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

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

Effects of Strong Inhibition of Cytochrome P450 3A and UDP glucuronosyltransferase 1A9 and Strong Induction of Cytochrome P450 3A on the Pharmacokinetics, Safety, and Tolerability of Soticlestat: Two Drug-Drug Interaction Studies in Healthy Volunteers.

Two open-label, phase 1 studies (NCT05064449, NCT05098041) investigated the effects of cytochrome P450 (CYP) 3A inhibition (via itraconazole), UDP glucuronosyltransferase (UGT) 1A9 inhibition (via mefenamic acid), and CYP3A induction (via rifampin) on the pharmacokinetics of soticlestat and its metabolites M-I and M3. In period 1 of both studies, participants received a single dose of soticlestat 300 mg. In period 2, participants received itraconazole on days 1-11 and soticlestat 300 mg on day 5 (itraconazole/mefenamic acid study; part 1); mefenamic acid on days 1-7 and soticlestat 300 mg on day 2 (itraconazole/mefenamic acid study; part 2); or rifampin on days 1-13 and soticlestat 300 mg on day 11 (rifampin study). Twenty-eight healthy adults participated in the itraconazole/mefenamic acid study (14 per part) and 15 participated in the rifampin study (mean age, 38.1-40.7 years; male, 79-93%). For maximum observed concentration, the geometric mean ratios (GMRs) of soticlestat + itraconazole, mefenamic acid, or rifampin to soticlestat alone were 116.6%, 107.3%, and 13.2%, respectively, for soticlestat; 10.7%, 118.0%, and 266.1%, respectively, for M-I, and 104.6%, 88.2%, and 66.6%, respectively, for M3. For area under the curve from time 0 to infinity, the corresponding GMRs were 124.0%, 100.6%, and 16.4% for soticlestat; 13.3%, 117.0%, and 180.8% for M-I; and 120.3%, 92.6%, and 58.4% for M3. Soticlestat can be administered with strong CYP3A and UGT1A9 inhibitors, but not strong CYP3A inducers (except for antiseizure medications, which will be further evaluated in ongoing phase 3 studies). In both studies, all treatment-emergent adverse events were mild or moderate. SIGNIFICANCE STATEMENT: These drug-drug interaction studies improve our understanding of the potential changes that may arise in soticlestat exposure in patients being treated with CYP3A inhibitors, UGT1A9 inhibitors, or CYP3A inducers. The results build on findings from previously published soticlestat studies and provide important information to help guide clinical practice. Soticlestat has shown positive phase 2 results and is currently in phase 3 development for the treatment of seizures in patients with Dravet syndrome and Lennox-Gastaut syndrome.

Pharmacokinetics and Drug-Drug Interaction of Ocedurenone (KBP-5074) in vitro and in vivo.

BACKGROUND AND OBJECTIVES: Ocedurenone (KBP-5074) is a novel nonsteroidal mineralocorticoid receptor antagonist that has demonstrated safety and efficacy in clinical trials in patients with uncontrolled hypertension and stage 3b/4 chronic kidney disease. This study evaluated the involvement of cytochrome P450 (CYP) isozymes and drug transporters in the biotransformation of ocedurenone, and whether ocedurenone inhibited or induced CYP enzymes and transporters. Clinical pharmacokinetic drug-drug interaction (DDI) of ocedurenone with CYP3A inhibitor and inducer were investigated in healthy volunteers. METHODS: In vitro tests were conducted to determine which CYP enzymes were involved in ocedurenone's metabolism and whether ocedurenone inhibited or induced these CYP enzymes; ocedurenone substrate characteristics for efflux and uptake transporters and its inhibitory potential on major drug transporters were also assessed. A clinical DDI study was conducted in healthy volunteers to evaluate the effects of a strong CYP3A inhibitor (itraconazole) and inducer (rifampin) on ocedurenone's pharmacokinetics. RESULTS: The in vitro study showed that ocedurenone was primarily metabolized by CYP3A4 and that it did not inhibit CYP enzymes. Ocedurenone appeared to be a substrate of BCRP and P-gp efflux transporters and inhibited BCRP, BSEP, MDR1, MATE1 and 2-K, OATP1B1/3, and OCT1. The clinical DDI study showed that itraconazole reduced ocedurenone's oral clearance by 51% and increased area under the plasma concentration-time curve extrapolated to infinity (AUC0-inf) by 104%, while rifampin increased its oral clearance by 6.4-fold and decreased plasma AUC0-inf by 84%. CONCLUSION: Ocedurenone was shown to be a CYP3A substrate, with no inhibition potential on major drug metabolizing CYP enzymes and transporters at clinical efficacious doses. Ocedurenone did not induce CYP1A2 and 3A4 activity in cultured human primary hepatocytes. Clinical DDI study indicated ocedurenone was well tolerated when administered as a single 0.5-mg dose both alone and with itraconazole or rifampin, and while itraconazole had a weak effect on ocedurenone's pharmacokinetics, rifampin had a significant effect reducing systemic exposures.

A Phase 1, Open-Label, Fixed-Sequence, Drug-Drug Interaction Study of Zanubrutinib with Rifabutin in Healthy Volunteers.

Zanubrutinib is a second-generation Bruton tyrosine kinase inhibitor that is primarily metabolized by CYP3A enzymes. Previous drug-drug interaction (DDI) studies have demonstrated that co-administration of zanubrutinib with rifampin, a strong CYP3A inducer, reduces zanubrutinib plasma concentrations, potentially impacting activity. The impact of the co-administration of zanubrutinib with less potent CYP3A inducers is unclear. This phase 1, open-label, fixed-sequence DDI study evaluated the pharmacokinetics, safety, and tolerability of zanubrutinib when co-administered with steady-state rifabutin, a known CYP3A inducer less potent than rifampin, in 13 healthy male volunteers (NCT04470908). Co-administration of zanubrutinib with rifabutin resulted in a less than 2-fold reduction of zanubrutinib exposures. Overall, zanubrutinib was well tolerated. The results of this study provide useful information for the evaluation of the DDI between rifabutin and zanubrutinib. In conjunction with safety and efficacy data from other clinical studies, these results will be taken into consideration to determine the appropriate dose recommendation of zanubrutinib when co-administered with CYP3A inducers.

Effect of CYP3A4 induction and inhibition on the pharmacokinetics of SHR0302 in healthy subjects.

AIMS: SHR0302 is a selective Janus kinase (JAK) 1 inhibitor under clinical investigation for the treatment of rheumatoid arthritis (RA). As SHR0302 is metabolized mainly by cytochrome P450 (CYP) 3A4, clinical studies were performed to evaluate the effects of a strong CYP3A4 inducer, rifampin, and a strong CYP3A4 inhibitor, itraconazole, on the pharmacokinetics of SHR0302 in healthy subjects. METHODS: Two phase I, open-label, fixed-sequence drug interaction studies enrolled 28 subjects. In Study A, 14 subjects received 8 mg SHR0302 on Days 1 and 10, and 600 mg rifampin once daily on Days 3-11. In Study B, 14 subjects received 4 mg SHR0302 on Days 1 and 8, and 200 mg itraconazole once daily on Days 4-10. Blood samples were collected to measure SHR0302 concentrations. Pharmacokinetic parameters were calculated using non-compartmental analysis. Treatment comparisons were made using mixed-effect models. RESULTS: Co-administration with rifampin decreased the exposures of SHR0302 with geometric mean ratios (GMRs) (90% confidence intervals [CIs]) for AUC0-inf of 0.51 (0.49, 0.54) and Cmax of 0.91 (0.84, 0.98). Co-administration with itraconazole increased the exposures of SHR0302 with GMR (90% CIs) for AUC0-inf of 1.48 (1.41, 1.56) and Cmax of 1.06 (0.982, 1.14). Single oral doses of SHR0302 administered with or without rifampin or itraconazole were generally safe. CONCLUSIONS: Strong CYP3A4 induction and inhibition both resulted in a weak effect on the clinical exposures of SHR0302. These present studies provided valuable information that helps inform SHR0302 dosing instructions and concomitant medication precautions.

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.

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

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

Evaluation of Absorption and Metabolism-Based DDI Potential of Pexidartinib in Healthy Subjects.

BACKGROUND AND OBJECTIVE: Pexidartinib is a novel oral small-molecule inhibitor that selectively targets colony-stimulating factor 1 receptor, KIT proto-oncogene receptor tyrosine kinase, and FMS-like tyrosine kinase 3 harboring an internal tandem duplication mutation. It is approved in the United States for the treatment of adult patients with symptomatic tenosynovial giant cell tumor (TGCT) associated with severe morbidity or functional limitations and not amenable to improvement with surgery. Pexidartinib in vitro data indicate the potential for absorption- and metabolism-related drug-drug interactions (DDIs). The objective was to present a comprehensive DDI risk assessment of agents that can impact pexidartinib exposure by altering its absorption and metabolism potentially affecting efficacy and safety of pexidartinib. METHODS: Four open-label crossover studies were performed to assess the effects of a pH modifier (esomeprazole), a strong cytochrome P450 (CYP) 3A4 inhibitor (itraconazole), a strong CYP3A/5'-diphospho-glucuronosyltransferase (UGT) inducer (rifampin), and a UGT inhibitor (probenecid) on the single-dose pharmacokinetics of pexidartinib. In addition, a physiologically based pharmacokinetic model was developed to predict the effect of a moderate CYP3A4 inhibitor (fluconazole) and a moderate CYP3A inducer (efavirenz) on the pharmacokinetics of pexidartinib. RESULTS: Co-administration of pexidartinib with esomeprazole modestly decreased pexidartinib exposure (maximum plasma concentration [Cmax], ng/mL: geometric mean ratio [90% confidence interval (CI)], 45.4% [36.8-55.9]; area under the drug plasma concentration-time curve from time 0 to infinity [AUC∞], ng•h/mL: geometric mean ratio [90% CI], 53.1% [47.4-59.3]), likely related to decreased solubility of pexidartinib at increased pH levels. As expected, the strong CYP3A4 inhibitor itraconazole increased pexidartinib exposure (Cmax, ng/mL: geometric mean ratio [90% CI], 148.3% [127.8-172.0]; AUC∞, ng•h/mL: geometric mean ratio [90% CI], 173.0% [160.7-186.3]) while the strong CYP3A/UGT inducer rifampin decreased exposure (Cmax, ng/mL: geometric mean ratio [90% CI], 67.1% [53.1-84.8]; AUC∞, ng•h/mL: geometric mean ratio [90% CI], 37.0% [30.6-44.8]). In addition, UGT inhibition increased pexidartinib exposure (Cmax, ng/mL: geometric mean ratio [90% CI], 105.8% [92.4-121.0]; AUC∞, ng•h/mL: geometric mean ratio [90% CI], 159.8% [143.4-178.0]), consistent with the fact that pexidartinib is a substrate of the UGT1A4 enzyme, which is responsible for the generation of the major metabolite, ZAAD-1006a. CONCLUSIONS: The physiologically based pharmacokinetic model predicted that a moderate CYP3A4 inhibitor and a moderate CYP3A inducer would produce modest increases and decreases, respectively, in pexidartinib exposure. These results provide a basis for pexidartinib dosing recommendations when administered concomitantly with drugs with drug-drug interaction potential, including dose adjustments when concomitant administration cannot be avoided. CLINICAL TRIAL REGISTRATION: Probenecid: phase I trial, NCT03138759, 3 May, 2017; esomeprazole, itraconazole, rifampin: phase I trials, not registered with ClinicalTrials.gov.

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.

Evaluation of Safety and Clinically Relevant Drug-Drug Interactions with Tucatinib in Healthy Volunteers.

BACKGROUND AND OBJECTIVE: Tucatinib is approved for treatment of human epidermal growth factor receptor 2-positive metastatic breast cancer. Understanding potential drug-drug interactions (DDIs) informs proper dosing when co-administering tucatinib with other therapies. The aim of this study was to evaluate DDIs between tucatinib and metabolizing enzymes and transporters in healthy volunteers. METHODS: Parts A-C assessed the impact of itraconazole (cytochrome P450 [CYP] 3A4 inhibitor), rifampin (CYP3A4/CYP2C8 inducer), or gemfibrozil (CYP2C8 inhibitor) on the pharmacokinetics of a single 300 mg dose of tucatinib administered orally and its primary metabolite, ONT-993. Parts D and E assessed the effect of steady-state tucatinib on the pharmacokinetics of repaglinide (CYP2C8 substrate), tolbutamide (CYP2C9 substrate), midazolam (CYP3A4 substrate), and digoxin (P-glycoprotein substrate). RESULTS: Tucatinib area under the concentration-time curve from time 0 extrapolated to infinity (AUC0-inf) increased by ~ 1.3- and 3.0-fold with itraconazole and gemfibrozil, respectively, and decreased by 48% with rifampin, indicating that tucatinib is metabolized primarily by CYP2C8, and to a lesser extent via CYP3A. Tucatinib was a strong inhibitor of CYP3A (midazolam AUC0-inf increased 5.7-fold), a weak inhibitor of CYP2C8 and P-glycoprotein, and had no impact on CYP2C9-mediated metabolism in humans. Tucatinib was well tolerated, alone and with co-administered drugs. CONCLUSION: The potential DDIs identified here may be mitigated by avoiding concomitant use of tucatinib with strong CYP3A inducers, moderate CYP2C8 inducers, CYP3A substrates with a narrow therapeutic window (modifying substrate dose where concomitant use is unavoidable), and strong CYP2C8 inhibitors (decreasing tucatinib dose where concomitant use is unavoidable), or by reducing the dose of P-glycoprotein substrates with a narrow therapeutic window. TRIAL REGISTRATION: This trial (NCT03723395) was registered on October 29, 2018.

Pharmacokinetics of asciminib in the presence of CYP3A or P-gp inhibitors, CYP3A inducers, and acid-reducing agents.

Asciminib is a first-in-class inhibitor of BCR::ABL1, specifically targeting the ABL myristoyl pocket. Asciminib is a substrate of CYP3A4 and P-glycoprotein (P-gp) and possesses pH-dependent solubility in aqueous solution. This report summarizes the results of two phase I studies in healthy subjects aimed at assessing the impact of CYP3A and P-gp inhibitors, CYP3A inducers and acid-reducing agents (ARAs) on the pharmacokinetics (PK) of asciminib (single dose of 40 mg). Asciminib exposure (area under the curve [AUC]) unexpectedly decreased by ~40% when administered concomitantly with the strong CYP3A inhibitor itraconazole oral solution, whereas maximum plasma concentration (Cmax ) decreased by ~50%. However, asciminib exposure was slightly increased in subjects receiving an itraconazole capsule (~3%) or clarithromycin (~35%), another strong CYP3A inhibitor. Macroflux studies showed that cyclodextrin (present in high quantities as excipient [40-fold excess to itraconazole] in the oral solution formulation of itraconazole) decreased asciminib flux through a lipid membrane by ~80%. The AUC of asciminib was marginally decreased by concomitant administration with the strong CYP3A inducer rifampicin (by ~13-15%) and the strong P-gp inhibitor quinidine (by ~13-16%). Concomitant administration of the ARA rabeprazole had little or no effect on asciminib AUC, with a 9% decrease in Cmax . The treatments were generally well tolerated. Taking into account the large therapeutic window of asciminib, the observed changes in asciminib PK following multiple doses of P-gp, CYP3A inhibitors, CYP3A inducers, or ARAs are not considered to be clinically meaningful. Care should be exercised when administering asciminib concomitantly with cyclodextrin-containing drug formulations.

Effects of rifampicin on antineoplastic drug pyrotinib maleate pharmacokinetics in healthy subjects.

PURPOSE: Pyrotinib (PTN), an irreversible EGFR/HER2 dual tyrosine kinase inhibitor used for treating HER2-positive breast cancer, is primarily metabolized by cytochrome P450 (CYP)3A4 isozyme. Rifampicin (RIF) is a strong index CYP3A4 inducer. Therefore, the study aimed to elucidate the effect of RIF on PTN pharmacokinetics (PK) in Chinese healthy volunteers. METHODS: This phase I, open-label study investigated the effects of steady-state RIF administration on single-dose PK of PTN. 18 healthy participants were enrolled in this trial, who received a single oral dose of 400 mg of PTN on days 1 and 13, and were administrated with RIF 600 mg qd on days 6 through 16. RIF was administrated on an empty stomach, PTN were administrated orally in the morning 30 min after the start of the standard meal. Serial PK samples for PTN were collected on days 1 and days 13. Safety assessments were performed via clinical laboratory tests throughout the study. RESULTS: 18 subjects were enrolled and 16 completed the study. RIF significantly reduced PTN exposure: Geometric least-squares mean ratios (90% CI) for PTN + RIF versus PTN alone were 0.04 (0.034,0.049), 0.04 (0.037,0.054), and 0.11 (0.09,0.124) for area under the curve from time zero to time of last quantifiable concentration (AUC0 - t), area under the curve from time zero to infinity (AUC0-∞ ), and maximum observed plasma concentration(Cmax), respectively. PTN alone and co-administered with RIF was well tolerated. CONCLUSION: The exposure of PTN was significantly affected by the action of RIF. The findings suggest that concomitant strong CYP3A4 inducers should be avoided during PTN treatment. Concurrent administration of PTN and RIF was well tolerated.

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

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

Physiologically-Based Pharmacokinetic Modelling of Entrectinib Parent and Active Metabolite to Support Regulatory Decision-Making.

BACKGROUND AND OBJECTIVE: Entrectinib is a selective inhibitor of ROS1/TRK/ALK kinases, recently approved for oncology indications. Entrectinib is predominantly cleared by cytochrome P450 (CYP) 3A4, and modulation of CYP3A enzyme activity profoundly alters the pharmacokinetics of both entrectinib and its active metabolite M5. We describe development of a combined physiologically based pharmacokinetic (PBPK) model for entrectinib and M5 to support dosing recommendations when entrectinib is co-administered with CYP3A4 inhibitors or inducers. METHODS: A PBPK model was established in Simcyp® Simulator. The initial model based on in vitro-in vivo extrapolation was refined using sensitivity analysis and non-linear mixed effects modeling to optimize parameter estimates and to improve model fit to data from a clinical drug-drug interaction study with the strong CYP3A4 inhibitor, itraconazole. The model was subsequently qualified against clinical data, and the final qualified model used to simulate the effects of moderate to strong CYP3A4 inhibitors and inducers on entrectinib and M5 pharmacokinetics. RESULTS: The final model showed good predictive performance for entrectinib and M5, meeting commonly used predictive performance acceptance criteria in each case. The model predicted that co-administration of various moderate CYP3A4 inhibitors (verapamil, erythromycin, clarithromycin, fluconazole, and diltiazem) would result in an average increase in entrectinib exposure between 2.2- and 3.1-fold, with corresponding average increases for M5 of approximately 2-fold. Co-administration of moderate CYP3A4 inducers (efavirenz, carbamazepine, phenytoin) was predicted to result in an average decrease in entrectinib exposure between 45 and 79%, with corresponding average decreases for M5 of approximately 50%. CONCLUSIONS: The model simulations were used to derive dosing recommendations for co-administering entrectinib with CYP3A4 inhibitors or inducers. PBPK modeling has been used in lieu of clinical studies to enable regulatory decision-making.

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

BACKGROUND: Isavuconazole is a triazole antifungal drug, approved for the treatment of invasive aspergillosis and mucormycosis. Isavuconazole is metabolised by CYP3A4 and CYP3A5, and it has been shown that the CYP3A inducer rifampin reduces isavuconazole exposure. By extrapolation, the concomitant use of isavuconazole with moderate and strong CYP450 inducers is contraindicated, although it is known that some CYP450 inducers are less potent in comparison with rifampin. OBJECTIVES: We aim to document exposure to isavuconazole in patients concomitantly treated with a CYP450 inducer that is less potent compared to rifampin. Moreover, although it is well known that CYP3A enzymes are important for the metabolism of isavuconazole, this induction effect has never been studied in combination with the patient's CYP3A genotype. PATIENTS: We report three patients treated with both isavuconazole and a CYP3A inducer that is less potent compared to rifampin (rifabutin or phenobarbital), in whom we determined isavuconazole concentrations. RESULTS: These cases suggest that the CYP3A4/5 genotype is an important determinant for isavuconazole exposure and that it might also influence the CYP450 induction interaction. CONCLUSIONS: CYP3A inducers that are less potent compared to rifampin, may be combined with isavuconazole in patients with loss of CYP3A5 activity (CYP3A5*3/*3). Therapeutic drug monitoring is recommended during this combination. However, low-isavuconazole exposure was observed in the extensive metaboliser with CYP3A4*1/*1 and CYP3A5*1/*3 alleles.

Investigating the Utility of Humanized Pregnane X Receptor-Constitutive Androstane Receptor-CYP3A4/7 Mouse Model to Assess CYP3A-Mediated Induction.

Clinical induction liability is assessed with human hepatocytes. However, underpredictions in the magnitude of clinical induction have been reported. Unfortunately, in vivo studies in animals do not provide additional insight because of species differences in drug metabolizing enzymes and their regulatory pathways. To circumvent this limitation, transgenic animals expressing human orthologs were developed. The aim of this work was to investigate the utility of mouse models expressing human orthologs of pregnane X receptor, constitutive androstane receptor, and CYP3A4/7 (Tg-Composite) in evaluating clinical induction. Rifampin, efavirenz, and pioglitazone, which were employed to represent strong, moderate, and weak inducers, were administered at multiple doses to Tg-Composite animals. In vivo CYP3A activity was monitored by measuring changes in the exposure of the CYP3A probe substrate triazolam. After the in vivo studies, microsomes were prepared from their livers to measure changes of in vitro CYP3A4 activity. In both in vivo and in vitro, distinction of clinic induction was recapitulated as rifampin yielded the greatest inductive effect followed by efavirenz and pioglitazone. Interestingly, with rifampin, in vivo CYP3A activity was approximately 4-fold higher than in vitro activity. Conversely, there was no difference between in vivo and in vitro CYP3A activity with efavirenz. These findings are consistent with the report that, although rifampin exhibits differential inductive effects between the intestines and liver, efavirenz does not. These data highlight the promise of transgenic models, such as Tg-Composite, to complement human hepatocytes to enhance the translatability of clinical induction as well as become a powerful tool to further study mechanisms of drug disposition. SIGNIFICANCE STATEMENT: Underprediction of the magnitude of clinical induction when using human hepatocytes has been reported, and transgenic models may improve clinical translatability. The work presented here showcases the human orthologs of pregnane X receptor, constitutive androstane receptor, and CYP3A4/7 model, which was able to recapitulate the magnitude of clinical induction and to differentiate tissue-dependent induction observed with rifampin but not with efavirenz. These results not only foreshadow the potential application of such transgenic models in assessing clinical induction but also in further investigation of the mechanism of drug disposition.

Evaluation of Methods to Assess CYP3A Induction Risk in Clinical Practice Using in Vitro Induction Parameters.

Established guidelines have recommended a number of methods based on in vitro data to assess the CYP3A induction risk of new chemical entities in clinical practice. In this study, we evaluated the predictability of various assessment methods. We collected in vitro parameters from a variety of literature that includes data on 19 batches of hepatocytes. Clinical CYP3A induction was predicted using 3 direct approaches-the fold-change, basic model, and mechanistic static models-as well as 5 correlation approaches, including the relative induction score (RIS) and the relative factor (RF) method. These predictions were then compared with data from 30 clinical inductions. Collected in vitro parameters varied greatly between hepatocyte batches. Direct assessment methods using fixed cut-off values provided a lot of false predictions due to hepatocyte variability, which can overlook induction risk or lead to needless clinical drug-drug interaction (DDI) studies. On the other hand, correlation methods with the cut-off values set for each batch of hepatocytes accurately predicted the induction risk. Among these, the AUCu/inducer concentrations for half the maximum induction (EC50) and the RF methods which use the area under the curve (AUC) of the unbound inducers for calculating induction potential showed an especially good correlation with clinical induction. Correlation methods were better at predicting clinical induction risk than the other methods, regardless of hepatocyte variability. The AUCu/EC50 and the RF methods in particular had a small number of false predictions, and can therefore be used to assess induction risk along with the other correlation methods recommended in guidelines.

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

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

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

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