Inflammation-mediated drug interactions of olokizumab and cytochrome P450 activities in patients with rheumatoid arthritis.

AIMS: In patients with rheumatoid arthritis (RA), interleukin (IL)-6 affects the activity of cytochrome P450 (CYP) enzymes. Treatment with anti-IL-6 therapy can reverse the IL-6-mediated downregulation of CYP enzymes, resulting in changes in plasma levels of CYP substrates. The primary objective of this study was to evaluate the impact of the IL-6 inhibitor olokizumab on the pharmacokinetics of CYP probe substrates in subjects with active RA. METHODS: Seventeen patients with active RA were orally administered a phenotyping cocktail of midazolam (CYP3A4 substrate), omeprazole (CYP2C19 substrate), warfarin (CYP2C9 substrate) and caffeine (CYP1A2 substrate) alone and 2 weeks after a single subcutaneous injection of 128 mg olokizumab. The pharmacokinetic parameters of each substrate were calculated using noncompartmental analysis. RESULTS: Sixteen of 17 enrolled patients received the complete doses of the cocktail drugs and olokizumab and were eligible for the pharmacokinetic evaluations. After single-dose administration of olokizumab, the exposure of midazolam and omeprazole decreased by 30-33% and 26-32%, respectively, compared to when the substrates were administered along via cocktail. In the presence of olokizumab, caffeine exposure increased by 19-23% compared to caffeine administration alone. There were no significant changes in S-warfarin exposure. CONCLUSION: In patients with active RA, olokizumab potentially reverses the IL-6-mediated suppression of CYP3A4 and CYP2C19. According to FDA guidance, olokizumab is considered a weak inducer of CYP3A4 and CYP2C19.

DKPE-GraphSYN: a drug synergy prediction model based on joint dual kernel density estimation and positional encoding for graph representation.

Introduction: Synergistic medication, a crucial therapeutic strategy in cancer treatment, involves combining multiple drugs to enhance therapeutic effectiveness and mitigate side effects. Current research predominantly employs deep learning models for extracting features from cell line and cancer drug structure data. However, these methods often overlook the intricate nonlinear relationships within the data, neglecting the distribution characteristics and weighted probability densities of gene expression data in multi-dimensional space. It also fails to fully exploit the structural information of cancer drugs and the potential interactions between drug molecules. Methods: To overcome these challenges, we introduce an innovative end-to-end learning model specifically tailored for cancer drugs, named Dual Kernel Density and Positional Encoding (DKPE) for Graph Synergy Representation Network (DKPEGraphSYN). This model is engineered to refine the prediction of drug combination synergy effects in cancer. DKPE-GraphSYN utilizes Dual Kernel Density Estimation and Positional Encoding techniques to effectively capture the weighted probability density and spatial distribution information of gene expression, while exploring the interactions and potential relationships between cancer drug molecules via a graph neural network. Results: Experimental results show that our prediction model achieves significant performance enhancements in forecasting drug synergy effects on a comprehensive cancer drug and cell line synergy dataset, achieving an AUPR of 0.969 and an AUC of 0.976. Discussion: These results confirm our model's superior accuracy in predicting cancer drug combinations, providing a supportive method for clinical medication strategy in cancer.

Warfarin and Polypharmacy Challenges in Sudan: Drug Interactions in Patient Cohort.

Purpose: Warfarin plays an important role in anticoagulation therapy despite the availability of the newest oral anticoagulants, and achieving optimal anticoagulation is challenging due to its narrow therapeutic range and variable dose. This study aimed to highlight polypharmacy and drug interactions in patients receiving warfarin therapy at Medani Heart Centre, Sudan. Methods: This retrospective hospital-based study was conducted from May 2017 to October 2018. Each concurrent medication prescribed for 104 patients was collected and checked for drug-drug interactions using Medscape Reference-Drug Interaction Checker. The data were analysed by using SPSS 20, and descriptive statistics were used. Results: The results revealed that 95.2% of patients had more than three medications in their profile, (3-5), (6-9) and more than 10 medications were prescribed for 40.4%, 44.2% and 10.6% of patients, respectively. A total of 93.3% of patients had drug-drug interactions, as follows: (1-5), (6-10), (11-15), (16-20) and more than 20 drug-drug interactions were found in 31.7%, 32.7%, 19.2%, 5.8% and 3.8% of patients, respectively. A total of 178 warfarin-drug interactions were identified in 88.5% of the patients. The INR ranged between 2 and 2.99 in 13.4% of patients, and INR values below 2 and above 5 were found in 44.2% and 21.2% of patients, respectively. Analgesics (n=54; 30.3%), cardiovascular drugs (n=51; 28.6%), and anticoagulants (n=46; 25.8%) were the most common drug classes that interact with warfarin. Significant and serious types of interactions with warfarin were found in 51% and 37.5% of patients, respectively. Conclusion: This study highlights the complexity of managing warfarin therapy amid prevalent polypharmacy. A substantial majority of patients experienced multiple drug interactions. The identification of significant and serious interactions emphasizes the need for vigilant management strategies, including improved communication among healthcare professionals and targeted education for both providers and patients, to enhance the safety and efficacy of warfarin therapy.

Preclinical pharmacokinetic characterization of amdizalisib, a novel PI3Kδ inhibitor for the treatment of hematological malignancies.

Amdizalisib, also named HMPL-689, a novel selective and potent PI3Kδ inhibitor, is currently under Phase II clinical development in China for treating hematological malignancies. The preclinical pharmacokinetics (PK) of amdizalisib were extensively characterized in vitro and in vivo to support the further development of amdizalisib. We characterized the plasma protein binding, blood-to-plasma partition ratio, cell permeability, hepatic microsomal metabolic stability, and drug-drug interaction potential of amdizalisib using in vitro experiments. In vivo PK assessment was undertaken in mice, rats, dogs, and monkeys following a single intravenous or oral administration of amdizalisib. The tissue distribution and excretion of amdizalisib were evaluated in rats. The PK parameters (CL and Vss) of amdizalisib in preclinical species (mice, rats, dogs, and monkeys) were utilized for the human PK projection using the allometric scaling (AS) approach. Amdizalisib was well absorbed and showed low-to-moderate clearance in mice, rats, dogs, and monkeys. It had high cell permeability without P-glycoprotein (P-gp) or breast cancer resistance protein (BCRP) substrate liability. Plasma protein binding of amdizalisib was high (approximately 90%). It was extensively distributed but with a low brain-to-plasma exposure ratio in rats. Amdizalisib was extensively metabolized in vivo, and the recovery rate of the prototype drug was low in the excreta. Amdizalisib and/or its metabolites were primarily excreted via the bile and urine in rats. Amdizalisib showed inhibition potential on P-gp but not on BCRP and was observed to inhibit CYP2C8 and CYP2C9 with IC50 values of 30.4 and 10.7 µM, respectively. It exhibited induction potential on CYP1A2, CYP2B6, CYP3A4, and CYP2C9. The preclinical data from these ADME studies demonstrate a favorable pharmacokinetic profile for amdizalisib, which is expected to support the future clinical development of amdizalisib as a promising anti-cancer agent.

Clinical Pharmacokinetics of Semaglutide: A Systematic Review.

Purpose: 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.

Drug-drug interaction and initial dosage optimization of aripiprazole in patients with schizophrenia based on population pharmacokinetics.

Background: The present study aimed to investigate the drug-drug interaction and initial dosage optimization of aripiprazole in patients with schizophrenia based on population pharmacokinetics. Research design and methods: A total of 119 patients with schizophrenia treated with aripiprazole were included to build an aripiprazole population pharmacokinetic model using nonlinear mixed effects. Results: The weight and concomitant medication of fluoxetine influenced aripiprazole clearance. Under the same weight, the aripiprazole clearance rates were 0.714:1 in patients with or without fluoxetine, respectively. In addition, without fluoxetine, for the once-daily aripiprazole regimen, dosages of 0.3 and 0.2 mg kg-1 day-1 were recommended for patients with schizophrenia weighing 40-95 and 95-120 kg, respectively, while for the twice-daily aripiprazole regimen, 0.3 mg kg-1 day-1 was recommended for those weighing 40-120 kg. With fluoxetine, for the once-daily aripiprazole regimen, a dosage of 0.2 mg kg-1 day-1 was recommended for patients with schizophrenia weighing 40-120 kg, while for the twice-daily aripiprazole regimen, 0.3 and 0.2 mg kg-1 day-1 were recommended for those weighing 40-60 and 60-120 kg, respectively. Conclusion: This is the first investigation of the effects of fluoxetine on aripiprazole via drug-drug interaction. The optimal aripiprazole initial dosage is recommended in patients with schizophrenia.

Hepatic injury and hepatic failure adverse events in 3,4-methylenedioxymethamphetamine users reported to the FDA Adverse Event Reporting System.

3,4-Methylenedioxymethamphetamine (MDMA) is being investigated in controlled clinical trials for use as an adjunct medication treatment for post-traumatic stress disorder. MDMA is metabolized by N-demethylation, primarily by CYP2D6, to its main inactive metabolite, 4-hydroxy-3-methoxymethamphetamine. It is also metabolized to a lesser extent by CYP1A2, CYP2B6, and CYP3A4 to its active metabolite, 3,4-methylenedioxyamphetamine. Considering the extensive hepatic metabolism and excretion, MDMA use in psychiatry raises concerns over drug-induced liver injury (DILI), a rare but dangerous event. Majority of the drugs withdrawn from the market for liver injury caused death or transplantation at frequencies under 0.01%. Unfortunately, markers for liver injury were not measured in most published clinical trials. At the same time, no visible DILI-related symptoms and adverse events were observed. Idiosyncratic DILI cases are rarely registered during clinical trials due to their rare nature. In this study, we surveyed a larger, over 1,500, and a more diverse set of reports from the FDA Adverse Event Reporting System and found 23 cases of hepatic injury and hepatic failure, in which MDMA was reported to be taken in addition to one or more substances. Interestingly, 22 out of 23 cases had one or more listed drugs with a known DILI concern based on the FDA's DILIrank dataset. Furthermore, only one report had MDMA listed as the primary suspect. Considering the nearly 20 million doses of MDMA used annually, this single report is insufficient for establishing a significant association with DILI.

Inhibitory effect of imperatorin on dabrafenib metabolism in vitro and in vivo.

Dabrafenib is a BRAF inhibitor that has been demonstrated to be efficacious in the treatment of melanoma and non-small-cell lung cancer patients with BRAF V600E mutations. The objective of this study was to investigate the effects of 51 traditional Chinese medicines on the metabolism of dabrafenib and to further investigate the inhibitory effect of imperatorin. The quantification of dabrafenib and its metabolite hydroxy-dabrafenib was carried out using a sensitive, rapid, and accurate assay method based on ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The results of in vitro experiments showed that 20 drugs inhibited the metabolism of dabrafenib by more than 80 %. In a further study of imperatorin on dabrafenib, the half-maximal inhibitory concentration (IC50) values of imperatorin on dabrafenib were 0.22 µM and 3.68 µM in rat liver microsomes (RLM) and human liver microsomes (HLM), respectively, while the inhibition mechanisms were non-competitive and mixed type inhibition, respectively. The results of in vivo experiments demonstrated that in the presence of imperatorin, the AUC(0-t), AUC(0-∞), Cmax, and Tmax of dabrafenib were increased by 2.38-, 2.26-, 1.05-, and 6.10-fold, respectively, while CLz/F was decreased by 67.9 %. In addition, Tmax of hydroxy-dabrafenib was increased by 1.4-fold. The results of the research showed that imperatorin had a consistent inhibitory effect on dabrafenib in vitro and in vivo. When the concurrent use of dabrafenib and imperatorin is unavoidable, clinicians should closely monitor for potential adverse events and make timely adjustments to the administered dosage.

DAS-DDI: A dual-view framework with drug association and drug structure for drug-drug interaction prediction.

In drug development and clinical application, drug-drug interaction (DDI) prediction is crucial for patient safety and therapeutic efficacy. However, traditional methods for DDI prediction often overlook the structural features of drugs and the complex interrelationships between them, which affect the accuracy and interpretability of the model. In this paper, a novel dual-view DDI prediction framework, DAS-DDI is proposed. Firstly, a drug association network is constructed based on similarity information among drugs, which could provide rich context information for DDI prediction. Subsequently, a novel drug substructure extraction method is proposed, which could update the features of nodes and chemical bonds simultaneously, improving the comprehensiveness of the feature. Furthermore, an attention mechanism is employed to fuse multiple drug embeddings from different views dynamically, enhancing the discriminative ability of the model in handling multi-view data. Comparative experiments on three public datasets demonstrate the superiority of DAS-DDI compared with other state-of-the-art models under two scenarios.

Evaluation of the effect of modafinil on the pharmacokinetics of encorafenib and binimetinib in patients with BRAF V600-mutant advanced solid tumors.

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.

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.

BACKGROUND: Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction between two drugs, but cannot directly determine their accurate risk level. METHODS: In this study, we propose a multi-class drug combination risk prediction model named AERGCN-DDI, utilizing a relational graph convolutional network with a multi-head attention mechanism. Drug-drug interaction events with varying risk levels are modeled as a heterogeneous information graph. Attribute features of drug nodes and links are learned based on compound chemical structure information. Finally, the AERGCN-DDI model is proposed to predict drug combination risk level based on heterogenous graph neural network and multi-head attention modules. RESULTS: To evaluate the effectiveness of the proposed method, five-fold cross-validation and ablation study were conducted. Furthermore, we compared its predictive performance with baseline models and other state-of-the-art methods on two benchmark datasets. Empirical studies demonstrated the superior performances of AERGCN-DDI. CONCLUSIONS: AERGCN-DDI emerges as a valuable tool for predicting the risk levels of drug combinations, thereby aiding in clinical medication decision-making, mitigating severe drug side effects, and enhancing patient clinical prognosis.

Evaluation of Sources of Drug Interaction Information for Nirmatrelvir/ritonavir.

The Japanese package insert (J-PI) for nirmatrelvir/ritonavir (N/r) (specially approved pharmaceutical) includes numerous warnings about drug interactions. However, discrepancies in the information on drug interaction are reported between J-PI and foreign databases. This study aimed to evaluate various information sources on N/r drug interactions. We categorized and compared information on N/r drug interactions from the J-PI, prescribing information from foreign regulatory agencies, guidance from the National Institutes of Health and University Health Network, the Ontario coronavirus disease 2019 (COVID-19) Science Advisory Table, University of Liverpool, Lexicomp, and the Japanese Society of Pharmaceutical Health Care and Sciences (JSPHCS). We assessed information quantity, missing data in J-PI, predicted change of the area under the blood concentration-time curve (AUC) for nirmatrelvir or co-administered drugs, and the information source consistency. From these information sources, we compiled a dataset with 115 contraindications and 203 precautions for N/r co-administration, and 51 contraindications are missing in J-PI. Among them, at least 12 drugs have large predicted AUC changes with N/r (AUC ≥5-fold or <1/5 of the baseline value). Nine of these 12 drugs are included as contraindications in Lexicomp and the JSPHCS. The consistency among the information sources is low. Information in the J-PI alone may be insufficient and Lexicomp or the JSPHCS guidelines should be useful because of their large amounts of information and wide coverage of drugs with large AUC changes. Due to low source consistency, multiple sources are needed for clinical management.

Plasma Venetoclax Concentrations in Patients with Acute Myeloid Leukemia Treated with CYP3A4 Inhibitors.

Venetoclax (VEN) is used in patients with acute myeloid leukemia (AML) and is primarily metabolized by CYP3A4, a major drug-metabolizing enzyme. Patients with AML simultaneously administered VEN and CYP3A4 inhibitors require a more appropriate management of drug-drug interactions (DDIs). Here, we report two cases of patients with AML (54-year-old man and 22-year-old woman) administrated VEN and CYP3A4 inhibitors, such as posaconazole, cyclosporine, or danazol. In the first case, we evaluated the appropriateness of timing for adjusting VEN dosage subsequent to the cessation of posaconazole. Consequently, modifying the VEN dosage in conjunction with the cessation of Posaconazole simultaneously may result in elevated plasma VEN levels. In the second case, plasma VEN concentrations were markedly elevated when co-administered with several CYP3A4 inhibitors. Additionally, in vitro assays were conducted for reverse translational studies to analyze CYP3A4 inhibition. CYP3A4 inhibition by combinatorial administration of cyclosporine A and danazol was demonstrated in vitro, which potentially explains the increasing plasma VEN concentrations observed in clinical settings. Although the acquisition of therapeutic effects is a major priority for patients, frequent therapeutic drug monitoring and dosage adjustments considering DDIs would be important factors in chemotherapy.

Minimal Involvement of P-gp and BCRP in Oral Absorption of Ensitrelvir, An Oral SARS-CoV-2 3C-like Protease Inhibitor, in a Non-Clinical Investigation.

P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) are important transporters causing drug-drug interaction (DDI). Here, we investigated the involvement of P-gp and BCRP in the oral absorption of ensitrelvir in non-clinical studies and estimated the DDI risk mediated by P-gp and BCRP inhibition in humans. Although ensitrelvir is an in vitro P-gp and BCRP substrate, it demonstrated high bioavailability in rats and monkeys after oral administration. Plasma exposures of ensitrelvir following oral administration were comparable in wild type (WT) and Bcrp (-/-) mice. On the other hand, the area under the plasma concentration-time curve (AUC) ratio of ensitrelvir in the Mdr1a/1b (-/-) mice to the WT mice was 1.92, indicating that P-gp, but not BCRP, was involved in the oral absorption of ensitrelvir. Based on our previous retrospective analyses, such a low AUC ratio (<3) in the Mdr1a/1b (-/-) mice indicates a minimal impact of P-gp on the oral absorption in humans. In conclusion, our studies demonstrate that the involvement of both P-gp and BCRP in the oral absorption of ensitrelvir is minimal, and suggest that ensitrelvir has a low risk for DDIs mediated by P-gp and BCRP inhibition in humans.

MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction.

The combinatorial therapy with multiple drugs may lead to unexpected drug-drug interactions (DDIs) and result in adverse reactions to patients. Predicting DDI events can mitigate the potential risks of combinatorial therapy and enhance drug safety. In recent years, deep models based on heterogeneous graph representation learning have attracted widespread interest in DDI event prediction and have yielded satisfactory results, but there is still room for improvement in prediction performance. In this study, we proposed a meta-path-based heterogeneous graph contrastive learning model, MPHGCL-DDI, for DDI event prediction. The model constructs two contrastive views based on meta-paths: an average graph view and an augmented graph view. The former represents that there are connections between drugs, while the latter reveals how the drugs connect with each other. We defined three levels of data augmentation schemes in the augmented graph view and adopted a combination of three losses in the model training phase: multi-relation prediction loss, unsupervised contrastive loss and supervised contrastive loss. Furthermore, the model incorporates indirect drug information, protein-protein interactions (PPIs), to reveal latent relations of drugs. We evaluated MPHGCL-DDI on three different tasks of two datasets. Experimental results demonstrate that MPHGCL-DDI surpasses several state-of-the-art methods in performance.

Icaritin exhibits potential drug-drug interactions through the inhibition of human UDP-glucuronosyltransferase in vitro.

Icaritin is a prenylflavonoid derivative of the genus Epimedium (Berberidaceae) and has a variety of pharmacological actions. Icaritin is approved by the National Medical Products Administration as an anticancer drug that exhibits efficacy and safety advantages in patients with hepatocellular carcinoma cells. This study aimed to evaluate the inhibitory effects of icaritin on UDP-glucuronosyltransferase (UGT) isoforms. 4-Methylumbelliferone (4-MU) was employed as a probe drug for all the tested UGT isoforms using in vitro human liver microsomes (HLM). The inhibition potentials of UGT1A1 and 1A9 in HLM were further tested by employing 17ß-estradiol (E2) and propofol (PRO) as probe substrates, respectively. The results showed that icaritin inhibits UGT1A1, 1A3, 1A4, 1A7, 1A8, 1A10, 2B7, and 2B15. Furthermore, icaritin exhibited a mixed inhibition of UGT1A1, 1A3, and 1A9, and the inhibition kinetic parameters (Ki) were calculated to be 3.538, 2.117, and 0.306 (µM), respectively. The inhibition of human liver microsomal UGT1A1 and 1A9 both followed mixed mechanism, with Ki values of 2.694 and 1.431 (µM). This study provides supporting information for understanding the drug-drug interaction (DDI) potential of the flavonoid icaritin and other UGT-metabolized drugs in clinical settings. In addition, the findings provide safety evidence for DDI when liver cancer patients receive a combination therapy including icaritin.

Effect of drug interactions with non-vitamin-K oral anticoagulants on thromboembolic events in patients with nonvalvular atrial fibrillation.

Introduction: Few real-world studies have investigated drug-drug interactions (DDIs) involving non-vitamin-K antagonist oral anticoagulants (NOACs) in patients with nonvalvular atrial fibrillation (NVAF). The interactions encompass drugs inducing or inhibiting cytochrome P450 3A4 and permeability glycoprotein. These agents potentially modulate the breakdown and elimination of NOACs. This study investigated the impact of DDIs on thromboembolism in this clinical scenario. Method: Patients who had NVAF and were treated with NOACs were selected as the study cohort from the National Health Insurance Research Database of Taiwan. Cases were defined as patients hospitalised for a thromboembolic event and who underwent a relevant imaging study within 7 days before hospitalisa-tion or during hospitalisation. Each case was matched with up to 4 controls by using the incidence density sampling method. The concurrent use of a cytochrome P450 3A4/permeability glycoprotein inducer or inhibitor or both with NOACs was identified. The effects of these interactions on the risk of thromboembolic events were examined with univariate and multivariate conditional logistic regressions. Results: The study cohort comprised 60,726 eligible patients. Among them, 1288 patients with a thromboembolic event and 5144 matched control patients were selected for analysis. The concurrent use of a cytochrome P450 3A4/permeability glycoprotein inducer resulted in a higher risk of thromboembolic events (adjusted odds ratio [AOR] 1.23, 95% confidence interval [CI] 1.004-1.51). Conclusion: For patients with NVAF receiving NOACs, the concurrent use of cytochrome P450 3A4/ permeability glycoprotein inducers increases the risk of thromboembolic events.

Pharmacokinetic assessment of low dose decitabine in combination therapies: Development and validation of a sensitive UHPLC-MS/MS method for murine plasma analysis.

Decitabine is a DNA methyltransferase inhibitor used in the treatment of acute myeloid leukemia and myelodysplastic syndrome. The notion that ongoing trials are presently exploring the combined use of decitabine, with or without the cytidine deaminase inhibitor cedazuridine, and other antileukemic drugs necessitates a comprehensive understanding of pharmacokinetic properties and an evaluation of drug-drug interaction liabilities. We report here the development and validation of a sensitive UHPLC-MS/MS method for quantifying decitabine in mouse plasma, which should be useful for such studies. The method involved a one-step protein precipitation extraction, and chromatographic separation on an XBridge HILIC column using gradient elution. The method was found to be robust, accurate, precise, and sufficiently sensitive (lower limit of quantitation, 0.4 ng/mL) to determine decitabine concentrations in microvolumes of plasma from mice receiving the agent orally or intravenously in the presence or absence of cedazuridine.

Evaluation of Drug-Drug Interaction Between Henagliflozin and Hydrochlorothiazide in Healthy Chinese Volunteers.

Purpose: Henagliflozin is an original, selective sodium-glucose cotransporter 2 (SGLT2) inhibitor. Hydrochlorothiazide (HCTZ) is a common anti-hypertensive drug. This study aimed to evaluate the potential interaction between henagliflozin and HCTZ. Methods: This was a single-arm, open-label, multi-dose, three-period study that was conducted in healthy Chinese volunteers. Twelve subjects were treated in three periods, period 1: 25 mg HCTZ for four days, period 2: 10 mg henagliflozin for four days and period 3: 25 mg HCTZ + 10 mg henagliflozin for four days. Blood samples and urine samples were collected before and up to 24 hours after drug administrations on day 4, day 10 and day 14. The plasma concentrations of henagliflozin and HCTZ were analyzed using LC-MS/MS. The urine samples were collected for pharmacodynamic glucose and electrolyte analyses. Tolerability was also evaluated. Results: The 90% CI of the ratio of geometric means (combination: monotherapy) for AUCτ,ss of henagliflozin and HCTZ was within the bioequivalence interval of 0.80-1.25. For henagliflozin, co-administration increased Css, max by 24.32% and the 90% CI of the GMR was (108.34%, 142.65%), and the 24-hour urine volume and glucose excretion decreased by 0.43% and 19.6%, respectively. For HCTZ, co-administration decreased Css, max by 19.41% and the 90% CI of the GMR was (71.60%, 90.72%), and the 24-hour urine volume and urinary calcium, potassium, phosphorus, chloride, and sodium excretion decreased by 11.7%, 20.8%, 11.8%, 11.9%, 22.0% and 15.5%, respectively. All subjects (12/12) reported adverse events (AEs), but the majority of theses AEs were mild and no serious AEs were reported. Conclusion: Although Css,max was affected by the combination of henagliflozin and HCTZ, there was no clinically meaningful safety interaction between them. Given these results, coadministration of HCTZ should not require any adaptation of henagliflozin dosing. Trial Registration: ClinicalTrials.gov NCT06083116.

A drug-drug interaction study and physiologically based pharmacokinetic modelling to assess the effect of an oral 5-lipoxygenase activating protein inhibitor on the pharmacokinetics of oral midazolam.

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.