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 Majority of the drugs withdrawn from the market for liver injury caused death or transplantation at frequencies under 0.01%.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.

Transporter-mediated drug-drug interactions: regulatory guidelines, in vitro and in vivo methodologies and translation, special populations, and the blood-brain barrier.

This review, part of a special issue on drug-drug interactions (DDIs) spearheaded by the International Society for the Study of Xenobiotics (ISSX) New Investigators, explores the critical role of drug transporters in absorption, disposition, and clearance in the context of DDIs. Over the past two decades, significant advances have been made in understanding the clinical relevance of these transporters. Current knowledge on key uptake and efflux transporters that affect drug disposition and development is summarized. Regulatory guidelines from the FDA, EMA, and PMDA that inform the evaluation of potential transporter-mediated DDIs are discussed in detail. Methodologies for preclinical and clinical testing to assess potential DDIs are reviewed, with an emphasis on the utility of physiologically based pharmacokinetic (PBPK) modeling. This includes the application of relative abundance and expression factors to predict human pharmacokinetics (PK) using preclinical data, integrating the latest regulatory guidelines. Considerations for assessing transporter-mediated DDIs in special populations, including pediatric, hepatic, and renal impairment groups, are provided. Additionally, the impact of transporters at the blood-brain barrier (BBB) on the disposition of CNS-related drugs is explored. Enhancing the understanding of drug transporters and their role in drug disposition and toxicity can improve efficacy and reduce adverse effects. Continued research is essential to bridge remaining gaps in knowledge, particularly in comparison with cytochrome P450 (CYP) enzymes.

No dose adjustment of metformin or substrates of organic cation transporters (OCT)1 and OCT2 and multidrug and toxin extrusion protein (MATE)1/2K with fostemsavir coadministration based on modeling approaches.

Fostemsavir is an approved gp120-directed attachment inhibitor and prodrug for the treatment of human immunodeficiency virus type 1 infection in combination with other antiretrovirals (ARVs) in heavily treatment-experienced adults with multi-drug resistance, intolerance, or safety concerns with their current ARV regimen. Initial in vitro studies indicated that temsavir, the active moiety of fostemsavir, and its metabolites, inhibited organic cation transporter (OCT)1, OCT2, and multidrug and toxin extrusion transporters (MATEs) at tested concentration of 100 uM, although risk assessment based on the current Food and Drug Administration in vitro drug-drug interaction (DDI) guidance using the mechanistic static model did not reveal any clinically relevant inhibition on OCTs and MATEs. However, a DDI risk was flagged with EMA static model predictions. Hence, a physiologically based pharmacokinetic (PBPK) model of fostemsavir/temsavir was developed to further assess the DDI risk potential of OCT and MATEs inhibition by temsavir and predict changes in metformin (a sensitive OCT and MATEs substrate) exposure. No clinically relevant impact on metformin concentrations across a wide range of temsavir concentrations was predicted; therefore, no dose adjustment is recommended for metformin when co-administered with fostemsavir.

Assessment of drug-drug interaction of dapagliflozin with LCZ696 based on an LC-MS/MS method.

The co-administration of dapagliflozin (DPF) and sacubitril/valsartan (LCZ696) has emerged as a promising therapeutic approach for managing heart failure. Given that DPF and LCZ696 are substrates for P-glycoprotein, there is a plausible potential for drug-drug interactions when administered concomitantly. To investigate the pharmacokinetic changes when these drugs are co-administered, we have established and validated a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method capable of simultaneously detecting DPF, LBQ657 (the active metabolite of sacubitril) and valsartan in rat plasma. This method has demonstrated selectivity, sensitivity, and accuracy. Drug-drug interactions were examined by the LC-MS/MS method. The mechanisms were investigated using everted intestinal sac models and Caco-2 cells. The results showed that DPF significantly increased the area under the curve (AUC(0-t)) (3,563.3 ± 651.7 vs. 7,146.5 ± 1,714.9 h µg/L) of LBQ657 (the active metabolite of sacubitril) and the AUC(0-t) (24,022.4 ± 6,774.3 vs. 55,728.3 ± 32,446.3 h µg/L) of valsartan after oral co-administration. Dapagliflozin significantly increased the amount of LBQ657 and valsartan in intestinal sacs by 1- and 1.25-fold at 2.25 h. Caco-2 cell uptake studies confirmed that P-glycoprotein is the transporter involved in this interaction. This finding enhances the understanding of drug-drug interactions in the treatment of heart failure and provides a guidence for clinical therapy.

Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine's Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes.

Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ's nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent-metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus®. The model was utilized for Drug-Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ's nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUClast ratio was 0.49 (slightly outside of the two-fold range). CBZ's nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

Donor-derived mold infections in lung transplant recipients: The importance of active surveillance.

Unexpected donor-derived fungal infections represent a rare but potentially fatal complication in lung transplant (Tx) recipients. Timely communication of the results of donor cultures and prompt treatment of recipients are crucial to mitigate the consequences of donor-derived transmissions. In this prospective cohort study, all consecutive patients who underwent lung transplantation from 2015 to 2022 were included. In December 2015, a Local Active Surveillance System has been implemented to provide biovigilance of donor culture results and optimize recipients' management. The aim of this study is to investigate the incidence of unexpected, mold-positive cultures among lung donors and the rate of transmission to recipients. Furthermore, management strategies and outcome of recipients with mold transmission are described. In case of isolation of the same mold in donor and recipient cultures, when possible, transmission was confirmed by dendrogram analysis. During the study period, 82 lung Tx were performed from 80 donors. The prevalence of donors with "unexpected" mold isolation from the respiratory tract was 3.75% (3/80). Isolated molds were Aspergillus niger, Rhizopus oryzae, and Aspergillus flavus. Transmissions occurred in all the three cases (100%) with a mean time of 5 days from lung Tx but none of the recipients developed invasive mold disease. Our Local Active Surveillance System allowed prompt recognition of lung donors unexpected mold colonization. Even though transmission occurred, introduction of early targeted antifungal therapy prevented potential catastrophic consequence of mold donor-derived infection in the immediate post-Tx period.

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.

Clinical implications of the Drug-Drug Interaction in Cancer Patients treated with innovative oncological treatments.

In the last two-decades, innovative drugs have revolutionized cancer treatments, demonstrating a significant improvement in overall survival. These drugs may present several pharmacokinetics interactions with non-oncological drugs, and vice versa, and, non-oncological drugs can modify oncological treatment outcome both with pharmacokinetic interaction and with an "off-target impact" on the tumor microenvironment or on the peripheral immune response. It's supposed that the presence of a drug-drug interaction (DDI) is associated with an increased risk of reduced anti-tumor effects or severe toxicities. However, clinical evidence that correlate the DDI presence with outcome are few, and results are difficult to compare because of difference in data collection and heterogeneous population. This review reports all the clinical evidence about DDI to provide an easy-to-use guide for DDI management and dose adjustment in solid tumors treated with inhibitors of the cyclin-dependent kinases CDK4-6, Antibody-drug conjugates, Poly ADPribose polymerase inhibitors, androgen-receptor targeted agents, or immunecheckpoints inhibitors.

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.

Assessing and mitigating pH-mediated DDI risks in drug development - formulation approaches and clinical considerations.

pH-mediated drug-drug interactions (DDI) is a prevalent DDI in drug development, especially for weak base compounds with highly pH-dependent solubility. FDA has released a guidance on the evaluation of pH-mediated DDI assessments using in vitro testing and clinical studies. Currently, there is no common practice of ways of testing across the academia and industry. The development of biopredictive method and physiologically-based biopharmaceutics modeling (PBBM) approaches to assess acid-reducing agent (ARA)-DDI have been proven with accurate prediction and could decrease drug development burden, inform clinical design and potentially waive clinical studies. Formulation strategies and careful clinical design could help mitigate the pH-mediated DDI to avoid more clinical studies and label restrictions, ultimately benefiting the patient. In this review paper, a detailed introduction on biorelevant dissolution testing, preclinical and clinical study requirement and PBPK modeling approaches to assess ARA-DDI are described. An improved decision tree for pH-mediated DDI is proposed. Potential mitigations including clinical or formulation strategies are discussed.

Domains in Action: Understanding Ddi1's Diverse Functions in the Ubiquitin-Proteasome System.

The ubiquitin-proteasome system (UPS) is a pivotal cellular mechanism responsible for the selective degradation of proteins, playing an essential role in proteostasis, protein quality control, and regulating various cellular processes, with ubiquitin marking proteins for degradation through a complex, multi-stage process. The shuttle proteins family is a very unique group of proteins that plays an important role in the ubiquitin-proteasome system. Ddi1, Dsk2, and Rad23 are shuttle factors that bind ubiquitinated substrates and deliver them to the 26S proteasome. Besides mediating the delivery of ubiquitinated proteins, they are also involved in many other biological processes. Ddi1, the least-studied shuttle protein, exhibits unique physicochemical properties that allow it to play non-canonical functions in the cells. It regulates cell cycle progression and response to proteasome inhibition and defines MAT type of yeast cells. The Ddi1 contains UBL and UBA domains, which are crucial for binding to proteasome receptors and ubiquitin respectively, but also an additional domain called RVP. Additionally, much evidence has been provided to question whether Ddi1 is a classical shuttle protein. For many years, the true nature of this protein remained unclear. Here, we highlight the recent discoveries, which shed new light on the structure and biological functions of the Ddi1 protein.

Comparing the risk of deep vein thrombosis of two combined oral contraceptives: Norethindrone/ethinyl estradiol and drospirenone/ethinyl estradiol.

Background: Deep vein thrombosis (DVT) has been reported as an adverse event for patients receiving combined oral contraceptives. Norethindrone/ethinyl estradiol (NET/EE) and drospirenone/ethinyl estradiol (DRSP/EE) are two commonly prescribed combined hormonal oral contraceptive agents used in the United States, differing in their progestin component. Objective: The purpose of this study was to determine the association between the progestin component of a combined oral contraceptive and the risk of DVT in patients taking oral contraceptives for birth control using data derived from the FDA Adverse Event Reporting System (FAERS). Methods: The risk of DVT was compared between patients that had taken NET/EE with those that had taken the DRSP/EE COC formulation for birth control. In addition, age was assessed as a possible confounder and the outcome severity for those diagnosed with DVT were compared between the two groups. Finally, association rule mining was utilized to identify possible drug-drug interactions that result in elevated DVT risk. Results: DVT was the fourth most commonly adverse event reported for patients taking DRSP/EE accounting for 8558 cases and the seventeenth most commonly reported adverse event for NET/EE accounting for 298 cases. Age was found to be a significant confounder for users of DRSP/EE with regards to DVT risk across all age groups assessed: 2040 (ROR = 3.69, 95% CI 3.37-4.04) However, there was only a statistically significant elevated risk in patients over 40 years of age taking NET/EE (ROR = 1.98, 95% CI 1.36-2.88). Patients that had taken DRSP/EE and the corticosteroid prednisone simultaneously had an approximately 3-fold increase in DVT risk (ROR = 2.77, 95% CI 2.43-3.15) relative to individuals that had only taken DRSP/EE. Conclusion: Based on this analysis, there is a higher risk of developing DVT when taking DRSP/EE than when taking NET/EE as hormonal contraception. In addition, a possibly significant drug-drug interaction between different COC formulations and prednisone were identified. This interaction may result in elevated DVT risk due to a synergistic impairment of fibrinolysis and a decrease in plasmin production.

Assessment of TGx-DDI genes for genotoxicity in a comprehensive panel of chemicals.

BACKGROUND: The TGx-DDI biomarker identifies transcripts specifically induced by primary DNA damage. Profiling similarity of TGx-DDI signatures can allow clustering compounds by genotoxic mechanism. This transcriptomics-based approach complements conventional toxicology testing by enhancing mechanistic resolution. METHODS: Unsupervised hierarchical clustering and t-distributed stochastic neighbor embedding (tSNE) were utilized to assess similarity of publicly-available per- and polyfluoroalkyl substances (PFAS) and ToxCast chemicals based on TGx-DDI modulation. TempO-seq transcriptomic data after highest chemical concentrations were analyzed. RESULTS: Clustering discriminated between genotoxic and non-genotoxic compounds while drawing similarity among chemicals with shared mechanisms. PFAS largely clustered distinctly from classical mutagens. However, dynamic range across PFAS types and durations indicated variable potential for DNA damage. tSNE visualization reinforced phenotypic groupings, with genotoxins clustering separately from non-DNA damaging agents. DISCUSSION: Unsupervised learning approaches applied to TGx-DDI profiles effectively categorizes chemical genotoxicity potential, aiding elucidation of biological response pathways. This transcriptomics-based strategy gives further insight into the role and effect of individual TGx-DDI biomarker genes and complements existing assays by enhancing mechanistic resolution. Overall, TGx-DDI biomarker profiling holds promise for predictive safety screening.

Docetaxel, cyclophosphamide, and epirubicin: application of PBPK modeling to gain new insights for drug-drug interactions.

The new adjuvant chemotherapy of docetaxel, epirubicin, and cyclophosphamide has been recommended for treating breast cancer. It is necessary to investigate the potential drug-drug Interactions (DDIs) since they have a narrow therapeutic window in which slight differences in exposure might result in significant differences in treatment efficacy and tolerability. To guide clinical rational drug use, this study aimed to evaluate the DDI potentials of docetaxel, cyclophosphamide, and epirubicin in cancer patients using physiologically based pharmacokinetic (PBPK) models. The GastroPlus™ was used to develop the PBPK models, which were refined and validated with observed data. The established PBPK models accurately described the pharmacokinetics (PKs) of three drugs in cancer patients, and the predicted-to-observed ratios of all the PK parameters met the acceptance criterion. The PBPK model predicted no significant changes in plasma concentrations of these drugs during co-administration, which was consistent with the observed clinical phenomenon. Besides, the verified PBPK models were then used to predict the effect of other Cytochrome P450 3A4 (CYP3A4) inhibitors/inducers on these drug exposures. In the DDI simulation, strong CYP3A4 modulators changed the exposure of three drugs by 0.71-1.61 fold. Therefore, patients receiving these drugs in combination with strong CYP3A4 inhibitors should be monitored regularly to prevent adverse reactions. Furthermore, co-administration of docetaxel, cyclophosphamide, or epirubicin with strong CYP3A4 inducers should be avoided. In conclusion, the PBPK models can be used to further investigate the DDI potential of each drug and to develop dosage recommendations for concurrent usage by additional perpetrators or victims.

High-throughput assay to simultaneously evaluate activation of CYP3A and the direct and time-dependent inhibition of CYP3A, CYP2C9, and CYP2D6 using liquid chromatography-tandem mass spectrometry.

In the early stages of drug discovery, adequate evaluation of the potential drug-drug interactions (DDIs) of drug candidates is important. Several CYP3A activators are known to lead to underestimation of DDIs. These compounds affect midazolam 1'-hydroxylation but not midazolam 4-hydroxylation.We used both metabolic reactions of midazolam to evaluate the activation and inhibition of CYP3A activators simultaneously. For our CYP inhibition assay using cocktail probe substrates, simultaneous liquid chromatography-tandem mass spectrometry monitoring of 1'-hydroxymidazolam and 4-hydroxymidazolam for CYP3A was established in addition to monitoring of 4-hydroxydiclofenac and 1'-hydroxybufuralol for CYP2C9 and CYP2D6.The results of our cocktail inhibition assay were well correlated with those of a single inhibition assay, as were the estimated inhibition parameters for typical CYP3A inhibitors. In our assay, a proprietary compound that activated midazolam 1'-hydroxylation and tended to inhibit 4-hydroxylation was evaluated along with known CYP3A activators. All compounds were well characterised by comparison of the results of midazolam 1'- and 4-hydroxylation.In conclusion, our CYP cocktail inhibition assay can detect CYP3A activation and assess the direct and time-dependent inhibition potentials for CYP3A, CYP2C9, and CYP2D6. This method is expected to be very efficient in the early stages of drug discovery.

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

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

Exploring the impact of CYP2D6 and UGT2B7 gene-drug interactions, and CYP-mediated DDI on oxycodone and oxymorphone pharmacokinetics using physiologically-based pharmacokinetic modeling and simulation.

Oxycodone is one of the most commonly used opioids to treat moderate to severe pain. It is metabolized mainly by CYP3A4 and CYP2D6, while only a small fraction of the dose is excreted unchanged into the urine. Oxymorphone, the metabolite primarily formed by CYP2D6, has a 40- to 60-fold higher mu-opioid receptor affinity than the parent compound. While CYP2D6-mediated gene-drug-interactions (GDIs) and drug-drug interactions (DDIs) are well-studied, they only account for a portion of the variability in oxycodone and oxymorphone exposure. The combined impact of CYP2D6-mediated GDIs and DDIs, CYP3A4-mediated DDIs, and UGT2B7 GDIs is not fully understood yet and hard to study in head-to-head clinical trials given the relatively large number of scenarios. Instead, we propose the use of a physiologically-based pharmacokinetic model that integrates available information on oxycodone's metabolism to characterize and predict the impact of DDIs and GDIs on the exposure of oxycodone and its major, pharmacologically-active metabolite oxymorphone. To this end, we first developed and verified a PBPK model for oxycodone and its metabolites using published clinical data. The verified model was then applied to determine the dose-exposure relationship of oxycodone and oxymorphone stratified by CYP2D6 and UGT2B7 phenotypes respectively, and administered perpetrators of CYP-based drug interactions. Our simulations demonstrate that the combination of CYP2D6 UM and a UGT2B7Y (268) mutation may lead to a 2.3-fold increase in oxymorphone exposure compared to individuals who are phenotyped as CYP2D6 NM / UGT2B7 NM. The extent of oxymorphone exposure increases up to 3.2-fold in individuals concurrently taking CYP3A4 inhibitors, such as ketoconazole. Inhibition of the CYP3A4 pathway results in a relative increase in the partial metabolic clearance of oxycodone to oxymorphone. Oxymorphone is impacted to a higher extent by GDIs and DDIs than oxycodone. We predict oxymorphone exposure to be highest in CYP2D6 UMs/UGT2B7 PMs in the presence of ketoconazole (strong CYP3A4 index inhibitor) and lowest in CYP2D6 PMs/UGT2B7 NMs in the presence of rifampicin (strong CYP3A4 index inducer) covering a 55-fold exposure range.

SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug-drug interaction prediction.

BACKGROUND: Drug-drug interactions (DDI) are prevalent in combination therapy, necessitating the importance of identifying and predicting potential DDI. While various artificial intelligence methods can predict and identify potential DDI, they often overlook the sequence information of drug molecules and fail to comprehensively consider the contribution of molecular substructures to DDI. RESULTS: In this paper, we proposed a novel model for DDI prediction based on sequence and substructure features (SSF-DDI) to address these issues. Our model integrates drug sequence features and structural features from the drug molecule graph, providing enhanced information for DDI prediction and enabling a more comprehensive and accurate representation of drug molecules. CONCLUSION: The results of experiments and case studies have demonstrated that SSF-DDI significantly outperforms state-of-the-art DDI prediction models across multiple real datasets and settings. SSF-DDI performs better in predicting DDI involving unknown drugs, resulting in a 5.67% improvement in accuracy compared to state-of-the-art methods.

Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review.

Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisions and establish optimal treatment regimens. However, manually detecting these interactions is time-consuming and labor-intensive. Utilizing the advancements in Artificial Intelligence (AI) is essential for achieving accurate forecasts of DDIs. In this review, DDI prediction tasks are classified into three types according to the type of DDI prediction: undirected DDI prediction, DDI events prediction, and Asymmetric DDI prediction. The paper then reviews the progress of AI for each of these three prediction tasks in DDI and provides a summary of the data sets used as well as the representative methods used in these three prediction directions. In this review, we aim to provide a comprehensive overview of drug interaction prediction. The first section introduces commonly used databases and presents an overview of current research advancements and techniques across three domains of DDI. Additionally, we introduce classical machine learning techniques for predicting undirected drug interactions and provide a timeline for the progression of the predicted drug interaction events. At last, we debate the difficulties and prospects of AI approaches at predicting DDI, emphasizing their potential for improving clinical decision-making and patient outcomes.

The impact of a non-restrictive Antimicrobial Stewardship Program in the emergency department of a secondary-level Italian hospital.

Evidence supporting the effectiveness of Antimicrobial Stewardship (AMS) Programs in the emergency department (ED) setting is limited. We conducted a prospective cohort study to assess the efficacy of an AMS program in an ED and a short-stay observation unit. The intervention included periodic prospective audits (twice a week), conducted by four infectious disease consultants. Primary outcomes included the difference in the hospital mortality rate, antibiotic consumption, and the incidence of bloodstream infections (BSI) caused by multidrug resistant (MDR) bacteria, before March 2020-February 2021 and after March 2021-February 2022 when the program was implemented. Interrupted time-series analysis was performed to assess the effect of our program. During the 12-month program, we performed 152 audits and evaluated 366 antibiotic therapies out of a total of 853 patients admitted. In the intervention period, we observed a non-statistically significant decrease in total antibiotic consumption, with a change in level of - 31.2 defined daily dose/100 patient-days (PD) (p = 0.71). Likewise, we found no significant variations in the rate of BSI due to MDR Gram-positive (CT - 0.02 events/PD, p = 0.84), MDR Gram-negative bacteria (CT 0.08, p = 0.71), or Candida spp. (CT 0.008, p = 0.86). Conversely, we found a significant decrease in the mortality rate between the pre- and post-intervention periods (- 1.98 deaths/100 PD, CI - 3.9 to - 0.007, p = 0.049). The Antibiotic Stewardship Program in the ED was associated with a significant decrease in the mortality rate. More high-quality studies are needed to determine the most effective ASP strategies in this unique setting.