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    Physiologically based pharmacokinetic modeling to assess metabolic drug-drug interaction risks and inform the drug label for fedratinib. Wu Fan,Krishna Gopal,Surapaneni Sekhar Cancer chemotherapy and pharmacology PURPOSE:Fedratinib (INREBIC), a Janus kinase 2 inhibitor, is approved in the United States to treat patients with myelofibrosis. Fedratinib is not only a substrate of cytochrome P450 (CYP) enzymes, but also exhibits complex auto-inhibition, time-dependent inhibition, or mixed inhibition/induction of CYP enzymes including CYP3A. Therefore, a mechanistic modeling approach was used to characterize pharmacokinetic (PK) properties and assess drug-drug interaction (DDI) potentials for fedratinib under clinical scenarios. METHODS:The physiologically based pharmacokinetic (PBPK) model of fedratinib was constructed in Simcyp (V17R1) by integrating available in vitro and in vivo information and was further parameterized and validated by using clinical PK data. RESULTS:The validated PBPK model was applied to predict DDIs between fedratinib and CYP modulators or substrates. The model simulations indicated that the fedratinib-as-victim DDI extent in terms of geometric mean area under curve (AUC) at steady state is about twofold or 1.2-fold when strong or moderate CYP3A4 inhibitors, respectively, are co-administered with repeated doses of fedratinib. In addition, the PBPK model successfully captured the perpetrator DDI effect of fedratinib on a sensitive CY3A4 substrate midazolam and predicted minor effects of fedratinib on CYP2C8/9 substrates. CONCLUSIONS:The PBPK-DDI model of fedratinib facilitated drug development by identifying DDI potential, optimizing clinical study designs, supporting waivers for clinical studies, and informing drug label claims. Fedratinib dose should be reduced to 200 mg QD when a strong CYP3A4 inhibitor is co-administered and then re-escalated to 400 mg in a stepwise manner as tolerated after the strong CYP3A4 inhibitor is discontinued. 10.1007/s00280-020-04131-y
    Antimicrobial agents, drug adverse reactions and interactions, and cancer. Millan Ximena,Muggia Victoria,Ostrowsky Belinda Cancer treatment and research The intent of this chapter is to review the types of adverse drug reactions and interactions associated with antimicrobial agents, specifically in the setting of patients with malignancies. The initial sections will discuss categorizing and describing the mechanisms of adverse reactions and interactions. The later sections include a detailed discussion about adverse reactions and drug interactions associated with commonly used antibacterial, antiviral, and antifungal agents in this subpopulation. Where relevant, the clinical use and indication for the drugs will be reviewed. The antibacterial section will specifically address the emergence of antimicrobial resistance and drugs of last resort (newer agents, such as linezolid and daptomycin and novel uses of older previously retired agents, such as polymyxin B). The antifungal section will address the ramification of pharmacokinetic interactions and the need to measure drug levels. The chapter is not meant to be exhaustive and as such will not extensively address all antimicrobials or all interactions for each of these agents. 10.1007/978-3-319-04220-6_14
    Clinical Guidance for Managing Statin and Antimicrobial Drug-Drug Interactions. Hylton Gravatt Leigh Anne,Flurie Rachel W,Lajthia Estela,Dixon Dave L Current atherosclerosis reports PURPOSE OF REVIEW:This review discusses potential drug-drug interactions between statins and antimicrobials and provides clinician's guidance on how to manage these interactions. RECENT FINDINGS:In addition to statin utilization increasing in recent years, there is greater emphasis on using moderate to high-intensity statin doses. Statin-related adverse effects are often dose-dependent; therefore, patients may be at increased risk. Antimicrobial use has also increased in recent years, and various efforts have been implemented to ensure appropriate use of antimicrobials. Commonly used antimicrobials, such as macrolide antibiotics and azole antifungals, interact significantly with the CYP3A4 enzyme pathway similarly to lovastatin, simvastatin, and atorvastatin. Consequently, the potential for significant drug-drug interactions is increasing. In 2012, the US Food and Drug Administration strengthened warning labels for statins and dose adjustments related to drug-drug interactions. As such, it is imperative that clinicians are comfortable identifying drug-drug interactions between statins and antimicrobials and making appropriate therapy modifications as clinically warranted. Statins and antimicrobials are frequently coprescribed, and the available pharmacokinetic data supports the potential for clinically significant drug-drug interactions. Macrolides and selected antifungals can significantly increase drug levels of select statins, particularly those metabolized by the CYP3A4 pathway. Contrarily, rifampin can significantly reduce drug levels of statins, limiting their efficacy. Future research efforts should identify interventions to improve clinician recognition of these drug-drug interactions and the prevention of unwarranted statin-related adverse effects. 10.1007/s11883-017-0682-x
    Effect of P-glycoprotein (P-gp) Inducers on Exposure of P-gp Substrates: Review of Clinical Drug-Drug Interaction Studies. Elmeliegy Mohamed,Vourvahis Manoli,Guo Cen,Wang Diane D Clinical pharmacokinetics Understanding transporter-mediated drug-drug interactions (DDIs) for investigational agents is important during drug development to assess DDI liability, its clinical relevance, and to determine appropriate DDI management strategies. P-glycoprotein (P-gp) is an efflux transporter that influences the pharmacokinetics (PK) of various compounds. Assessing transporter induction in vitro is challenging and is not always predictive of in vivo effects, and hence there is a need to consider clinical DDI studies; however, there is no clear guidance on when clinical evaluation of transporter induction is required. Furthermore, there is no proposed list of index transporter inducers to be used in clinical studies. This review evaluated DDI studies with known P-gp inducers to better understand the mechanism and site of P-gp induction, as well as the magnitude of induction effect on the exposure of P-gp substrates. Our review indicates that P-gp and cytochrome P450 (CYP450) enzymes are co-regulated via the pregnane xenobiotic receptor (PXR) and the constitutive androstane receptor (CAR). The magnitude of the decrease in substrate drug exposure by P-gp induction is generally less than that of CYP3A. Most P-gp inducers reduced total bioavailability with a minor impact on renal clearance, despite known expression of P-gp at the apical membrane of the kidney proximal tubules. Rifampin is the most potent P-gp inducer, resulting in an average reduction in substrate exposure ranging between 20 and 67%. For other inducers, the reduction in P-gp substrate exposure ranged from 12 to 42%. A lower reduction in exposure of the P-gp substrate was observed with a lower dose of the inducer and/or if the administration of the inducer and substrate was simultaneous, i.e. not staggered. These findings suggest that clinical evaluation of the impact of P-gp inducers on the PK of investigational agents that are substrates for P-gp might be warranted only for compounds with a relatively steep exposure-efficacy relationship. 10.1007/s40262-020-00867-1
    A pharmacovigilance study of pharmacokinetic drug interactions using a translational informatics discovery approach. Wang Lei,Shendre Aditi,Chiang Chien-Wei,Cao Weidan,Ning Xia,Zhang Ping,Zhang Pengyue,Li Lang British journal of clinical pharmacology BACKGROUND:While the pharmacokinetic (PK) mechanisms for many drug interactions (DDIs) have been established, pharmacovigilance studies related to these PK DDIs are limited. Using a large surveillance database, a translational informatics approach can systematically screen adverse drug events (ADEs) for many DDIs with known PK mechanisms. METHODS:We collected a set of substrates and inhibitors related to the cytochrome P450 (CYP) isoforms, as recommended by the United States Food and Drug Administration (FDA) and Drug Interactions Flockhart table™. The FDA's Adverse Events Reporting System (FAERS) was used to obtain ADE reports from 2004 to 2018. The substrate and inhibitor information were used to form PK DDI pairs for each of the CYP isoforms and Medical Dictionary for Regulatory Activities (MedDRA) preferred terms used for ADEs in FAERS. A shrinkage observed-to-expected ratio (Ω) analysis was performed to screen for potential PK DDI and ADE associations. RESULTS:We identified 149 CYP substrates and 62 CYP inhibitors from the FDA and Flockhart tables. Using FAERS data, only those DDI-ADE associations were considered that met the disproportionality threshold of Ω > 0 for a CYP substrate when paired with at least two inhibitors. In total, 590 ADEs were associated with 2085 PK DDI pairs and 38 individual substrates, with ADEs overlapping across different CYP substrates. More importantly, we were able to find clinical and experimental evidence for the paclitaxel-clopidogrel interaction associated with peripheral neuropathy in our study. CONCLUSION:In this study, we utilized a translational informatics approach to discover potentially novel CYP-related substrate-inhibitor and ADE associations using FAERS. Future clinical, population-based and experimental studies are needed to confirm our findings. 10.1111/bcp.14762
    Ketoconazole-Associated Liver Injury in Drug-Drug Interaction Studies in Healthy Volunteers. Banankhah Peymaan S,Garnick Kyle A,Greenblatt David J Journal of clinical pharmacology Ketoconazole is a potent CYP3A inhibitor in vivo, and frequently serves as an index CYP3A inhibitor in drug-drug interaction (DDI) studies with healthy volunteers. Limitations restricting the use of systemic ketoconazole in such studies have been recently imposed by regulatory agencies in the United States, the European Union, and elsewhere. A risk of ketoconazole-associated liver injury in the context of DDI studies was cited as the primary justification for these measures. To evaluate the basis for these restrictions, we analyzed a series of published DDI studies identified from a review of existing literature. The study set consisted of 53 DDI studies, and included 971 healthy volunteers with systemic ketoconazole exposure in addition to the victim drug under study. Ketoconazole-associated abnormalities in serum chemistry values indicative of liver injury were observed in 4 subjects, representing a prevalence of 0.41% within the study population. There were no major adverse reactions or instances of hepatic failure. All abnormalities indicative of liver injury resolved upon discontinuation of ketoconazole treatment. The findings from this review do not support restriction of ketoconazole as an index CYP3A inhibitor in DDI studies involving healthy volunteers. 10.1002/jcph.711
    Investigating Transporter-Mediated Drug-Drug Interactions Using a Physiologically Based Pharmacokinetic Model of Rosuvastatin. Wang Q,Zheng M,Leil T CPT: pharmacometrics & systems pharmacology Rosuvastatin is a frequently used probe in transporter-mediated drug-drug interaction (DDI) studies. This report describes the development of a physiologically based pharmacokinetic (PBPK) model of rosuvastatin for prediction of pharmacokinetic (PK) DDIs. The rosuvastatin model predicted the observed single (i.v. and oral) and multiple dose PK profiles, as well as the impact of coadministration with transporter inhibitors. The predicted effects of rifampin and cyclosporine (6.58-fold and 5.07-fold increase in rosuvastatin area under the curve (AUC), respectively) were mediated primarily via inhibition of hepatic organic anion-transporting polypeptide (OATP)1B1 (Inhibition constant (K ) ∼1.1 and 0.014 µM, respectively) and OATP1B3 (K ∼0.3 and 0.007 µM, respectively), with cyclosporine also inhibiting intestinal breast cancer resistance protein (BCRP; K ∼0.07 µM). The predicted effects of gemfibrozil and its metabolite were moderate (1.88-fold increase in rosuvastatin AUC) and mediated primarily via inhibition of hepatic OATP1B1 and renal organic cation transporter 3. This model of rosuvastatin will be useful in prospectively predicting transporter-mediated DDIs with novel pharmaceutical agents in development. 10.1002/psp4.12168
    Physiologically Based Pharmacokinetic Modeling Suggests Limited Drug-Drug Interaction for Fesoterodine When Coadministered With Mirabegron. Lin Jian,Goosen Theunis C,Tse Susanna,Yamagami Hidetomi,Malhotra Bimal Journal of clinical pharmacology 5-Hydroxymethyl tolterodine (5-HMT; the active fesoterodine metabolite) is metabolized via the cytochrome P450 (CYP) 2D6 and CYP3A pathways. Mirabegron is a moderate CYP2D6 inhibitor and weak CYP3A inhibitor. Potential drug-drug interactions (DDIs) following coadministration of these 2 overactive bladder treatments were estimated using physiologically based pharmacokinetic models, developed and verified by comparing predicted and observed pharmacokinetic profiles from clinical studies. Models predicted and verified mirabegron and desipramine (CYP2D6 substrate) and 5-HMT and ketoconazole (strong CYP3A inhibitor) DDIs. Mirabegron model-predicted mean steady-state AUC and C were within 11% of clinical observations. The predicted versus observed geometric mean ratio (GMR) of AUC for CYP2D6 substrates desipramine and metoprolol coadministered with mirabegron 100 or 160 mg once daily were 3.47 versus 3.41 and 2.97 versus 3.29, respectively, indicating that the mirabegron model can be used to predict clinical CYP2D6 inhibition. 5-HMT fractional clearance by CYP3A and CYP2D6 was verified from clinical DDI studies with a potent CYP3A4 inhibitor (ketoconazole) and inducer (rifampicin) in CYP2D6 extensive and poor metabolizers and with a moderate CYP3A inhibitor (fluconazole) in healthy volunteers. 5-HMT AUC and C GMRs for fesoterodine DDIs were all predicted within 1.26-fold of clinical observation, providing verification for the fesoterodine substrate model. The predicted changes in 5-HMT AUC and C ratios for 8 mg fesoterodine when coadministered with 50 mg mirabegron were 1.22-fold and 1.17-fold, respectively, relative to 8 mg fesoterodine given alone. This modest increase in 5-HMT exposures by approximately 20% is considered clinically insignificant and would not require fesoterodine dose adjustment when coadministered with mirabegron within approved daily-dose ranges. 10.1002/jcph.1438
    Quantitative Assessment of Levonorgestrel Binding Partner Interplay and Drug-Drug Interactions Using Physiologically Based Pharmacokinetic Modeling. Cicali Brian,Lingineni Karthik,Cristofoletti Rodrigo,Wendl Thomas,Hoechel Joachim,Wiesinger Herbert,Chaturvedula Ayyappa,Vozmediano Valvanera,Schmidt Stephan CPT: pharmacometrics & systems pharmacology Levonorgestrel (LNG) is the active moiety in many hormonal contraceptive formulations. It is typically coformulated with ethinyl estradiol (EE) to decrease intermenstrual bleeding. Due to its widespread use and CYP3A4-mediated metabolism, there is concern regarding drug-drug interactions (DDIs), particularly a suboptimal LNG exposure when co-administered with CYP3A4 inducers, potentially leading to unintended pregnancies. The goal of this analysis was to determine the impact of DDIs on the systemic exposure of LNG. To this end, we developed and verified a physiologically-based pharmacokinetic (PBPK) model for LNG in PK-Sim (version 8.0) accounting for the impact of EE and body mass index (BMI) on LNG's binding to sex-hormone binding globulin. Model parameters were optimized following intravenous and oral administration of 0.09 mg LNG. The combined LNG-EE PBPK model was verified regarding CYP3A4-mediated interaction by comparing to published clinical DDI study data with carbamazepine, rifampicin, and efavirenz (CYP3A4 inducers). Once verified, the model was applied to predict systemic LNG exposure in normal BMI and obese women (BMI ≥ 30 kg/m ) with and without co-administration of itraconazole (competitive CYP3A4 inhibitor) and clarithromycin (mechanism-based CYP3A4 inhibitor). Total and free LNG exposures, when co-administered with EE, decreased 2-fold in the presence of rifampin, whereas they increased 1.5-fold in the presence of itraconazole. Although changes in total and unbound exposure were decreased in obese women compared with normal BMI women, the relative impact of DDIs on LNG exposure was similar between both groups. 10.1002/psp4.12572
    Physiologically Based Pharmacokinetic Model Predictions of Panobinostat (LBH589) as a Victim and Perpetrator of Drug-Drug Interactions. Einolf Heidi J,Lin Wen,Won Christina S,Wang Lai,Gu Helen,Chun Dung Y,He Handan,Mangold James B Drug metabolism and disposition: the biological fate of chemicals Panobinostat (Farydak) is an orally active hydroxamic acid-derived histone deacetylase inhibitor used for the treatment of relapsed or refractory multiple myeloma. Based on recombinant cytochrome P450 (P450) kinetic analyses in vitro, panobinostat oxidative metabolism in human liver microsomes was mediated primarily by CYP3A4 with lower contributions by CYP2D6 and CYP2C19. Panobinostat was also an in vitro reversible and time-dependent inhibitor of CYP3A4/5 and a reversible inhibitor of CYP2D6 and CYP2C19. Based on a previous clinical drug-drug interaction study with ketoconazole (KTZ), the contribution of CYP3A4 in vivo was estimated to be ∼40%. Using clinical pharmacokinetic (PK) data from several trials, including the KTZ drug-drug interaction (DDI) study, a physiologically based pharmacokinetic (PBPK) model was built to predict panobinostat PK after single and multiple doses (within 2-fold of observed values for most trials) and the clinical DDI with KTZ (predicted and observed area under the curve ratios of 1.8). The model was then applied to predict the drug interaction with the strong CYP3A4 inducer rifampin (RIF) and the sensitive CYP3A4 substrate midazolam (MDZ) in lieu of clinical trials. Panobinostat exposure was predicted to decrease in the presence of RIF (65%) and inconsequentially increase MDZ exposure (4%). Additionally, PBPK modeling was used to examine the effects of stomach pH on the absorption of panobinostat in humans and determined that absorption of panobinostat is not expected to be affected by increases in stomach pH. The results from these studies were incorporated into the Food and Drug Administration-approved product label, providing guidance for panobinostat dosing recommendations when it is combined with other drugs. 10.1124/dmd.117.076851
    Physiologically Based Pharmacokinetic Modeling of Doravirine and Its Major Metabolite to Support Dose Adjustment With Rifabutin. Yee Ka Lai,Cabalu Tamara D,Kuo Yuhsin,Fillgrove Kerry L,Liu Yang,Triantafyllou Ilias,McClain Sasha,Dreyer Daniel,Wenning Larissa,Stoch S Aubrey,Iwamoto Marian,Sanchez Rosa I,Khalilieh Sauzanne G Journal of clinical pharmacology Doravirine, a novel nonnucleoside reverse transcriptase inhibitor for the treatment of human immunodeficiency virus 1 (HIV-1), is predominantly cleared by cytochrome P450 (CYP) 3A4 and metabolized to an oxidative metabolite (M9). Coadministration with rifabutin, a moderate CYP3A4 inducer, decreased doravirine exposure. Based on nonparametric superposition modeling, a doravirine dose adjustment from 100 mg once daily to 100 mg twice daily during rifabutin coadministration was proposed. However, M9 exposure may also be impacted by induction, in addition to the dose adjustment. As M9 concentrations have not been quantified in previous clinical studies, a physiologically based pharmacokinetic model was developed to investigate the change in M9 exposure when doravirine is coadministered with CYP3A inducers. Simulations demonstrated that although CYP3A induction increases doravirine clearance by up to 4.4-fold, M9 exposure is increased by only 1.2-fold relative to exposures for doravirine 100 mg once daily in the absence of CYP3A induction. Thus, a 2.4-fold increase in M9 exposure relative to the clinical dose of doravirine is anticipated when doravirine 100 mg twice daily is coadministered with rifabutin. In a subsequent clinical trial, doravirine and M9 exposures, when doravirine 100 mg twice daily was coadministered with rifabutin, were found to be consistent with model predictions using rifampin and efavirenz as representative inducers. These findings support the dose adjustment to doravirine 100 mg twice daily when coadministered with rifabutin. 10.1002/jcph.1747
    Application of physiologically based pharmacokinetic modeling to the prediction of drug-drug and drug-disease interactions for rivaroxaban. Xu Ruijuan,Ge Weihong,Jiang Qing European journal of clinical pharmacology PURPOSE:Rivaroxaban is a direct oral anticoagulant with a large inter-individual variability. The present study is to develop a physiologically based pharmacokinetic (PBPK) model to predict several scenarios in clinical practice. METHODS:A whole-body PBPK model for rivaroxaban, which is metabolized by the cytochrome P450 (CYP) 3A4/5, 2J2 pathways and excreted via kidneys, was developed to predict the pharmacokinetics at different doses in healthy subjects and patients with hepatic or renal dysfunction. Hepatic clearance and drug-drug interactions (DDI) were estimated by in vitro in vivo extrapolation (IVIVE) based on parameters obtained from in vitro experiments. To validate the model, observed concentrations were compared with predicted concentrations, and the impact of special scenarios was investigated. RESULTS:The PBPK model successfully predicted the pharmacokinetics for healthy subjects and patients as well as DDIs. Sensitivity analysis shows that age, renal, and hepatic clearance are important factors affecting rivaroxaban pharmacokinetics. The predicted fold increase of rivaroxaban AUC values when combined administered with the inhibitors such as ketoconazole, ritonavir, and clarithromycin were 2.3, 2.2, and 1.3, respectively. When DDIs and hepatic dysfunction coexist, the fold increase of rivaroxaban exposure would increase significantly compared with one factor alone. CONCLUSIONS:Our study using PBPK modeling provided a reasonable approach to evaluate exposure levels in special patients under special scenarios. Although further clinical study or real-life experience would certainly merit the current work, the modeling work so far would at least suggest caution of using rivaroxaban in complicated clinical settings. 10.1007/s00228-018-2430-8
    Evaluation of the Drug-Drug Interaction Potential of Acalabrutinib and Its Active Metabolite, ACP-5862, Using a Physiologically-Based Pharmacokinetic Modeling Approach. Zhou Diansong,Podoll Terry,Xu Yan,Moorthy Ganesh,Vishwanathan Karthick,Ware Joseph,Slatter J Greg,Al-Huniti Nidal CPT: pharmacometrics & systems pharmacology Acalabrutinib, a selective, covalent Bruton tyrosine kinase inhibitor, is a CYP3A substrate and weak CYP3A/CYP2C8 inhibitor. A physiologically-based pharmacokinetic (PBPK) model was developed for acalabrutinib and its active metabolite ACP-5862 to predict potential drug-drug interactions (DDIs). The model indicated acalabrutinib would not perpetrate a CYP2C8 or CYP3A DDI with the sensitive CYP substrates rosiglitazone or midazolam, respectively. The model reasonably predicted clinically observed acalabrutinib DDI with the CYP3A perpetrators itraconazole (4.80-fold vs. 5.21-fold observed) and rifampicin (0.21-fold vs. 0.23-fold observed). An increase of two to threefold acalabrutinib area under the curve was predicted for coadministration with moderate CYP3A inhibitors. When both the parent drug and active metabolite (total active components) were considered, the magnitude of the CYP3A DDI was much less significant. PBPK dosing recommendations for DDIs should consider the magnitude of the parent drug excursion, relative to safe parent drug exposures, along with the excursion of total active components to best enable safe and adequate pharmacodynamic coverage. 10.1002/psp4.12408
    Physiologically-Based Pharmacokinetic Modeling Approach to Predict Rifampin-Mediated Intestinal P-Glycoprotein Induction. Yamazaki Shinji,Costales Chester,Lazzaro Sarah,Eatemadpour Soraya,Kimoto Emi,Varma Manthena V CPT: pharmacometrics & systems pharmacology Physiologically-based pharmacokinetic (PBPK) modeling is a powerful tool to quantitatively describe drug disposition profiles in vivo, thereby providing an alternative to predict drug-drug interactions (DDIs) that have not been tested clinically. This study aimed to predict effects of rifampin-mediated intestinal P-glycoprotein (Pgp) induction on pharmacokinetics of Pgp substrates via PBPK modeling. First, we selected four Pgp substrates (digoxin, talinolol, quinidine, and dabigatran etexilate) to derive in vitro to in vivo scaling factors for intestinal Pgp kinetics. Assuming unbound Michaelis-Menten constant (K ) to be intrinsic, we focused on the scaling factors for maximal efflux rate (J ) to adequately recover clinically observed results. Next, we predicted rifampin-mediated fold increases in intestinal Pgp abundances to reasonably recover clinically observed DDI results. The modeling results suggested that threefold to fourfold increases in intestinal Pgp abundances could sufficiently reproduce the DDI results of these Pgp substrates with rifampin. Hence, the obtained fold increases can potentially be applicable to DDI prediction with other Pgp substrates. 10.1002/psp4.12458
    Physiologically-based pharmacokinetic modeling to predict drug interactions of lemborexant with CYP3A inhibitors. Ueno Takashi,Miyajima Yukiko,Landry Ishani,Lalovic Bojan,Schuck Edgar CPT: pharmacometrics & systems pharmacology 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. 10.1002/psp4.12606
    A Physiologically Based Pharmacokinetic Modeling Approach to Predict Drug-Drug Interactions of Buprenorphine After Subcutaneous Administration of CAM2038 With Perpetrators of CYP3A4. Liu Tao,Gobburu Jogarao V S Journal of pharmaceutical sciences CAM2038, FluidCrystal injection depot, is an extended release formulation of buprenorphine given subcutaneously every 1 week (Q1W) or every 4 weeks (Q4W). The purpose of this research was to predict the magnitude of drug-drug interaction (DDI) after coadministration of a strong CYP3A4 inducer or inhibitor using physiologically based pharmacokinetic (PBPK) modeling. A PBPK model was developed for CAM2038 based on the previously published buprenorphine PBPK model after intravenous and sublingual administration and the PK profiles after subcutaneous administration of CAM2038 from 2 phase I clinical trials. The strong CYP3A4 inhibitor ketoconazole was predicted to increase the buprenorphine exposure by 35% for the Q1W formulation and 34% for Q4W formulation, respectively. Also, the strong CYP3A4 inducer rifampin was predicted to decrease the buprenorphine exposure by 26% for both the Q1W and Q4W formulations. The results provided insight into the potential DDI effect for CAM2038 and suggested a lack of clinically meaningful DDI when CAM2038 is coadministered with CYP3A4 inhibitor or inducer. Therefore, no dose adjustment is required when CAM2038 is coadministered with CYP3A4 perpetrators. 10.1016/j.xphs.2017.10.035
    Verification of a physiologically based pharmacokinetic model of ritonavir to estimate drug-drug interaction potential of CYP3A4 substrates. Umehara Ken-Ichi,Huth Felix,Won Christina S,Heimbach Tycho,He Handan Biopharmaceutics & drug disposition Ritonavir is one of several ketoconazole alternatives used to evaluate strong CYP3A4 inhibition potential in clinical drug-drug interaction (DDI) studies. In this study, four physiologically based pharmacokinetic (PBPK) models of ritonavir as an in vivo time-dependent inhibitor of CYP3A4 were created and verified for oral doses of 20, 50, 100 and 200 mg using the fraction absorbed (F ) and oral clearance (CL ) values reported in the literature, because transporter and CYP enzyme reaction phenotyping data were not available. The models were used subsequently to predict and compare the magnitude of the AUC increase in nine reference DDI studies evaluating the effect of ritonavir at steady-state on midazolam (CYP3A4 substrate) exposure. Midazolam AUC and C ratios were predicted within 2-fold of the respective observations in seven studies. Simulations of the hepatic and gut CYP3A4 abundance after multiple oral dosing of ritonavir indicated that a 3-day treatment with ritonavir 100 mg twice daily is sufficient to reach maximal CYP3A4 inhibition and subsequent systemic exposure increase of a CYP3A4 substrate, resulting in the reliable estimation of f . The ritonavir model was submitted as part of the new drug application for Kisqali® (ribociclib) and accepted by health authorities. 10.1002/bdd.2122
    A Physiologically Based Pharmacokinetic Model for Optimally Profiling Lamotrigine Disposition and Drug-Drug Interactions. Conner Todd M,Reed Ronald C,Zhang Tao European journal of drug metabolism and pharmacokinetics BACKGROUND AND OBJECTIVES:Lamotrigine (Lamictal) is a broad-spectrum antiepileptic drug available in both immediate-(IR) and extended-release (XR) formulations. Here, we present a new physiologically based pharmacokinetic (PBPK) model for IR and XR formulations of lamotrigine to predict disposition in adults and children, plus drug-drug interactions (DDIs). METHODS:Models for lamotrigine IR and XR formulations were constructed using a Simcyp Simulator. Concentration-time profiles were simulated for lamotrigine IR single (S) and steady-state (SS) doses ranging from 25 to 200 mg in adults, as well as 2 mg/kg (S), and 7.7-9.4 mg/kg (SS) in children aged between 4 and 17 years. Lamotrigine XR profiles were simulated for S and SS doses ranging from 250 to 400 mg. DDI prediction with lamotrigine was simulated in adults with enzyme-inducing drugs, rifampin (rifampicin) and ritonavir, as well as the enzyme inhibitor, valproic acid. RESULTS:The lamotrigine model predicted adult area-under-the-curve (AUC) and peak plasma concentration (C) results for IR S within 35% of observed data; lamotrigine IR SS dosing was within 10% and 30% of observed data, respectively. Pediatric lamotrigine IR S AUC and C values were within 10% and 15% of observed data, respectively. AUC and C values for lamotrigine XR S simulated in adults were within 20% of observed data; similarly lamotrigine XR SS parameters were within 10%. Concerning DDI simulation in adults, predicted-to-observed lamotrigine AUC ratios [AUC/AUC] were within 15% for ritonavir and rifampin, and 20% for valproic acid. CONCLUSIONS:Our developed PBPK lamotrigine profile accurately predicts DDIs and lamotrigine IR/XR formulation disposition in adults and children. This PBPK model will be helpful in designing future DDI studies for co-administration of lamotrigine with other drugs and in designing individualized patient dosing regimens. 10.1007/s13318-018-0532-4
    When special populations intersect with drug-drug interactions: Application of physiologically-based pharmacokinetic modeling in pregnant populations. Sychterz Caroline,Galetin Aleksandra,Taskar Kunal S Biopharmaceutics & drug disposition Pregnancy results in significant physiological changes that vary across trimesters and into the postpartum period, and may result in altered disposition of endogenous substances and drug pharmacokinetics. Pregnancy represents a unique special population where physiologically-based pharmacokinetic modeling (PBPK) is well suited to mechanistically explore pharmacokinetics and dosing paradigms without subjecting pregnant women or their fetuses to extensive clinical studies. A critical review of applications of pregnancy PBPK models (pPBPK) was conducted to understand its current status for prediction of drug exposure in pregnant populations and to identify areas of further expansion. Evaluation of existing pPBPK modeling efforts highlighted improved understanding of cytochrome P450 (CYP)-mediated changes during pregnancy and identified knowledge gaps for non-CYP enzymes and the physiological changes of the postpartum period. Examples of the application of pPBPK beyond simple dose regimen recommendations are limited, particularly for prediction of drug-drug interactions (DDI) or differences between genotypes for polymorphic drug metabolizing enzymes. A raltegravir pPBPK model implementing UGT1A1 induction during the second and third trimesters of pregnancy was developed in the current work and verified against clinical data. Subsequently, the model was used to explore UGT1A1-related DDI risk with atazanavir and rifampicin along with the effect of enzyme genotype on raltegravir apparent clearance. Simulations of pregnancy-related induction of UGT1A1 either exacerbated UGT1A1 induction by rifampicin or negated atazanavir UGT1A1 inhibition. This example illustrated the advantages of pPBPK modeling for mechanistic evaluation of complex interplays of pregnancy- and drug-related effects in support of model-informed approaches in drug development. 10.1002/bdd.2272
    Physiologically Based Pharmacokinetic Modeling of Palbociclib. Yu Yanke,Loi Cho-Ming,Hoffman Justin,Wang Diane Journal of clinical pharmacology Palbociclib is an orally available CDK4/6 inhibitor. In humans, palbociclib undergoes metabolism mediated primarily by CYP3A and SULT2A1, and it is also a weak time-dependent CYP3A inhibitor. The objectives of the current study are to (1) develop a physiologically based pharmacokinetic (PBPK) model of palbociclib based on the in silico, in vitro, and in vivo pharmacokinetic data of palbociclib, (2) verify the PBPK model with clinical drug-drug interaction (DDI) results of palbociclib with strong CYP3A inhibitor (itraconazole), inducer (rifampin), and a sensitive CYP3A substrate (midazolam), and (3) predict the DDI risk of palbociclib with moderate/weak CYP3A inhibitors. The developed PBPK model adequately described the observed pharmacokinetics of palbociclib after administration of a single oral or intravenous dose of palbociclib. The model-predicted DDIs of palbociclib with itraconazole, rifampin, and midazolam were consistent with the observed DDIs, with the discrepancies of the predicted vs observed AUCR and C R within 20%, except for the AUC ratio of palbociclib with coadministration of rifampin. Using this final PBPK model, it was predicted that weak CYP3A inhibitors (fluoxetine and fluvoxamine) are anticipated to have negligible DDI risk with palbociclib, whereas moderate CYP3A inhibitors (diltiazem and verapamil) may increase plasma palbociclib AUC by ∼40%. A moderate CYP3A inducer (efavirenz) may decrease plasma palbociclib AUC by ∼40%. The established model is considered sufficiently robust for other applications in support of the continued development for palbociclib. 10.1002/jcph.792
    Physiologically based modeling of pravastatin transporter-mediated hepatobiliary disposition and drug-drug interactions. Varma Manthena V S,Lai Yurong,Feng Bo,Litchfield John,Goosen Theunis C,Bergman Arthur Pharmaceutical research PURPOSE:To develop physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics and drug-drug interactions (DDI) of pravastatin, using the in vitro transport parameters. METHODS:In vitro hepatic sinusoidal active uptake, passive diffusion and canalicular efflux intrinsic clearance values were determined using sandwich-culture human hepatocytes (SCHH) model. PBPK modeling and simulations were implemented in Simcyp (Sheffield, UK). DDI with OATP1B1 inhibitors, cyclosporine, gemfibrozil and rifampin, was also simulated using inhibition constant (Ki) values. RESULTS:SCHH studies suggested active uptake, passive diffusion and efflux intrinsic clearance values of 1.9, 0.5 and 1.2 μL/min/10(6)cells, respectively, for pravastatin. PBPK model developed, using transport kinetics and scaling factors, adequately described pravastatin oral plasma concentration-time profiles at different doses (within 20% error). Model based prediction of DDIs with gemfibrozil and rifampin was similar to that observed. However, pravastatin-cyclosporine DDI was underpredicted (AUC ratio 4.4 Vs ~10). Static (R-value) model predicted higher magnitude of DDI compared to the AUC ratio predicted by the PBPK modeling. CONCLUSIONS:PBPK model of pravastatin, based on in vitro transport parameters and scaling factors, was developed. The approach described can be used to predict the pharmacokinetics and DDIs associated with hepatic uptake transporters. 10.1007/s11095-012-0792-7
    Developing a physiologically based pharmacokinetic model of apixaban to predict scenarios of drug-drug interactions, renal impairment and paediatric populations. Xu Ruijuan,Tang Hong,Chen Lin,Ge Weihong,Yang Jin British journal of clinical pharmacology AIMS:To develop a physiologically based pharmacokinetic (PBPK) model for apixaban, an oral anticoagulant with a narrow therapeutic index, and to predict PK profiles and potential drug-drug interactions (DDIs) in patients with renal impairment and paediatrics. METHODS:A whole-body apixaban PBPK model was developed and validated in SimCYP for healthy adults with or without interacting drugs. The model was extended to renal impairment and paediatrics. Observed PK data in adults were compared with predicted data. The effect of renal function, age and DDIs on apixaban PK was investigated. RESULTS:The PBPK model successfully predicted the PK of apixaban alone and under the influence of interacting drugs. For patients with renal impairment, the PBPK model successfully predicted the fold change in each impairment group; inhibitory DDI and renal impairment had a synergistic effect on the increase of apixaban exposure (e.g., almost 3-fold increase of AUC in ketoconazole + severe renal impairment group). For infants younger than 1 year, the exposure of apixaban decreased with increased weight-normalized clearance. For newborn infants, AUC of apixaban was >2-fold higher than that in children older than 1 year. Meanwhile, the effect of DDI seems to be weakened while the effect of renal impairment might be enhanced in infants younger than 1 year. CONCLUSION:Our study provides a reasonable approach to estimate the dose adjustment for the first use of apixaban in special populations with complex situations, which has the opportunity to make the clinical practice much safer. 10.1111/bcp.14743
    Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network. Marok Fatima Zahra,Fuhr Laura Maria,Hanke Nina,Selzer Dominik,Lehr Thorsten Pharmaceutics The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6 and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drug-drug interaction (DDI) studies. The aim of this study was to build a whole-body physiologically based pharmacokinetic (PBPK) model of bupropion including its DDI-relevant metabolites, and to qualify the model using clinical drug-gene interaction (DGI) and DDI data. The model was built in PK-Sim applying clinical data of 67 studies. It incorporates CYP2B6-mediated hydroxylation of bupropion, metabolism via CYP2C19 and 11β-HSD, as well as binding to pharmacological targets. The impact of CYP2B6 polymorphisms is described for normal, poor, intermediate, and rapid metabolizers, with various allele combinations of the genetic variants , , and . DDI model performance was evaluated by prediction of clinical studies with rifampicin (CYP2B6 and CYP2C19 inducer), fluvoxamine (CYP2C19 inhibitor) and voriconazole (CYP2B6 and CYP2C19 inhibitor). Model performance quantification showed 20/20 DGI ratios of hydroxybupropion to bupropion AUC ratios (DGI AUC ratios), 12/13 DDI AUC ratios, and 7/7 DDGI AUC ratios within 2-fold of observed values. The developed model is freely available in the Open Systems Pharmacology model repository. 10.3390/pharmaceutics13030331
    Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes. Dmitriev Alexander V,Lagunin Alexey A,Karasev Dmitry А,Rudik Anastasia V,Pogodin Pavel V,Filimonov Dmitry A,Poroikov Vladimir V Current topics in medicinal chemistry Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods. 10.2174/1568026619666190123160406
    Prediction of the effect of voriconazole on the pharmacokinetics of non-steroidal anti-inflammatory drugs. Li Na,Zhu Liqin,Qi Fang,Li Mengxue,Xu Gaoqi,Ge Tingyue Journal of chemotherapy (Florence, Italy) To evaluate the impact of voriconazole on the pharmacokinetics of Non-steroidal anti-inflammatory drugs (NSAIDs). Pharmacokinetic and physiochemical properties of voriconazole and NSAIDs are applied to build physiologically based pharmacokinetic (PBPK) models using Gastroplus. The PBPK models of voriconazole and NSAIDs are verified by published studies, respectively. After the successful verification, DDI simulations are applied to predict the effect of voriconazole on the pharmacokinetics of NSAIDs. The area under the plasma concentration-time curves extrapolated to infinity (AUC) of celecoxib, ibuprofen, tenoxicam and piroxicam are increased by 51%, 7%, 2% and 1% in concurrent use with voriconazole, respectively. The maximum concentration (C) of celecoxib, ibuprofen, tenoxicam and piroxicam are increased by 21%, 1%, 1% and 1% in the presence of concomitant voriconazole, respectively. Considering the inter-patient variability, changes in AUC or C are not clinically meaningful. Therefore, adverse events and toxicity that are associated with celecoxib should be closely monitored when in combination with voriconazole, and this result can provide guidance for clinical DDI studies. 10.1080/1120009X.2018.1500197
    A Mechanistic, Enantioselective, Physiologically Based Pharmacokinetic Model of Verapamil and Norverapamil, Built and Evaluated for Drug-Drug Interaction Studies. Hanke Nina,Türk Denise,Selzer Dominik,Wiebe Sabrina,Fernandez Éric,Stopfer Peter,Nock Valerie,Lehr Thorsten Pharmaceutics The calcium channel blocker and antiarrhythmic agent verapamil is recommended by the FDA for drug-drug interaction (DDI) studies as a moderate clinical CYP3A4 index inhibitor and as a clinical Pgp inhibitor. The purpose of the presented work was to develop a mechanistic whole-body physiologically based pharmacokinetic (PBPK) model to investigate and predict DDIs with verapamil. The model was established in PK-Sim, using 45 clinical studies (dosing range 0.1-250 mg), including literature as well as unpublished Boehringer Ingelheim data. The verapamil R- and S-enantiomers and their main metabolites R- and S-norverapamil are represented in the model. The processes implemented to describe the pharmacokinetics of verapamil and norverapamil include enantioselective plasma protein binding, enantioselective metabolism by CYP3A4, non-stereospecific Pgp transport, and passive glomerular filtration. To describe the auto-inhibitory and DDI potential, mechanism-based inactivation of CYP3A4 and non-competitive inhibition of Pgp by the verapamil and norverapamil enantiomers were incorporated based on in vitro literature. The resulting DDI performance was demonstrated by prediction of DDIs with midazolam, digoxin, rifampicin, and cimetidine, with 21/22 predicted DDI AUC ratios or C ratios within 1.5-fold of the observed values. The thoroughly built and qualified model will be freely available in the Open Systems Pharmacology model repository to support model-informed drug discovery and development. 10.3390/pharmaceutics12060556
    Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug-Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach. Fuhr Laura Maria,Marok Fatima Zahra,Hanke Nina,Selzer Dominik,Lehr Thorsten Pharmaceutics 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 AUC ratios and 11/15 DDI C 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. 10.3390/pharmaceutics13020270
    Physiologically Based Pharmacokinetic Modelling of Cytochrome P450 2C9-Related Tolbutamide Drug Interactions with Sulfaphenazole and Tasisulam. Perkins Everett J,Posada Maria,Kellie Turner P,Chappell Jill,Ng Wee Teck,Twelves Chris European journal of drug metabolism and pharmacokinetics BACKGROUND AND OBJECTIVES:Cytochrome P450 2C9 (CYP2C9) is involved in the biotransformation of many commonly used drugs, and significant drug interactions have been reported for CYP2C9 substrates. Previously published physiologically based pharmacokinetic (PBPK) models of tolbutamide are based on an assumption that its metabolic clearance is exclusively through CYP2C9; however, many studies indicate that CYP2C9 metabolism is only responsible for 80-90% of the total clearance. Therefore, these models are not useful for predicting the magnitude of CYP2C9 drug-drug interactions (DDIs). This paper describes the development and verification of SimCYP-based PBPK models that accurately describe the human pharmacokinetics of tolbutamide when dosed alone or in combination with the CYP2C9 inhibitors sulfaphenazole and tasisulam. METHODS:A PBPK model was optimized in SimCYP for tolbutamide as a CYP2C9 substrate, based on published in vitro and clinical data. This model was verified to replicate the magnitude of DDI reported with sulfaphenazole and was further applied to simulate the DDI with tasisulam, a small molecule investigated for the treatment of cancer. A clinical study (CT registration # NCT01185548) was conducted in patients with cancer to assess the pharmacokinetic interaction of tasisulum with tolbutamide. A PBPK model was built for tasisulam, and the clinical study design was replicated using the optimized tolbutamide model. RESULTS:The optimized tolbutamide model accurately predicted the magnitude of tolbutamide AUC increase (5.3-6.2-fold) reported for sulfaphenazole. Furthermore, the PBPK simulations in a healthy volunteer population adequately predicted the increase in plasma exposure of tolbutamide in patients with cancer (predicted AUC ratio = 4.7-5.4; measured mean AUC ratio = 5.7). CONCLUSIONS:This optimized tolbutamide PBPK model was verified with two strong CYP2C9 inhibitors and can be applied to the prediction of CYP2C9 interactions for novel inhibitors. Furthermore, this work highlights the utility of mechanistic models in navigating the challenges in conducting clinical pharmacology studies in cancer patients. 10.1007/s13318-017-0447-5
    Physiologically Based Pharmacokinetic Model of Itraconazole and Two of Its Metabolites to Improve the Predictions and the Mechanistic Understanding of CYP3A4 Drug-Drug Interactions. Prieto Garcia Luna,Janzén David,Kanebratt Kajsa P,Ericsson Hans,Lennernäs Hans,Lundahl Anna Drug metabolism and disposition: the biological fate of chemicals Physiologically based pharmacokinetic (PBPK) modeling for itraconazole using a bottom-up approach is challenging, not only due to complex saturable pharmacokinetics (PK) and the presence of three metabolites exhibiting CYP3A4 inhibition, but also because of discrepancies in reported in vitro data. The overall objective of this study is to provide a comprehensive mechanistic PBPK model for itraconazole in order to increase the confidence in its drug-drug interaction (DDI) predictions. To achieve this, key in vitro and in vivo data for itraconazole and its major metabolites were generated. These data were crucial to developing a novel bottom-up PBPK model in Simcyp (Simcyp Ltd., Certara, Sheffield, United Kingdom) for itraconazole and two of its major metabolites: hydroxy-itraconazole (OH-ITZ) and keto-itraconazole (keto-ITZ). Performance of the model was validated using prespecified acceptance criteria against different dosing regimens, formulations for 29 PK, and DDI studies with midazolam and other CYP3A4 substrates. The main outcome is an accurate PBPK model that simultaneously predicts the PK profiles of itraconazole, OH-ITZ, and keto-ITZ. In addition, itraconazole DDIs with midazolam and other CYP3A4 substrates were successfully predicted within a 2-fold error. Prediction precision and bias of DDI expressed as geometric mean fold error were for the area under the concentration-time curve and peak concentration, 1.06 and 0.96, respectively. To conclude, in this paper a comprehensive data set for itraconazole and its metabolites is provided that enables bottom-up mechanism-based PBPK modeling. The presented model is applicable for studying the contribution from the metabolites and allows improved assessments of itraconazole DDI. 10.1124/dmd.118.081364
    Physiologically based pharmacokinetic modelling of oxycodone drug-drug interactions. Rytkönen Jaana,Ranta Veli-Pekka,Kokki Merja,Kokki Hannu,Hautajärvi Heidi,Rinne Valtteri,Heikkinen Aki T Biopharmaceutics & drug disposition Oxycodone is an opioid analgesic with several pharmacologically active metabolites and relatively narrow therapeutic index. Cytochrome P450 (CYP) 3A4 and CYP2D6 play major roles in the metabolism of oxycodone and its metabolites. Thus, inhibition and induction of these enzymes may result in substantial changes in the exposure of both oxycodone and its metabolites. In this study, a physiologically based pharmacokinetic (PBPK) model was built using GastroPlus™ software for oxycodone, two primary metabolites (noroxycodone, oxymorphone) and one secondary metabolite (noroxymorphone). The model was built based on literature and in house in vitro and in silico data. The model was refined and verified against literature clinical data after oxycodone administration in the absence of drug-drug interactions (DDI). The model was further challenged with simulations of oxycodone DDI with CYP3A4 inhibitors ketoconazole and itraconazole, CYP3A4 inducer rifampicin and CYP2D6 inhibitor quinidine. The magnitude of DDI (AUC ratio) was predicted within 1.5-fold error for oxycodone, within 1.8-fold and 1.3-4.5-fold error for the primary metabolites noroxycodone and oxymorphone, respectively, and within 1.4-4.5-fold error for the secondary metabolite noroxymorphone, when compared to the mean observed AUC ratios. This work demonstrated the capability of PBPK model to simulate DDI of the administered compounds and the formed metabolites of both DDI victim and perpetrator. However, the predictions for the formed metabolites tend to be associated with higher uncertainty than the predictions for the administered compound. The oxycodone model provides a tool for forecasting oxycodone DDI with other CYP3A4 and CYP2D6 DDI perpetrators that may be co-administered with oxycodone. 10.1002/bdd.2215
    Clinical Data Combined With Modeling and Simulation Indicate Unchanged Drug-Drug Interaction Magnitudes in the Elderly. Stader Felix,Courlet Perrine,Kinvig Hannah,Penny Melissa A,Decosterd Laurent A,Battegay Manuel,Siccardi Marco,Marzolini Catia Clinical pharmacology and therapeutics Age-related comorbidities and consequently polypharmacy are highly prevalent in the elderly, resulting in an increased risk for drug-drug interactions (DDIs). The effect of aging on DDI magnitudes is mostly uncertain, leading to missing guidance regarding the clinical DDI management in the elderly. Clinical data obtained in aging people living with HIV ≥ 55 years, who participated in the Swiss HIV Cohort Study, demonstrated unchanged DDI magnitudes with advanced aging for four studied DDI scenarios. These data plus published data for midazolam in the presence of clarithromycin and rifampicin in elderly individuals assessed the predictive potential of the used physiologically-based pharmacokinetic (PBPK) model to simulate DDIs in the elderly. All clinically observed data were generally predicted within the 95% confidence interval of the PBPK simulations. The verified model predicted subsequently the magnitude of 50 DDIs across adulthood (20-99 years) with 42 scenarios being only verified in adults aged 20-50 years in the absence of clinically observed data in the elderly. DDI magnitudes were not impacted by aging regardless of the involved drugs, DDI mechanism, mediators of DDIs, or the sex of the investigated individuals. The prediction of unchanged DDI magnitudes with advanced aging were proofed by 17 published, independent DDIs that were investigated in young and elderly subjects. In conclusion, this study demonstrated by combining clinically observed data with modeling and simulation that aging does not impact DDI magnitudes and thus, clinical management of DDIs can a priori be similar in aging men and women in the absence of severe comorbidities. 10.1002/cpt.2017
    Physiologically-Based Pharmacokinetic Modeling to Support the Clinical Management of Drug-Drug Interactions With Bictegravir. Stader Felix,Battegay Manuel,Marzolini Catia Clinical pharmacology and therapeutics 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. 10.1002/cpt.2221
    Physiologically Based Pharmacokinetic Models of Probenecid and Furosemide to Predict Transporter Mediated Drug-Drug Interactions. Britz Hannah,Hanke Nina,Taub Mitchell E,Wang Ting,Prasad Bhagwat,Fernandez Éric,Stopfer Peter,Nock Valerie,Lehr Thorsten Pharmaceutical research PURPOSE:To provide whole-body physiologically based pharmacokinetic (PBPK) models of the potent clinical organic anion transporter (OAT) inhibitor probenecid and the clinical OAT victim drug furosemide for their application in transporter-based drug-drug interaction (DDI) modeling. METHODS:PBPK models of probenecid and furosemide were developed in PK-Sim®. Drug-dependent parameters and plasma concentration-time profiles following intravenous and oral probenecid and furosemide administration were gathered from literature and used for model development. For model evaluation, plasma concentration-time profiles, areas under the plasma concentration-time curve (AUC) and peak plasma concentrations (C) were predicted and compared to observed data. In addition, the models were applied to predict the outcome of clinical DDI studies. RESULTS:The developed models accurately describe the reported plasma concentrations of 27 clinical probenecid studies and of 42 studies using furosemide. Furthermore, application of these models to predict the probenecid-furosemide and probenecid-rifampicin DDIs demonstrates their good performance, with 6/7 of the predicted DDI AUC ratios and 4/5 of the predicted DDI C ratios within 1.25-fold of the observed values, and all predicted DDI AUC and C ratios within 2.0-fold. CONCLUSIONS:Whole-body PBPK models of probenecid and furosemide were built and evaluated, providing useful tools to support the investigation of transporter mediated DDIs. 10.1007/s11095-020-02964-z
    PBPK modeling of CYP3A and P-gp substrates to predict drug-drug interactions in patients undergoing Roux-en-Y gastric bypass surgery. Chen Kuan-Fu,Chan Lingtak-Neander,Lin Yvonne S Journal of pharmacokinetics and pharmacodynamics Roux-en-Y gastric bypass surgery (RYGBS) is an effective surgical intervention to reduce mortality in morbidly obese patients. Following RYGBS, the disposition of drugs may be affected by anatomical alterations and changes in intestinal and hepatic drug metabolizing enzyme activity. The aim of this study was to better understand the drug-drug interaction (DDI) potential of CYP3A and P-gp inhibitors. The impacts of RYGBS on the absorption and metabolism of midazolam, acetaminophen, digoxin, and their major metabolites were simulated using physiologically-based pharmacokinetic (PBPK) modeling. PBPK models for verapamil and posaconazole were built to evaluate CYP3A- and P-gp-mediated DDIs pre- and post-RYGBS. The simulations suggest that for highly soluble drugs, such as verapamil, the predicted bioavailability was comparable pre- and post-RYGBS. For verapamil inhibition, RYGBS did not affect the fold-change of the predicted inhibited-to-control plasma AUC ratio or predicted inhibited-to-control peak plasma concentration ratio for either midazolam or digoxin. In contrast, the predicted bioavailability of posaconazole, a poorly soluble drug, decreased from 12% pre-RYGBS to 5% post-RYGBS. Compared to control, the predicted posaconazole-inhibited midazolam plasma AUC increased by 2.0-fold pre-RYGBS, but only increased by 1.6-fold post-RYGBS. A similar trend was predicted for pre- and post-RYGBS inhibited-to-control midazolam peak plasma concentration ratios (2.0- and 1.6-fold, respectively) following posaconazole inhibition. Absorption of highly soluble drugs was more rapid post-RYGBS, resulting in higher predicted midazolam peak plasma concentrations, which was further increased following inhibition by verapamil or posaconazole. To reduce the risk of a drug-drug interaction in patients post-RYGBS, the dose or frequency of object drugs may need to be decreased when administered with highly soluble inhibitor drugs, especially if toxicities are associated with plasma peak concentrations. 10.1007/s10928-020-09701-4
    Dose adjustment of venetoclax when co-administered with posaconazole: clinical drug-drug interaction predictions using a PBPK approach. Bhatnagar Sumit,Mukherjee Dwaipayan,Salem Ahmed Hamed,Miles Dale,Menon Rajeev M,Gibbs John P Cancer chemotherapy and pharmacology PURPOSE:Venetoclax, a targeted anticancer agent approved for the treatment of chronic lymphocytic leukemia and acute myeloid leukemia, is a substrate of cytochrome P450 (CYP) 3A enzyme (CYP3A4). Posaconazole, commonly used to prevent invasive fungal infections in neutropenic patients with hematological malignancies, potently inhibits CYP3A4. The purpose of this evaluation was to predict venetoclax exposures following co-administration of posaconazole at doses not previously studied clinically. METHODS:Two physiologically based pharmacokinetic (PBPK) models were developed for posaconazole based on published parameters, one for an oral suspension and another for delayed released tablets. Parameter optimization, guided by sensitivity analyses, was conducted such that the models could replicate clinical exposures of posaconazole and drug-drug interactions with sensitive CYP3A substrates including venetoclax. The clinically verified posaconazole PBPK models were then utilized to predict DDI with a previously published venetoclax PBPK model at clinically relevant dosing scenarios. RESULTS:The posaconazole PBPK models predicted posaconazole exposure and DDI related fold changes with acceptable prediction errors for both posaconazole formulations. The model predicted exposures of venetoclax, when co-administered with a 300 mg QD dose of delayed release tablets of posaconazole, were in concordance with observed data. Increasing the posaconazole dose to 500 mg QD increased venetoclax exposures by about 12% relative to 300 mg QD, which were still within the venetoclax safe exposure range. CONCLUSIONS:The posaconazole PBPK models were developed and clinically verified. Predictions using the robust PBPK model confirmed the venetoclax label recommendation of 70 mg in the presence of posaconazole at doses up to 500 mg QD. 10.1007/s00280-020-04179-w
    Simultaneous Physiologically Based Pharmacokinetic (PBPK) Modeling of Parent and Active Metabolites to Investigate Complex CYP3A4 Drug-Drug Interaction Potential: A Case Example of Midostaurin. Gu Helen,Dutreix Catherine,Rebello Sam,Ouatas Taoufik,Wang Lai,Chun Dung Yu,Einolf Heidi J,He Handan Drug metabolism and disposition: the biological fate of chemicals Midostaurin (PKC412) is being investigated for the treatment of acute myeloid leukemia (AML) and advanced systemic mastocytosis (advSM). It is extensively metabolized by CYP3A4 to form two major active metabolites, CGP52421 and CGP62221. In vitro and clinical drug-drug interaction (DDI) studies indicated that midostaurin and its metabolites are substrates, reversible and time-dependent inhibitors, and inducers of CYP3A4. A simultaneous pharmacokinetic model of parent and active metabolites was initially developed by incorporating data from in vitro, preclinical, and clinical pharmacokinetic studies in healthy volunteers and in patients with AML or advSM. The model reasonably predicted changes in midostaurin exposure after single-dose administration with ketoconazole (a 5.8-fold predicted versus 6.1-fold observed increase) and rifampicin (90% predicted versus 94% observed reduction) as well as changes in midazolam exposure (1.0 predicted versus 1.2 observed ratio) after daily dosing of midostaurin for 4 days. The qualified model was then applied to predict the DDI effect with other CYP3A4 inhibitors or inducers and the DDI potential with midazolam under steady-state conditions. The simulated midazolam area under the curve ratio of 0.54 and an accompanying observed 1.9-fold increase in the CYP3A4 activity of biomarker 4-hydroxycholesterol indicated a weak-to-moderate CYP3A4 induction by midostaurin and its metabolites at steady state in patients with advSM. In conclusion, a simultaneous parent-and-active-metabolite modeling approach allowed predictions under steady-state conditions that were not possible to achieve in healthy subjects. Furthermore, endogenous biomarker data enabled evaluation of the net effect of midostaurin and its metabolites on CYP3A4 activity at steady state and increased confidence in DDI predictions. 10.1124/dmd.117.078006
    Physiologically Based Pharmacokinetic (PBPK) Modeling of Pitavastatin and Atorvastatin to Predict Drug-Drug Interactions (DDIs). Duan Peng,Zhao Ping,Zhang Lei European journal of drug metabolism and pharmacokinetics BACKGROUND:The disposition of statins varies and involves both metabolizing enzymes and transporters, making predictions of statin drug-drug interactions (DDIs) challenging. Physiologically based pharmacokinetic (PBPK) models have, however, demonstrated ability to predict complex DDIs. OBJECTIVE:In this study, PBPK models of two statins (pitavastatin and atorvastatin) were developed and applied to predict pitavastatin and atorvastatin associated DDIs. METHOD:Pitavastatin and atorvastatin PBPK models were developed using in vitro and human pharmacokinetic data in a population-based PBPK software (SimCYP) by considering the contribution of both metabolizing enzymes and transporters to their overall pharmacokinetics. The statin PBPK models and software's built-in or published models of inhibitors were used to predict DDIs under different scenarios. RESULTS:The statin models reasonably predicted the observed exposure change due to Organic Anion Transporting Polypeptide (OATP) 1B1 polymorphism or clinical DDIs with itraconazole, erythromycin, and gemfibrozil, while under-predicted the observed DDIs caused by rifampin and cyclosporine. Further analysis demonstrated that OATP1B1 inhibition by rifampin or cyclosporine in the existing inhibitor models needs to be approximately tenfold stronger to recapitulate the observed DDI with these two inhibitors. CONCLUSION:Through quantitative assessment of the effect of OATP1B1 genetic polymorphism and inhibitors of transporters and metabolizing enyzmes via PBPK modeling, we confirmed the importance of OATP1B1 in the disposition of these two statins, and explored potential causes for under-prediction of the inhibitory effect of rifampin and cyclosporine. 10.1007/s13318-016-0383-9
    Calibrating the In Vitro-In Vivo Correlation for OATP-Mediated Drug-Drug Interactions with Rosuvastatin Using Static and PBPK Models. Sane Rucha,Cheung Kit Wun Kathy,Kovács Péter,Farasyn Taleah,Li Ruina,Bui Annamaria,Musib Luna,Kis Emese,Plise Emile,Gáborik Zsuzsanna Drug metabolism and disposition: the biological fate of chemicals Organic anion-transporting polypeptide (OATP) 1B1/3-mediated drug-drug interaction (DDI) potential is evaluated in vivo with rosuvastatin (RST) as a probe substrate in clinical studies. We calibrated our assay with RST and estradiol 17--D-glucuronide (E17G)/cholecystokinin-8 (CCK8) as in vitro probes for qualitative and quantitative prediction of OATP1B-mediated DDI potential for RST. In vitro OATP1B1/1B3 inhibition using E17G and CCK8 yielded higher area under the curve (AUC) ratio (AUCR) values numerically with the static model, but all probes performed similarly from a qualitative cutoff-based prediction, as described in regulatory guidances. However, the magnitudes of DDI were not captured satisfactorily. Considering that clearance of RST is also mediated by gut breast cancer resistance protein (BCRP), inhibition of BCRP was also incorporated in the DDI prediction if the gut inhibitor concentrations were 10 × IC for BCRP inhibition. This combined static model closely predicted the magnitude of RST DDI with root-mean-square error values of 0.767-0.812 and 1.24-1.31 with and without BCRP inhibition, respectively, for in vitro-in vivo correlation of DDI. Physiologically based pharmacokinetic (PBPK) modeling was also used to simulate DDI between RST and rifampicin, asunaprevir, and velpatasvir. Predicted AUCR for rifampicin and asunaprevir was within 1.5-fold of that observed, whereas that for velpatasvir showed a 2-fold underprediction. Overall, the combined static model incorporating both OATP1B and BCRP inhibition provides a quick and simple mathematical approach to quantitatively predict the magnitude of transporter-mediated DDI for RST for routine application. PBPK complements the static model and provides a framework for studying molecules when a dynamic model is needed. SIGNIFICANCE STATEMENT: Using 22 drugs, we show that a static model for organic anion-transporting polypeptide (OATP) 1B1/1B3 inhibition can qualitatively predict potential for drug-drug interaction (DDI) using a cutoff-based approach, as in regulatory guidances. However, consideration of both OATP1B1/3 and gut breast cancer resistance protein inhibition provided a better prediction of the magnitude of the transporter-mediated DDI of these inhibitors with rosuvastatin. Based on these results, we have proposed an empirical mechanistic-static approach for a more reliable prediction of transporter-mediated DDI liability with rosuvastatin that drug development teams can leverage. 10.1124/dmd.120.000149
    PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin. Hanke Nina,Frechen Sebastian,Moj Daniel,Britz Hannah,Eissing Thomas,Wendl Thomas,Lehr Thorsten CPT: pharmacometrics & systems pharmacology According to current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidance documents, physiologically based pharmacokinetic (PBPK) modeling is a powerful tool to explore and quantitatively predict drug-drug interactions (DDIs) and may offer an alternative to dedicated clinical trials. This study provides whole-body PBPK models of rifampicin, itraconazole, clarithromycin, midazolam, alfentanil, and digoxin within the Open Systems Pharmacology (OSP) Suite. All models were built independently, coupled using reported interaction parameters, and mutually evaluated to verify their predictive performance by simulating published clinical DDI studies. In total, 112 studies were used for model development and 57 studies for DDI prediction. 93% of the predicted area under the plasma concentration-time curve (AUC) ratios and 94% of the peak plasma concentration (C ) ratios are within twofold of the observed values. This study lays a cornerstone for the qualification of the OSP platform with regard to reliable PBPK predictions of enzyme-mediated and transporter-mediated DDIs during model-informed drug development. All presented models are provided open-source and transparently documented. 10.1002/psp4.12343