Search Results (24)

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: Colistin (polymyxin E) has re-emerged as a last-hope treatment against MDR Gram-negative pathogens due to the development of extensively drug-resistant Gram-negative bacteria. Unfortunately, rapid global resistance towards colistin has emerged, which represents a major public health concern. In this context (CBD), a lipophilic molecule derived from Cannabis sativa, exhibits antimicrobial activity mainly against Gram-positive bacteria but is generally ineffective against Gram-negative species. However, synergistic antibacterial activity between CBD and polymyxin B has been reported. The objective of this work is to analyze the colistin–CBD synergy against clinically relevant Gram-negative isolates displaying diverse mechanisms of colistin resistance and to explore the basis of the possible mechanism of action involved in the first steps of this synergy. Methods: Microbiological assays, minimal inhibitory concentration, cell culture, synergy tests by checker board and time kill, biofilm inhibition evaluation by crystal violet and MTT, SEM (scanning electron microscopy), molecules interaction analysis by nuclear magnetic resonance (NMR). Results: The colistin–CBD combination displayed synergy in colistin resistant Gram-negative bacteria and also disrupted preformed biofilms and killed bacteria within them. Time-kill assays revealed rapid bactericidal activity and SEM showed mild surface alterations on bacterial outer membranes after sublethal colistin monotherapy. Furthermore, a series of sequential treatment assays on colistin-resistant Escherichia coli showed that simultaneous exposure to both compounds was required for activity, as introducing a washing step between treatments abolished the antibacterial effect. In order to obtain deeper insight into this mechanism, NMR analyses were performed, revealing specific molecular interactions between CBD and colistin molecules. Conclusions: These results provide evidence for the first time that both molecules engage through a specific and structurally meaningful interaction and only display synergy when acting together on colistin-resistant bacteria. 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

The significant burden of polypharmacy in clinical settings contrasts sharply with the narrow research focus on drug–drug interactions (DDIs), revealing an important gap in understanding the complexity of real-world multi-drug regimens. The present study addresses this gap by conducting a high-resolution, multidimensional bibliometric and network analysis of 19,151 DDI publications indexed in the Web of Science Core Collection (1975–2025). Using advanced tools, including VOSviewer version 1.6.20, Bibliometrix 5.0.0, and AI-enhanced terminology normalization, global research trajectories, knowledge clusters, and collaborative dynamics were systematically mapped. The analysis revealed an exponential growth in publication volume (from 55 in 1990 to 1194 in 2024), with output led by the United States and a marked acceleration in Chinese contributions after 2015. Key pharmacological agents frequently implicated in DDI research included CYP450-dependent drugs such as statins, antiretrovirals, and central nervous system drugs. Thematic clusters evolved from mechanistic toxicity assessments to complex frameworks involving clinical risk management, oncology co-therapies, and pharmacokinetic modeling. The citation impact peaked at 3.93 per year in 2019, reflecting the increasing integration of DDI research into mainstream areas of pharmaceutical science. The findings highlight a shift toward addressing polypharmacy risks in aging populations, supported by novel computational methodologies. This comprehensive assessment offers insights for researchers and academics aiming to navigate the evolving scientific landscape of DDIs and underlines the need for more nuanced system-level approaches to interaction risk assessment. Future studies should aim to incorporate patient-level real-world data, expand bibliometric coverage to underrepresented regions and non-English literature, and integrate pharmacogenomic and time-dependent variables to enhance predictive models of interaction risk. Cross-validation of AI-based approaches against clinical outcomes and prospective cohort data are also needed to bridge the translational gap and support precision dosing in complex therapeutic regimens. Full article

Background: Drug-drug interactions (DDIs) are a growing safety concern in dental care, particularly among patients with comorbidities and polypharmacy. However, real-world data (RWD) on the prevalence and severity of DDIs in dental settings remain scarce. Objectives: This study aimed to assess the frequency, severity, and clinical relevance of DDIs in dental patients and to identify age- and comorbidity-related risk patterns. Methods: This retrospective study analyzed a cohort of 105 dental patients, considering demographics, preexisting diseases, dental procedures, and prescribed medications. We examined drug-drug interactions (DDIs) employing the DrugBank Drug Interaction Checker, which yields DDI severity into major, moderate, or minor. Results: 45.7% of patients had preexisting diseases, with cardiovascular diseases most prevalent (19.0%). Higher prevalent dental diagnoses and procedures included apical lesions (47.6%) and tooth extractions (53.3%), suggesting frequent pharmacotherapy exposure. We identified 542 DDIs out of 1332 drug pairs and found 2.3% major, 25.0% moderate, and 13.4% minor, with 59.3% showing no interactions. Key high-risk DDIs included epinephrine with beta-blockers. Fifteen patients aged 31–60 years experienced the most major DDIs of 61.3%, patients ≥ 61 years faced 38.7%, and the 0–30 group had none, highlighting age-specific risks. The higher DDIs burden in the 31–60 age group may reflect better knowledge of the drugs they used and accurate reporting of them. Conclusions: Our retrospective study addresses the paucity of dental DDIs real-world data (RWD) studies, pleading for improved drug reconciliation, systematic screening, and age- and comorbidity-tailored strategies to enhance patient safety. Full article

Complete atrioventricular (AV) block is a severe conduction abnormality caused by intrinsic cardiac disease, ischemia, electrolyte imbalances, or drug interactions. Elderly patients on multiple medications are particularly vulnerable to polypharmacy-related interactions. This case report describes an 82-year-old female presenting to the emergency department with fatigue, syncope, and disorientation. Her medical history included atrial fibrillation, hypertension, and heart failure, with a medication regimen of digoxin 0.25 mg given daily 5 days out of 7, metoprolol 50 mg twice daily, lisinopril 10 mg daily, furosemide 40 mg daily, and spironolactone 50 mg daily. Clinical examination revealed bradycardia and a holosystolic murmur in the mitral valve area, while the electrocardiogram showed complete AV block at a ventricular rate of 35 bpm. Laboratory results indicated mild hyperkalemia (4.9 mmol/L). Suspecting a digoxin–beta-blocker interaction, antiarrhythmic therapy was discontinued. Within three days, the AV block resolved, transitioning to atrial fibrillation with a high ventricular rate. Bisoprolol was introduced for rate control, and hemodynamic stability was achieved. The patient was discharged with a revised medication regimen and showed no recurrence of AV block. This case emphasizes the importance of recognizing drug interactions as a reversible cause of AV block and using drug interaction checkers to manage polypharmacy, especially in elderly patients with multiple comorbidities. It also highlights the rare and paradoxical combination of atrial flutter and complete AV block. Full article

Drug interactions are a significant and integral part of the concept of medication-related adverse events, whether referring to potential interactions or those currently observed in real-world conditions. The high global consumption of antibiotics and their pharmacokinetic and pharmacodynamic mechanisms make antibiotic-drug interactions a key element that requires continuous study due to their clinical relevance. In the present work, the current state of knowledge on antibiotic-drug interactions, which are less studied than other drug-drug interactions despite their frequent use in acute settings, has been consolidated and updated. The focus was on the interactions of the commonly used antibiotics in clinical practice, on the characteristics of the geriatric population susceptible to interactions, and on the impact of online drug interaction checkers. Additionally, strategies for optimizing the management of these interactions, including spacing out administrations, monitoring, or avoiding certain combinations, are suggested. Sustained research and careful monitoring are critical for improving antibiotic safety and efficacy, especially in susceptible populations, to enhance precision in managing antibiotic-drug interactions. Full article

Metabolic syndrome (MetS) presents a significant global health challenge, characterized by a cluster of metabolic alterations including obesity, hypertension, insulin resistance/dysglycemia, and atherogenic dyslipidemia. Advances in understanding and pharmacotherapy have added complexity to MetS management, particularly concerning drug interactions and pharmacogenetic variations. Limited literature exists on drug–drug–gene interactions (DDGIs) and drug–drug–transporter gene interactions (DDTGIs), which can significantly impact pharmacokinetics and pharmacodynamics, affecting treatment outcomes. This narrative review aims to address the following three key objectives: firstly, shedding a light on the PK metabolism, transport, and the pharmacogenetics (PGx) of medicines most commonly used in the MetS setting (relevant lipid-lowering drugs, antihypertensives and antihyperglycemics agents); secondly, exemplifying potential clinically relevant pharmacokinetic drug interactions, including drug–drug interactions, DDGIs, and DDTGIs; and, thirdly, describing and discussing their potential roles in clinical practice. This narrative review includes relevant information found with the use of interaction checkers, pharmacogenetic databases, clinical pharmacogenetic practice guidelines, and literature sources, guided by evidence-based medicine principles. Full article

Objective: This study assessed the patterns and clinical significance of potential drug–drug interactions (pDDIs) in patients with diseases of the cardiovascular system. Methods: Electronic health records (EHRs), established in 2018–2023, were selected using the probability serial nested sampling method (n = 1030). Patients were aged 27 to 95 years (65.0% men). Primary diagnosis of COVID-19 was present in 17 EHRs (1.7%). Medscape Drug Interaction Checker was used to characterize pDDIs. The Mann–Whitney U test and chi-square test were used for statistical analysis. Results: Drug numbers per record ranged from 1 to 23 in T-List and from 1 to 20 in P-List. In T-List, 567 drug combinations resulted in 3781 pDDIs. In P-List, 584 drug combinations resulted in 5185 pDDIs. Polypharmacy was detected in 39.0% of records in T-List versus 65.9% in P-List (p-value < 0.05). The rates of serious and monitor-closely pDDIs due to ‘aspirin + captopril’ combinations were significantly higher in P-List than in T-List (p-value < 0.05). The rates of serious pDDIs due to ‘aspirin + enalapril’ and ‘aspirin + lisinopril’ combinations were significantly lower in P-List compared with the corresponding rates in T-List (p-value < 0.05). Serious pDDIs due to administration of aspirin with fosinopril, perindopril, and ramipril were detected less frequently in T-List (p-value < 0.05). Conclusions: Obtained data may suggest better patient adherence to ‘aspirin + enalapril’ and ‘aspirin + lisinopril’ combinations, which are potentially superior to the combinations of aspirin with fosinopril, perindopril, and ramipril. An abundance of high-order pDDIs in real-world clinical practice warrants the development of a decision support system aimed at reducing pharmacotherapy-associated risks while integrating patient pharmacokinetic, pharmacodynamic, and pharmacogenetic information. Full article

In recent years, several changes have occurred in the management of chronic immunological conditions with the emerging use of targeted therapies. This two-phase cross-sectional study was conducted through structured in-person interviews in 2018–2019 and 2022. Additional data sources included ambulatory medical records and the itemized reimbursement reporting interface of the National Health Insurance Fund. Drug interactions were analyzed using the UpToDate Lexicomp, Medscape drug interaction checker, and Drugs.com databases. The chi-square test was used, and odds ratios (ORs) were calculated. In total, 185 patients participated. In 53% of patients (n = 53), a serious drug–drug interaction (DDI) was identified (mean number: 1.07 ± 1.43, 0–7), whereas this value was 38% (n = 38) for potential drug–supplement interactions (mean number: 0.58 ± 0.85, 0–3) and 47% (n = 47) for potential targeted drug interactions (0.72 ± 0.97, 0–5) in 2018. In 2022, 78% of patients (n = 66) were identified as having a serious DDI (mean number: 2.27 ± 2.69, 0–19), 66% (n = 56) had a potential drug–supplement interaction (mean number: 2.33 ± 2.69, 0–13), and 79% (n = 67) had a potential targeted drug interactions (1.35 ± 1.04, 0–5). Older age (>60 years; OR: 2.062), female sex (OR: 3.387), and polypharmacy (OR: 5.276) were identified as the main risk factors. Screening methods and drug interaction databases do not keep pace with the emergence of new therapeutics. Full article

Drug–drug interactions (DDIs) can either enhance or diminish the positive or negative effects of the associated drugs. Multiple drug combinations create difficulties in identifying clinically relevant drug interactions; this is why electronic drug interaction checkers frequently report DDI results inconsistently. Our paper aims to analyze drug interactions in cardiovascular diseases by selecting drugs from pharmacotherapeutic subcategories of interest according to Level 2 of the Anatomical Therapeutic Chemical (ATC) classification system. We checked DDIs between 9316 pairs of cardiovascular drugs and 25,893 pairs of cardiovascular and other drugs. We then evaluated the overall agreement on DDI severity results between two electronic drug interaction checkers. Thus, we obtained a fair agreement for the DDIs between drugs in the cardiovascular category, as well as for the DDIs between drugs in the cardiovascular and other (i.e., non-cardiovascular) categories, as reflected by the Fleiss’ kappa coefficients of $\kappa =0.3363$ and $\kappa =0.3572$, respectively. The categorical analysis of agreement between ATC-defined subcategories reveals Fleiss’ kappa coefficients that indicate levels of agreement varying from poor agreement ($\kappa <0$) to perfect agreement ($\kappa =1$). The main drawback of the overall agreement assessment is that it includes DDIs between drugs in the same subcategory, a situation of therapeutic duplication seldom encountered in clinical practice. Our main conclusion is that the categorical analysis of the agreement on DDI is more insightful than the overall approach, as it allows a more thorough investigation of the disparities between DDI databases and better exposes the factors that influence the different responses of electronic drug interaction checkers. Using categorical analysis avoids potential inaccuracies caused by particularizing the results of an overall statistical analysis in a heterogeneous dataset. Full article

The study aimed to assess clinical pharmacology patterns of prescribed and taken medications in older cardiovascular patients using electronic health records (EHRs) (n = 704) (2019–2022). Medscape Drug Interaction Checker was used to identify pairwise drug–drug interactions (DDIs). Prevalence rates of DDIs were 73.5% and 68.5% among taken and prescribed drugs, respectively. However, the total number of DDIs was significantly higher among the prescribed medications (p < 0.05). Serious DDIs comprised 16% and 7% of all DDIs among the prescribed and taken medications, respectively (p < 0.05). Median numbers of DDIs between the prescribed vs. taken medications were Me = 2, IQR 0–7 vs. Me = 3, IQR 0–7 per record, respectively. Prevalence of polypharmacy was significantly higher among the prescribed medications compared with that among the taken drugs (p < 0.05). Women were taking significantly more drugs and had higher prevalence of polypharmacy and DDIs (p < 0.05). No sex-related differences were observed in the list of prescribed medications. ICD code U07.1 (COVID-19, virus identified) was associated with the highest median DDI number per record. Further research is warranted to improve EHR structure, implement patient engagement in reporting adverse drug reactions, and provide genetic profiling of patients to avoid potentially serious DDIs. Full article

Patients in intensive care units (ICU) are at high risk to experience potential drug-drug interactions (pDDIs) because of the complexity of their drug regimens. Such pDDIs may be driven by pharmacokinetic or pharmacodynamic mechanisms with clinically relevant consequences in terms of treatment failure or development of drug-related adverse events. The aim of this paper is to review the pharmacokinetic-driven pDDIs involving antibiotics in ICU adult patients. A MEDLINE Pubmed search for articles published from January 2000 to June 2022 was completed matching the terms “drug-drug interactions” with “pharmacokinetics”, “antibiotics”, and “ICU” or “critically-ill patients”. Moreover, additional studies were identified from the reference list of retrieved articles. Some important pharmacokinetic pDDIs involving antibiotics as victims or perpetrators have been identified, although not specifically in the ICU settings. Remarkably, most of them relate to the older antibiotics whereas novel molecules seem to be associated with a low potential for pDDIs with the exceptions of oritavancin as potential perpetrator, and eravacicline that may be a victim of strong CYP3A inducers. Personalized therapeutic drug regimens by means of available web-based pDDI checkers, eventually combined with therapeutic drug monitoring, when available, have the potential to improve the response of ICU patients to antibiotic therapies. Full article

Purpose: Several studies have shown that polypharmacy is the main cause of drug interactions, and the prevalence and the level of the severity varied with the duration of stay in the hospital, sex and race of the patients. The aims of this investigation were to identify the drug-drug interactions in hospitalized pediatric patients associated with polypharmacy, and to categorize the drug interactions in pharmacokinetic or pharmacodynamic interactions according to their level of severity. Methods: A cross-sectional, prospective analytical study was performed at a pediatric tertiary care hospital in Lahore, Pakistan for the duration of 4 months, which included prescription orders for 300 patients. Data were collected from patient medical files about previous and current medication history. Drug interactions were analyzed using interaction checker on Medscape and categorized according to the severity levels. Results: Out of 300 patients, the occurrence of drug interactions was found in 157 (52.3%) patients, while in 143 (47.7%), no interaction was found. Among these interactions, 50.7% were pharmacodynamic interactions, and 49.30% were pharmacokinetic interactions. Eighty-one percent of prescription orders with drug interactions contained more than three drugs, and 11.9% of interactions were severe. The majority of interactions were of amikacin-vancomycin, piroxicam-captopril and captopril-ciprofloxacin. Conclusion: Most of the interactions were moderate among patients with multiple drug prescriptions. The drug interactions can be minimized by providing special patient monitoring and adequate management with prior knowledge of these drug interaction. Full article

Background: This study measures the use of drugs within the therapeutic areas of antithrombotic agents (B01), the cardiovascular system (C), analgesics (N02), psycholeptics (N05), and psychoanaleptics (N06) among the general population (GP) in comparison to persons with diabetes in Denmark. The study focuses on drugs having pharmacogenomics (PGx) based dosing guidelines for CYP2D6, CYP2C19, and SLCO1B1 to explore the potential of applying PGx-based decision-making into clinical practice taking drug–drug interactions (DDI) and drug–gene interactions (DGI) into account. Methods: This study is cross-sectional, using The Danish Register of Medicinal Product Statistics as the source to retrieve drug consumption data. Results: The prevalence of use in particular for antithrombotic agents (B01) and cardiovascular drugs (C) increases significantly by 4 to 6 times for diabetic users compared to the GP, whereas the increase for analgesics (N02), psycoleptics, and psychoanaleptics (N06) was somewhat less (2–3 times). The five most used PGx drugs, both in the GP and among persons with diabetes, were pantoprazole, simvastatin, atorvastatin, metoprolol, and tramadol. The prevalence of use for persons with diabetes compared to the GP (prevalence ratio) increased by an average factor of 2.9 for all PGx drugs measured. In addition, the prevalence of use of combinations of PGx drugs was 4.6 times higher for persons with diabetes compared to GP. In conclusion, the findings of this study clearly show that a large fraction of persons with diabetes are exposed to drugs or drug combinations for which there exist PGx-based dosing guidelines related to CYP2D6, CYP2C19, and SLCO1B1. This further supports the notion of accessing and accounting for not only DDI but also DGI and phenoconversion in clinical decision-making, with a particular focus on persons with diabetes. Full article