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The document is a corrigendum for an article titled "Inhibitory effects of calcium channel blockers nisoldipine and nimodipine on ivacaftor metabolism and their underlying mechanism." The correction addresses errors in Figure 7 and Table 2 related to the unit of concentration values used. The corrected Ki values are now 3.26 and 5.87, respectively. The authors assure that these corrections do not impact the scientific conclusions of the original article. [Extracted from the article]

Objective: Darunavir (DRV) is a P-glycoprotein (P-gp) inhibitor. Dabigatran etexilate, prodrug of the anticoagulant dabigatran, is a P-gp probe substrate. This study evaluated the effect of single and multiple doses of DRV, coadministered with cobicistat (COBI) or ritonavir (rtv), on the pharmacokinetics (PK) of single-dose dabigatran etexilate. Methods: This was an open-label, fixed-sequence, single-center, 2-panel, phase 1 study in which healthy adult participants were equally divided over 2 panels. In panel 1, participants received single and multiple doses of DRV/COBI 800/150 mg coadministered with single-dose dabigatran etexilate 150 mg. In panel 2, participants received single and multiple doses of DRV 800 mg + rtv 100 mg coadministered with single-dose dabigatran etexilate 150 mg. Key PK parameters evaluated were maximum plasma concentration (Cmax) and area under the plasma concentration-time curve from time zero to infinity (AUCinf) for free and total dabigatran. Results: Overall, 28 participants were enrolled and treated (n = 14 per panel). Dabigatran Cmax and AUCinf increased 2.64-fold after a single dose of DRV/COBI and 1.99- and 1.88-fold, respectively, after multiple doses of DRV/COBI. Dabigatran Cmax and AUCinf increased 1.64- and 1.72-fold, respectively, after a single dose of DRV + rtv and 1.22- and 1.18-fold, respectively, after multiple doses of DRV + rtv. In both panels, the most commonly reported adverse events were diarrhea and headache. Conclusion: Findings of increased dabigatran exposure with DRV/COBI or DRV + rtv coadministration indicate an inhibitory effect of single-dose boosted DRV on P-gp, and a mixed inhibitory/inductive effect of multiple doses of boosted DRV on P-gp. Trial registration: ClinicalTrials.gov, NCT04208061. Registered December 19, 2019 [ABSTRACT FROM AUTHOR]

Ivacaftor is the first potentiator of the cystic fibrosis transmembrane conductance regulator (CFTR) protein approved for use alone in the treatment of cystic fibrosis (CF). Ivacaftor is primarily metabolized by CYP3A4 and therefore may interact with drugs that are CYP3A4 substrates, resulting in changes in plasma exposure to ivacaftor. The study determined the levels of ivacaftor and its active metabolite M1 by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). We screened 79 drugs and 19 severely inhibited ivacaftor metabolism, particularly two cardiovascular drugs (nisoldipine and nimodipine). In rat liver microsomes (RLM) and human liver microsomes (HLM), the half-maximal inhibitory concentrations (IC50) of nisoldipine on ivacaftor metabolism were 6.55 μM and 9.10 μM, respectively, and the inhibitory mechanism of nisoldipine on ivacaftor metabolism was mixed inhibition; the IC50 of nimodipine on ivacaftor metabolism in RLM and HLM were 4.57 μM and 7.15 μM, respectively, and the inhibitory mechanism of nimodipine on ivacaftor was competitive inhibition. In pharmacokinetic experiments in rats, it was observed that both nisoldipine and nimodipine significantly altered the pharmacokinetic parameters of ivacaftor, such as AUC(0-t) and CLz/F. However, this difference may not be clinically relevant. In conclusion, this paper presented the results of studies investigating the interaction between these drugs and ivacaftor in vitro and in vivo. The objective is to provide a rationale for the safety of ivacaftor in combination with other drugs. [ABSTRACT FROM AUTHOR]

Background: Voriconazole (VRZ) is involved in a variety of drug‒drug interactions (DDIs), but few studies have reported adverse events (AEs) associated with the DDIs of VRZ. The primary goal of this study was to analyse the potential risk factors for AEs caused by DDIs between VRZ and other drugs via the OpenVigil FDA platform and to provide a reference for preventing VRZ DDIs and monitoring clinically related adverse drug events. Methods: A retrospective pharmacovigilance study was conducted to investigate the AEs related to DDIs between VRZ and four categories of drugs: proton pump inhibitors (PPIs), non-steroidal anti-inflammatory drugs (NSAIDs), immunosuppressants, and other antibacterial drugs. AE information for the target drugs from the first quarter of 2004 to the third quarter of 2022 was downloaded from the OpenVigil FDA data platform. Four frequency statistical models—the reporting ratio method, Ω shrinkage measure model, combination risk ratio model, and the chi-square statistics model—were used to analyse the AEs related to DDIs and evaluate the correlation and influence of sex and age between the drug(s) and the target AEs detected. Results: A total of 38 drugs were included, with 262 AEs detected by at least one of the four models and 48 AEs detected by all four models. Some 77 detected AEs were significantly positively correlated with DDIs and were related to higher reporting rates of AEs than when used alone. Graft-versus-host disease was the AE that had the strongest correlation with the drug interaction between VRZ and immunosuppressants (tacrolimus, mycophenolate mofetil, cyclophosphamide, and cyclosporine), and multiple organ dysfunction syndrome was correlated with VRZ in combination with other antibacterial drugs (linezolid, meropenem, cefepime, and vancomycin). Significant sex and age differences in the target AEs were detected for five and nine target drugs, respectively. For VRZ in combination with linezolid, aggravated conditions and respiratory failure should be given more attention in male patients, and mycophenolate mofetil and respiratory failure in female patients. When conditions are aggravated, febrile neutropenia and septic shock should be of particular concern in patients over 18 years of age who use VRZ in combination with ceftazidime, ciprofloxacin, or cytarabine. In patients aged under 18, septic shock should be considered when VRZ is used in combination with meropenem and dexamethasone. Conclusion: AEs related to DDIs should receive more attention when VRZ is used in combination with PPIs (renal impairment), NSAIDs (constipation and renal failure), immunosuppressants (graft versus host disease, septic shock) and other antibacterial drugs (multiple organ dysfunction syndrome, febrile neutropenia, and respiratory failure). Considering the influence of sex and age differences in VRZ DDIs, these factors need to be considered when assessing the risk of AEs in patients receiving VRZ and other drugs. [ABSTRACT FROM AUTHOR]

Objective Background Methods Results Conclusion To evaluate the pharmacodynamic (PD) and pharmacokinetic (PK) interactions between zavegepant and sumatriptan in healthy adults.Zavegepant is a high‐affinity, selective, small‐molecule calcitonin gene–related peptide receptor antagonist administered as a nasal spray approved in the United States for the acute treatment of migraine. Triptans, including sumatriptan, are a different class of drugs for acute migraine treatment and are associated with a risk of increased blood pressure (BP). Hence, it is important to study the drug–drug interactions between zavegepant and sumatriptan due to potential coadministration in clinical settings.This was a Phase 1, single‐center, partially blind, randomized, placebo‐controlled, single‐arm study. Eligible participants were males aged ≥ 18 and ≤ 40 years or females aged ≥ 18 and ≤ 50 years. On Day 1, participants received sumatriptan 2 × 6 mg subcutaneous injections (1 h apart) and were then randomized (6:1 ratio) to receive zavegepant 2 × 10 mg nasal spray (1 in each nostril) or placebo on Days 2 and 3. On Day 4, zavegepant or placebo was coadministered with sumatriptan after the second sumatriptan injection. BP, PK, and safety were evaluated at pre‐specified time points.Forty‐two participants enrolled in the study received at least one dose of any treatment and were included in the safety analyses. Forty‐one participants who completed the study were included in the BP and PK analyses. The mean (standard deviation) time‐weighted average (TWA) of mean arterial pressure (MAP [sumatriptan + zavegepant 87.2 (6.8) vs. sumatriptan 86.9 (6.0)]), diastolic BP (DBP [sumatriptan + zavegepant 72.3 (6.8) vs. sumatriptan 72.1 (6.2)]), and systolic BP (SBP [sumatriptan + zavegepant 116.8 (10.2) vs. sumatriptan 116.2 (8.6)]) did not change following zavegepant and sumatriptan coadministration on Day 4 compared to sumatriptan alone on Day 1. Statistical comparisons of the TWA of MAP, DBP, and SBP between sumatriptan and zavegepant coadministration and sumatriptan alone were similar; the differences observed were 0.04 mmHg for MAP (90% confidence interval [CI]: −0.69, 0.77 mmHg), 0.00 mmHg for DBP (90% CI: −0.76, 0.76 mmHg), and 0.33 mmHg for SBP (90% CI: −0.97, 1.63 mmHg). Sumatriptan PK after sumatriptan and zavegepant coadministration versus sumatriptan alone was similar; the comparison ratios were 102.5% (90% CI: 100.7%, 104.2%) for AUC0‐inf and 104.1% (90% CI: 98.0%, 110.6%) for Cmax. A small difference in zavegepant PK exposure after sumatriptan and zavegepant coadministration versus zavegepant alone was not considered clinically relevant: the comparison ratios were 112.4% (90% CI: 103.4%, 122.3%) for AUC0–24 and 96.7% (90% CI: 88.9%, 105.2%) for Cmax. Overall, 90% (38/42) of participants experienced ≥ 1 treatment‐emergent adverse event that was mild or moderate in severity. All treatments were generally safe and well tolerated.Coadministration of zavegepant with sumatriptan was safe and without PD or PK interactions in healthy adults. [ABSTRACT FROM AUTHOR]

Riociguat, an orally soluble guanylate cyclase (sGC)-promoting drug, is mainly used in the clinical treatment of pulmonary hypertension (PH). In this study, a novel ultra-performance liquid chromatography-tandem mass spectrometry method was developed to quantify the concentrations of riociguat and its metabolite (M1) in plasma. The precision, stability, accuracy, matrix effect, and recovery of the methodology were satisfactory. Quercetin, a well-recognized compound, functions as a novel anticancer agent with the potential to alleviate symptoms of PH. Therefore, the potential interaction between quercetin and riociguat was investigated in this study. The levels of riociguat and M1 in rat plasma were measured using the method developed in this study to evaluate the interactions between riociguat and quercetin in rats. The results revealed that quercetin significantly inhibited riociguat and M1 metabolism with increased systemic exposure. [ABSTRACT FROM AUTHOR]

Background/Objectives: Rivaroxaban and dabigatran are commonly used for thromboembolic disease management in active cancer patients. However, limited research explores the impact of concurrent chemotherapy on the pharmacodynamics of direct oral anticoagulants (DOAC). The aim of our study was to evaluate the impact of combined chemotherapy with rivaroxaban and dabigatran on the pharmacodynamics in patients with diffuse large B-cell lymphoma (DLBCL).; Methods: This was a prospective, pharmacodynamic study. Eligible subjects were ≥18 years old, diagnosed with DLBCL and initiating R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) immunochemotherapy. The enrolled adults received either rivaroxaban (10 mg once daily) or dabigatran etixalate (110 mg twice daily). Plasma anti-factor Xa (FXa) in participants on rivaroxaban and diluted thrombin time (dTT) in participants on dabigatran were assessed over the dosing interval before and after R-CHOP administration. Pharmacodynamic parameters of rivaroxaban and dabigatran were determined using a non-compartmental analysis.; Results: Twenty-six adults participated, with twelve in the rivaroxaban group and fourteen in the dabigatran group. The mean age was 59 ± 14.4 years. In the rivaroxaban group, the AUEC of FXa inhibition showed no significant change after R-CHOP (mean difference 3.8 ng·h/mL, 95% confidence interval (CI) −155.4 to 163.0, p = 0.96). Similarly, in the dabigatran group, the AUEC of dTT remained unchanged post R-CHOP (mean difference 54.41 ng·h/mL, 95% CI −99.09 to 207.9 ng/mL, p = 0.46). However, the median time-to-peak dTT was significantly faster with R-CHOP (3 h, [min–max, 1.5–8] compared to without it (4 h, [min–max, 3–8], p = 0.04); Conclusions: Concurrent R-CHOP chemotherapy did not significantly impact FXa inhibition by rivaroxaban or dTT by dabigatran. The time-to-peak dTT was faster when dabigatran was administered with R-CHOP. [ABSTRACT FROM AUTHOR]

Cytochrome P450 enzymes (CYPs) play a crucial role in phase I metabolic reactions. The activity of CYPs would affect therapeutic efficacy and may even induce toxicity. Given the complex components of traditional Chinese medicine, it is important to understand the effect of active ingredients on CYPs activity to guide their prescription. This study aimed to evaluate the effect of polyphyllin H on the activity of CYPs major isoforms providing a reference for the clinical prescription of polyphyllin H and its source herbs. The effects of polyphyllin H were evaluated in pooled human liver microsomes using probe substrates of CYP1A2, 2A6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4 to determine their activities. The Lineweaver‐Burk was used to model the inhibition, and a time‐dependent inhibition experiment was performed to understand the characteristics of the inhibition. Polyphyllin H significantly suppressed the activity of CYP1A2, 2D6, and 3A4 with IC50 values of 6.44, 13.88, and 4.52 μM, respectively. The inhibition of CYP1A2 and 2D6 was best fitted with a competitive model, yielding the inhibition constant (Ki) values of 3.18 and 6.77 μM, respectively. The inhibition of CYP3A4 was fitted with the non‐competitive model with the Ki value of 2.38 μM. Moreover, the inhibition of CYP3A4 was revealed to be time‐dependent with the inhibition parameters inhibition constant (KI) and inactivation rate constant (Kinact) values of 2.26 μM−1 and 0.045 min−1. Polyphyllin H acted as a competitive inhibitor of CYP1A2 and 2D6 and a non‐competitive and time‐dependent inhibitor of CYP3A4. [ABSTRACT FROM AUTHOR]

Purpose: Midostaurin, approved for FLT3-mutated acute myeloid leukemia and advanced systemic mastocytosis, is mainly metabolized by cytochrome P450 (CYP) 3A4. Midostaurin exhibited potential inhibitory effects on P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion-transporting polyprotein 1B1, and CYP2D6 in in vitro studies. This study investigated the pharmacokinetic (PK) effects of midostaurin on P-gp (digoxin), BCRP (rosuvastatin) and CYP2D6 (dextromethorphan) substrates in healthy adults. Methods: This was an open-label, single-sequence, phase I clinical study evaluating the effect of single-dose midostaurin (100 mg) on the PK of digoxin and rosuvastatin (Arm 1), and dextromethorphan (Arm 2). Participants were followed up for safety 30 days after last dose. In addition, the effect of midostaurin on the PK of dextromethorphan metabolite (dextrorphan) was assessed in participants with functional CYP2D6 genes in Arm 2. Results: The effect of midostaurin on digoxin was minor and resulted in total exposure (AUC) and peak plasma concentration (Cmax) that were only 20% higher. The effect on rosuvastatin was mild and led to an increase in AUCs of approximately 37-48% and of 100% in Cmax. There was no increase in the primary PK parameters (AUCs and Cmax) of dextromethorphan in the presence of midostaurin. The study treatments were very well tolerated with no occurance of severe adverse events (AEs), AEs of grade ≥ 2, or deaths. Conclusion: Midostaurin showed only a minor inhibitory effect on P-gp, a mild inhibitory effect on BCRP, and no inhibitory effect on CYP2D6. Study treatments were well tolerated in healthy adults. [ABSTRACT FROM AUTHOR]

Purpose: To report an incident of a breakthrough deep vein thrombosis (DVT) and potential example of a drug-drug interaction in a patient treated with edoxaban and rifabutin who was being treated for respiratory tuberculosis. Case: A 76-year-old male presented with anemia requiring transfusion and subsequent shortness of breath that was later diagnosed to be respiratory tuberculosis. He experienced a prolonged hospital stay due to persistently positive Mycobacterium tuberculosis respiratory samples and a complicated social situation that required continuous hospitalization for approximately five months. During his treatment the patient was transitioned from apixaban to edoxaban due to a drug-drug interaction with rifabutin. He subsequently had a DVT while on edoxaban after two months of therapy that would require him to transition to warfarin. Conclusion: This case represents an example of a potentially significant drug-drug interaction between edoxaban and rifabutin. Other direct oral anticoagulants (DOACs) exhibit a potential drug-drug interaction that limit their effectiveness when used with rifamycins. This report describes the first known case of a patient experiencing a DVT after prolonged edoxaban use in combination with rifabutin. Treatment with DOACs for patients taking concomitant cytochrome P450 (CYP) inducers such as rifabutin may be more complicated than previously believed. [ABSTRACT FROM AUTHOR]

Purpose: The optimal design for pharmacoepidemiologic drug–drug interactions (DDIs) studies is unclear. Using the association between concomitant use of sulfonylureas and warfarin and the risk of severe hypoglycemia as a case study, a DDI with little or no clinical impact, we tested whether the prevalent new‐user design can be applied in the area. Methods: Among all patients initiating sulfonylureas in the UK's Clinical Practice Research Datalink (1998–2020), we identified those adding‐on warfarin while on a sulfonylurea. For each co‐exposed patient, we defined a prescription‐based exposure set including other sulfonylurea users not adding‐on warfarin (comparators). Within each exposure set, we matched each co‐exposed patient to five comparators on time‐conditional propensity scores (TCPS) and followed them using an as‐treated approach. Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) of severe hypoglycemia associated with concomitant use of sulfonylureas and warfarin compared to use of sulfonylureas alone. Sensitivity analyses addressed the impact of different potential sources of bias. Results: The study cohort included 17 890 patients co‐exposed to sulfonylureas and warfarin and 88 749 matched comparators. After TCPS matching, patient characteristics were well‐balanced between groups. Compared to use of sulfonylureas alone, concomitant use of sulfonylureas and warfarin was not associated with the risk of severe hypoglycemia (HR, 1.04; 95% CI, 0.92–1.17). Sensitivity analyses were consistent with the primary analysis (HRs ranging from 1.01 to 1.15, all not statistically significant). Conclusions: Our study suggests that the prevalent new‐user design could be used for the assessment of clinical effects of DDIs. [ABSTRACT FROM AUTHOR]

Background: Combining immune checkpoint and proton pump inhibitors is widely used in cancer treatment. However, the drug-drug interactions of these substances are currently unknown. This study aimed to explore drug-drug interactions associated with concomitant immune checkpoint and proton pump inhibitors. Methods: Data were obtained from the US Food and Drug Administration Adverse Event Reporting System from 2014 to 2023. Disproportionality analysis was used for data mining by calculating the reporting odds ratios (RORs) with 95% confidence intervals (95%Cls). The adjusted RORs (RORadj) were then analysed using logistic regression analysis, considering age, sex, and reporting year. Drug-drug interactions occur when a combination treatment enhances the frequency of an event. Further confirmation of the robustness of the findings was achieved using additive and multiplicative models, which are the two statistical methodologies for signal detection of DDIs using spontaneous reporting system. Results: The total number of reports on immune checkpoint combined with proton pump inhibitors was 4,276. Median patient age was 66 years (interquartile range [IQR]: 60–74 years). Significant interaction signals were observed for congenital, familial and genetic disorders (RORadj = 2.66, 95%CI, 1.38–5.14, additive models = 0.7322, multiplicative models = 3.5142), hepatobiliary disorders (RORcrude = 6.64, 95%CI, 5.82–7.58, RORadj = 7.10, 95%CI, 6.16–8.18, additive models = 2.0525, multiplicative models = 1.1622), metabolism and nutrition disorders (RORcrude = 3.27, 95%CI, 2.90–3.69, RORadj = 2.66, 95%CI, 2.30–3.08, additive models = 0.6194), and skin and subcutaneous tissue disorders (RORcrude = 1.41, 95%CI, 1.26–1.58, RORadj = 1.53, 95%CI, 1.34–1.75, additive models = 0.6927, multiplicative models = 5.3599). Subset data analysis showed that programmed death-1 combined with proton pump inhibitors was associated with congenital, familial, and genetic disorders; hepatobiliary disorders; and skin and subcutaneous tissue disorders. Programmed death ligand-1 combined with proton pump inhibitors was associated with adverse reactions of metabolism and nutrition disorders. Cytotoxic T-lymphocyte antigen-4 combined with proton pump inhibitors was associated with congenital, familial, and genetic disorders, and skin and subcutaneous tissue disorders. Conclusions: Based on real-world data, four Standardized MedDRA Query System Organ Class toxicities were identified as drug-drug interactions associated with combining immune checkpoint and proton pump inhibitors. Clinicians should be cautious when administering these drugs concomitantly. Preclinical trials and robust clinical studies are required to explore the mechanisms and relationships underlying interactions, thus improving understanding of drug-drug interactions associated with this combination therapy. [ABSTRACT FROM AUTHOR]

Vatiquinone, a 15‐lipoxygenase inhibitor, is in development for patients with Friedreich's ataxia. Physiologically based pharmacokinetic (PBPK) modeling addressed drug–drug interaction gaps without additional studies. A PBPK model (Simcyp Simulator version 21, full model) was developed using parameters obtained from in vitro studies, in silico estimation and optimization, and two clinical studies. A venous blood dosing model best characterized vatiquinone lymphatic absorption. Apparent oral clearance (CL/F) was used to optimize intrinsic clearance (CLint). Intestinal availability (Fg) was estimated using the hybrid flow term (Qgut), unbound fraction in the enterocytes (fugut), and gut intrinsic metabolic clearance (CLuG,int). Renal clearance (CLR) was set to zero. Assuming an Fa of 1, CYP3A4 contribution (fmCYP3A4) was further optimized. The PBPK model was verified with two clinical studies and demonstrated that it adequately characterized vatiquinone PK. As a perpetrator, the model predicted no risk for vatiquinone to significantly alter the drug exposures of CYP3A4 and CYP1A2 substrates as evident bynegligible reduction in both midazolam and caffeine area under the curve (AUC)inf and Cmax. As a victim, the model predicted that vatiquinone exposures are weakly influenced by moderate CYP3A4 inhibitors and inducers. With fluconazole coadministration, vatiquinone AUCinf and Cmax increased by nearly 50% and 25%, respectively. With efavirenz coadministration, vatiquinone AUCinf and Cmax decreased by approximately 20% and 10%, respectively. Results suggested that vatiquinone does not significantly impact CYP3A4 and CYP1A2 substrates and that moderate CYP3A4 inhibitors and inducers weakly impact vatiquinone AUC. [ABSTRACT FROM AUTHOR]

Background: Tacrolimus is widely used to treat pediatric nephrotic range proteinuria (NRP). Diltiazem, a CYP3A4/5 inhibitor, is often administered with tacrolimus, affecting its pharmacokinetic profile. The impact of this combination on tacrolimus exposure, particularly in CYP3A5*3 genetic polymorphism, remains unclear in pediatric NRP patients. This study aimed to evaluate the effects of diltiazem on tacrolimus pharmacokinetics, focusing on the CYP3A5*3 polymorphism. Methods: We conducted a retrospective clinical study involving pediatric NRP patients, divided into two groups: those receiving tacrolimus with diltiazem and those receiving tacrolimus alone. Propensity score matching (PSM) was used to balance the baseline characteristics between the groups. We compared daily dose-adjusted trough concentrations (C0/D) of tacrolimus in both the original and PSM cohorts. The influence of diltiazem on tacrolimus C0/D, stratified by CYP3A5*3 genetic polymorphism, was assessed in a self-controlled case series study. Results: Before PSM, the tacrolimus C0/D in patients taking diltiazem was significantly higher compared to those with tacrolimus alone (75.84 vs. 56.86 ng/mL per mg/kg, P = 0.034). This finding persisted after PSM (75.84 vs. 46.93 ng/mL per mg/kg, P= 0.028). In the self-controlled case study, tacrolimus C0/D elevated about twofold (75.84 vs. 34.76 ng/mL per mg/kg, P < 0.001) after diltiazem administration. CYP3A5 expressers (CYP3A5*1/*1 and *1/*3) and CYP3A5 non-expressers (CYP3A5*3/*3) experienced a 1.8-fold and 1.3-fold increase in tacrolimus C0/D when combined with diltiazem, respectively. Conclusion: Diltiazem significantly increased tacrolimus C0/D, with CYP3A5*3 expressers showing higher elevations than non-expressers among pediatric NRP patients. These findings highlight the importance of personalized tacrolimus therapy based on CYP3A5*3 genotypes in pediatric patients taking diltiazem. [ABSTRACT FROM AUTHOR]

Objective: The metabolism- and transporter-based drug-drug interactions (DDIs) between mycophenolate mofetil (MMF) and co-administered medications may be key factors for the high individual variability in MMF exposure. This study systematically assessed the influence of co-medications on the mycophenolic acid (MPA) pharmacokinetic (PK) process in vitro, particularly to provide mechanistic evidence of the metabolic interaction among steroids, cyclosporine (CsA), and MMF. Methods: Based on a previous study, we hypothesized that there are three main DDI pathways affecting MMF PK in vivo. A human hepatocyte induction study, transporter substrate/inhibition study using human embryonic kidney 293 cells, and multidrug resistance-associated protein 2 (MRP2) substrate/inhibition study using vesicle membrane were conducted to assess the mechanistic evidence of the metabolic interaction in triple therapies. The potential DDI risks associated with seven medications commonly co-administered with MMF in clinical practice were further evaluated. Results: The in vitro results suggested that prednisolone, the active metabolite of prednisone, induces the enzymatic activity of uridine 5'-diphosphoglucuronosyltransferase (UGT), particularly the UGT1A9 and UGT2B7 isoforms, resulting in increased metabolism of MPA to MPA glucuronide (MPAG). This induction potential was not observed in CsA-treated human hepatocytes. CsA inhibits organic anion-transporting polypeptide (OATP) 1B1- and OATP1B3- mediated MPAG. Prednisolone and CsA showed no inhibitory effect on MRP2- mediated MPAG efflux. Salvia miltiorrhiza significantly inhibited organic aniontransporting polypeptide and OAT 3 activities, suggesting that it affects the hepatic uptake and renal excretion of MPAG, causing increased MPAG exposure in vivo. Conclusion: These identified factors may contribute to the high inter-individual variability in MMF exposure and facilitate further development of mechanistic MMF PK models and individualized therapies. [ABSTRACT FROM AUTHOR]

This study aimed to determine changes in the hydrolysis of vicagrel, a substrate drug of arylacetamide deacetylase (Aadac) and carboxylesterase 2 (Ces2), in P-glycoprotein (P-gp)-deficient or P-gp-inhibited mice and to elucidate the mechanisms involved. Male wild-type (WT) and P-gp knock-out (KO) mice were used to investigate the systemic exposure of vicagrel thiol active metabolite H4 and platelet response to vicagrel, and the mRNA and protein expression levels of intestinal Aadac and Ces2. Moreover, WT mice were administered vicagrel alone or in combination with elacridar (a potent P-gp inhibitor) to determine drug-drug interactions. Compared with WT mice, P-gp KO mice exhibited significant increases in the systemic exposure of H4, the protein expression levels of intestinal Aadac and Ces2, and inhibition of ADP-induced platelet aggregation by vicagrel. Further, the H4 exposure was positively correlated with intestinal Aadac protein expression levels but did not vary with short-term inhibition of P-gp efflux activity by elacridar. P-gp-deficient mice, rather than elacridar-treated mice, exhibited significant upregulation of intestinal Aadac and Ces2 and thus, enhanced metabolic activation of and platelet response to vicagrel, suggesting that the metabolic activation of vicagrel may vary with P-gp deficiency, not P-gp inhibition, in mice. [ABSTRACT FROM AUTHOR]

Purpose: Patient sedation and analgesia are vital for safety and comfort during extracorporeal membrane oxygenation (ECMO). However, adsorption by the circuit may alter drug pharmaco-kinetics and remains poorly characterized. This study is the first to examine the concentrations of DEX and MDZ in the presence of drug–drug interactions using an in vitro extracorporeal circuit system that incorporates a polymer-coated polyvinyl chloride tube, but not a membrane oxygenator. Methods and Results: Nine in vitro extracorporeal circuits were prepared using polymer-coated PVC tubing. Once the circuits were primed and running, either a single drug or two drugs were injected as boluses into the circuit with three circuits per drug. Drug samples were drawn following injection at 2, 5, 15, 30, 60, and 120 min and at 4, 12, and 24 h. They were then analyzed using high-performance liquid chromatography with mass spectrometry. When compared with an injection of DEX alone, the combination of DEX and MDZ is highly changed, with DEX and MDZ affecting the availability of free drugs in the circuit. Conclusions: The change of DEX and MDZ concentrations was confirmed by a combination of both drugs as compared with either single-infusion DEX or MDZ in an in vitro extracorporeal circuit. Drug–drug interactions developed between DEX and MDZ through albumin in an extracorporeal circuit; as a result, the unbounded drugs might change in the circuit. [ABSTRACT FROM AUTHOR]

Background: A clinical drug-drug interaction (DDI) study was designed to evaluate the effect of multiple doses of modafinil, a moderate CYP3A4 inducer at a 400 mg QD dose, on the multiple oral dose pharmacokinetics (PK) of encorafenib and its metabolite, LHY746 and binimetinib and its metabolite, AR00426032. Methods: This study was conducted in patients with BRAF V600-mutant advanced solid tumors. Treatment of 400 mg QD modafinil was given on Day 15 through Day 21. Encorafenib 450 mg QD and binimetinib 45 mg BID were administered starting on Day 1. PK sampling was conducted from 0 to 8 h on Day 14 and Day 21. Exposure parameters were calculated for each patient by noncompartmental analysis and geometric least-squares mean ratio. Corresponding 90% confidence intervals were calculated to estimate the magnitude of effects. Results: Among 11 PK evaluable patients, encorafenib Cmax and AUClast were decreased in presence of steady-state modafinil by 20.2% and 23.8%, respectively. LHY746 exposures were not substantially changed in the presence of steady-state modafinil. Conclusion: The results from this clinical study indicate modafinil 400 mg QD had a weak effect on encorafenib PK. Based on these results, encorafenib can be coadministered with a moderate CYP3A4 inducer without dosing adjustment. Clinical trial registration: ClinicalTrials.gov NCT03864042, registered 6 March 2019. [ABSTRACT FROM AUTHOR]

Aims: Early clinical studies have indicated that the pharmacokinetics of Atuliflapon (AZD5718) are time and dose dependent. The reason(s) for these findings is(are) not fully understood, but pre‐clinical profiling suggests that time‐dependent CYP3A4 inhibition cannot be excluded. In clinical practice, Atuliflapon will be co‐administered with CYP3A4 substrates; thus, it is important to determine the impact of Atuliflapon on the pharmacokinetics (PK) of CYP3A4 substrates. The aim of this study was to evaluate the effect of Atuliflapon on the pharmacokinetics of a sensitive CYP3A4 substrate, midazolam, and to explore if the time‐/dose‐dependent effect seen after repeated dosing could be an effect of change in CYP3A4 activity. Methods: Open‐label, fixed‐sequence study in healthy volunteers to assess the PK of midazolam alone and in combination with Atuliflapon. Fourteen healthy male subjects received single oral dose of midazolam 2 mg on days 1 and 7 and single oral doses of Atuliflapon (125 mg) from days 2 to 7. A physiologically based pharmacokinetic (PBPK) model was developed to assess this drug–drug interaction. Results: Mean midazolam values of maximum plasma concentration (Cmax) and area under the curve (AUC) to infinity were increased by 39% and 56%, respectively, when co‐administered with Atuliflapon vs. midazolam alone. The PBPK model predicted a 27% and 44% increase in AUC and a 23% and 35% increase in Cmax of midazolam following its co‐administrations with two predicted therapeutically relevant doses of Atuliflapon. Conclusions: Atuliflapon is a weak inhibitor of CYP3A4; this was confirmed by the validated PBPK model. This weak inhibition is predicted to have a minor PK effect on CYP3A4 metabolized drugs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Ritonavir (RTV), which is used in combination with nilmatrelvir (NMV) to treat coronavirus disease 2019 (COVID-19), inhibits cytochrome P450 (CYP) 3A, thereby increasing blood tacrolimus (TAC) levels through a drug–drug interaction (DDI). We experienced a case in which a DDI between the two drugs led to markedly increased blood TAC levels, resulting in vasospastic angina (VSA) and acute kidney injury (AKI). Rifampicin (RFP) was administered to induce CYP3A and promote TAC metabolism. A 60-year-old man with dermatomyositis who was taking 3 mg/day TAC contracted COVID-19. The patient started oral NMV/RTV therapy, and he was admitted to the hospital after 4 days because of chest pain and AKI. On day 5, his blood TAC level increased markedly to 119.8 ng/mL. RFP 600 mg was administered once daily for 3 days, and his blood TAC level decreased to the therapeutic range of 9.6 ng/mL on day 9, leading to AKI improvement. Transient complete atrioventricular block and nonsustained ventricular tachycardia were present during chest pain. In the coronary spasm provocation test, complete occlusion was observed in the right coronary artery, leading to a diagnosis of VSA. VSA and AKI are possible side effects of high blood TAC levels caused by DDI, and attention should be paid to cardiovascular side effects such as VSA and AKI associated with increased blood levels of TAC when it is used together with NMV/RTV. When blood levels of TAC increase, oral RFP can rapidly decrease TAC blood levels and potentially reduce its toxicity. [ABSTRACT FROM AUTHOR]

Identifying drug–drug interactions (DDIs) is an important aspect of drug design research, and predicting DDIs serves as a crucial guarantee for avoiding potential adverse effects. Current substructure‐based prediction methods still have some limitations: (i) The process of substructure extraction does not fully exploit the graph structure information of drugs, as it only evaluates the importance of different radius substructures from a single perspective. (ii) The process of constructing drug representations has overlooked the significant impact of relation embedding on optimizing drug representations. In this work, we propose a substructure‐aware graph neural network incorporating relation features (RFSA‐DDI) for DDI prediction, which introduces a directed message passing neural network with substructure attention mechanism based on graph self‐adaptive pooling (GSP‐DMPNN) and a substructure‐aware interaction module incorporating relation features (RSAM). GSP‐DMPNN utilizes graph self‐adaptive pooling to comprehensively consider node features and local drug information for adaptive extraction of substructures. RSAM interacts drug features with relation representations to enhance their respective features individually, highlighting substructures that significantly impact predictions. RFSA‐DDI is evaluated on two real‐world datasets. Compared to existing methods, RFSA‐DDI demonstrates certain advantages in both transductive and inductive settings, effectively handling the task of predicting DDIs for unseen drugs and exhibiting good generalization capability. The experimental results show that RFSA‐DDI can effectively capture valuable structural information of drugs more accurately for DDI prediction, and provide more reliable assistance for potential DDIs detection in drug development and treatment stages. [ABSTRACT FROM AUTHOR]

Prostate cancer (PC) represents the second most common diagnosed cancer in men. The burden of diagnosis and long-term treatment may frequently cause psychiatric disorders in patients, particularly depression. The most common PC treatment option is androgen deprivation therapy (ADT), which may be associated with taxane chemotherapy. In patients with both PC and psychiatric disorders, polypharmacy is frequently present, which increases the risk of drug–drug interactions (DDIs) and drug-related adverse effects. Therefore, this study aimed to conduct a pharmacoepidemiologic study of the concomitant administration of PC drugs and psychotropics using three drug interaction databases (Lexicomp®, drugs.com®, and Medscape®). This study assayed 4320 drug–drug combinations (DDCs) and identified 814 DDIs, out of which 405 (49.63%) were pharmacokinetic (PK) interactions and 411 (50.37%) were pharmacodynamic (PD) interactions. The most common PK interactions were based on CYP3A4 induction (n = 275, 67.90%), while the most common PD interactions were based on additive torsadogenicity (n = 391, 95.13%). Proposed measures for managing the identified DDIs included dose adjustments, drug substitutions, supplementary agents, parameters monitoring, or simply the avoidance of a given DDC. A significant heterogenicity was observed between the selected drug interaction databases, which can be mitigated by cross-referencing multiple databases in clinical practice. [ABSTRACT FROM AUTHOR]

Objectives: To describe the pharmacokinetic (PK) characteristics of nirmatrelvir/ritonavir in renal transplant recipients and explore the potential factors that related to the PK variance of nirmatrelvir/ritonavir and its interaction with calcineurin inhibitor (CNI). Methods: Renal transplant recipients treated with CNI and nirmatrelvir/ritonavir were prospectively enrolled. Steady-state plasma concentrations of nirmatrelvir/ritonavir were determined by high-performance liquid chromatography-tandem mass spectrometry, and the PK parameters were calculated using non-compartmental analysis. Spearman correlation analysis was used for exploring influencing factors. Results: A total of eight recipients were enrolled; for nirmatrelvir and ritonavir, AUC/dose was 0.24179 ± 0.14495 and 0.06196 ± 0.03767 μg·h·mL-1·mg-1. Red blood cell (RBC), hematocrit (Ht), hemoglobins (Hb), and creatinine clearance (Ccr) were negatively correlated with AUC/dose of nirmatrelvir, while Ccr, CYP3A5 genotype, and CYP3A4 genotype were related to the AUC/dose of ritonavir. Ccr was negatively correlated with the C0/dose of tacrolimus (TAC) after termination of nirmatrelvir/ritonavir (rs = -0.943, p = 0.008). Conclusions: The PK characteristics of nirmatrelvir/ritonavir vary greatly among renal transplant recipients. Factors including Ccr and CYP3A5 genotype were related to the in vivo exposure of nirmatrelvir/ritonavir. During the whole process before and after nirmatrelvir/ritonavir therapy, it is recommended to adjust the CNI basing on renal function to avoid CNI toxicity exposure. [ABSTRACT FROM AUTHOR]

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 168 hours, 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. [ABSTRACT FROM AUTHOR]

Cytochrome P450 enzymes are a superfamily of enzymes responsible for the metabolism of a variety of medicines and xenobiotics. Among the Cytochrome P450 family, five isozymes that include 1A2, 2C9, 2C19, 2D6, and 3A4 are most important for the metabolism of xenobiotics. Inhibition of any of these five CYP isozymes causes drug-drug interactions with high pharmacological and toxicological effects. So, the inhibition or non-inhibition prediction of these isozymes is of great importance. Many techniques based on machine learning and deep learning algorithms are currently being used to predict whether these isozymes will be inhibited or not. In this study, three different molecular or substructural properties that include Morgan, MACCS and Morgan (combined) and RDKit of the various molecules are used to train a distinct SVM model against each isozyme (1A2, 2C9, 2C19, 2D6, and 3A4). On the independent dataset, Morgan fingerprints provided the best results, while MACCS and Morgan (combined) achieved comparable results in terms of balanced accuracy (BA), sensitivity (Sn), and Mathews correlation coefficient (MCC). For the Morgan fingerprints, balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4 on an independent dataset ranged between 0.81 and 0.85, 0.61 and 0.70, 0.72 and 0.83, respectively. Similarly, on the independent dataset, MACCS and Morgan (combined) fingerprints achieved competitive results in terms of balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4, which ranged between 0.79 and 0.85, 0.59 and 0.69, 0.69 and 0.82, respectively. [ABSTRACT FROM AUTHOR]

A drug–drug interaction (DDI) trial of cytochrome P450 3A (CYP3A) is a necessary part of early‐phase trials of drugs mainly metabolized by this enzyme, but CYP3A DDI clinical trials do not have a standard design, especially for Chinese people. We aimed to offer specific recommendations for CYP3A DDI clinical trial design. This was an open, three‐cycle, self‐controlled study. Healthy subjects were given different administration strategies of CYP3A4 perpetrators. In each cycle, blood samples were collected before and within 24 h after the administration of midazolam, the CYP3A indicator substrate. The plasma concentrations of midazolam and 1‐hydroxymidazolam was obtained using liquid chromatography tandem mass spectrometry assay. For CYP3A inhibition, itraconazole exposure with a loading dose could increase the exposure of midazolam by 3.21‐fold based on maximum plasma concentration (Cmax), 8.37‐fold based on area under the curve Pharmacology Research & Perspectives for review only from zero to the time point (AUC0–t), and 11.22‐fold based on area under the curve from zero to infinity (AUC0–∞). The data were similar for itraconazole pretreatment without a loading dose. For CYP3A induction, the exposure of rifampin for 7 days decreased the plasma concentration of midazolam ~0.27‐fold based on Cmax, ~0.18‐fold based on AUC0–t, and ~0.18‐fold based on AUC0–∞. Midazolam exposure did not significantly change when the pretreatment of rifampin increased to 14 days. This study showed that itraconazole pretreatment for 3 days without a loading dose was enough for CYP3A inhibition, and pretreatment with rifampin for 7 days could induce near‐maximal CYP3A levels. [ABSTRACT FROM AUTHOR]

Detailed data on safety associated with drug–drug interactions (DDIs) between Linezolid (LZD) and other antibiotics are limited. The aim of this study was to investigate the safety signals related to these DDIs and to provide a reference for clinically related adverse drug event monitoring. Adverse event (AE) information from 1 January 2004 to 16 June 2022 of the target antibiotics including LZD using alone or in combination with LZD was extracted from the OpenVigil FDA data platform for safety signal analysis. The combined risk ratio model, reporting ratio method, Ω shrinkage measure model, and chi‐square statistics model were used to analyze the safety signals related to DDIs. Meanwhile, we evaluated the correlation and the influence of sex and age between the drug(s) and the target AE detected. There were 18991 AEs related to LZD. There were 2293, 1726, 4449, 821, 2431, 1053, and 463 AE reports when LZD was combined with amikacin, voriconazole, meropenem, clarithromycin, levofloxacin, piperacillin‐tazobactam, and azithromycin, respectively. Except for azithromycin, there were positive safety signals related to DDIs between LZD and these antibiotics. These DDIs might influence the incidence of 13, 16, 7, 7, 6, and 15 types of AEs, respectively, and is associated with higher reporting rates of AEs compared with use alone. Moreover, sex and age might influence the occurrence of AEs. We found that the combinations of LZD and other antibiotics are related to multiple AEs, such as hepatotoxicity, drug resistance and electrocardiogram QT prolonged, but further research is still required to investigate their underlying mechanisms. This study can provide a new reference for the safety monitoring of LZD combined with other antibiotics in clinical practice. [ABSTRACT FROM AUTHOR]

Bicyclol is a hepatoprotective agent widely used for treating chronic hepatitis and drug-induced liver injuries in clinics. The purpose of the study was to elucidate the contribution of CYP450 enzymes to the metabolism of bicyclol using the relative activity factor approach. After incubation with human liver microsomes and recombinant human liver CYP450 enzymes, the calculated contribution of CYP3A4 and 2C19 to the metabolism of bicyclol was 85.6–90.3% and 9.2–9.7%, respectively. The metabolism was interrupted in the presence of CYP3A4 and 2C19 selective inhibitors. These findings help to predict or avoid metabolic drug–drug interactions or toxicity in clinical applications of bicyclol. [ABSTRACT FROM AUTHOR]

Background: Drug-drug interactions (DDIs) increase the incidence of adverse drug reactions (ADRs). In a previous report, we revealed that the incidence of potential DDIs due to the same CYP molecular species in one prescription exceeds 90% among patients taking six or more drugs and that CYP3A4 markedly influences the increase in the number of potential DDIs in clinical practice. However, the factors contributing to an increased number of potential DDIs in prescriptions from multiple clinical departments remain poorly clarified. Methods: This observational study was performed at five pharmacies in Okayama Prefecture, Japan. Patients who visited these pharmacies from 11 April 2022 to 24 April 2022 were included, except those who had prescriptions only from a single clinical department. A stratified analysis was performed to determine the incidence of CYP3A4-related potential DDIs according to the number of drugs taken. Additionally, factors associated with an increase in the number of drugs involved in CYP3A4-related potential DDIs were identified using multiple linear regression analysis. In this study, potential DDIs for the prescription data subdivided by clinical department, containing two or more drugs, were used as control data. Results: Overall, 372 outpatients who received prescriptions from multiple clinical departments were included in the current study. The number of drugs contributing to CYP3A4-related potential DDIs increased with an increase in the number of clinical departments. Notably, in cases taking fewer than six drugs, prescriptions from multiple clinical departments had a higher frequency of CYP3A4-related potential DDIs than those in prescriptions subdivided by clinical department. Multiple regression analysis identified "Cardiovascular agents", "Agents affecting central nervous system", and "Urogenital and anal organ agents" as the top three drug classes that increase CYP3A4-related potential DDIs. Conclusion: Collectively, these results highlight the importance of a unified management strategy for prescribed drugs and continuous monitoring of ADRs in outpatients receiving prescriptions from multiple clinical departments even if the number of drugs taken is less than six. [ABSTRACT FROM AUTHOR]

Drug efficacy and effectiveness are assessed respectively through clinical trials and pharmaco-epidemiological studies. However, relative and absolute benefits of drugs are distinct measures that must be considered in relation to the baseline risk of disease incidence, complication or progression. On the other hand, adverse drug reactions are independent of the basic risk but depend on the characteristics of the population treated. Given these prerequisites, how can we balance the benefits and risks of drugs? We use the example of therapeutics evaluated during Covid to describe how assessing the benefit-risk balance of drugs is a complex process. Clinical trials are not designed to identify rare adverse events, underscoring the necessity for a pharmacovigilance system. Evaluating the balance between the benefits and risks of drugs is an ongoing process, demanding the simultaneous analysis of data from clinical trials, potential drug-drug interactions, pharmacovigilance monitoring and pharmaco-epidemiological studies, to identify potential safety concerns. In addition, pharmacologists must play a major role in educating the general public about drugs, aiding in the accurate interpretation of the benefit-risk balance and preventing misinformation. [ABSTRACT FROM AUTHOR]

Purpose: We assessed the differential effect of clarithromycin, a strong inhibitor of cytochrome P450 (CYP) 3A4 and P-glycoprotein, on the pharmacokinetics of a regular dose of edoxaban and on a microdose cocktail of factor Xa inhibitors (FXaI). Concurrently, CYP3A activity was determined with a midazolam microdose. Methods: In an open-label fixed-sequence trial in 12 healthy volunteers, the pharmacokinetics of a microdosed FXaI cocktail (μ-FXaI; 25 μg apixaban, 50 μg edoxaban, and 25 μg rivaroxaban) and of 60 mg edoxaban before and during clarithromycin (2 x 500 mg/d) dosed to steady-state was evaluated. Plasma concentrations of study drugs were quantified using validated ultra-performance liquid chromatography–tandem mass spectrometry methods. Results: Therapeutic clarithromycin doses increased the exposure of a therapeutic 60 mg dose of edoxaban with a geometric mean ratio (GMR) of the area under the plasma concentration-time curve (AUC) of 1.53 (90 % CI: 1.37–1.70; p < 0.0001). Clarithromycin also increased the GMR (90% CI) of the exposure of microdosed FXaI apixaban to 1.38 (1.26–1.51), edoxaban to 2.03 (1.84–2.24), and rivaroxaban to 1.44 (1.27–1.63). AUC changes observed for the therapeutic edoxaban dose were significantly smaller than those observed with the microdose (p < 0.001). Conclusion: Clarithromycin increases FXaI exposure. However, the magnitude of this drug interaction is not expected to be clinically relevant. The edoxaban microdose overestimates the extent of the drug interaction with the therapeutic dose, whereas AUC ratios for apixaban and rivaroxaban were comparable to the interaction with therapeutic doses as reported in the literature. Trial Registration: EudraCT Number: 2018-002490-22 [ABSTRACT FROM AUTHOR]

Purpose: Abiraterone, apalutamide, darolutamide and enzalutamide are second-generation hormone therapies used for advanced prostate cancer; the majority of patients receiving these treatments are elderly, poly-medicated patients. Since their first market authorizations, their pharmacokinetic (PK) characteristics are increasingly well known. A potential risk of drug-drug interaction (DDI), especially with cardiovascular drugs, needs to be considered. In the case of antithrombotics, treatment imbalance can lead to severe consequences. Objectives: To describe PK profiles of hormone therapies and antithrombotics and to predict DDIs and potentially related clinical events. Methods: PK profiles (CYP450 and P-gp substrate, inducer or inhibitor) are described by cross-referencing data sources (summary of product characteristics, European public assessment reports, PubMed database, Micromedex®, etc.); a description of the potential interactions with anti-cancer drugs for each DDI and related clinical events is provided. We discuss management recommendations, including those set out in international guidelines. Results: Antithrombotics are mainly metabolized by CYP 2C9, 2C19 or 3A4. For abiraterone (CYP 2C8, 2D6 inhibitor) and darolutamide (CYP 3A4 inducer), no interaction was identified with antithrombotics. For apalutamide (CYP 2C9, 2C19, 3A4 and P-gp inducer) and enzalutamide (CYP 2C9, 2C19, 3A4 inducer and P-gp inhibitor), several PK interactions were identified with antithrombotics, which could lead to various clinical events (haemorrhage or thromboembolism). Conclusion: Numerous interactions are expected between enzalutamide or apalutamide and antithrombotics, for which management should be deployed on a case-by-case basis. PK and pharmaco-epidemiological studies could shed light on whether or not there are clinically significant events related to DDIs with antithrombotics. [ABSTRACT FROM AUTHOR]

Background: We examined whether the pharmacodynamic drug–drug interaction between esaxerenone and trimethoprim enhances the hyperkalemic effect. Methods: A retrospective observational study was conducted to identify patients >18 years undertaking esaxerenone alone or esaxerenone plus trimethoprim at Mie University Hospital from May 2019 to December 2022. We performed propensity score-matching (1:1) to compare between-group differences in the maximum change in serum potassium levels (ΔK) using the Mann–Whitney U test. For esaxerenone plus trimethoprim, Spearman's correlation coefficients were used to examine correlations between ΔK and variables, including changes in blood urea nitrogen (ΔBUN), serum creatinine levels (ΔCr), and weekly trimethoprim cumulative dose. Results: Out of propensity score-matched groups (n=8 each), serum potassium levels significantly increased after administration of esaxerenone alone (4.4 [4.2 to 4.7] meq/L to 5.2 [4.7 to 5.4] meq/L, p=0.008) and esaxerenone plus trimethoprim (4.2 [4.0 to 5.1] meq/L to 5.4 [4.7 to 5.5] meq/L, p=0.023). ΔK did not significantly differ between the groups (esaxerenone alone; 0.6 [0.3 to 0.9] meq/L vs. esaxerenone plus trimethoprim; 1.0 [0.4 to 1.3] meq/L, p=0.342). ΔK positively correlated with ΔBUN (r=0.988, p<0.001) or ΔCr (r=0.800, p=0.017). There was a trend of correlation of ΔK with a weekly cumulative trimethoprim dose (r=0.607, p=0.110). Conclusions: The hyperkalemic effect of the drug–drug interaction between esaxerenone and trimethoprim is not notable and related to renal function and trimethoprim dosage. [ABSTRACT FROM AUTHOR]

Objective & methods: This study tested associations of genotype-predicted activity of CYP3A4, other pharmacogenes, SLC28A7 (rs11648166) and ALPPL2 (rs28845026) with systemic concentrations of the endocrine therapies anastrozole and fulvestrant in SWOG S0226 trial participants. Results: Participants in the anastrozole-only arm with low CYP3A4 activity (i.e. CYP3A4*22 carriers) had higher systemic anastrozole concentrations than patients with high CYP3A4 activity (β-coefficient = 10.03; 95% CI: 1.42, 18.6; p = 0.025). In an exploratory analysis, participants with low CYP2C9 activity had lower anastrozole concentrations and higher fulvestrant concentrations than participants with high CYP2C9 activity. Conclusion: Inherited genetic variation in CYP3A4 and CYP2C9 may affect concentrations of endocrine therapy and may be useful to personalize dosing and improve treatment outcomes.

ObjectiveThe present study aims to explore the initial dosage optimization of sertraline in pediatric major depressive disorder (MDD) patients based on model-informed precision dosing (MIPD).MethodsA total of 111 pediatric MDD patients treated with sertraline were included for analysis using MIPD. Sertraline concentration levels, physiological and biochemical indexes of pediatric MDD patients, combined drug information were included in the construction of model.ResultsWeight and zopiclone co-administration influenced sertraline clearance in pediatric MDD patients. With the same weight, the sertraline clearance rates were 0.453:1 in patients with, or without zopiclone, respectively. Furthermore, without zopiclone, for once-daily sertraline scheme, the dosages of 4.0, and 3.0 mg/kg/day were suggested for pediatric MDD patients with body weight of 30–38.5, and 38.5–80 kg, respectively; for twice-daily sertraline scheme, the dosage of 2.0 mg/kg/day was suggested for pediatric MDD patients with body weight of 30–80 kg. With zopiclone, for once-daily sertraline scheme, the dosage of 1.0 mg/kg/day was suggested for pediatric MDD patients with body weight of 30–80 kg; for twice-daily sertraline scheme, the dosage of 0.5 mg/kg/day was suggested for pediatric MDD patients with body weight of 30–80 kg.ConclusionThis study first explored the initial dosage optimization of sertraline in pediatric MDD patients based on MIPD, and recommended the optimal sertraline initial dosage in pediatric MDD patients based on zopiclone co-administration.

Background/objectivePreventive medications are crucial in migraine prevention. In cases of refractory migraine headaches, multiple medications may be required. We seek to identify a comprehensive list of preventive migraine headache medications that can be used as two, three, and four drug combinations without drug–drug interactions.MethodsWe compiled a list of prevention medications from Szperka et al.’s “Migraine Care in the Era of COVID-19” as well as American Headache Society’s 2018 and 2021 “Consensus Statements on Integrating New Migraine Treatments into Clinical Practice.” We obtained all possible two to four combinations of prevention medications through this list. We then filtered out all combinations containing at least one interaction based on DrugBank database and also identified least to most interacting medications.ResultsA total of 26 unique prevention medications are identified. This results in a total of 325 combinations of two preventives, 2,600 combinations of three preventives, and 14,950 combinations of four preventives. There are a total of 124, 146, and 0 non-interacting two, three, and four preventive combinations, respectively. All except 16 combinations of pick-twos can be placed within a pick-three combinations. The resulting distinct non-interacting medications can be represented by a condensed list of 162 unique combinations of medications. CGRP antagonists, Botulinum toxin A, melatonin, and candesartan are least interacting.ConclusionThis list of migraine preventive medications without drug–drug interactions is a useful tool for clinicians seeking to manage refractory headaches more effectively by implementing an evidence-based polypharmacy.

IntroductionSulfatinib is a novel oral tyrosine kinase inhibitor (TKI) with selective inhibition of fibroblast growth factor (FGFR), colony-stimulating factor 1 receptor (CSF-1R) and vascular endothelial growth factor receptor (VEGFR) 1, 2, and 3. It has been approved for the therapy of neuroendocrine tumors arising in the non-pancreatic (December 2020) and pancreatic (June 2021) glands. Until now, there has no research on the determination of sulfatinib in biological medium by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method.MethodsThe current study validated a sensitive and reliable quantitative detection of sulfatinib in plasma using UPLC-MS/MS for the first time, and investigated the interaction with myricetin in rats. Acetonitrile was used to precipitate the plasma protein, and lenvatinib was employed as the internal standard (IS).ResultsThe method demonstrated that sulfatinib presented high linearity over the concentration of 11–2,000 ng/mL with the lower limit of quantification (LLOQ) of 1 ng/mL. It was validated methodologically that the precision, matrix effect, stability, accuracy and extraction recovery were all within the allowable values. Moreover, male Sprague-Dawley (SD) rats were assigned randomly to assess the interaction between sulfatinib (30 mg/kg) and myricetin (50 mg/kg). Nevertheless, no significant differences of the main pharmacokinetic parameters were revealed. This may be due to insufficient doses of myricetin, or failure of myricetin to act in a timely manner in vivo.DiscussionThe findings contributed to a better understanding of the metabolism and drug-drug interaction of sulfatinib, but the presence or absence of interactions needs to be confirmed by further studies.

Background This study investigated drug–drug interactions in patients with atrial fibrillation taking both a direct oral anticoagulant (DOAC) and an antiarrhythmic drug. Methods and Results Using data from the National Health Insurance database (2012–2018), we identified 78 805 patients with atrial fibrillation on DOACs, with 24 142 taking amiodarone, 8631 taking propafenone, 2784 taking dronedarone, 297 taking flecainide, 177 taking sotalol, and 42 772 on DOACs alone. Patients with bradycardia, heart block, heart failure, mitral stenosis, prosthetic valves, or incomplete data were excluded. Propensity score matching compared those taking both DOACs and antiarrhythmic drugs with those on DOACs alone. There was an increased risk of major bleeding in patients concomitantly taking DOACs with amiodarone when compared with matched patients taking DOACs alone (hazard ratio [HR],1.13 [95% CI, 1.04–1.23]; P=0.0044), particularly in patients taking dabigatran (HR, 1.19 [95% CI, 1.03–1.38]; P=0.0175). No significant difference in bleeding risk was found for propafenone, dronedarone, flecainide, or sotalol. The small sample sizes in the flecainide and sotalol groups limit interpretation. Notably, intracranial bleeding risk was higher in patients on DOACs and amiodarone, regardless of age. Additionally, patients

BackgroundAlthough super-boosted lopinavir/ritonavir (LPV/r; ratio 4:4 instead of 4:1) is recommended for infants living with HIV and receiving concomitant rifampicin, in clinical practice, many different LPV/r dosing strategies are applied due to poor availability of pediatric separate ritonavir formulations needed to superboost. We evaluated LPV pharmacokinetics in infants with HIV receiving LPV/r dosed according to local guidelines in various sub-Saharan African countries with or without rifampicin-based tuberculosis (TB) treatment.MethodsThis was a 2-arm pharmacokinetic substudy nested within the EMPIRICAL trial (#NCT03915366). Infants aged 1-12 months recruited into the main study were administered LPV/r according to local guidelines and drug availability either with or without rifampicin-based TB treatment; during rifampicin cotreatment, they received double-dosed (ratio 8:2) or semisuperboosted LPV/r (adding a ritonavir 100 mg crushed tablet to the evening LPV/r dose). Six blood samples were taken over 12 hours after intake of LPV/r.ResultsIn total, 14/16 included infants had evaluable pharmacokinetic curves; 9/14 had rifampicin cotreatment (5 received double-dosed and 4 semisuperboosted LPV/r). The median (IQR) age was 6.4 months (5.4-9.8), weight 6.0 kg (5.2-6.8), and 10/14 were male. Of those receiving rifampicin, 6/9 infants (67%) had LPV Ctrough

Abstract Background Among the oral antivirals used for treating patients with mild-to-moderate novel coronavirus disease 2019 (COVID-19), nirmatrelvir/ritonavir (NMV/RTV) and ensitrelvir (ESV) are inhibitors of cytochrome P450 (CYP) 3A, and therefore, can cause drug–drug interactions with concomitant medications. Tacrolimus (TAC), a substrate of CYP3A4/5, is administered for a long period to prevent rejection after kidney transplantation. TAC should be discontinued while using NMV/RTV because blood TAC levels significantly increase when these drugs are concomitantly administered. However, the influence of ESV on blood TAC levels has not yet been reported, and the management of TAC doses during the use of ESV remains unclear. Case presentation We experienced three kidney transplant recipients with COVID-19, whose blood trough levels of TAC increased by the concomitant use of NMV/RTV or ESV. In two patients administering NMV/RTV, blood trough levels of TAC increased more than tenfold after combination therapy, whereas in one patient administering ESV, TAC level increased approximately threefold. Conclusions These cases suggest that TAC administration should be discontinued during NMV/RTV treatment to maintain blood TAC levels within the therapeutic range, and a reduced TAC dose is sufficient during ESV treatment.

Jin Wang, Chaoying Hu, Lan Zhang Department of Pharmacy, Phase I Clinical Trial Center, Xuanwu Hospital Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Chaoying Hu; Lan Zhang, Email hucarol@126.com; xwzhanglan@126.comPurpose: SHR6390 is an oral, potent and selective small-molecule CDK4/6 inhibitor for the treatment of human breast, ovarian and colon cancer. Previous studies have shown that SHR6390 in combination with rifampicin, a potent inducer of CYP3A4, significantly reduces exposure levels. Therefore, we further investigated the effect of efavirenz, a moderate CYP3A4 inducer, on a single oral dose of SHR6390 in healthy volunteers.Patients and Methods: Twenty healthy subjects were enrolled in this single-center, open, single-dose, self-controlled DDI study. On Day 1, subjects received a single oral dose of 150mg SHR6390; on Day 8-26, subjects received 600 mg efavirenz orally at night, with a single dose of 150 mg SHR6390 on Day 22. Blood samples for pharmacokinetic analyses were collected.Results: The geometric mean ratios of the maximum concentration(Cmax) and the area under the concentration curve from zero to infinity (AUC0-inf) between combination therapy and SHR6390 monotherapy (combination therapy/SHR6390 monotherapy) and their 90% confidence intervals were 0.562 (0.482, 0.654) and 0.328 (0.278, 0.386), respectively. This indicates that the Cmax and AUC0 inf of SHR6390 decreased by approximately 43.8% and 67.2%, respectively. Oral administration of 150 mg SHR6390 alone or together with efavirenz was safe and tolerable in healthy subjects.Conclusion: It is suggested that under the action of the moderate CPY3A4 inducer efavirenz, the exposure AUC of SHR6390 exhibits a moderate level of induction. It is recommended to avoid concomitant administration of moderate inducers of CYP3A4 during treatment with SHR6390.Trial Registration: http://www.chinadrugtrials.org.cn/index.html, CTR20211571/ https://classic.clinicaltrials.gov, NCT04973020.Keywords: SHR6390, efavirenz, drug-drug interaction, pharmacokinetics, tolerability

Xi-Ding Yang,1,2 Yong-Yu Yang1,3 1Department of Pharmacy, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People’s Republic of China; 2Phase I Clinical Trial Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People’s Republic of China; 3Hunan Provincial Engineering Research Central of Translational Medical and Innovative Drug, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People’s Republic of ChinaCorrespondence: Yong-Yu Yang, Department of Pharmacy, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People’s Republic of China, Email yongyuyang@csu.edu.cnPurpose: The aim of this review was to provide all the pharmacokinetic data for semaglutide in humans concerning its pharmacokinetics after subcutaneously and oral applications in healthy and diseased populations, to provide recommendations for clinical use.Methodology: The PubMed and Embase databases were searched to screen studies associated with the pharmacokinetics of semaglutide. The pharmacokinetic parameters included area under the curve plasma concentrations (AUC), maximal plasma concentration (Cmax), time to Cmax, half-life (t1/2), and clearance. The systematic literature search retrieved 17 articles including data on pharmacokinetic profiles after subcutaneously and oral applications of semaglutide, and at least one of the above pharmacokinetic parameter was reported in all included studies.Results: Semaglutide has a predictable pharmacokinetic profile with a long t1/2 that allows for once-weekly subcutaneous administration. The AUC and Cmax of both oral and subcutaneous semaglutide increased with dose. Food and various dosing conditions including water volume and dosing schedules can affect the oral semaglutide exposure. There are limited drug–drug interactions and no dosing adjustments in patients with upper gastrointestinal disease, renal impairment or hepatic impairment. Body weight may affect semaglutide exposure, but further studies are needed to confirm this.Conclusion: This review encompasses all the pharmacokinetic data for subcutaneous and oral semaglutide in both healthy and diseased participants. The existing pharmacokinetic data can assist in developing and evaluating pharmacokinetic models of semaglutide and will help clinicians predict semaglutide dosages. In addition, it can also help optimize future clinical trials.Keywords: Pharmacokinetics, semaglutide, curve plasma concentrations, type 2 diabetes, obesity, glucagon-like peptide-1, drug–drug interaction