Pharmacokinetics of olverembatinib (HQP1351) in the presence of a strong CYP3A4 inhibitor (itraconazole) or inducer (rifampin) in healthy volunteers.

Olverembatinib (HQP1351) is a BCR-ABL1 tyrosine kinase inhibitor with promising clinical activity. It is approved in China for the treatment of patients with chronic myeloid leukemia harboring drug-resistant mutations, such as T315I. In vitro studies suggested that metabolism of olverembatinib is primarily mediated by cytochrome P450 (CYP3A4). The effects of CYP3A4 inhibition and induction on the pharmacokinetics of olverembatinib were evaluated in an open-label, 2-part, fixed-sequence study in healthy volunteers. In Part 1 of this study, 16 participants received a single oral dose of olverembatinib (20 mg) and the oral CYP3A4 inhibitor itraconazole (200 mg). In Part 2, 16 participants received a single oral dose of olverembatinib (40 mg) and the oral CYP3A4 inducer rifampin (600 mg). To measure pharmacokinetic parameters, serial blood samples were collected after administration of olverembatinib alone and combined with itraconazole or rifampin. Coadministration of olverembatinib with itraconazole increased the peak plasma concentration of olverembatinib, its area under the time-concentration curve (AUC)0-last, and AUC0-inf by 75.63%, 147.06%, and 158.66%, respectively. Coadministration with rifampin decreased these same variables by 61.27%, 74.21%, and 75.19%, respectively. These results confirm that olverembatinib is primarily metabolized by CYP3A4 in humans, suggesting that caution should be exercised with concurrent use of olverembatinib and strong CYP3A4 inhibitors or inducers.

Value of Assessing 1-Hydroxymidazolam in Drug-Drug Interaction Studies with Midazolam as a Substrate of Cytochrome P450 3A.

The purpose of this overview was to perform an exploratory analysis of in-house drug-drug interaction (DDI) studies conducted with investigational drugs and oral midazolam to assess the value of measuring 1-OH-midazolam (1-OHM) in such studies. The perpetrator effect of the investigational drugs on cytochrome P450 3A (CYP3A) was assessed by analyzing both midazolam and 1-OHM in plasma and evaluating their pharmacokinetic parameters. Given the almost exclusive metabolism of the parent drug by CYP3A to the main metabolite 1-OHM, an increase in midazolam and a decrease in 1-OHM exposure in the case of CYP3A inhibition caused by a perpetrator drug would be expected. The opposite would be anticipated in the case of CYP3A induction. For this analysis, the perpetrator potential of eight different investigational drugs was incorporated. Among the 10 studies included, the identified CYP3A inhibitors (n = 4) and inducers (n = 1) were classified based on the data generated with midazolam per se, with 1-OHM levels not contributing to the interpretation of the data as they did not corroborate the findings of the parent compound. Therefore, it was concluded that continued analysis of 1-OHM in plasma may be questionable as it does not add value to the interpretation of the results when performing CYP3A DDI studies with an investigational drug as a perpetrator.

Mechanistic Account of Distinct Change in Organic Anion Transporting Polypeptide 1B (OATP1B) Substrate Pharmacokinetics during OATP1B-Mediated Drug-Drug Interactions Using Physiologically Based Pharmacokinetic Modeling.

The role of transporters in drug clearance is widely acknowledged, directly and indirectly by facilitating tissue/enzyme exposure. Through the latter, transporters also affect volume of distribution. Drug-drug interactions (DDIs) involving organic anion transporting polypeptides (OATPs) 1B1/1B3 and SLCO1B1 pharmacogenetics lead to altered pharmacokinetics of OATP1B substrates; however, several factors may confound direct interpretation of pharmacokinetic parameters from these clinical studies using noncompartmental analysis (NCA). A review of clinical data herein indicates a single dose of OATP1B inhibitor rifampin almost never leads to increased substrate half-life but often a decrease and that most clinical OATP1B substrates are CYP3A4 substrates and/or undergo enterohepatic cycling (EHC). Using hypothetically simple OATP1B substrate physiologically based pharmacokinetic (PBPK) models, simulated effect of rifampin differed from specific OATP1B inhibition due to short rifampin half-life causing dissipation of OATP1B inhibition over time combined with CYP3A4 induction. Calculated using simulated tissue data, volume of distribution indeed decreased with OATP1B inhibition and was expectedly limited to the contribution of liver volume. However, an apparent and counterintuitive effect of rifampin on volume greater than that on clearance resulted for CYP3A4 substrates using NCA. The effect of OATP1B inhibition and rifampin on OATP1B substrate models incorporating EHC plus or minus renal clearance was distinct compared with simpler models. Using PBPK models incorporating reversible lactone metabolism for clinical OATP1B substrates atorvastatin and pitavastatin, DDIs reporting decreased half-life with rifampin were reproduced. These simulations provide an explanation for the distinct change in OATP1B substrate pharmacokinetics observed in clinical studies, including changes in volume of distribution and additional mechanisms. SIGNIFICANCE STATEMENT: Transporters are involved in drug clearance and volume of distribution, and distinct changes in OATP1B substrate pharmacokinetics are observed with OATP1B inhibitor rifampin. Using hypothetical and validated PBPK models and simulations, this study addresses the limitations of single-dose rifampin and complicated clinical OATP1B substrate disposition in evaluating the pharmacokinetic parameters of OATP1B substrates during rifampin drug-drug interactions (DDIs). These models account for change in volume of distribution and identify additional mechanisms underlying apparent pharmacokinetic changes in OATP1B DDIs.

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

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

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

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

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.

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.

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.

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.