Search Results (278)

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

Pangenotypic direct-acting antivirals (pDAAs) have transformed hepatitis C virus (HCV) treatment. In Italy, sofosbuvir/velpatasvir (SOF/VEL) and glecaprevir/pibrentasvir (GLE/PIB) are available. While both show similar efficacy, differences in patient profiles and potential drug–drug interactions (DDIs) may influence treatment choice. This study examined factors affecting pDAA selection and potential prescribing gaps. Using administrative databases (2018–2023) covering 3.7 million citizens, HCV patients were divided into SOF/VEL and GLE/PIB cohorts and compared by demographic, clinical, and therapeutic data. Among 5565 patients, 2837 (51%) received SOF/VEL and 2728 (49%) received GLE/PIB. SOF/VEL patients were older (60.8 vs. 57.6 years, p < 0.001) and had more comorbidities: diabetes (24% vs. 17%), mental disorders (22% vs. 14%), cancer (14% vs. 9%), and cardiovascular disease (31% vs. 22%). Hospitalization rates were higher (19% vs. 13%), as were exemption codes for chronic hepatitis (58% vs. 50%) and hypertension (32% vs. 23%). Polypharmacy was more common with SOF/VEL; 25% used ≥10 non-pDAA drugs (vs. 17%), and mean medications per patient were higher (6.3 ± 5.6 vs. 4.9 ± 5.2). SOF/VEL was often used for older, frailer patients, likely due to a more favourable DDI profile. These prescribing trends highlight the importance of tailoring pDAA choice to patient comorbidity profiles, ensuring appropriate and individualized HCV treatment. Full article

Drug–drug interactions (DDIs) are one of the most common problems related to drug administration which represent a risk for patient safety. Considering their position in the healthcare system, pharmacists should be more proactively involved in DDI management. The paper shows representation of DDI intervention models in each DDI category. This research enrolled outpatients prescribed pharmacotherapies from 40 randomly selected community pharmacies. DDIs were analyzed using Lexicomp^® Lexi-InteractTM Online (Lexi-Comp, Inc., Hudson, NY, USA) software. Clinical pharmacists’ panel, according to the necessary interventions, determined an independent model of pharmacist interventions (category 1) and models that require cooperation with physicians (category 2) for DDI management. In total, 4107 patients were enrolled in the study. Mean patient age was 67.5; they were mostly women (56.5%) and had on average of 3.4 diagnosis and 5.5 prescription drugs. Overall, 14,175 potential clinically significant DDIs were identified: 83.3% of C, 15.4% of D, and 1.3% of X category. At least one potential DDI was found in 78.6% of patients. Models of pharmacist DDI interventions in collaboration with a physician (category 2) were more prevalent than independent models (category 1): 57.5% vs. 42.5% in C category DDIs, 97.8% vs. 2.2% in D category, and 100% vs. 0% in category X DDIs. This research aimed to gain an insight into the distribution of interventions in DDI management models between physicians and pharmacists, which can contribute to more efficient pharmaceutical care and visibility. Full article

Background: Dravet syndrome, a severe early-onset epileptic encephalopathy, is treated with multiple antiepileptic drugs such as clobazam (CLB) and stiripentol (STP), increasing the risk of drug–drug interactions (DDIs). Given the limited pediatric pharmacokinetic data, this study developed physiologically based pharmacokinetic (PBPK) models for CLB and STP to optimize dosing and assess DDI risk across pediatric age groups. Methods: We developed PBPK models for CLB, its active metabolite, N-desmethylclobazam (N-CLB), and STP in healthy adults and pediatric patients with Dravet syndrome aged two years and older. We evaluated the inhibitory effect of STP on CLB and N-CLB metabolism, accounting for CYP2C19 phenotypes. The model was extrapolated to predict drug exposure in pediatric patients under two years of age. Results: PBPK models for CLB, N-CLB, and STP successfully recapitulated observed pharmacokinetics in healthy adults and pediatric patients older than two years. Model verification against clinical DDI data showed that co-administration of STP with CLB resulted in a clinically insignificant increase in CLB exposure (C_min ratio = 1.77). In contrast, N-CLB exposure increased approximately 7-fold in CYP2C19 extensive metabolizers (C_min ratio ≈ 7) and slightly decreased in poor metabolizers (C_min ratio = 0.9), consistent with the CYP2C19-dependent metabolism of N-CLB. Extrapolation to pediatric patients under two years of age predicted CLB, N-CLB, and STP exposures that were comparable to older children and remained within their reported efficacy and safety margins, suggesting no major ontogeny-related effect on exposure. Conclusions: The PBPK model supports the safe extrapolation of CLB and STP co-administration to pediatric Dravet syndrome patients as young as six months. Full article

Introduction: Cannabidiol (CBD) is widely available over the counter for presumed medical and recreational purposes. Despite its non-psychoactive nature, CBD exhibits intrinsic pharmacological activity that may lead to potential adverse drug events (ADEs) and drug–drug interactions (DDI) with common prescription medications through cytochrome P450 inhibition. Due to their largely unregulated nature and widespread advertising, consumers who use CBD products may not be aware of these potential negative drug interactions. The purpose of this study was to determine how frequently patients who use CBD products concurrently take prescription medication with known drug–drug interaction (DDI) potential, and to identify specific therapeutic classes most commonly involved. Methods: In this cross-sectional study, a survey was distributed to patients and family members in the adult and pediatric Emergency Departments of a Level 1 Trauma Center in eastern North Carolina. Respondents reported household CBD use and selected from a list of conditions for which they take prescription medications. Results: Of 681 eligible respondents, 254 (37.3%) reported CBD use in their household (CBDUIH). Among those with CBDUIH, 69.7% reported concurrent use of 1 or more medications with a potential DDI risk. The most common categories of prescriptions were antidepressants (64.4%) and antihypertensives (41.8%), followed by agents for diabetes, hyperlipidemia, and immune disorders. Conclusions: The majority of CBD users in this population are concurrently taking medications with DDI potential, highlighting the need for patient and provider education, and improved labeling of CBD-based products to accurately reflect risks. Further study of clinically significant interactions is needed to determine which medications within these common categories have the most substantial risk of DDI. Full article

Background: Physiologically based pharmacokinetic (PBPK) modeling is a mathematical approach that integrates human physiological parameters with drug-specific characteristics (including both active pharmaceutical ingredients and excipients), and it has emerged as one of the core technologies for optimizing the efficiency and reliability of drug development. Methods: This study synthesizes applications of PBPK models in FDA-approved drugs (2020–2024), systematically analyzing model utilization frequency, indication distribution, application domains and choice of modeling platforms, to reveal their substantive contributions to regulatory submissions. Additionally, we conducted an in-depth analysis of the PBPK models for 2024, classifying models into three tiers based on critical assessment of FDA reviewer comments. Results: Among 245 FDA-approved new drugs during this period, 65 NDAs/BLAs (26.5%) submitted PBPK models as pivotal evidence. Oncology drugs accounted for the highest proportion (42%). In application scenarios, drug–drug interaction (DDI) was predominant (81.9%), followed by dose recommendations for patients with organ impairment (7.0%), pediatric population dosing prediction (2.6%), and food-effect evaluation. Regarding modeling platforms, Simcyp^® emerged as the industry-preferred modeling platform, with an 80% usage rate. In terms of regulatory evaluation, a core concern for reviewers is whether the model establishes a complete and credible chain of evidence from in vitro parameters to clinical predictions. Conclusions: Detailed regulatory reviews demonstrate that although some PBPK models exhibit certain limitations and shortcomings, this does not preclude them from demonstrating notable strengths and practical value in critical applications. Benefiting from the strong support these successful implementations provide for regulatory decision-making, the technology is gaining increasing recognition across the industry. Looking forward, the integration of PBPK modeling with artificial intelligence (AI) and multi-omics data will unprecedentedly enhance predictive accuracy, thereby providing critical and actionable insights for decision-making in precision medicine and global regulatory strategies. Full article

Direct-acting antivirals (DAAs) have transformed hepatitis C virus (HCV) management, offering high cure rates, favorable safety, and simplified regimens. Management in immunosuppressed patients remains challenging due to drug–drug interactions (DDIs). The objective of this review is to summarize clinical outcomes, safety, and pharmacologic considerations of DAA therapy in immunosuppressed patients, including solid organ transplant recipients and those on biological agents. We reviewed clinical studies, pharmacologic databases, and guidelines to characterize DAA classes, mechanisms, and relevant DDIs in immunosuppressed HCV patients. In transplant recipients, DAAs achieved sustained virological response (SVR) > 90% with minimal graft rejection. Safety profiles were favorable, and immunosuppressant dose adjustments were rarely needed. DDIs, particularly with calcineurin inhibitors (tacrolimus, cyclosporine), require careful monitoring due to variable trough-level effects. Evidence also supports the efficacy and safety of DAAs in patients on biological agents, without compromising SVR. Pharmacokinetic data indicate DAAs maintain antiviral activity across HCV genotypes in the presence of immunosuppressants, though mTOR inhibitors may alter efficacy in certain HCV genotypes. DAAs are highly effective and safe in immunosuppressed patients, achieving high SVR rates and potential graft survival benefits. Prospective studies are needed to assess DAA therapy in patients receiving biological agents and to optimize co-administration strategies with immunosuppressive agents. Full article

The forecasting of Drug-Drug Interactions (DDIs) is essential in pharmacology and clinical practice to prevent adverse drug reactions. Existing approaches, often based on neural networks and knowledge graph embedding, face limitations in modeling correlations among drug features and in handling complex BioKG relations, such as one-to-many, hierarchical, and composite interactions. To address these issues, we propose Rot4Cap, a novel framework that embeds drug entity pairs and BioKG relationships into a four-dimensional vector space, enabling effective modeling of diverse mapping properties and hierarchical structures. In addition, our method integrates molecular structures and drug descriptions with BioKG entities, and it employs capsule network–based attention routing to capture feature correlations. Experiments on three benchmark BioKG datasets demonstrate that Rot4Cap outperforms state-of-the-art baselines, highlighting its effectiveness and robustness. Full article

Background and Objective: Tofacitinib (TOF), an oral Janus kinase inhibitor used to treat rheumatoid arthritis (RA), is extensively metabolized by cytochrome P450 (CYP) 3A4. Ritonavir (RTV), a protease inhibitor, is commonly used as a pharmacokinetic (PK) enhancer due to its potent CYP3A4 inhibitory effects. Considering the prevalence of comorbidities in RA patients, it is possible to use TOF and RTV concurrently, raising concerns about potential drug–drug interactions (DDIs). The current study aims to assess the potential DDIs between RTV and TOF. Methods: An in vivo rat study was conducted to investigate the impacts of RTV on the PK of TOF. Rats were randomly divided into three groups: vehicle, RTV 10 mg/kg, and RTV 20 mg/kg, each undergoing four days of pretreatment. On the test day, TOF (10 mg/kg) was administered following co-administration of the respective RTV doses. Blood samples were collected at the pre-specified time points. Plasma concentrations of TOF were quantified using liquid chromatography coupled with mass spectrometry, and PK parameters were analyzed using non-compartmental analysis. Results: RTV (10 and 20 mg/kg) increased the area under the curve of TOF by 2.53-fold (95% CI: 1.64–3.43) and 5.39-fold (95% CI: 4.47–6.33), respectively, and the maximum concentration by 1.47-fold (95% CI: 0.99–2.00) and 2.86-fold (95% CI: 2.39–3.37), respectively. Whereas the half-life (t_1/2) remained unchanged. Conclusions: RTV substantially increased TOF exposure in rats. These results suggest the need for dose adjustments of TOF during co-administration with RTV in clinical settings. Further clinical research is needed to confirm these findings. Full article