Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 5 Articles
Background: Anti-MRSA agents are essential for treating severe infections, yet their use is constrained by distinct toxicity profiles. However, comparative real-world data remain scarce. Methods: This nationwide pharmacovigilance study used the Japanese Adverse Drug Event Report (JADER) database (2004–2025). Disproportionality analyses (proportional reporting ratio [PRR]) were performed at the Standardized MedDRA Query and Preferred Term levels, complemented by Weibull-based time-to-onset modeling, to characterize AE paerns associated with vancomycin (VCM), teicoplanin (TEIC), arbekacin (ABK), daptomycin (DAP), linezolid (LZD), and tedizolid (TZD). Results: Distinct agentspecific AE profiles were observed. VCM showed disproportionate reporting of acute renal failure (PRR 6.66) and severe cutaneous reactions. TEIC displayed fewer renal signals but relatively higher reporting of hematologic events (PRR 3.51). ABK demonstrated high disproportionality in acute and chronic renal failure, reflecting aminoglycoside nephrotoxicity. DAP showed a high reporting signal for eosinophilic pneumonia (PRR 23.30), interstitial lung disease, and creatine kinase elevation/rhabdomyolysis, with wearout hazard patterns suggesting a possible time-dependent reporting tendency. LZD exhibited hematopoietic signals (PRR 6.13) and additional associations with hyponatremia, lactic acidosis, and optic neuropathy, consistent with marrow suppression and mitochondrial toxicity. Weibull analysis indicated cumulative “wear-out” risks for renal, hepatic, and hematologic events, whereas hypersensitivity and many pulmonary events followed random-failure paerns. Conclusions: This large-scale JADER analysis delineated the distinct safety profiles of the six anti-MRSA agents. The key findings included DAP pulmonary and muscle toxicities, LZD hematological events, and VCM nephrotoxicity. Time-toonset modeling indicates potential cumulative versus random risk patterns, suggesting the need for individualized monitoring and cross-validation....
Objective: Describe a mobile health clinic program led by pharmacists to provide services in a primary care shortage area. Methods: ONU HealthWise is a comprehensive pharmacy service offered by Ohio Northern University Raabe College of Pharmacy with a mobile clinic initiated in 2015. ONU HealthWise is located in an HRSA-designated medically underserved and primary care shortage area and the mobile health clinic visits 11–18 locations monthly plus additional sites for screening or vaccinations. Medical residents from a healthsystem attend some locations and collaborative practice agreements allow pharmacists to initiate and adjust medications. Student pharmacists rotate through the mobile clinic to gain experiential training toward their Doctor of Pharmacy. The mobile clinic is an integral part of the learning and precepting for ONU HealthWise PGY-1 residents. Results: Over a 12-month period (July 2024–June 2025), the mobile clinic held 148 clinics across 7 rural counties in northwest Ohio. A total of 1265 screenings were conducted at 713 patient encounters (604 unique patients). Of the screenings, 38.1% of blood glucose, 21.6% of cholesterol, and 60.1% of blood pressures were abnormal. All abnormal tests resulted in either a medication adjustment, scheduled follow-up at future mobile clinic, or referral to a provider. Student pharmacists spent more than 3670 h on the mobile health clinic in experiential education. Conclusion: Pharmacists can be an integral healthcare provider by increasing access to primary care services through a mobile health clinic in a medically underserved area. The service provides learners with vital patient experiences....
Background: Diabetes mellitus is frequently associated with complications and comorbidities that often require hospitalization and the use of multiple medications for effective management. However, the simultaneous use of these treatments significantly increases the risk of potential drug–drug interactions (pDDIs). Objectives: This study assessed the prevalence, levels, and associated predictors of pDDIs among hospitalized participants with type 2 diabetes mellitus (T2DM) and evaluated their clinical relevance and implications for monitoring and management. Methods: This retrospective cross-sectional study included 430 inpatients with T2DM at Universitas Indonesia Hospital, Indonesia. Lexicomp® Lexi-Interact™ software Wolters Kluwer was used to analyze and classify pDDIs based on severity, risk rating, and documentation levels. Additionally, logistic regression analysis was conducted to identify the predictors of pDDIs, and the study assessed the clinical relevance of major pDDIs. Results: Of the total participants, 84.7% (n = 364) experienced pDDIs, with 1642 interactions identified. Moderate interactions accounted for 77.5% (n = 1273), whereas major interactions constituted 12.2% (n = 201). The most common risk rating was category C (77.5%, n = 1187), and the predominant evidence support level was ‘fair’ (64.8%, n = 1064). Multivariate logistic regression analysis showed a significant association between pDDIs and of 7–12 medications used (OR = 30.1; p < 0.001), and hospital stays ≥4 days (OR = 9.7; p = 0.001). Major pDDIs were significantly linked to ≥13 medications (OR = 5.5; p = 0.002), ≥4 days hospitalization (OR = 11.3; p < 0.001), and urinary tract infections (OR = 3.5; p = 0.02). Participants with major pDDIs exhibited hypoglycemia, hyperglycemia, electrolyte imbalances, and reduced therapeutic responses. Conclusions: The findings indicate a high prevalence of pDDIs among participants with T2DM, highlighting the impact of polypharmacy, prolonged hospitalization, and comorbidities. Implementing software-based screening, close monitoring, and targeted interventions are essential to reduce adverse clinical outcomes and enhance patient safety....
The use of generic medicines (GEs) is being promoted to reduce healthcare costs. This study aims to evaluate the effect of a pharmacist-led educational intervention on the number of patients switching from brand-name medicines to GEs among patients who preferred brand-name products. Pharmacists provided a standardized explanation using pamphlets about GEs to patients who wished to use brand-name medicines and allocated time to answer questions. Basic knowledge about GEs (nine items) and perceptions of GE use (three items) were assessed before and after the intervention to determine whether changes influenced switching behavior. Between 1 March and 30 July 2025, 40 patients were enrolled in the analysis. Following the intervention, the number of patients using one or more brand-name medicines significantly decreased to 25. Knowledge and perceptions of GEs significantly increased after the intervention and were associated with an increased rate of switching to GEs. Binominal logistic regression analysis identified age ≥ 65 years as a strong factor associated with preference for brand-name medicines (OR, 16.45; 95% CI, 1.48–182.65; p = 0.02). These findings indicate that pharmacist-led educational interventions are effective in promoting GE use among patients who prefer brand-name medicines. In the future, we plan to conduct comparative studies with a control group to further investigate the factors that lead to patients strongly preferring the use of brand-name medicines....
Bringing a new drug to market is a complex, costly, and lengthy process, averaging $2.6 billion and about ten years of research and development. It involves multiple stages, from target discovery to post-approval monitoring, and relies heavily on innovation driven by collaboration among pharmaceutical sciences, biology, biochemistry, engineering, and artificial intelligence. Drug discovery can be divided into four main stages: target selection and validation; compound screening and optimization; preclinical studies; and clinical trials. First, researchers identify and validate a biological target associated with a disease using genomic, proteomic, and bioinformatic approaches. Next, potential compounds (“hits”) are identified through methods such as high-throughput and virtual screening, followed by iterative chemical optimization and functional testing. Promising candidates undergo preclinical in vivo studies to assess pharmacokinetics, pharmacodynamics, and toxicity. Clinical development proceeds in three phases: Phase I evaluates safety in healthy volunteers; Phase II assesses efficacy in patients; and Phase III confirms efficacy and safety in larger populations. After successful trials, regulatory agencies review the data for approval. While small molecules have long dominated due to their stability and oral bioavailability, biologics—such as monoclonal antibodies and mRNA-based therapies—have grown rapidly, highlighted by COVID-19 vaccine development and increasing FDA approvals....
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