Journal of Research in Pharmacy Practice最新文献

Objective: The use of rapamycin (sirolimus) in the treatment of infantile hemangioma (IH) is a persistent challenge in dermatology. We aim to systematically review the efficacy and safety of rapamycin for IH treatment.

Methods: We systematically searched PubMed, Web of Science, and Scopus for relevant English papers up to December 2024. After screening the full texts, eight studies met the inclusion criteria. The quality of the included articles was assessed using the Joanna Briggs Institute checklist. Detailed data about the effectiveness and safety of rapamycin in IH were collected.

Findings: A total of 8 articles published between 2013 and 2021 were included, with 8 cases. The ages of the participants ranged from 3 weeks to 10 years. The most common dose of rapamycin used in these papers was 0.8 mg/m2 twice daily, with a range of 0.1 mg/m2 to 2 mg/m2 daily. Most patients had a response to rapamycin therapy (five out of eight patients). Two cases reported gastrointestinal adverse effects, whereas one developed severe, fatal hypoglycemia after angiography, which the authors considered the role that rapamycin in causing this complication to be unlikely.

Conclusion: Rapamycin can be a safe and efficient medication in the treatment of IH. Combination therapy, especially in patients resistant to treatment for IH, is a preferred choice for IH treatment and reduces the side effects of propranolol. It is also preferred for the treatment of lesions in the mucosal areas and near important organs.

Dispensing errors in outpatient and community pharmacies pose significant safety risks and contribute to increased global healthcare costs. Despite their frequency, comprehensive studies on rates, causes, and interventions remain limited. This review examines current evidence on dispensing error prevalence, key risk factors, and the impact of mitigation strategies. Following PRISMA guidelines, we systematically searched PubMed, Embase, and CINAHL for studies on dispensing error rates in community and outpatient pharmacies, covering the period from January 2010 to June 2025, including English-language studies and grey literature. Eligible studies reported calculable prevalence using cross-sectional, cohort, or longitudinal designs. Two reviewers independently assessed records, extracted data, and evaluated bias using the NHLBI and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tools. In 22 studies (13 from developing, 9 from developed countries), dispensing error rates ranged from 0.001% to 11.53%, higher in community (mean 4.12%) than in outpatient pharmacies (1.85%). Common errors were wrong dose/ strength (58.6%) and look-/sound-alike drugs (47.90%). Key contributors were workload (55%), staff shortages (22%), and illegible prescriptions (30.70%). While one study reported a modest 1.40% decrease with electronic prescribing, another observed up to a 98.20% reduction using automated systems. GRADE evaluation found very low confidence in the data due to variability in study settings and methods. Dispensing errors remain a major issue, particularly in community pharmacies and underserved areas. Multimodal strategies - integrating technology, standardizing workflows, and training staff - represent a potentially effective approach to reducing errors. Although the certainty of the evidence is very low, the findings may still help guide future policies aimed at introducing error reporting systems, adopting technology, and improving consistency in research methods.

Objective: Chronic diseases such as diabetes mellitus, dyslipidemia, and hypertension (HTN) are the leading causes of global morbidity and mortality. Pharmacological management of these conditions is crucial. Demographic factors may affect medication utilization. Thus, our aim is to investigate associations between patients' demographics and chronic medication utilization.

Methods: A retrospective cross-sectional study was conducted by reviewing electronic medical records of adult patients with at least one chronic condition. Data were collected over 12 months, from September 2023 to September 2024, across multiple internal medicine clinics within a secondary care hospital. Our principal outcome was the association between patients' demographics (age and gender) and chronic medication use. Data collected included demographic details, diagnoses, laboratory results, and prescribed medications. Chi-square test was used to evaluate associations between demographic variables and chronic medication classes.

Findings: This study included 501 patients with different chronic diseases such as diabetes, HTN, and dyslipidemia. More than half of the cohort was females (54.1%). The mean age was 64 years. Significant age-related differences were observed across multiple medication classes including antihypertensives, lipid-lowering agents, and anticoagulants (P < 0.001). Gender differences were minimal, with the only significant association was found in the use of antiplatelets, which were prescribed more in males (P = 0.0001).

Conclusion: This study offers data about the relationship between chronic medication utilization and demographics. Age is a significant determinant of chronic medication prescribed in a secondary care setting, while gender has a more limited impact. Our findings can be instrumental in policy decisions and serve as a basis for future research.

Objective: This study aims to investigate how machine learning (ML) contributes to drug repurposing efforts in oncology, considering the pharmaceutical industry's mounting R and D inefficiencies and economic pressures. Through qualitative interviews with experts across artificial intelligence, oncology, and pharmaceutical development, this paper explores the real-world applications of ML in this field, the challenges to its implementation, and its future potential to streamline drug discovery.

Methods: This study employed the "research onion" framework (Saunders et al., 2016), adopting an interpretivist philosophy and inductive approach to explore stakeholder perspectives on integrating ML into oncological drug repurposing. A multimethod strategy combined a narrative literature review with 13 semi-structured interviews, selected through purposive and snowball sampling. Data were thematically analyzed using Braun and Clarke's six-step framework, supported by NVivo. Research trustworthiness was ensured via Lincoln and Guba's criteria, and ethical approval was granted by Imperial College London.

Findings: Three major thematic domains emerged: The technological, regulatory, and business landscapes. Technological challenges included poor data quality, limited accessibility to real-world datasets, and the need for robust infrastructure to support predictive modeling. Regulatory barriers are centered on ethical concerns in data governance and the difficulty of securing exclusivity and market protection for repurposed drugs. From a business perspective, profitability concerns, generic competition, and fragmented data ownership underscored the need for more collaborative and economically sustainable models.

Conclusion: ML offers potential for oncological drug repurposing, but realizing its benefits requires addressing key technological, regulatory, and economic challenges.

Objective: Many antibiotics currently in use show limited effectiveness against Acinetobacter baumannii due to its high resistance. The current investigation set out to assess the synergistic effect of colistin in combination with levofloxacin and meropenem to provide an appropriate antibiotic regimen for infections caused by multidrug-resistant A. baumannii (MDR-AB).

Methods: Utilizing the broth microdilution approach, the minimum inhibitory concentration (MIC) of levofloxacin, colistin, and meropenem versus isolates of MDR-AB obtained from hospitalized individuals with nosocomial infections caused by this pathogen was ascertained. The checkerboard approach was also used to determine the antibiotics' simultaneous effects on the pathogen.

Findings: Twenty isolates underwent testing. Every isolate was meropenem-resistant. Conversely, all 20 isolates showed sensitivity to colistin, with a MIC range of 0.031-2 µg/mL. The synergy test revealed that no instances of antagonistic interactions existed between meropenem and colistin in 60% of isolates, indicating partial synergy (additive impact). However, only 40% of isolates demonstrated partial synergism with no instances of antagonistic behavior when colistin and levofloxacin were administered together.

Conclusion: Levofloxacin and meropenem exhibit respectable synergistic effects when combined with colistin; as a result, these combinations may be clinically assessed for MDR-AB infections.

Objective: The objective of this study was to evaluate factors associated with glucocorticoid (GC) self-medication during the COVID-19 pandemic and to describe its clinical consequences.

Methods: Self-medication practices and disease activity were assessed in rheumatoid arthritis patients before and after the hospital closure due to the COVID-19 pandemic through telephone survey and clinical record review. GC toxicity was evaluated using a modified Glucocorticoid Toxicity Index (GTI). Comparisons were performed using statistical tests as appropriate.

Findings: Following the hospital's reopening, 51% of patients had modified their GC dose ("changers"), while 49% maintained the same dose ("nonchangers"). Lower adherence to methotrexate (MTX) was significantly associated with self-medication (P < 0.001). Self-medication correlated with increased C-reactive protein levels and pain perception but did not alter clinical activity score. The GTI revealed more infections and neurological effects in the changer group.

Conclusion: MTX nonadherence was the primary determinant of GC self-medication during the hospital closure. Although transient, this behavior was associated with increased inflammatory markers, pain perception, and adverse events.

Objective: There is no evidence regarding the efficacy of bamboo salt in the treatment of recurrent aphthous ulceration. The objective of the present study was to investigate the effect of bamboo salt mouthwash in comparison to benzydamine hydrochloride in the treatment of recurrent aphthous ulceration.

Methods: This randomized double-blind clinical trial was performed on 40 patients diagnosed with minor recurrent aphthous ulceration. Patients were randomized to use bamboo salt or benzydamine mouthwashes for 5 days. Patients in both groups were instructed to use 30 mL of mouthwash every 8 h for 30 s each day. Study outcomes, comprising ulcer size, pain, healing time, and patients' satisfaction, were recorded by investigators.

Findings: The adjusted between-group analyses showed that the mean ulcer size reduction was significantly greater in the bamboo salt group than the benzydamine hydrochloride group (mean difference: -1.40 ± 0.53; P = 0.01) on day 3. In addition, a marginally significant difference was observed between the two groups in terms of ulcer size reduction on day 5 (P = 0.07) and pain score reduction on day 3 (P = 0.06). There was a significant difference between the two groups in the mean duration of ulcer healing, both in the crude and adjusted analyses (P < 0.05). Patients' satisfaction was significantly higher in the bamboo salt group than in the benzydamine hydrochloride group (P = 0.03).

Conclusion: The present study demonstrated that the mouthwash of bamboo salt may be considered an effective option in the management of recurrent aphthous ulceration. However, further studies are needed to confirm our findings.

This review aimed to comprehensively evaluate the impact of artificial intelligence (AI) on the future of clinical pharmacy practice and hospital settings. To achieve this, a thorough literature search was conducted across major databases, including PubMed, Scopus, Web of Science, Embase, and Google Scholar, covering studies published up to July 2025. Relevant keywords - such as "artificial intelligence," "machine learning," "clinical pharmacy," "hospital pharmacy," "medication management," and "decision support systems" - were used to identify studies assessing the applications, challenges, and outcomes of AI in these contexts. All eligible articles were narratively analyzed. The findings indicated that AI has a significant role in optimizing pharmaceutical inventory management, reducing medication errors, enhancing the accuracy and safety of drug dispensing, and streamlining drug distribution processes in hospital environments. Advancements in machine learning algorithms and clinical decision support systems not only assist pharmacists in selecting optimal therapeutic regimens and detecting drug-drug interactions but also facilitate the delivery of personalized patient care and prediction of medication needs. In addition, AI integration was shown to improve workflow efficiency, reduce administrative workload, and enhance both pharmacist training and job satisfaction. In conclusion, the integration of AI into clinical pharmacy and hospital settings emerges as a transformative factor, promising to elevate the quality of pharmaceutical care, improve patient outcomes, and shape a more dynamic and efficient future for the pharmacy profession.

Objective: Mobile health (mHealth) applications are widely acknowledged as beneficial interventions for enhancing medication adherence in patients with chronic conditions, including type 2 diabetes mellitus (T2DM). Despite the extensive smartphone prevalence in Saudi Arabia, the practical utilization of mHealth applications for diabetes self-management remains constrained. This study aimed to investigate the attitudes, willingness, and obstacles regarding the use of mHealth applications for medication adherence among patients with type 2 diabetes mellitus in Saudi Arabia.

Methods: A cross-sectional study was performed with 489 adult patients with type 2 diabetes mellitus across healthcare facilities and digital platforms in Saudi Arabia. The questionnaire had five domains: demographics, technological utilization, attitudes, willingness, and perceived obstacles. Descriptive analyses were conducted with SPSS.

Findings: The majority of participants had a favorable disposition toward mHealth tools. The majority concurred that applications may facilitate adherence and favored interfaces in Arabic. Whereas 71.0% indicated a readiness to utilize mHealth, barely 37.0% were actively employing such applications. Principal obstacles encompassed forgetfulness, feelings of being overwhelmed, adverse effects, financial constraints, and restricted digital accessibility. Cultural and behavioral factors, including the utilization of herbal treatments and religious considerations, also impacted app usage.

Conclusion: mHealth possesses significant potential to enhance medication adherence among Saudi patients with type 2 diabetes mellitus. However, successful implementation relies on overcoming behavioral, cultural, and infrastructural obstacles. These findings underscore the necessity for customized, Arabic-compatible digital health solutions that match with the objectives of Saudi Vision 2030.

Objective: In many emerging economies, simple cost-plus markups push medicine prices far above reference benchmarks, undermining affordability and access. Iran exemplifies this problem, with a public sector median price ratio (MPR) of 6.70 - nearly seven times the reference price - and an out-of-pocket (OOP) burden exceeding 50%. This study aims to benchmark Iran's cost-plus pricing framework against international alternatives, quantify the independent effects of cost-plus dominance and inflation on relative prices, and model policy scenarios to reduce household spending.

Methods: We compared eleven countries (high-income: Germany, France, UK; upper-middle-income: South Korea, China, Brazil, Turkey, South Africa, Thailand; lower-middle-income: Iran, India) using WHO/HAI surveys (2023) to derive public/private MPRs, OOP percentages, and availability metrics. An ordinary-least-squares regression of ln (MPR) on cost-plus dominance (binary), log GDP per capita, and consumer-price-index (CPI) inflation quantified key drivers. Scenario modeling for Iran estimated OOP impacts under (i) a 30% CPI-adjusted cap (MPR = 4.69) and (ii) an health-technology assessment (HTA)-linked value-based regime (MPR = 2.00).

Findings: Iran's cost-plus system predicts a 178% higher MPR (β = 1.02; P < 0.001); each 1% inflation adds 2% to ln (MPR) (β = 0.02; P < 0.001). Under a CPI cap, OOP falls from 52% to 35%; HTA-based pricing could halve OOP to 20%.

Conclusion: Static cost-plus pricing perpetuates catastrophic patient costs. Phased reforms - beginning with CPI-indexed ceilings and advancing to HTA-driven value pricing - can transform unaffordable systems into sustainable, equitable models for emerging markets.