Physiologically based pharmacokinetic modeling of CYP2C8 substrate rosiglitazone and its metabolite to predict metabolic drug-drug interaction.

Rosiglitazone is an activator of nuclear peroxisome proliferator-activated (PPAR) receptor gamma used in the treatment of type 2 diabetes mellitus. The elimination of rosiglitazone occurs mainly via metabolism, with major contribution by enzyme cytochrome P450 (CYP) 2C8. Primary routes of rosiglitazone metabolism are N-demethylation and hydroxylation. Modulation of CYP2C8 activity by co-administered drugs lead to prominent changes in the exposure of rosiglitazone and its metabolites. Here, we attempt to develop mechanistic parent-metabolite physiologically based pharmacokinetic (PBPK) model for rosiglitazone. Our goal is to predict potential drug-drug interaction (DDI) and consequent changes in metabolite N-desmethyl rosiglitazone exposure. The PBPK modeling was performed in the PKSim® software using clinical pharmacokinetics data from literature. The contribution to N-desmethyl rosiglitazone formation by CYP2C8 was delineated using vitro metabolite formation rates from recombinant enzyme system. Developed model was verified for prediction of rosiglitazone DDI potential and its metabolite exposure based on observed clinical DDI studies. Developed model exhibited good predictive performance both for rosiglitazone and N-desmethyl rosiglitazone respectively, evaluated based on commonly acceptable criteria. In conclusion, developed model helps with prediction of CYP2C8 DDI using rosiglitazone as a substrate, as well as changes in metabolite exposure. In vitro data for metabolite formation can be successfully utilized to translate to in vivo conditions.

Grace periods and exposure misclassification in self-controlled case-series studies of drug-drug interactions.

The self-controlled case-series (SCCS) research design is increasingly used in pharmacoepidemiologic studies of drug-drug interactions (DDIs), with the target of inference being the incidence rate ratio (IRR) associated with concomitant exposure to the object plus precipitant drug versus the object drug alone. While day-level drug exposure can be inferred from dispensing claims, these inferences may be inaccurate, leading to biased IRRs. Grace periods (periods assuming continued treatment impact after days' supply exhaustion) are frequently used by researchers, but the impact of grace period decisions on bias from exposure misclassification remains unclear. Motivated by an SCCS study examining the potential DDI between clopidogrel (object) and warfarin (precipitant), we investigated bias due to precipitant or object exposure misclassification using simulations. We show that misclassified precipitant treatment always biases the estimated IRR toward the null, whereas misclassified object treatment may lead to bias in either direction or no bias, depending on the scenario. Further, including a grace period for each object dispensing may unintentionally increase the risk of misclassification bias. To minimize such bias, we recommend 1) avoiding the use of grace periods when specifying object drug exposure episodes; and 2) including a washout period following each precipitant exposed period.

Hyperkalemic effect of drug-drug interaction between esaxerenone and trimethoprim in patients with hypertension: a pilot study.

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.

Development of UPLC-MS/MS method for studying the pharmacokinetic interactions of fuzuloparib with curcumin in rats.

Fuzuloparib is a novel orally bioactive poly-ADP-ribose polymerase inhibitor (PARPi), which was approved by the Chinese Regulatory Agency (CRA) in 2020 for the treatment of platinum-sensitive recurrent ovarian, fallopian tube, and primary peritoneal cancers. This study firstly presents a rapid and accurate ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method for analyzing the levels of fuzuloparib and its major metabolite (SHR165202), and to investigate drug-drug interaction between fuzuloparib and curcumin in vitro and in vivo studies. After protein precipitation with acetonitrile, mobile phase consisted of acetonitrile and 0.1 % formic acid with a gradient elution was used to successfully separate fuzuloparib, SHR165202 and talazoparib (internal standard, IS). The results indicated that fuzuloparib and SHR165202 had good linearity over the calibration range of 2-50 ng/mL and 1-20 ng/mL, respectively. The precision, accuracy, stability, matrix effect, and extraction recovery required for methodological validation all complied with the requirements of the Bioanalytical Method Validation Guidelines. In vitro microsome incubation experiments, curcumin exhibited inhibitory effect on fuzuloparib in both rat liver microsomes (RLM) and human liver microsomes (HLM) with half-maximal inhibitory concentration (IC50) value of 10.54 µM and 47.64 µM, respectively, and the corresponding mechanism was non-competitive. Furthermore, the inhibitory mechanism of curcumin on fuzuloparib was validated through molecular docking. In pharmacokinetic experiments in rats, curcumin significantly altered the plasma exposure of fuzuloparib, resulting in significant increases in AUC(0-t) and Cmax of fuzuloparib and a significant decrease in CLz/F. Moreover, the metabolite SHR165202 showed significant increases in AUC(0-t), AUC(0-∞), Tmax and Cmax and a significant decrease in CLz/F. This further supports the notion that curcumin could inhibit the metabolism of fuzuloparib. Therefore, when co-administering fuzuloparib and curcumin in clinic, it is recommended to monitor plasma levels of fuzuloparib and pay close attention to adverse effects. If necessary, the dose of fuzuloparib needs to be reduced.

BiRNN-DDI: A Drug-Drug Interaction Event Type Prediction Model Based on Bidirectional Recurrent Neural Network and Graph2Seq Representation.

Research on drug-drug interaction (DDI) prediction, particularly in identifying DDI event types, is crucial for understanding adverse drug reactions and drug combinations. This work introduces a Bidirectional Recurrent Neural Network model for DDI event type prediction (BiRNN-DDI), which simultaneously considers structural relationships and contextual information. Our BiRNN-DDI model constructs drug feature graphs to mine structural relationships. For contextual information, it transforms drug graphs into sequences and employs a two-channel structure, integrating BiRNN, to obtain contextual representations of drug-drug pairs. The model's effectiveness is demonstrated through comparisons with state-of-the-art models on two DDI event-type benchmarks. Extensive experimental results reveal that BiRNN-DDI surpasses other models in accuracy, AUPR, AUC, F1 score, Precision, and Recall metrics on both small and large datasets. Additionally, our model exhibits a lower parameter space, indicating more efficient learning of drug feature representations and prediction of potential DDI event types.

Effect of Efavirenz on the Pharmacokinetics of SHR6390 in Healthy Volunteers.

Purpose: 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.

Quantitative prediction of CYP3A induction-mediated drug-drug interactions in clinical practice.

There have been no reports on the quantitative prediction of CYP3A induction-mediated decreases in AUC and Cmax for drug candidates identified as a "victims" of CYP3A induction. Our previous study separately evaluated the fold-induction of hepatic and intestinal CYP3A by known inducers using clinical induction data and revealed that we were able to quantitatively predict the AUC ratio (AUCR) of a few CYP3A substrates in the presence and absence of CYP3A inducers. In the present study, we investigate the predictability of AUCR and also Cmax ratio (CmaxR) in additional 54 clinical studies. The fraction metabolized by CYP3A (fm), the intestinal bioavailability (Fg), and the hepatic intrinsic clearance (CLint) of substrates were determined by the in vitro experiments as well as clinical data used for calculating AUCR and CmaxR. The result showed that 65-69% and 65-67% of predictions were within 2-fold of observed AUCR and CmaxR, respectively. A simulation using multiple parameter combinations suggested that the variability of fm and Fg within a certain range might have a minimal impact on the calculation output. These findings suggest that clinical AUCR and CmaxR of CYP3A substrates can be quantitatively predicted from the preclinical stage.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic.

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Clinical drug interactions between linezolid and other antibiotics: For adverse drug event monitoring.

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.

Influence of ensitrelvir or nirmatrelvir/ritonavir on tacrolimus clearance in kidney transplant recipients: a single-center case series.

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.

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.

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.

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.

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.

OCT1 (SLC22A1) transporter kinetics and regulation in primary human hepatocyte 3D spheroids.

3D spheroids of primary human hepatocytes (3D PHH) retain a differentiated phenotype with largely conserved metabolic function and proteomic fingerprint over weeks in culture. As a result, 3D PHH are gaining importance as a model for mechanistic liver homeostasis studies and in vitro to in vivo extrapolation (IVIVE) in drug discovery. However, the kinetics and regulation of drug transporters have not yet been assessed in 3D PHH. Here, we used organic cation transporter 1 (OCT1/SLC22A1) as a model to study both transport kinetics and the long-term regulation of transporter activity via relevant signalling pathways. The kinetics of the OCT1 transporter was studied using the fluorescent model substrate 4-(4-(dimethylamino)styryl)-N-methylpyridinium (ASP+) and known OCT1 inhibitors in individual 3D PHH. For long-term studies, 3D PHH were treated with xenobiotics for seven days, after which protein expression and OCT1 function were assessed. Global proteomic analysis was used to track hepatic phenotypes as well as prototypical changes in other regulated proteins, such as P-glycoprotein and Cytochrome P450 3A4. ASP+ kinetics indicated a fully functional OCT1 transporter with a Km value of 14 ± 4.0µM as the mean from three donors. Co-incubation with known OCT1 inhibitors decreased the uptake of ASP+ in the 3D PHH spheroids by 35-52%. The long-term exposure studies showed that OCT1 is relatively stable upon activation of nuclear receptor signalling or exposure to compounds that could induce inflammation, steatosis or liver injury. Our results demonstrate that 3D PHH spheroids express physiologically relevant levels of fully active OCT1 and that its transporter kinetics can be accurately studied in the 3D PHH configuration. We also confirm that OCT1 remains stable and functional during the activation of key metabolic pathways that alter the expression and function of other drug transporters and drug-metabolizing enzymes. These results will expand the range of studies that can be performed using 3D PHH.