Clinical Drug-drug Interaction Studies Research Articles

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy

Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.

A Case to Support the Continued Use of Rifampin in Clinical Drug-Drug Interaction Studies.

A Case to Support the Continued Use of Rifampin in Clinical Drug-Drug Interaction Studies.

Prediction of drug-drug interaction risk of P-glycoprotein substrate in drug discovery

We aimed to predict the drug-drug interaction (DDI) risk of P-glycoprotein (P-gp) substrates by using P-gp expressing LLC-PK1 cells and its knockout mice (KO). The area under the curve (AUC) of 16 marketed drugs and plasma concentration (Cplasma) of 207 screening compounds, with corrected efflux ratio (CER) ≥ 2, were compared between P-gp KO mice and wild type mice (WT). At permeability (Papp) ≥ 10 × 10−6 cm/sec in parent LLC-PK1 cells, AUC ratios (KO/WT) and Cplasma ratios (KO/WT) of these compounds were within 3-fold. Besides, AUC ratios (KO/WT) of clinical P-gp substrates, with human AUC ratio with and without P-gp inhibitor administration ≥2, were found to be higher than 8.7. Based on these observations of Papp and AUC ratios, we established a work-flow of P-gp substrate assessment with the threshold, AUC ratio (KO/WT) ≥ 9 leading to DDI risk of AUC ratio (human) ≥ 2. Subsequently, the screening compound showing high CER (=57.6), but its AUC ratio (KO/WT) was 3.7, had been presumed to be weak risk and its AUC ratio (human) was 1.2 at later clinical DDI study. Proposed workflow should be useful for predicting the DDI risk of P-gp substrates in drug discovery.

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

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

Clinical Evaluation of the Effect of Encorafenib on Bupropion, Rosuvastatin, and Coproporphyrin I and Considerations for Statin Coadministration.

Encorafenib is a kinase inhibitor indicated for the treatment of patients with unresectable or metastatic melanoma or metastatic colorectal cancer, respectively, with selected BRAF V600 mutations. A clinical drug-drug interaction (DDI) study was designed to evaluate the effect of encorafenib on rosuvastatin, a sensitive substrate of OATP1B1/3 and breast cancer resistance protein (BCRP), and bupropion, a sensitive CYP2B6 substrate. Coproporphyrin I (CP-I), an endogenous substrate for OATP1B1, was measured in a separate study to deconvolute the mechanism of transporter DDI. DDI study participants received a single oral dose of rosuvastatin (10 mg) and bupropion (75 mg) on days - 7, 1, and 14 and continuous doses of encorafenib (450 mg QD) and binimetinib (45 mg BID) starting on day 1. The CP-I data were collected from participants in a phase 3 study who received encorafenib (300 mg QD) and cetuximab (400 mg/m2 initial dose, then 250 mg/m2 QW). Pharmacokinetic and pharmacodynamic analysis was performed using noncompartmental and compartmental methods. Bupropion exposure was not increased, whereas rosuvastatin Cmax and area under the receiver operating characteristic curve (AUC) increased approximately 2.7 and 1.6-fold, respectively, following repeated doses of encorafenib and binimetinib. Increase in CP-I was minimal, suggesting that the primary effect of encorafenib on rosuvastatin is through BCRP. Categorization of statins on the basis of their metabolic and transporter profile suggests pravastatin would have the least potential for interaction when coadministered with encorafenib. The results from these clinical studies suggest that encorafenib does not cause clinically relevant CYP2B6 induction or inhibition but is an inhibitor of BCRP and may also inhibit OATP1B1/3 to a lesser extent. Based on these results, it may be necessary to consider switching statins or reducing statin dosage accordingly for coadministration with encorafenib. ClinicalTrials.gov NCT03864042, registered 6 March 2019.

Statin Drug-Drug Interactions: Pharmacokinetic Basis of FDA Labeling Recommendations and Comparison Across Common Tertiary Clinical Resources.

Statins are widely prescribed and highly susceptible to pharmacokinetic (PK)-based drug-drug interactions (DDIs). To date, there has not been a comprehensive analysis of the basis upon which statin DDI recommendations in US Food and Drug Administration (FDA) prescribing information (PI) are derived. We have conducted such an analysis. We also assessed the degree of concordance of statin DDI recommendations in FDA PI and those provided in common tertiary clinical resources. We catalogued statin DDI information, including PK data and management recommendations, for statin precipitant drugs approved from 2010 to 2021, available from FDA PI and tertiary clinical resource databases. Recommendations were categorized and mapped with associated PK data to assess consistency in the PK basis for labeling recommendations. From the 80 precipitant drugs evaluated, 180 statin DDIs were identified in FDA PI. Dedicated clinical DDI studies were conducted for 54% (n = 97) of these DDIs and 34% (n = 61) of DDI recommendations were extrapolated from clinical data with other statins. Overall, we found that PK-based statin recommendations were consistent across PI. These findings highlight regulatory precedence for translating information across statins without conducting dedicated clinical DDI studies, which may support future efforts toward streamlining the approach to investigation and labeling of statin DDIs. In addition, with the exception of some notable discrepancies, general concordance was observed between FDA and tertiary resources regarding "Dose Adjustment" and "Avoid Coadministration" recommendations. However, further analyses are warranted across other DDI pairs to determine whether discordance can routinely lead to different clinical recommendations depending on the drug information resource.

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. Invitro 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.

Assessment of the CYP3A4 Induction Potential by Carbamazepine: Insights from Two Clinical DDI Studies and PBPK Modeling.

In the past, rifampicin was well-established as strong index CYP3A inducer in clinical drug-drug interaction (DDI) studies. However, due to identified potentially genotoxic nitrosamine impurities, it should not any longer be used in healthy volunteer studies. Available clinical data suggestcarbamazepine as an alternative to rifampicin as strong index CYP3A4 inducer in clinical DDI studies. Further, physiologically-based pharmacokinetic (PBPK) modeling is a tool with increasing importance to support the DDI risk assessment of drugs during drug development. CYP3A4 induction properties and the safety profile of carbamazepine were investigated in two open-label, fixed sequence, crossover clinical pharmacology studies in healthy volunteers using midazolam as a sensitive index CYP3A4 substrate. Carbamazepine was up-titrated from 100 mg twice daily (b.i.d.) to 200 mg b.i.d., and to a final dose of 300 mg b.i.d. for 10 consecutive days. Mean area under plasma concentration-time curve from zero to infinity (AUC(0-∞)) of midazolam consistently decreased by 71.8% (ratio: 0.282, 90% confidence interval (CI): 0.235-0.340) and 67.7% (ratio: 0.323, 90% CI: 0.256-0.407) in study 1 and study 2, respectively. The effect was adequately described by an internally developed PBPK model for carbamazepine which has been made freely available to the scientific community. Further, carbamazepine was safe and well-tolerated in the investigated dosing regimen in healthy participants. The results demonstrated that the presented design is appropriate for the use of carbamazepine as alternative inducer to rifampicin in DDI studies acknowledging its CYP3A4 inductive potency and safety profile.

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

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

Physiologically Based Pharmacokinetic Modeling for Maribavir to Inform Dosing in Drug-Drug Interaction Scenarios with CYP3A4 Inducers and Inhibitors.

Maribavir, an orally available antiviral agent, has been approved in multiple countries for the treatment of patients with refractory post-transplant cytomegalovirus (CMV) infection and/or disease. Maribavir is primarily metabolized by CYP3A4; coadministration with CYP3A4 inducers and inhibitors may significantly alter maribavir exposure, thereby affecting its efficacy and safety. The effect of CYP3A4 inducers and inhibitors on maribavir exposure was evaluated based on a drug-drug interaction (DDI) study and physiologically-based pharmacokinetic (PBPK) modeling. The effect of rifampin (a strong inducer of CYP3A4 and moderate inducer of CYP1A2), administered at a 600mg dose once daily, on maribavir pharmacokinetics was assessed in a clinical phase1 DDI study in healthy participants. A full PBPK model for maribavir was developed and verified using invitro and clinical pharmacokinetic data from phase1 studies. The verified PBPK model was then used to simulate maribavir DDI interactions with various CYP3A4 inducers and inhibitors. The DDI study results showed that coadministration with rifampin decreased the maribavir maximum plasma concentration (Cmax), area under the plasma concentration-time curve (AUC), and trough concentration (Ctrough) by 39%, 60%, and 82%, respectively. Based on the results from the clinical DDI study, the coadministration of maribavir with rifampin is not recommended. The PBPK model did not predict a clinically significant effect of CYP3A4 inhibitors on maribavir exposure; however, it predicted that strong or moderate CYP3A4 inducers, including carbamazepine, efavirenz, phenobarbital, and phenytoin, may reduce maribavir exposure to a clinically significant extent, and may prompt the consideration of a maribavir dosing increase, in accordance with local approved labels and/or regulations.

Physiologically Based Pharmacokinetic Modeling of the Drug-Drug Interaction Between CYP3A4 Substrate Glasdegib and Moderate CYP3A4 Inducers in Lieu of a Clinical Study.

Glasdegib (DAURISMO) is a hedgehog pathway inhibitor approved for the treatment of acute myeloid leukemia (AML). Cytochrome P450 3A4 (CYP3A4) has been identified as a major metabolism and clearance pathway for glasdegib. The role of CYP3A4 in the clearance of glasdegib has been confirmed with clinical drug-drug interaction (DDI) studies following the coadministration of glasdegib with the strong CYP3A4 inhibitor ketoconazole and the strong inducer rifampin. To evaluate potential drug interactions with CYP3A4 modulators, the coadministration of glasdegib with a moderate CYP3A4 inducer, efavirenz, was evaluated using physiologically based pharmacokinetic (PBPK) modeling using the Simcyp simulator. The glasdegib compound file was developed using measured physicochemical properties, data from human intravenous and oral pharmacokinetics, absorption, distribution, metabolism, and excretion studies, and invitro reaction phenotyping results. The modeling assumptions, model parameters, and assignments of fractional CYP3A4 metabolism were verified using results from clinical pharmacokinetics (PK) and DDI studies with ketoconazole and rifampin. The verified glasdegib and efavirenz compound files, the latter of which was available in the Simcyp simulator, were used to estimate the potential impact of efavirenz on the PK of glasdegib. PBPK modeling predicted a glasdegib area under the concentration-time curve ratio of 0.45 and maximum plasma concentration ratio of 0.75 following coadministration with efavirenz. The PBPK results, in lieu of a formal clinical study, informed the drug label, with the recommendation to double the clinical dose of glasdegib when administered in conjunction with a moderate CYP3A4 inducer, followed by a resumption of the original dose 7days post-discontinuation.

Utilization of OATP1B Biomarker Coproporphyrin-I to Guide Drug-Drug Interaction Risk Assessment: Evaluation by the Pharmaceutical Industry.

Drug-drug interactions (DDIs) involving hepatic organic anion transporting polypeptides 1B1/1B3 (OATP1B) can be substantial, however, challenges remain for predicting interaction risk. Emerging evidence suggests that endogenous biomarkers, particularly coproporphyrin-I (CP-I), can be used to assess in vivo OATP1B activity. The present work under the International Consortium for Innovation and Quality in Pharmaceutical Development was aimed primarily at assessing CP-I as a biomarker for informing OATP1B DDI risk. Literature and unpublished CP-I data along with pertinent in vitro and clinical DDI information were collected to identify DDIs primarily involving OATP1B inhibition and assess the relationship between OATP1B substrate drug and CP-I exposure changes. Static models to predict changes in exposure of CP-I, as a selective OATP1B substrate, were also evaluated. Significant correlations were observed between CP-I area under the curve ratio (AUCR) or maximum concentration ratio (Cmax R) and AUCR of substrate drugs. In general, the CP-I Cmax R was equal to or greater than the CP-I AUCR. CP-I Cmax R < 1.25 was associated with absence of OATP1B-mediated DDIs (AUCR < 1.25) with no false negative predictions. CP-I Cmax R < 2 was associated with weak OATP1B-mediated DDIs (AUCR < 2). A correlation was identified between CP-I exposure changes and OATP1B1 static DDI predictions. Recommendations for collecting and interpreting CP-I data are discussed, including a decision tree for guiding DDI risk assessment. In conclusion, measurement of CP-I is recommended to inform OATP1B inhibition potential. The current analysis identified changes in CP-I exposure that may be used to prioritize, delay, or replace clinical DDI studies.

Quantitative prediction of transporter-mediated drug-drug interactions using the mechanistic static pharmacokinetic (MSPK) model

Guidance/guidelines on drug-drug interactions (DDIs) have been issued in Japan, the United States, and Europe. These guidance/guidelines provide decision trees for conducting metabolizing enzyme-mediated clinical DDI studies; however, the decision trees for transporter-mediated DDIs lack quantitative prediction methods. In this study, the accuracy of a net-effect mechanistic static pharmacokinetics (MSPK) model containing the fraction transported (ft) of transporters was examined to predict transporter-mediated DDIs. This study collected information on 25 oral drugs with new active reagents that were used in clinical DDI studies as perpetrators (42 cases) from drugs approved in Japan between April 2016 and June 2020. The AUCRs (AUC ratios with and without perpetrators) of victim drugs were predicted using the net-effect MSPK model. As a result, 83 and 95% of the predicted AUCRs were within 1.5- and 2-fold error in the observed AUCRs, respectively. In cases where the victims were statins in which pharmacokinetics several transporters are involved, 70 and 91% of the predicted AUCRs were within 1.5- and 2-fold errors, respectively. Therefore, the net-effect MSPK model was applicable for predicting the AUCRs of victims, which are substrates for multiple transporters.

Imeglimin: A Clinical Pharmacology Review.

Imeglimin (PXL008, EMD-387008, Twymeeg®) is a first-in-class novel oral hypoglycemic agent, launched in Japan, for the treatment of type 2 diabetes mellitus. Its mechanism of action targets mitochondrial bioenergetics to ameliorate insulin resistance and to enhance β-cell function. This review summarizes the properties underlying the pharmacokinetic profile of imeglimin, a small cationic drug belonging to the tetrahydrotriazine chemical class, with a complex mechanism of absorption involving an active transport through organic cation transporters (OCTs). Imeglimin absorption decreases when dose increases due to the saturation of the active uptake transport. Post absorption, imeglimin is rapidly and primarily distributed to organs and tissues, and has a half-life ranging from 9.03 to 20.2h. Plasma protein binding of imeglimin is low, which explains the rapid distribution to the organs observed in all species. Imeglimin is excreted unchanged in urine, indicating a low extent of metabolism. Imeglimin is a substrate of multidrug and toxic compound extrusion (MATE)2-K and a substrate and inhibitor of OCT1, OCT2, and MATE1. Clinical drug-drug interaction studies confirmed the absence of relevant clinical interaction with substrates or inhibitors of these transporters. Overall, the drug-drug interaction potential of imeglimin is low. Its pharmacokinetics profile has also been characterized in special populations, showing no influence of mild and moderate hepatic impairment but an impact of renal function on imeglimin renal clearance. Dosage adjustment is thus required in moderately and severely renally impaired patients. Imeglimin pharmacokinetics was shown to be insensitive to ethnicity and food intake and to have no effect on QTcF interval.

Prediction of Drug-Drug Interactions with Ensartinib as a Time-Dependent CYP3A Inhibitor Using Physiologically Based Pharmacokinetic Model.

Ensartinib (X-396) is a second-generation anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor (TKI) indicated for the treatment of ALK-positive patients with locally advanced or metastatic non-small cell lung cancer. Although in vitro experiments and molecular docking suggested its potential as a cytochrome P450 inhibitor, no further investigation or clinical trials have been conducted to assess its drug-drug interaction (DDI) risk. In this study, we conducted a series of in vitro experiments to elucidate the inhibition mechanism of ensartinib. Furthermore, a physiologically-based pharmacokinetic (PBPK) model was developed based on in vitro, in silico, and in vivo parameters, verified using clinical data, and applied to predict the clinical DDI mediated by ensartinib. The in vitro incubation experiments suggested that ensartinib exhibited strong time-dependent inhibition. Simulation results from the PBPK model indicated a significant increase in the exposure of CYP3A substrates in the presence of ensartinib, with the maximal plasma concentration and area under the plasma concentration-time curve increasing up to 12-fold and 29-fold for sensitive substrates. Based on these findings, it is evident that co-administration of ensartinib and CYP3A substrates requires careful regulatory consideration. SIGNIFICANCE STATEMENT: Ensartinib was found to be a strong time-dependent inhibitor of CYP3A for the first time based on in vitro experiments, but there was no research conducted to estimate the risk of drug-drug interaction (DDI) of ensartinib in clinic. Therefore, the first ensartinib physiologically based pharmacokinetic model was developed and applied to predict various untested scenarios. The simulation result indicated that the exposure of CYP3A substrate increased significantly and urged the further clinical DDI study.

Drug-drug interactions and dose management of BTK inhibitors when initiating nirmatrelvir/ritonavir (paxlovid) based on physiologically-based pharmacokinetic models

ObjectiveCo-administration of Bruton's tyrosine kinase (BTK) inhibitors with nirmatrelvir/ritonavir is challenging because of potential drug-drug interactions (DDIs). However, clinical trials specifically evaluating such DDIs are absent. To evaluate and quantify the DDIs between them and provide rational dose management strategies of BTK inhibitors, we conducted this study using physiologically-based pharmacokinetic (PBPK) models. MethodsPhysicochemical properties and pharmacokinetic parameters were acquired from the published literature and databases. The PBPK models were developed using Simcyp® software. These models were validated by comparing with published literature values. The successfully validated PBPK models were used to simulate the plasma concentration-time profiles and DDIs in a virtual healthy population receiving BTK inhibitors alone or with ritonavir. ResultsSimulated plasma concentration-time profiles and pharmacokinetic parameters of each drug were in agreement with clinically observed values from literatures. Ritonavir increased ibrutinib maximum plasma concentration (Cmax) and the area under plasma concentration-time curve (AUC) 33- and 53.88-fold, respectively, increased zanubrutinib Cmax and AUC 2.57- and 3.18-fold, respectively, and increased acalabrutinib Cmax and AUC 3.85- and 6.54-fold, respectively. Based on our simulations, dose-adjustment strategies may consist of ibrutinib at 25 mg q48h, zanubrutinib at 80 mg twice-daily and acalabrutinib at 25 mg twice-daily with nirmatrelvir/ritonavir. ConclusionsThe PBPK models predicted the in vivo pharmacokinetics and the DDIs of BTK inhibitors and ritonavir. The prospective simulations not only provided scientific evidence regarding rational dosing management strategies when initiating nirmatrelvir/ritonavir therapy but also provided a reference for the design of clinical DDIs study that may save resources and time. SummaryPaxlovid could increase Cmax and AUC0-τ of BTK inhibitors (ibrutinib, zanubrutinib and acalabrutinib), and dose adjustment strategy of ibrutinib (25 mg q48h), zanubrutinib (80 mg q12h) and acalabrutinib (25 mg q12h) should be considered when combination with nirmatrelvir/ritonavir.

Clinical Pharmacology of Brigatinib: A Next-Generation Anaplastic Lymphoma Kinase Inhibitor.

Brigatinib, a next-generation anaplastic lymphoma kinase (ALK) inhibitor designed to overcome mechanisms of resistance associated with crizotinib, is approved for the treatment of ALK-positive advanced or metastatic non-small cell lung cancer. After oral administration of single doses of brigatinib 30-240 mg, the median time to reach maximum plasma concentration ranged from 1 to 4 h. In patients with advanced malignancies, brigatinib showed dose linearity over the dose range of 60-240 mg once daily. A high-fat meal had no clinically meaningful effect on systemic exposures of brigatinib (area under the plasma concentration-time curve); thus, brigatinib can be administered with or without food. In a population pharmacokinetic analysis, a three-compartment pharmacokinetic model with transit absorption compartments was found to adequately describe brigatinib pharmacokinetics. In addition, the population pharmacokinetic analyses showed that no dose adjustment is required based on body weight, age, race, sex, total bilirubin (<1.5× upper limit of normal), and mild-to-moderate renal impairment. Data from dedicated phase I trials have indicated that no dose adjustment is required for patients with mild or moderate hepatic impairment, while a dose reduction of approximately 40% (e.g., from 180 to 120 mg) is recommended for patients with severe hepatic impairment, and a reduction of approximately 50% (e.g., from 180 to 90 mg) is recommended when administering brigatinib to patients with severe renal impairment. Brigatinib is primarily metabolized by cytochrome P450 (CYP) 3A, and results of clinical drug-drug interaction studies and physiologically based pharmacokinetic analyses have demonstrated that coadministration of strong or moderate CYP3A inhibitors or inducers with brigatinib should be avoided. If coadministration with a strong or moderate CYP3A inhibitor cannot be avoided, the dose of brigatinib should be reduced by approximately 50% (strong CYP3A inhibitor) or approximately 40% (moderate CYP3A inhibitor), respectively. Brigatinib is a weak inducer of CYP3A in vivo; data from a phase I drug-drug interaction study showed that coadministration of brigatinib 180 mg once daily reduced the oral midazolam area under the plasma concentration-time curve from time zero to infinity by approximately 26%. Brigatinib did not inhibit CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, or CYP2D6 at clinically relevant concentrations in vitro. Exposure-response analyses based on data from the ALTA (ALK in Lung Cancer Trial of AP26113) and ALTA-1L pivotal trials of brigatinib confirm the favorable benefit versus risk profile of the approved titration dosing regimen of 180 mg once daily (after a 7-day lead-in at 90 mg once daily).

Assessment of Drug-Drug Interaction Risk Between Intravenous Fentanyl and the Glecaprevir/Pibrentasvir Combination Regimen in Hepatitis C Patients Using Physiologically Based Pharmacokinetic Modeling and Simulations.

An unsafe injection practice is one of the major contributors to new hepatitis C virus (HCV) infections; thus, people who inject drugs are a key population to prioritize to achieve HCV elimination. The introduction of highly effective and well-tolerated pangenotypic direct-acting antivirals, including glecaprevir/pibrentasvir (GLE/PIB), has revolutionized the HCV treatment landscape. Glecaprevir is a weak cytochrome P450 3A4 (CYP3A4) inhibitor, so there is the potential for drug-drug interactions (DDIs) with some opioids metabolized by CYP3A4, such as fentanyl. This study estimated the impact of GLE/PIB on the pharmacokinetics of intravenous fentanyl by building a physiologically based pharmacokinetic (PBPK) model. A PBPK model was developed for intravenous fentanyl by incorporating published information on fentanyl metabolism, distribution, and elimination in healthy individuals. Three clinical DDI studies were used to verify DDIs within the fentanyl PBPK model. This model was integrated with a previously developed GLE/PIB PBPK model. After model validation, DDI simulations were conducted by coadministering GLE 300mg + PIB 120mg with a single dose of intravenous fentanyl (0.5µg/kg). The predicted maximum plasma concentration ratio between GLE/PIB + fentanyl and fentanyl alone was 1.00, and the predicted area under the curve ratio was 1.04, suggesting an increase of only 4% in fentanyl exposure. The administration of a therapeutic dose of GLE/PIB has very little effect on the pharmacokinetics of intravenous fentanyl. This negligible increase would not be expected to increase the risk of fentanyl overdose beyond the inherent risks related to the amount and purity of the fentanyl received during recreational use.

Impact of Obesity on the Drug-Drug Interaction Between Dolutegravir and Rifampicin or Any Other Strong Inducers.

Obesity is increasingly prevalent among people with HIV. Obesity can impact drug pharmacokinetics and consequently the magnitude of drug-drug interactions (DDIs) and, thus, the related recommendations for dose adjustment. Virtual clinical DDI studies were conducted using physiologically based pharmacokinetic (PBPK) modeling to compare the magnitude of the DDI between dolutegravir and rifampicin in nonobese, obese, and morbidly obese individuals. Each DDI scenario included a cohort of virtual individuals (50% female) between 20 and 50 years of age. Drug models for dolutegravir and rifampicin were verified against clinical observed data. The verified models were used to simulate the concurrent administration of rifampicin (600 mg) at steady state with dolutegravir (50 mg) administered twice daily in normal-weight (BMI 18.5-30 kg/m2), obese (BMI 30-40 kg/m2), and morbidly obese (BMI 40-50 kg/m2) individuals. Rifampicin was predicted to decrease dolutegravir area under the curve (AUC) by 72% in obese and 77% in morbidly obese vs 68% in nonobese individuals; however, dolutegravir trough concentrations were reduced to a similar extent (83% and 85% vs 85%). Twice-daily dolutegravir with rifampicin resulted in trough concentrations always above the protein-adjusted 90% inhibitory concentration for all BMI groups and above the 300 ng/mL threshold in a similar proportion for all BMI groups. The combined effect of obesity and induction by rifampicin was predicted to further decrease dolutegravir exposure but not the minimal concentration at the end of the dosing interval. Thus, dolutegravir 50 mg twice daily with rifampicin can be used in individuals with a high BMI up to 50 kg/m2.

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

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. 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. 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) by104%, while rifampin increased its oral clearance by 6.4-fold and decreased plasma AUC0-inf by 84%. 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.