The effect of itraconazole, a strong CYP3A4 inhibitor, on the pharmacokinetics of the first-in-class ACKR3/CXCR7 antagonist, ACT-1004-1239.

Cytochrome P450 (CYP) 3A4 is an enzyme involved in the metabolism of many drugs that are currently on the market and is therefore a key player in drug-drug interactions (DDIs). ACT-1004-1239 is a potent and selective, first-in-class ACKR3/CXRC7 antagonist being developed as a treatment for demyelinating diseases including multiple sclerosis. Based on the human absorption, distribution, metabolism, and excretion (ADME) study results, ACT-1004-1239 is predominantly metabolized by CYP3A4. This study investigated the effect of the strong CYP3A4 inhibitor, itraconazole, on the pharmacokinetics of single-dose ACT-1004-1239 in healthy male subjects. In the open-label, fixed-sequence DDI study, a total of 16 subjects were treated. Each subject received a single dose of 10 mg ACT-1004-1239 (Treatment A) in the first period followed by concomitant administration of multiple doses of 200 mg itraconazole and a single dose of 10 mg ACT-1004-1239 in the second period. We report a median of difference in tmax (90% confidence interval, CI) of 0.5 h (0.0, 1.0) comparing both treatments. The geometric mean ratio (GMR) (90% CI) of Cmax and AUC0-∞ was 2.16 (1.89, 2.47) and 2.77 (2.55, 3.00), respectively. The GMR (90% CI) of t1/2 was 1.46 (1.26, 1.70). Both treatments were well-tolerated with an identical incidence in subjects reporting treatment-emergent adverse events (TEAE). The most frequently reported TEAEs were headache and nausea. In conclusion, ACT-1004-1239 is classified as a moderately sensitive CYP3A4 substrate (i.e., increase of AUC ≥2- to <5-fold), and this should be considered in further clinical studies if CYP3A4 inhibitors are concomitantly administered.

A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions.

Dasatinib, a second-generation tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pH-sensitive solubility, dasatinib is susceptible to enzyme-mediated drug-drug interactions (DDIs) with CYP3A4 perpetrators and pH-dependent DDIs with acid-reducing agents. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model of dasatinib to describe and predict enzyme-mediated and pH-dependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentration-time curve from the first to the last timepoint of measurement (AUClast) and maximum plasma concentration (Cmax) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUClast and Cmax ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%-80% for strong and 0%-70% for moderate CYP3A4 inhibitors and a 2.3-3.1-fold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patient-related factors, such as elevated gastric pH.

Pharmacokinetics and Safety of Lurbinectedin Administrated with Itraconazole in Cancer Patients: A Drug-Drug Interaction Study.

This open-label, two-part, phase Ib drug-drug interaction study investigated whether the pharmacokinetic (PK) and safety profiles of lurbinectedin (LRB), a marine-derived drug, are affected by co-administration of itraconazole (ITZ), a strong CYP3A4 inhibitor, in adult patients with advanced solid tumors. In Part A, three patients were sequentially assigned to Sequence 1 (LRB 0.8 mg/m2, 1-h intravenous [IV] + ITZ 200 mg/day oral in Cycle 1 [C1] and LRB alone 3.2 mg/m2, 1 h, IV in Cycle 2 [C2]). In Part B, 11 patients were randomized (1:1) to receive either Sequence 1 (LRB at 0.9 mg/m2 + ITZ in C1 and LRB alone in C2) or Sequence 2 (LRB alone in C1 and LRB + ITZ in C2). Eleven patients were evaluable for PK analysis: three in Part A and eight in Part B (four per sequence). The systemic total exposure of LRB increased with ITZ co-administration: 15% for Cmax, area under the curve (AUC) 2.4-fold for AUC0-t and 2.7-fold for AUC0-∞. Co-administration with ITZ produced statistically significant modifications in the unbound plasma LRB PK parameters. The LRB safety profile was consistent with the toxicities described in previous studies. Co-administration with multiple doses of ITZ significantly altered LRB systemic exposure. Hence, to avoid LRB overexposure when co-administered with strong CYP3A4 inhibitors, an LRB dose reduction proportional to CL reduction should be applied.

Docetaxel, cyclophosphamide, and epirubicin: application of PBPK modeling to gain new insights for drug-drug interactions.

The new adjuvant chemotherapy of docetaxel, epirubicin, and cyclophosphamide has been recommended for treating breast cancer. It is necessary to investigate the potential drug-drug Interactions (DDIs) since they have a narrow therapeutic window in which slight differences in exposure might result in significant differences in treatment efficacy and tolerability. To guide clinical rational drug use, this study aimed to evaluate the DDI potentials of docetaxel, cyclophosphamide, and epirubicin in cancer patients using physiologically based pharmacokinetic (PBPK) models. The GastroPlus™ was used to develop the PBPK models, which were refined and validated with observed data. The established PBPK models accurately described the pharmacokinetics (PKs) of three drugs in cancer patients, and the predicted-to-observed ratios of all the PK parameters met the acceptance criterion. The PBPK model predicted no significant changes in plasma concentrations of these drugs during co-administration, which was consistent with the observed clinical phenomenon. Besides, the verified PBPK models were then used to predict the effect of other Cytochrome P450 3A4 (CYP3A4) inhibitors/inducers on these drug exposures. In the DDI simulation, strong CYP3A4 modulators changed the exposure of three drugs by 0.71-1.61 fold. Therefore, patients receiving these drugs in combination with strong CYP3A4 inhibitors should be monitored regularly to prevent adverse reactions. Furthermore, co-administration of docetaxel, cyclophosphamide, or epirubicin with strong CYP3A4 inducers should be avoided. In conclusion, the PBPK models can be used to further investigate the DDI potential of each drug and to develop dosage recommendations for concurrent usage by additional perpetrators or victims.

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).

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.

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.

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.

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.