Drug-drug Interaction Studies Research Articles

Effect of Carbamazepine on the Pharmacokinetics of Erdafitinib in Healthy Participants.

Erdafitinib, a selective and potent oral pan-FGFR inhibitor, is metabolized mainly through CYP2C9 and CYP3A4 enzymes. This phase 1, open-label, single-sequence, drug-drug interaction study evaluated the pharmacokinetics, safety, and tolerability of a single oral dose of erdafitinib alone and when co-administered with steady state oral carbamazepine, a dual inducer of CYP3A4 and CYP2C9, in 13 healthy adult participants (NCT04330248). Compared with erdafitinib administration alone, carbamazepine co-administration decreased total and free maximum plasma concentrations of erdafitinib (Cmax) by 35% (95% CI 30%-39%) and 22% (95% CI 17%-27%), respectively. The areas under the concentration-time curve over the time interval from 0 to 168hours, to the last quantifiable data point, and to time infinity (AUC168h, AUClast, AUCinf), were markedly decreased for both total erdafitinib (56%-62%) and free erdafitinib (48%-55%). The safety profile of erdafitinib was consistent with previous clinical studies in healthy participants, with no new safety concerns when administered with or without carbamazepine. Co-administration with carbamazepine may reduce the activity of erdafitinib due to reduced exposure. Concomitant use of strong CYP3A4 inducers with erdafitinib should be avoided.

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.

Pharmacokinetics and Safety of Atogepant Co-administered with Quinidine Gluconate in Healthy Participants: A Phase 1, Open-Label, Drug-Drug Interaction Study.

Atogepant, an oral calcitonin gene-related peptide receptor antagonist, is approved for the preventive treatment of migraine. Atogepant is a substrate of P-glycoprotein (P-gp), breast cancer resistance protein, organic anion transporting polypeptide transporters, and cytochrome P450 (CYP)3A4 and 2D6. Quinidine is a strong P-gp and CYP2D6 inhibitor. A phase 1 open-label study evaluated the effect of P-gp and CYP2D6 inhibition by quinidine on the pharmacokinetics of atogepant, and the safety and tolerability of atogepant and quinidine gluconate (QG) when co-administered and when given alone in 33 healthy adults. There was no significant change in the atogepant maximum plasma concentration with QG co-administration. The overall systemic exposure, the area under the plasma concentration-time curve (from time 0 to time t or to infinity), of atogepant increased by 25% when co-administered with QG. However, such an increase was not considered clinically relevant. Atogepant did not alter the mean plasma concentration of quinidine at steady state. The incidence of treatment-emergent adverse events (TEAEs) was highest when QG was administered alone (42.4%), which was primarily due to QT prolongation. Most TEAEs reported were mild in severity and resolved within 1-2 days. Co-administration of atogepant with QG did not result in any unexpected tolerability findings in this phase 1 study in healthy participants. The increase in atogepant exposure during QG co-administration could be due to inhibition of CYP2D6 (a minor contributor to atogepant clearance) as well as inhibition of P-gp.

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

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

Zibotentan Can Be Co-administered with Contraceptives Containing Ethinyl Estradiol and Levonorgestrel: A Pharmacokinetic Drug-Drug Interaction Study.

The endothelin A receptor antagonist zibotentan, combined with the sodium-glucose co-transporter-2 inhibitor dapagliflozin, is being investigated for the treatment of chronic kidney disease with high proteinuria. To allow women of childbearing potential access to this treatment, highly effective contraception is required and drug interactions compromising contraception reliability must be avoided. This study investigated the risk of pharmacokinetic (PK) interaction between zibotentan and the contraceptives ethinyl estradiol and levonorgestrel. A single-sequence, within-participant comparison study was conducted in 24 healthy women of non-childbearing potential, comparing the PK of ethinyl estradiol/levonorgestrel alone and with zibotentan. Single oral doses of 0.06 mg ethinyl estradiol/0.3 mg levonorgestrel were administered on Days 1 and 15; zibotentan 10 mg was dosed orally, once-daily through Days 6-19. PK profiles were determined and ethinyl estradiol/levonorgestrel PK was compared between Day 1 and 15 based on geometric least-squares mean ratios of PK parameters, including maximum observed concentration (Cmax) and area under the plasma concentration-time curve from zero to infinity (AUCinf). Co-administration with zibotentan did not affect ethinyl estradiol PK (geometric mean ratio [90% confidence interval] Cmax 1.05 [0.99-1.11], AUCinf 1.00 [0.96-1.05]), while a weak interaction (increased exposure) was observed for levonorgestrel (Cmax 1.12 [1.02-1.23], AUCinf 1.30 [1.21-1.39]), which was regarded as without clinical relevance. Plasma exposure of ethinyl estradiol/levonorgestrel was not reduced by multiple-dose zibotentan. In conclusion, contraception containing ethinyl estradiol/levonorgestrel is regarded possible under zibotentan-containing treatments. This expands choices for women of childbearing potential, supporting diversity in the ZENITH High Proteinuria trial.

The Use of Microdosing for In vivo Phenotyping of Cytochrome P450 Enzymes: Where Do We Stand? A Narrative Review.

Cytochrome P450 (CYP) enzymes play a central role in the elimination of approximately 80% of all clinically used drugs. Differences in CYP enzyme activity between individuals can contribute to interindividual variability in exposure and, therefore, treatment outcome. In vivo CYP enzyme activity could be determined with phenotyping. Currently, (sub)therapeutic doses are used for in vivo phenotyping, which can lead to side effects. The use of microdoses (100µg) for in vivo phenotyping for CYP enzymes could overcome the limitations associated with the use of (sub)therapeutic doses of substrates. The aim of this review is to provide a critical overview of the application of microdosing for in vivo phenotyping of CYP enzymes. A literature search was performed to find drug-drug interaction studies of CYP enzyme substrates that used microdoses of the respective substrates. A substrate was deemed sensitive to changes in CYP enzyme activity when the pharmacokinetics of the substrate significantly changed during inhibition and induction of the enzyme. On the basis of the currently available evidence, the use of microdosing for in vivo phenotyping for subtypes CYP1A2, CYP2C9, CYP2D6, and CYP2E1 is not recommended. Microdosing can be used for the in vivo phenotyping of CYP2C19 and CYP3A. The recommended microdose phenotyping test for CYP2C19 is measuring the omeprazole area-under-the-concentration-time curve over 24h (AUC0-24) after administration of a single 100µg dose. CYP3A activity could be best determined with a 0.1-75µg dose of midazolam, and subsequently measuring AUC extrapolated to infinity (AUC∞) or clearance. Moreover, there are two metrics available for midazolam using a limited sampling strategy: AUC over 10h (AUC0-10) and AUC from 2 to 4h (AUC2-4).

Unveiling Pharmacokinetics and Drug Interaction of Linagliptin and Pioglitazone HCl in Rat Plasma Using LC-MS/MS.

The linagliptin (LIN) and pioglitazone HCl (PIO) combination, currently undergoing phase III clinical trials for diabetes mellitus treatment, demonstrated significant improvements in glycemic control. However, the absence of an analytical method for simultaneous determination in biological fluids highlights a crucial gap. This underscores the pressing need for sensitive bioanalytical methods, emphasizing the paramount importance of developing such tools to advance diabetes management strategies and enhance patient care. Herein, a sensitive reverse-phase high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry method was developed for simultaneous determination of LIN and PIO in rat plasma using alogliptin as an internal standard. Chromatographic separation was performed on an Agilent Eclipse Plus C18 (4.6 × 100 mm, 3.5 μm) using an isocratic mobile phase system consisting of ammonium formate (pH 4.5) and methanol using an acetonitrile-induced protein precipitation technique for sample preparation. Multiple reaction monitoring in positive ion mode was used for quantitation of the precursor to production at m/z 473.2 → 419.9 for LIN, 357.1 → 134.2 for PIO, and 340.3 → 116.1 for ALO. The linearity range was 0.5 to 100 and 1 to 2000 ng/mL for LIN and PIO, respectively. The developed method was validated as per US-FDA guidelines and successfully applied to clinical pharmacokinetic and drug-drug interaction studies with a single oral administration of LIN and PIO in rat plasma. Pharmacokinetic parameters of LIN were significantly influenced by the concomitant administration of PIO and vice versa. Molecular modeling revealed the significant interaction of LIN and PIO with P-glycoprotein. Therefore, the drug-drug interaction between LIN and PIO deserves further study to improve drug therapy and prevent dangerous adverse effects.

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

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

CBD and THC in Special Populations: Pharmacokinetics and Drug-Drug Interactions.

Cannabinoid use has surged in the past decade, with a growing interest in expanding cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC) applications into special populations. Consequently, the increased use of CBD and THC raises the risk of drug-drug interactions (DDIs). Nevertheless, DDIs for cannabinoids, especially in special populations, remain inadequately investigated. While some clinical trials have explored DDIs between therapeutic drugs like antiepileptic drugs and CBD/THC, more potential interactions remain to be examined. This review summarizes the published studies on CBD and THC-drug interactions, outlines the mechanisms involved, discusses the physiological considerations in pharmacokinetics (PK) and DDI studies in special populations (including pregnant and lactating women, pediatrics, older adults, patients with hepatic or renal impairments, and others), and presents modeling approaches that can describe the DDIs associated with CBD and THC in special populations. The PK of CBD and THC in special populations remain poorly characterized, with limited studies investigating DDIs involving CBD/THC in these populations. Therefore, it is critical to evaluate potential DDIs between CBD/THC and medications that are commonly used in special populations. Modeling approaches can aid in understanding these interactions.

Assessment of the Effects of Abrocitinib on the Pharmacokinetics of Probe Substrates of Cytochrome P450 1A2, 2B6 and 2C19 Enzymes and Hormonal Oral Contraceptives in Healthy Individuals.

Abrocitinib is an oral small-molecule Janus kinase (JAK)-1 inhibitor approved for the treatment of moderate-to-severe atopic dermatitis. In vitro studies indicated that abrocitinib is a weak time-dependent inhibitor of cytochrome P450 (CYP)2C19/3A and a weak inducer of CYP1A2/2B6/2C19/3A. To assess the potential effect of abrocitinib on concomitant medications, drug-drug interaction (DDI) studies were conducted for abrocitinib with sensitive probe substrates of these CYP enzymes. The impact of abrocitinib on hormonal oral contraceptives (ethinyl estradiol and levonorgestrel), as substrates of CYP3A and important concomitant medications for female patients, was also evaluated. Three Phase 1 DDI studies were performed to assess the impact of abrocitinib 200 mg once daily (QD) on the probe substrates of: (1) 1A2 (caffeine), 2B6 (efavirenz) and 2C19 (omeprazole) in a cocktail study; (2) 3A (midazolam); and (3) 3A (oral contraceptives). After multiple doses of abrocitinib 200 mg QD, there is a lack of effect on the pharmacokinetics of midazolam, efavirenz and contraceptives. Abrocitinib increased the area under the concentration time curve from 0 to infinity (AUCinf) and the maximum concentration (Cmax) of omeprazole by approximately 189 and 134%, respectively. Abrocitinib increased the AUCinf of caffeine by 40% with lack of effect on Cmax. Based on the study results, abrocitinib is a moderate inhibitor of CYP2C19. Caution should be exercised when using abrocitinib concomitantly with narrow therapeutic index medicines that are primarily metabolized by CYP2C19 enzyme. Abrocitinib is a mild inhibitor of CYP1A2; however, the impact is not clinically relevant, and no general dose adjustment is recommended for CYP1A2 substrates. Abrocitinib does not inhibit CYP3A or induce CYP1A2/2B6/2C19/3A and does not affect the pharmacokinetics of contraceptives. ClinicalTrials.gov registration IDs: NCT03647670, NCT05067439, NCT03662516.

Oral Pharmacokinetic Drug-drug Interactions between Amifampridine and Acetaminophen in Rats

Amifampridine, the first-line medication for Lambert-Eaton myasthenic syndrome (LEMS), is extensively metabolized by N-acetyltransferase 2 (NAT2). Drug-drug interactions (DDIs) can occur when co-administered with a NAT2 inhibitor and amifampridine. Acetaminophen is a widely used analgesic for mild to moderate pain, which is also known as a NAT2 inhibitor. In this work, we studied the effects of acetaminophen on the amifampridine pharmacokinetics in rats. Both acetaminophen (300 mg/kg) and amifampridine (2 mg/kg) were administered orally. In acetaminophen-treated rats, the systemic exposure to amifampridine significantly increased, and the ratio of the area under the plasma concentration-time curve for 3-N-acetylamifampridine to amifampridine (AUCm/AUCp) decreased markedly, which is likely due to the inhibition of NAT2 by acetaminophen. Also, the urinary amount excreted was increased in the acetaminophen-treated group, but the renal clearance remained unchanged. This oral pharmacokinetic drug-drug interaction study showed that orally administered acetaminophen significantly inhibits the NAT2-based metabolism of amifampridine and may cause meaningful DDIs.

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.

Effect of Daridorexant on the Pharmacokinetics of Midazolam, and on the Pharmacokinetics and Pharmacodynamics of Warfarin in Healthy Male Subjects.

Daridorexant, a dual orexin receptor antagonist was recently approved for the treatment of insomnia at doses up to 50 mg once per night. This study investigated the effect of single-dose and multiple-dose daridorexant 50 mg at steady state on the pharmacokinetics (PK)of the cytochrome P450 (CYP) 3A4-sensitive substrate midazolam, and the effect of single-dose daridorexant 50 mg on the PK and pharmacodynamics(PD) of the CYP2C9-sensitive substrate warfarin. In this prospective, single-center, open-label, fixed-sequence, phase I, drug-drug interaction study, 18 healthy male subjects sequentially received Treatment A, B, and C in three periods. Treatment A consisted of a single oral concomitant administration of midazolam 2 mg and warfarin 25 mg on day 1 of the first period. Treatment B consisted of one oral administration of daridorexant 50 mg followed 1 h later by a single oral dose of midazolam 2 mg concomitantly with a single oral dose of warfarin 25 mg on day 1 and a once-daily oral administration of daridorexant 50 mg for 6 days of the second period. Treatment C consisted of a single oral administration of daridorexant 50 mg at steady state followed 1 h later by a single oral administration of midazolam 2 mg on day 1 of the third period. Blood samples were assessed for midazolam and S-warfarin PK, and PD (international normalized ratio and factor VII). Noncompartmental PK parameters and PD variables were evaluated with geometric mean ratios and 90% confidence intervals of Treatment B/A versus C/A for midazolam, and treatment B/A for warfarin. Safety and tolerability of each treatment were also assessed. Midazolam maximum plasma concentration(Cmax) and area under the plasma concentration-time curve from 0 to 24 h(AUC0-24) were 1.13- and 1.42-fold higher, respectively, after single-dose administration of daridorexant 50 mg compared to administration of midazolam alone, while Cmax and AUC0-24 were 1.12- and 1.35-fold higher, respectively, after administration of daridorexant 50 mg once daily at steady state. Terminal half-life and time to maximum plasma concentration were comparable between treatments. Daridorexant had no influence on the PK and PD of warfarin. All treatments were safe and well tolerated. Daridorexant at 50 mg is classified as a weak CYP3A4 inhibitor after single- and multiple-dose administration once daily at steady state. Daridorexant 50 mg did not induce CYP3A4 activity or inhibit CYP2C9 activity. This trial (NCT05480488) was registered on 29 July, 2022.

Methodological Considerations on the Use of Cohort Designs in Drug-Drug Interaction Studies in Pharmacoepidemiology

Abstract Purpose of Review The evidence regarding the clinical effects of drug-drug interactions (DDIs) is scarce and limited. Pharmacoepidemiologic studies could help fill in this important knowledge gap. Here, we review the pharmacoepidemiology of DDIs with a focus on cohort designs. We also highlight the decision-making process with respect to different aspects of cohort study design, potential biases that may arise during this decision process, and mitigation strategies. Recent Findings Considering the pharmacologic mechanism of the DDI of interest as well as of the object drug and the precipitant drug separately at the design stage of cohort studies for DDIs will help minimize major biases such as prevalent user bias and confounding by indication. Confounding by indication could also be mitigated by using control precipitants. Further, the correct assignment of the cohort entry date via the application of a time-varying exposure definition can help minimize immortal time bias and prevalent user bias. Minimization of these biases may also potentially be achieved with recently developed tools such as target trial emulation and the prevalent new-user design; however, more research is needed in the area. Summary Careful consideration of the underlying pharmacology and the specifics of study design will help minimize major biases in cohort studies that aim to assess the clinical effects of DDIs. Recent methodological developments from other areas of pharmacoepidemiology could further improve the internal validity of DDI 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.

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.

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).

Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict CYP3A-Mediated Drug Interaction between Saxagliptin and Nicardipine: Bridging Rat-to-Human Extrapolation.

The aim of this study was to predict the cytochrome P450 3A (CYP3A)-mediated drug-drug interactions (DDIs) between saxagliptin and nicardipine using a physiologically based pharmacokinetic (PBPK) model. Initially, in silico and in vitro parameters were gathered from experiments or the literature to construct PBPK models for each drug in rats. These models were integrated to predict the DDIs between saxagliptin, metabolized via CYP3A2, and nicardipine, exhibiting CYP3A inhibitory activity. The rat DDI PBPK model was completed by optimizing parameters using experimental rat plasma concentrations after co-administration of both drugs. Following co-administration in Sprague-Dawley rats, saxagliptin plasma concentration significantly increased, resulting in a 2.60-fold rise in AUC, accurately predicted by the rat PBPK model. Subsequently, the workflow of the rat PBPK model was applied to humans, creating a model capable of predicting DDIs between the two drugs in humans. Simulation from the human PBPK model indicated that nicardipine co-administration in humans resulted in a nearly unchanged AUC of saxagliptin, with an approximate 1.05-fold change, indicating no clinically significant changes and revealing a lack of direct translation of animal interaction results to humans. The animal-to-human PBPK model extrapolation used in this study could enhance the reliability of predicting drug interactions in clinical settings where DDI studies are challenging.

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.