuzuloparib by fluconazole, which is a broad antifungal agent and a moderate inhibitor of CYP3A4. METHODS: In this study, the effects of CYP3A4 inhibition on the pharmacokinetics of fuzuloparib were assessed in a total of 20 healthy Chinese male subjects in an open-label, two-period, single-sequence, crossover study. RESULTS: Pharmacokinetic parameters, including the maximal plasma concentration (Cmax), the plasma concentration-time curve from time 0 to last measurable area under concentration (AUC0-t), and from time 0 to infinity (AUC0-∞), were increased by 32.4%, 104.5%, and 109.6%, with corresponding 90% confidence intervals of (23-43%), (93-116%), and (98-122%), respectively, when fluconazole was combined with fuzuloparib compared to fuzuloparib alone. There was also a slight increase in the incidence of treatment emergent adverse events, including hyperlipidemia and elevated aspartate transaminase. CONCLUSION: The fuzuloparib is 150 mg b.i.d in clinics use. Our results suggest that fuzuloparib could well be tolerated when administered as a single 20 mg oral dose alone or co-administered with 400 mg fluconazole in healthy male subjects. It is recommended to avoid using moderate CYP3A4 inhibitors together with fuzuloparib or instead of 50 mg when necessary.