Search Results (348)

Introduction: Drug–drug interactions (DDI) are a major, preventable cause of patient harm, a challenge amplified in Jordan by rising polypharmacy and documented high rates of medication errors. To date, no study in Jordan has systematically compared hospital and community pharmacists. This study aimed to conduct the first national, comparative assessment of DDI management among these two cadres. Materials and Methods: A national, cross-sectional study was conducted with 380 licensed pharmacists (175 hospitals, 205 community) recruited via proportionate stratified random sampling. A validated online questionnaire assessed demographics, objective DDI knowledge, professional attitudes, practices, and barriers. Multivariable logistic regression was used to identify independent predictors of high knowledge and optimal practice. All collected data were coded, cleaned, and analyzed using the Statistical Package for the Social Sciences (SPSS V28.0). Results: Hospital pharmacists achieved significantly higher mean objective knowledge scores than community pharmacists (10.3 vs. 8.1 out of 15, p < 0.001), a gap particularly wide for interactions involving high-risk OTC medications. The primary barrier for community pharmacists was a lack of access to patient data (85.4%), contrasting with high workload and physician resistance in hospitals. Optimal practice was independently predicted by higher knowledge (AOR = 1.25), a hospital practice setting (AOR = 3.65), and was inhibited by perceived physician resistance (AOR = 0.45). Conclusions: Jordanian hospital and community pharmacists operate in distinct worlds of knowledge and practice. A tailored, dual-pronged national strategy is essential. For hospitals, interventions should target interprofessional dynamics. For community pharmacies, health policy reform to provide access to integrated patient data is the most urgent priority. These findings highlight a globally relevant challenge of practice-setting disparities, offering a model for other nations to develop tailored, context-specific interventions to improve medication safety. Full article

Background: Asciminib (Scemblix^®) is approved for the first-line treatment of adult patients with chronic myeloid leukemia in the chronic phase at 40 mg twice daily (BID) and 80 mg once daily (QD) or 200 mg BID for patients harboring the T315I mutation. Objectives: (1) Extrapolate the DDI magnitude as the perpetrator or victim of other drugs and the effect of organ impairment to untested doses; (2) Predict clinically untested DDI scenarios. Methods: Asciminib is primarily cleared by cytochrome P450 (CYP)3A4, UDP-glucuronosyltransferases (UGT)2B7, UGT2B17, UGT1A3/4, and the breast-cancer-resistance protein (BCRP). In vitro asciminib is an inhibitor of several CYP, UGT enzymes, and transporters and is an inducer of CYP1A2 and CYP3A4. Clinical DDI studies assessed asciminib 40 mg BID as a perpetrator on CYP-sensitive substrates. Additional studies evaluated the impact of strong CYP3A4 perpetrators and imatinib on a single 40 mg dose of asciminib. Hepatic and renal impairment studies were also conducted at the 40 mg dose. A nonlinear whole-body physiologically based pharmacokinetic (PBPK) model was developed and verified for asciminib as a CYP3A4, UGT, and BCRP substrate and a perpetrator of several CYP and UGT enzymes. Results: This PBPK model was applied in lieu of clinical pharmacology studies to support the new drug application of Scemblix^® and to bridge data from 40 mg BID to the 80 mg QD and 200 mg BID dose regimens. Conclusions: The PBPK predictions informed the drug product label and are estimated to have replaced at least 10 clinical studies. Full article

Background: Drug–drug interactions (DDIs) involve pharmacokinetic or pharmacodynamic changes that occur when multiple drugs are co-administered, potentially leading to reduced efficacy or adverse effects. As polypharmacy becomes more prevalent, especially among patients with chronic diseases, scalable and accurate DDI prediction has become increasingly important. Although numerous computational approaches have been proposed to predict DDIs using various modalities such as chemical structure and biological networks, the intrinsic heterogeneity of these data complicates unified modeling; Methods: We address this challenge with a multimodal deep learning framework that integrates three complementary, heterogeneous modalities: (i) chemical structure, (ii) BioBERT-derived semantic embeddings (a domain-specific large language model, LLM), and (iii) pharmacological mechanisms through the CTET proteins. To incorporate indirect biological pathways within the PPI network, we apply a random walk with restart (RWR) algorithm. Results: Across features combinations, fusing structural feature with BioBERT embedding achieved the highest classification accuracy (0.9655), highlighting the value of readily available data and the capacity of domain-specific language models to encode pharmacological semantics from unstructured texts. Conclusions: BioBERT embeddings were particularly informative, capturing subtle pharmacological relationships between drugs and improving prediction of potential DDIs. Beyond predictive performance, the framework is readily applicable to real-world clinical workflows, providing rapid DDI references to support the polypharmacy decision-making. Full article

With the ongoing advancement of urban renewal and cultural tourism, public spaces within historic cultural districts face dual challenges of structural complexity and diverse user demands. There is an urgent need to establish a scientific, user-oriented evaluation system to enhance spatial quality and user satisfaction. This study takes the Nanhesha Historic and Cultural Quarter in Yangzhou as a case study, focusing on two primary user groups: tourists and local residents. Employing semi-structured interviews and grounded theory, it distils a demand evaluation framework comprising four dimensions—spatial structure, environmental perception, socio-cultural aspects, and facility systems—with a total of 21 indicators. Subsequently, employing the Delphi method, experts were invited to refine the indicators through two rounds of deliberation. The Kano model was then applied to classify the demand attributes of different groups, identifying five common demands and sixteen differentiated demands. These were categorised into three sensitivity levels. Further integrating the Satisfaction Increment Index (SII), Dissatisfaction Decrement Index (DDI), and sensitivity values, a two-dimensional prioritisation model was constructed. This yielded a unified three-tier priority system alongside independent ranking frameworks for each user group. Findings reveal that visitors prioritise immediate experiential attributes such as spatial accessibility, appropriate scale, and environmental cleanliness, whereas residents favour long-term usage-oriented aspects including cultural expression, convenient facilities, and climate adaptability. This research not only enriches the theoretical framework for studying public space perception in historic cultural districts but also provides actionable evaluation criteria and practical pathways for multi-stakeholder spatial optimisation design. It offers guidance for the high-quality, refined development of public spaces within historic quarters. Full article

Background: Drug–drug interactions (DDIs) are frequent and potentially harmful in pediatric cancer patients due to polypharmacy and complex chemotherapy regimens. However, data on DDIs in hospitalized pediatric oncology patients remain limited, particularly in Middle Eastern settings. Methods: In this prospective study, we analyzed prescriptions for hospitalized pediatric oncology patients in Iran to assess the prevalence, severity, and nature of potential DDIs (PDDIs). Chemotherapy and supportive medications were analyzed using two validated databases (Lexi-Interact™ and Drugs.com™) between November 2019 and June 2020. Results: Of 80 patients (median age 8.9 years), 21.2% had at least one documented PDDI. We identified 197 total PDDIs involving 42 unique drug pairs. The most common DDIs included acetaminophen and granisetron (severity rating: moderate). Methotrexate and vincristine were the most frequent antineoplastic DDI pair. Methotrexate alone accounted for 156 interactions. Conclusions: This is the first prospective study from Iran—and the largest in the region—investigating PDDIs in pediatric oncology. The dual-database screening approach improved PDDI detection. Clinical teams should routinely evaluate medication profiles in pediatric cancer patients to minimize avoidable harms from DDIs. Full article

Background and aim: Managing the therapy of patients with chronic liver diseases and comorbidities presents significant challenges for physicians and pharmacists, particularly regarding drug-induced liver damage and polypharmacy. Given the liver’s central role in drug detoxification, polypharmacy in liver disease requires special attention. The aim of the review was to assess the prevalence of polypharmacy among patients with chronic liver diseases. Approach and Results: A literature search focused on randomized controlled trials, database reviews, and medical records. Review of PubMed, SCOPUS, and ScienceDirect databases identified 2578 manuscripts, however only 11 studies met the inclusion criteria. The results of studies showed that the prevalence of polypharmacy among patients with chronic liver disease can exceed 50%, and can lead to high prevalence of MRP and pDDI among those patients. Conclusions: Findings reveal a critical link between polypharmacy and adverse outcomes in chronic liver diseases, including cirrhosis, hepatitis, and non-alcoholic fatty liver disease. Individualized treatment plans, considering factors such as age, gender, comorbidities, and liver disease severity are essential. The interventions focused on mitigating MRP and reducing pDDI need to be implemented in order to reduce the potential harm of polypharmacy. Full article

At present, polypharmacy—which is the use of several medications to treat a single case at the same time—has become a fairly common medical practice, particularly in chronic illnesses or with older patients. But this relatively ‘faster’ form of treatment brings the problem of cumulative polypharmacy, which occurs when there is an increase in drug–drug interactions (DDIs) due to the large number of medicines taken. While the aftermath, such as the reduction in strength of medication taken or catastrophic and fatal responses to certain drugs, is clearly not worth the initial effort put into trying to ease the condition, attempting to resolve these issues requires excessive research. With these difficulties in mind, we describe our research that uses graph neural networks (GNNs) focused on DDI prediction by modeling drugs and their interactions in the form of graphs. The research is divided into two parts. In this research, the relevant literature is reviewed in order to understand how modern GNN-based algorithms can be applied for the detection of optimal drugs. Full article

Background: Accurate assessment of CYP2C induction-mediated drug–drug interactions (DDIs) remains a challenge, despite the importance of CYP2C enzymes in drug metabolism. Limitations in available models and scarce clinical induction data have hampered quantitative preclinical DDI risk evaluation. Methods: In this study, the authors utilized an all-human hepatocyte triculture system to capture CYP2C induction using the perpetrators rifampicin, efavirenz, carbamazepine, and apalutamide. In vitro induction parameters were quantified by measuring changes in both mRNA and enzyme activities for CYP2C8, CYP2C9, and CYP2C19. These induction parameters, along with CYP-specific intrinsic clearance (CL_int) for the victim compounds, were incorporated into a physiologically based pharmacokinetic (PBPK) model, and pharmacokinetics (PK) of known CYP2C substrates were predicted with and without co-administration of perpetrator compounds using clinical dosing regimens. The results were quantitatively compared with the currently utilized mechanistic static modeling (MSM) approach and the reported clinical DDI outcomes. Results: By incorporating the measured f_m of CYP2C substrates into PBPK modeling, we observed a lower propensity to over- or underpredict the exposure of these substrates as victims of CYP2C induction-based DDIs when co-administered with known perpetrators, which resulted in an excellent correlation to observed clinical outcomes. The MSM approach predicted the CYP3A4 induction-based DDI risk accurately but could not capture CYP2C induction with similar precision. Conclusions: Overall, this is the first study that demonstrates the utility of PBPK modeling as a complementary approach to MSM for CYP2C induction-based DDI risk assessment. Full article

Background: Anterior cervical discectomy and fusion (ACDF) is a widely performed surgical intervention for cervical spine herniation (CSH) to alleviate symptoms such as pain, weakness, and restricted mobility. Despite its efficacy, ACDF is associated with postoperative complications, notably dysphagia and dysphonia (PDD). Objective: This study investigates the prevalence, severity, and risk factors associated with PDD following ACDF using the validated Dysphagia and Dysphonia Inventory (HSS-DDI) adapted into Greek. Methods: A prospective observational cohort study was conducted at the University General Hospital of Ioannina from May to November 2023. The study involved 40 adult patients who underwent ACDF for CSH. Postoperative dysphagia and dysphonia were assessed using the Ohkuma questionnaire and HSS-DDI at 1 week and 1 month postoperatively. Results: The mean age of participants was 54.78 years, with a majority being male (60%). In terms of body mass index (BMI), 30% of participants had a normal weight, 47.5% were overweight, and 22.5% were obese. This study revealed that dysphagia and dysphonia were common postoperative complications, with improvements noted after one month. Factors such as BMI were statistically significant in influencing dysphagia outcomes, with normal BMI individuals reporting better outcomes than obese participants. Confirmatory factor analysis indicated the need for a larger sample size to confirm subscale validity in the Greek population. Conclusions: Postoperative dysphagia and dysphonia are prevalent following ACDF, but most patients experience improvements within a short period. Identifying risk factors, such as BMI, and utilizing validated assessment tools like the HSS-DDI can help optimize surgical techniques and postoperative care. Further studies with larger sample sizes are recommended for a more comprehensive understanding of these complications. Full article

Despite the significant volume of empirical research found in student-authored academic theses—particularly in the social sciences—these works are often poorly documented and difficult to discover within institutional repositories. A key reason for this is the lack of appropriate metadata frameworks that balance descriptive richness with usability. General standards such as Dublin Core are too simplistic to capture critical research details, while more robust models like the Data Documentation Initiative (DDI) are too complex for non-specialist users and not designed for use with student theses. This paper presents the design and validation of a lightweight, web-based metadata framework specifically tailored to document empirical research in academic theses. We are the first to adapt existing hybrid Dublin Core–DDI approaches specifically for thesis documentation, with a novel focus on cross-methodological research and non-expert usability. The model was developed through a structured analysis of actual student theses and refined to support intuitive, structured metadata entry without requiring technical expertise. The resulting system enhances the discoverability, classification, and reuse of empirical theses within institutional repositories, offering a scalable solution to elevate the visibility of the gray literature in higher education. Full article

Background: Drug–drug interactions (DDIs) may occur when two or more drugs are taken together, leading to undesired side effects or potential synergistic effects. Most clinical effects of drug combinations have not been assessed in clinical trials. Therefore, predicting DDIs can provide better patient management, avoid drug combinations that can negatively affect patient care, and exploit potential synergistic combinations to improve current therapies in women’s healthcare. Methods: A DDI prediction model was built to describe relevant drug combinations affecting reproductive treatments. Approved drug features (chemical structure of drugs, side effects, targets, enzymes, carriers and transporters, pathways, protein–protein interactions, and interaction profile fingerprints) were obtained. A unified predictive score revealed unknown DDIs between reproductive and commonly used drugs and their associated clinical effects on reproductive health. The performance of the prediction model was validated using known DDIs. Results: This prediction model accurately predicted known interactions (AUROC = 0.9876) and identified 2991 new DDIs between 192 drugs used in different female reproductive conditions and other drugs used to treat unrelated conditions. These DDIs included 836 between drugs used for in vitro fertilization. Most new DDIs involved estradiol, acetaminophen, bupivacaine, risperidone, and follitropin. Follitropin, bupivacaine, and gonadorelin had the highest discovery rate (42%, 32%, and 25%, respectively). Some were expected to improve current therapies (n = 23), while others would cause harmful effects (n = 11). We also predicted twelve DDIs between oral contraceptives and HIV drugs that could compromise their efficacy. Conclusions: These results show the importance of DDI studies aimed at identifying those that might compromise or improve their efficacy, which could lead to personalizing female reproductive therapies. Full article

In clinical decision support systems (CDSSs), where accurate classification of drug–drug interactions (DDIs) can directly affect treatment safety and outcomes, identifying drug interactions is a major challenge, introducing a scalable approach for classifying DDIs utilizing a finely-tuned biomedical language model. The method shown here uses BiomedBERT, a domain-specific version of bidirectional encoder representations from transformers (BERT) that was pre-trained on biomedical literature, to reduce the number of resources needed during fine-tuning. Low-rank adaptation (LoRA) was used to fine-tune the model on the DrugBank dataset. The objective was to classify DDIs into two clinically distinct categories, that is, synergistic and antagonistic interactions. A pseudo-labeling strategy was created to deal with the problem of not having enough labeled data. A curated ground-truth dataset was constructed using polarity-labeled interaction entries from DrugComb and verified DrugBank antagonism pairs. The fine-tuned model is used to figure out what kinds of interactions there are in the rest of the unlabeled data. A checkpointing system saves predictions and confidence scores in small pieces, which means that the process can be continued and is not affected by system crashes. The framework is designed to log every prediction it makes, allowing results to be refined later, either manually or through automated updates, without discarding low-confidence cases, as traditional threshold-based methods often do. The method keeps a record of every output it generates, making it easier to revisit earlier predictions, either by experts or with improved tools, without depending on preset confidence cutoffs. It was built with efficiency in mind, so it can handle large amounts of biomedical text without heavy computational demands. Rather than focusing on model novelty, this research demonstrates how existing biomedical transformers can be adapted to polarity-aware DDI classification with minimal computational overhead, emphasizing deployment feasibility and clinical relevance. 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: Depression, a major global health issue, is commonly treated with selective serotonin reuptake inhibitors (SSRIs). Given the link between depression and inflammation, nonsteroidal anti-inflammatory drugs (NSAIDs) may have adjunctive benefits. Clinically, SSRIs and NSAIDs are often co-prescribed for comorbid pain or inflammatory conditions. However, both drug classes pose risks of adverse effects, and their interaction may lead to clinically significant drug–drug interactions. Objectives: This study analyzed FDA Adverse Event Reporting System (FAERS) data (2004–2024) to assess gastrointestinal bleeding, thrombocytopenia, and acute kidney injury (AKI) potential risks linked to SSRIs (citalopram, escitalopram, fluoxetine, paroxetine, fluvoxamine, and sertraline) and NSAIDs (propionic/acetic/enolic acid derivatives, COX-2 inhibitors) in depression patients, alone and combined. Methods: Disproportionality analysis (crude reporting odds ratios, cROR) identified possible associations; drug interactions were evaluated using Ω shrinkage, additive, multiplicative, and combination risk ratio (CRR) models. Results: Gastrointestinal bleeding risk was potentially elevated with citalopram (cROR = 2.81), escitalopram (2.27), paroxetine (2.17), fluvoxamine (3.58), sertraline (1.69), and propionic acid NSAIDs (3.17). Thrombocytopenia showed a potential correlation with fluoxetine (2.11) and paroxetine (2.68). AKI risk may be increased with citalopram (1.39), escitalopram (1.36), fluvoxamine (3.24), and COX-2 inhibitors (2.24). DDI signal analysis suggested that citalopram in combination with propionic acid derivatives (additive model = 0.01, multiplicative model = 1.14, and CRR = 3.13) might increase the risk of bleeding. Paroxetine combined with NSAIDs (additive model = 0.014, multiplicative model = 2.65, and CRR = 2.99) could potentially increase the risk of thrombocytopenia. Sertraline combined with NSAIDs (Ω₀₂₅ = 0.94, multiplicative model = 2.14) might be associated with an increasing risk of AKI. Citalopram combined with propionic acid derivatives (Ω₀₂₅ = 1.08, multiplicative model = 2.17, and CRR = 2.42) could be associated with an increased risk of acute kidney injury. Conclusions: Certain combinations of SSRIs and NSAIDs might further elevate these risks of gastrointestinal bleeding, thrombocytopenia, and acute kidney injury in patients with depression. Given the potential drug–drug interactions, heightened clinical vigilance is advised when prescribing SSRIs and NSAIDs in combination to patients with depression. Full article