Search Results (393)

Background: Cardiovascular polypharmacy inherently amplifies the risk of drug–drug interactions (DDIs), yet most studies remain limited to isolated drug pairs or predefined high-risk classes, without mapping the systemic architecture through which interactions accumulate. Objectives: To characterize the burden, severity, and network structure of potential DDIs in a real-world cohort of hospitalized cardiovascular patients using interaction profiling combined with graph-theoretic network analysis. Methods: This retrospective observational study included 250 hospitalized cardiovascular patients. All home medications at admission were analyzed using the Drugs.com interaction database, and a drug interaction network was constructed to compute topological metrics (i.e., degree, betweenness, and eigenvector centrality). Results: Polypharmacy was highly prevalent, with a mean of 7.7 drugs per patient, and 98.4% of patients exhibited at least one potential DDI. A total of 4353 interactions were identified, of which 12.1% were classified as major, and 35.2% of patients presented high-risk profiles with ≥3 major interactions. Interaction burden showed a strong correlation with medication count (r = 0.929). Network analysis revealed a limited cluster of hub medications, particularly pantoprazole, furosemide, spironolactone, amiodarone, and perindopril, that disproportionately governed both interaction density and high-severity risk. Conclusions: These findings move beyond conventional pairwise screening by demonstrating how interaction risk propagates through interconnected therapeutic networks. The study supports the integration of hub-focused deprescribing, targeted monitoring strategies, and network-informed clinical decision support to mitigate DDI risk in cardiovascular polypharmacy. Future studies should link potential DDIs to clinical outcomes and validate network-based prediction models in prospective settings. Full article

Background/Objectives: Blood thinners (anticoagulants) remain the first line pharmacotherapy for the management of cardiovascular and thromboembolic disorders. The increased utilization of polypharmacy, likely driven by the greater burden of comorbidities, elevates the risk of potential drug–drug interactions (pDDIs) and creates a significant challenge in anticoagulant management. The aim of the study was to assess the prescribing trend and impact of polypharmacy and pDDIs in patients receiving anticoagulant drug therapy in a public hospital providing secondary care. Methods: A cross-sectional observational study was undertaken between January–June 2023. Data from electronic medical records of prescriptions for anticoagulants were collected, analyzed for prescribing patterns, and checked for pDDIs using Micromedex database 2.0^®. Utilizing binary logistic regression, the relationship between polypharmacy and sociodemographic factors was assessed. Multivariate logistic regression analysis served to uncover determinants linked to pDDIs. Results: Of the total 130 patients, females were predominant (58.46%), with a higher prevalence among those aged 61–90 years. Atrial fibrillation emerged as the main clinical reason and apixaban (51.53%) ranked as the top prescribed anticoagulant in our cohort. Among the 766 pDDIs identified, the majority [401 (52.34%)] were categorized as moderate in severity. Polypharmacy was strongly linked to age (p = 0.001), the Charlson comorbidity index (CCI) (p = 0.040), and comorbidities (p = 0.005) in the binary logistic regression analysis. In the multivariable analysis, the number of medications remain a strong predictor of pDDIs (adjusted OR: 30.514, p = 0.001). Conclusions: Polypharmacy and pDDIs were exhibited in a significant segment of cohort receiving anticoagulant therapy, with strong correlations to age, CCI, comorbidities, and the number of medications. A multidimensional approach involving collaboration among healthcare providers assisted by clinical decision support systems can help optimize the management of polypharmacy, minimize the risks of pDDIs, and ultimately enhance health outcomes. Full article

The landscape of oncologic therapies has undergone large changes since the introduction of monoclonal antibody (mAb) based immunotherapies in the late 1990s and early 2000s. MAb-based therapeutics, also called biologics or large molecules, have distinct pharmacological characteristics compared to chemotherapeutics and small molecules. Development of biologics requires thorough assessment of pharmacokinetic (PK) and pharmacodynamic (PD) characteristics to ensure safety and demonstration of efficacy. This review provides an overview of the clinical pharmacology packages of biologics for the treatment of oncologic malignancies approved by the U.S. Food and Drug Administration (FDA) over the past decade (January 2015 and August 2025). The conduct of population PK (PopPK) and exposure-eesponse (E-R) analyses, as well as assessments for drug–drug interactions (DDIs), immunogenicity, and QT prolongation risk are discussed. The aim of this review is to provide insight into the clinical pharmacology assessments for approval of antibody-based therapies in oncology as well as provide a longitudinal view of clinical pharmacology packages in this space. Full article

Drug–drug interactions (DDIs) can cause adverse reactions or reduce the efficiency of a drug. Using computers to predict DDIs is now critical in pharmacology, as this reduces risks, improves drug outcomes and lowers healthcare costs. Clinical trials are slow, expensive, and require a lot of effort. The use of artificial intelligence (AI), primarily in the form of machine learning (ML) and its subfield deep learning (DL), has made DDI prediction more accurate and efficient when handling large datasets from biological, chemical, and clinical domains. Many ML and DL approaches are bio-inspired, taking inspiration from natural systems, and are considered part of the broader class of biomimetic methods. This review provides a comprehensive overview of AI-based methods currently used for DDI prediction. These include classical ML algorithms, such as logistic regression (LR) and support vector machines (SVMs); advanced DL models, such as deep neural networks (DNNs) and long short-term memory networks (LSTMs); graph-based models, such as graph convolutional networks (GCNs) and graph attention networks (GATs); and ensemble techniques. The use of knowledge graphs and transformers to capture relations and meaningful data about drugs is also investigated. Additionally, emerging biomimetic approaches offer promising directions for the future in designing AI models that can emulate the complexity of pharmacological interactions. These upgrades include using genetic algorithms with LR and SVM, neuroevaluation (brain-inspired model optimization) to improve DNN and LSTM architectures, ant-colony-inspired path exploration with GCN and GAT, and immune-inspired attention mechanisms in transformer models. This manuscript reviews the typical types of data employed in DDI (pDDI) prediction studies and the evaluation methods employed, discussing the pros and cons of each. There are useful approaches outlined that reveal important points that require further research and suggest ways to improve the accuracy, usability, and understanding of DDI prediction models. Full article

Over the past decade, significant progress has been made in the treatment of chronic hepatitis C virus (HCV) infection. The introduction of direct-acting antivirals (DAAs) has revolutionized the treatment of HCV infection, offering nearly 100% efficacy. Furthermore, additional therapeutic regimens with pangenotypic efficacy have been registered. These drugs are also characterized by a few adverse events and good treatment tolerance. As DAA therapy is now accessible to virtually all patients, including those with multimorbidity who often take multiple medications, drug interactions (DDIs) have become a significant clinical challenge. One of the groups of patients who are frequently infected with HCV is those with mental disorders. Due to frequently overlapping metabolic pathways, DDIs can occur, affecting the effectiveness of both psychiatric and antiviral therapy. Knowledge of these interactions is crucial in these cases and influences patient management. This paper discusses the most significant interactions between pangenotypic DAA regimens and psychotropic medications. Full article

Background: Potential drug–drug interactions (pDDIs) present substantial challenges to medication safety during care transitions. Warfarin, with its narrow therapeutic index and extensive interaction profile, provides a strategic model for examining pDDIs at discharge. This study aimed to characterize the burden and determinants of major warfarin pDDIs among patients discharged from a tertiary-care hospital. Methods: This retrospective cross-sectional study analyzed electronic health records of 1667 patients discharged home on warfarin. Major pDDIs were identified using the Micromedex^® Drug Interaction database. Log-binomial regression was used to assess predictors of ≥1 major pDDIs, and generalized Poisson regression was used to model the number of pDDIs per patient. Results: Major warfarin pDDIs were identified in 81.6% (95% CI: 79.6–83.4%) of patients at hospital discharge. The burden was considerable: 35.1% (95% CI: 32.8–37.4%) of patients had one major pDDI, while 46.5% (95% CI: 44.1–48.9%) had two or more. Polypharmacy (≥5 concurrent medications) was the strongest predictor, associated with a higher risk of any major pDDI (adjusted risk ratio 1.72, 95% CI: 1.46–2.02) and nearly three times the burden of interactions per patient (adjusted incidence rate ratio (IRR) 2.87, 95% CI: 2.36–3.49). When modeled as a continuous variable, each additional discharge medication was associated with a 9% increase in predicted pDDI burden (IRR 1.09, 95% CI: 1.08–1.10). Conclusions: Using warfarin as a model for high-risk medication safety, major pDDIs were highly prevalent at hospital discharge, with polypharmacy as a significant predictor of both the presence and burden of interactions. These findings emphasize the importance of identifying polypharmacy-related pDDIs to reduce potential drug interaction risk during care transitions. Full article

Background: Chronic insomnia is associated with elevated cardiovascular disease risk, and current therapeutic options for this condition remain inadequate. Melatonin (MT) combined with cannabidiol (CBD) may exert synergistic effects on improving sleep; the underlying pharmacological drug–drug interactions (DDI) and interspecies differences in their combined actions remain unknown. Purpose: This study aimed to evaluate the pharmacokinetic characteristics of combined drug formulations by utilizing DDI-based approaches so as to underpin the efficacy and safety of the formulation. Methods: Overexpressing hPEPT1 in MDCK cells, multiple species liver microsomes, equilibrium dialysis, and a static DDI model were employed to assess CBD’s effects on MT’s cellular uptake, inhibitory effect, enzymatic phenotype, protein binding, and human AUC changes. Results: CBD significantly increased MT exposure in dogs but caused dose-dependent biphasic changes in rats. MT negligibly affected CBD PK. In vitro, CBD inhibited MT metabolism with species differences: potent competitive inhibition in dogs (IC₅₀ = 3.42 ± 1.30 μM), weaker inhibition in rats/humans (IC₅₀ = 13.54 ± 1.15/16.47 ± 4.23 μM). CBD also demonstrated mechanism-based inhibition (K_I = 25.63 μM, K_inact = 0.063 min⁻¹) against human CYP1A2-mediated MT metabolism. Acidic conditions revealed that CBD inhibited PEPT1-mediated MT uptake. CBD exhibits high and MT moderate protein binding. Static model predictions aligned with in vivo dog/rat data project a worst-case human MT AUC increase up to 12-fold. Conclusions: This study identifies the critical role of PEPT1 in MT absorption and elucidates the dual mechanisms of CBD; namely, absorption inhibition and metabolic delay in regulating MT pharmacokinetics, which exhibits interspecies differences. Full article

Background/Objectives: Understanding the pharmacokinetics (PK) of antiepileptic and anti-inflammatory drugs under different physiological conditions is essential for optimizing therapy. Phenytoin, a widely used antiepileptic, and indomethacin, a nonsteroidal anti-inflammatory drug, are frequently prescribed in women of reproductive age. This study aimed to evaluate the influence of age, pregnancy, and dosing regimens on the PK of both drugs, as well as to investigate potential drug–drug interactions (DDIs). Methods: PK parameters of phenytoin and indomethacin were systematically analyzed in women aged 20–45 years under non-pregnant and pregnant conditions. Different dosing regimens were compared, and coadministration studies were conducted to assess DDI. Results: Phenytoin demonstrated stable absorption and bioavailability across ages and during pregnancy. Single daily dosing (300 mg once daily) yielded slightly higher peak concentration (C_max) values, while fractionated dosing (100 mg q8h) produced significantly higher drug exposure (AUC) and absorption fraction, particularly with prolonged administration, reflecting saturable metabolism. During pregnancy, systemic exposure (C_max and AUC) was modestly reduced, while absorption and distribution remained unchanged. Indomethacin showed minimal age-related variability and linear pharmacokinetics across dosing regimens. In pregnancy, exposure was reduced (lower C_max and AUC) with delayed T_max, indicating slower absorption. Importantly, no PK DDI was observed, as indomethacin parameters remained unchanged except for T_max, which was lower in the interaction scenario compared with baseline, suggesting a faster absorption rate without affecting overall exposure or peak concentration in the presence of phenytoin. Conclusions: Phenytoin and indomethacin exhibit stable and predictable PK across ages and during pregnancy, with dose-dependent characteristics that align with their known metabolic profiles. The absence of clinically relevant DDI supports their safe concomitant use. These findings provide preliminary reassuring evidence for clinicians and contribute to a better understanding of their pharmacological behavior in diverse patient populations. Full article

Sodium–glucose cotransporter 2 (SGLT2) inhibitors are widely used in type 2 diabetes and cardiometabolic diseases, and their pharmacokinetic characteristics generally confer a low risk of clinically relevant drug–drug interactions (DDIs). Most clinical studies demonstrate that these agents can be co-administered safely with commonly prescribed medications without dose adjustment, although strong enzyme inducers such as rifampin can reduce systemic exposure, and pharmacodynamic interactions may still arise. However, existing evidence is largely derived from short-term studies in healthy volunteers, with limited data in special populations and minimal evaluation of metabolite- or transporter-mediated interactions. This review summarizes the available in vitro and in vivo pharmacokinetic and DDI data for SGLT2 inhibitors, identifies key knowledge gaps related to polypharmacy, metabolite effects, and vulnerable patient groups, and outlines future research priorities to ensure their safe and effective use in real-world clinical practice. Full article

Background/Objectives: Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used globally to manage pain and inflammation. The rising prevalence of polypharmacy and potential drug–drug interactions (pDDIs) magnified by the prolonged and irrational use of NSAIDs may jeopardize patient medication safety. This study aims to analyze the pattern in prescribing NSAIDs and assess the extent of polypharmacy and pDDIs in community pharmacies located in Ras Al Khaimah. Methods: A quantitative cross-sectional study was conducted in randomly selected community pharmacies over six months (July 2024 to December 2024). Prescriptions pertaining to NSAIDs were assessed for prescribing patterns; incidence of polypharmacy and pDDIs were identified using Lexicomp’s drug interaction database. Chi-square tests assessed associations between treatment variables and polypharmacy, while logistic regression explored predictors of pDDIs. Results: In a total of 600 prescriptions, 1865 drugs were prescribed, including 908 NSAIDs. Celecoxib (28.2%) and ketoprofen (27.6%) remained the most predominant oral and topical NSAIDs prescribed. Aspirin and celecoxib were most commonly linked with pDDIs. A total of 357 pDDIs were identified, averaging 1.87 ± 1.39 per prescription. Most were of minor severity (60.22%), risk category C (43.97%), and fair reliability (59.38%). Gender, nationality, and comorbidities were significantly associated with polypharmacy (p < 0.001). Logistic regression showed nationality (p = 0.016), comorbidities (p < 0.001), and drug count (p = 0.007) as key predictors of pDDIs. Conclusions: Frequent NSAIDs prescribing, incidence of polypharmacy, and pDDIs underscore the attention for more cautious, evidence-based prescribing practice. Enforcing a robust regulatory framework, coupled with strengthening medication-use policies and pharmacist-led thorough medication history review and ongoing monitoring is paramount to improve patient safety and clinical outcomes. Full article

Personalizing therapy using medical marijuana (MM) is based on understanding the pharmacogenomics (PGx) and drug–drug interactions (DDIs) involved, as well as identifying potential epigenetic risk markers. In this work, the evidence regarding the role of variants in phase I (CYP2C9, CYP2C19, CYP3A4/5) and II (UGT1A9/UGT2B7) genes, transporters (ABCB1), and selected neurobiological factors (AKT1/COMT) in differentiating responses to Δ⁹-tetrahydrocannabinol (THC) and cannabidiol (CBD) has been reviewed. Data indicating enzyme inhibition by CBD and the possibility of phenoconversion were also considered, which highlights the importance of a dynamic interpretation of PGx in the context of current pharmacotherapy. Simultaneously, the results of epigenetic studies (DNA methylation, histone modifications, and ncRNA) in various tissues and developmental windows were summarized, including the reversibility of some signatures in sperm after a period of abstinence and the persistence of imprints in blood. Based on this, practical frameworks for personalization are proposed: the integration of PGx testing, DDI monitoring, and phenotype correction into clinical decision support systems (CDS), supplemented by cautious dose titration and safety monitoring. The culmination is a proposal of tables and diagrams that organize the most important PGx–DDI–epigenetics relationships and facilitate the elimination of content repetition in the text. The paper identifies areas of implementation maturity (e.g., CYP2C9/THC, CBD-CYP2C19/clobazam, AKT1, and acute psychotomimetic effects) and those requiring replication (e.g., multigenic analgesic signals), indicating directions for future research. Full article

Dexmedetomidine is a commonly used sedative because it has minimal adverse effects on respiratory function. Nevertheless, its cardiovascular safety profile, particularly bradycardia risk and drug–drug interactions (DDIs), remains incompletely understood. Additionally, current studies, including our previous analysis using the FDA adverse event reporting system (FAERS), hold several limitations. In this study, the electronic health record (EHR) platform TriNetX was utilized for pharmacovigilance analyses of dexmedetomidine. The significantly elevated incidence of bradycardia in dexmedetomidine-treated patients was demonstrated compared to other prevalent anesthetics. Age-stratified analyses revealed pronounced susceptibility in geriatric patients, while a slightly increased susceptibility in male patients was observed. In addition, elevated DDIs of dexmedetomidine with risperidone and albuterol were identified using disproportionality analysis with propensity score matching. Finally, to investigate molecular mechanisms of dexmedetomidine-associated bradycardia, analyses were conducted on a public microarray dataset, and nine differentially expressed miRNAs were identified following dexmedetomidine administration. Gene Ontology (GO) analysis of target genes of all five up-regulated miRNAs revealed rhythmic process and muscle tissue development as potential explanations. Notably, the target genes of the up-regulated miRNAs miR-26a-5p and miR-30c-5p were significantly enriched in GO terms associated with bradycardia. Together, this study identified bradycardia as a significant adverse drug event (ADE) of dexmedetomidine administration, observed possible clinically meaningful DDIs with dexmedetomidine, demonstrated a greater risk in elderly patients, and provided transcriptomic evidence that miRNA-mediated pathway dysregulation may contribute to dexmedetomidine-associated bradycardia. Full article

Patients undergoing oral surgery are frequently polymedicated and preoperative prescriptions (analgesics, corticosteroids, antibiotics) can generate clinically significant drug–drug interactions (DDIs) associated with bleeding risk, serotonin toxicity, cardiovascular instability and other adverse events. This study prospectively evaluated whether large language models (LLMs) can assist in detecting clinically relevant DDIs at the point of care. Five LLMs (ChatGPT-5, DeepSeek-Chat, DeepSeek-Reasoner, Gemini-Flash, and Gemini-Pro) were compared with a panel of experienced oral surgeons in 500 standardized oral-surgery cases constructed from realistic chronic medication profiles and typical postoperative regimens. For each case, all chronic and procedure-related drugs were provided and the task was to identify DDIs and rate their severity using an ordinal Lexicomp-based scale (A–X), with D/X considered “action required”. Primary outcomes were exact agreement with surgeon consensus and ordinal concordance; secondary outcomes included sensitivity for actionable DDIs, specificity, error pattern and response latency. DeepSeek-Chat reached the highest exact agreement with surgeons (50.6%) and showed perfect specificity (100%) but low sensitivity (18%), missing 82% of actionable D/X alerts. ChatGPT-5 showed the highest sensitivity (98.0%) but lower specificity (56.7%) and generated more false-positive warnings. Median response time was 3.6 s for the fastest model versus 225 s for expert review. These findings indicate that current LLMs can deliver rapid, structured DDI screening in oral surgery but exhibit distinct safety trade-offs between missed critical interactions and alert overcalling. They should therefore be considered as decision-support tools rather than substitutes for clinical judgment and their integration should prioritize validated, supervised workflows. Full article

Artificial Intelligence (AI) is reshaping pharmacy practice by enhancing decision-making, personalizing therapy, and improving medication safety. AI applications now span drug discovery, clinical decision support, and adherence monitoring. This narrative review explores key innovations, practical applications, and the implications of AI integration in pharmacy practice, with a focus on emerging tools, pharmacist roles, and ethical considerations. The review was conducted using literature from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar. Thematic synthesis included AI-based drug interaction checkers, Clinical Decision Support Systems (CDSS), telepharmacy, pharmacogenomics, and predictive analytics. AI enhances clinical decision-making, reduces medication errors, and supports precision medicine. AI tools support pharmacists and healthcare professionals in optimizing care. However, data privacy, algorithmic bias, and workflow integration continue to pose challenges. AI holds transformative potential in pharmacy, though its integration requires overcoming ethical and workflow-related challenges. Ethical and regulatory vigilance, coupled with pharmacist training and interdisciplinary collaboration, is essential to realize the full potential of AI. Full article