Intelligent Pharmacy最新文献

{"title":"Molecular recognition for anion detection: Progress and environmental significance","authors":"Harsha Narkhede ,&nbsp;Archana Tupe ,&nbsp;Manoj Kumbhare ,&nbsp;Ajaykumar Surana ,&nbsp;Kanchan Khedkar","doi":"10.1016/j.ipha.2025.07.001","DOIUrl":"10.1016/j.ipha.2025.07.001","url":null,"abstract":"<div><div>Supramolecular chemistry is the name given to the subfields of chemistry that deal with complex frameworks made up of a certain number of molecules. The host and visitor or guest involved in molecular recognition demonstrate molecular complementarity. Significant attention has been generated in the new year for the precise identification and detection of anion species using artificial sensors. Due to the fact that anion play a significant role in the climate or environment. Consideration of the type of non-covalent interaction used to complex the anion guest has allowed for the helpful classification of anion receptors. Calculation and simplicity are necessary for the plane of certain hosts for anion. Anion is important to the environment. Different detecting techniques have been discussed.</div><div>It also, review the effect of phosphate, nitrate anions on ecosystem. Phosphates are important components of medications and fertilizers. Phosphate and nitrogen are the essential supplements that in extreme amounts contaminate lakes, streams and waterway. Anion is important to the environment. Anions in water, air, and other environmental resources have been documented to be contaminated for many years. Eutrophication can occur due to excessive use of phosphate and nitrate anions. Overuse of fertilizers damages ecosystems and contributes to pollution of the air, water, and land. Therefore, it is important to highlight the finding of anion waste in the ecosystem. The phosphate and nitrate ions are two of the main polluting anions that will be used in the current paper to show the effects of fertilizers on the environment.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 6","pages":"Pages 387-400"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145719119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LC-MS/MS-guided profiling and network pharmacology analysis of bioactive compounds from Costus speciosus targeting type 2 diabetes: Insights from molecular docking and dynamics","authors":"Taufik Muhammad Fakih ,&nbsp;Jajang Japar Sodik ,&nbsp;Rifky Rahmadi Khaerulihsan ,&nbsp;Ihsan Jaya Fathurohman ,&nbsp;Livia Syafnir ,&nbsp;La Ode Akbar Rasydy ,&nbsp;Muchtaridi Muchtaridi","doi":"10.1016/j.ipha.2025.07.002","DOIUrl":"10.1016/j.ipha.2025.07.002","url":null,"abstract":"<div><h3>Background/objectives</h3><div><em>Costus speciosus</em> is a medicinal plant traditionally used in Southeast Asia for its metabolic and anti-inflammatory properties, yet the molecular mechanisms underlying its bioactivity remain underexplored. Among its phytoconstituents, various plant-derived lipophilic compounds have attracted attention due to their structural similarity to endogenous metabolites and their potential role in modulating metabolic pathways relevant to type 2 diabetes mellitus (T2DM). This study aimed to investigate the binding behavior and stability of <em>Costus speciosus</em>-derived metabolites against key molecular targets involved in insulin signaling and oncogenic transformation.</div></div><div><h3>Methods</h3><div>LC-MS/MS profiling was employed to identify major bioactive metabolites from the rhizome extract. Subsequently, a network pharmacology approach was used to filter relevant targets, followed by molecular docking and 200 ns molecular dynamics simulations to evaluate interaction stability. Binding free energy was computed using the MM-PBSA method to support thermodynamic relevance.</div></div><div><h3>Results</h3><div>A total of 18 compounds were identified via LC-MS/MS, of which 15 were successfully linked to at least one protein target through bioinformatics databases and proceeded to molecular docking analysis. Among these, campestanol showing the highest docking affinity (−10.73 ​kcal/mol) and the lowest inhibition constant (13.60 ​nM) toward PIK3CA. Molecular dynamics simulations revealed that the PIK3CA–campestanol complex exhibited comparable or superior stability metrics (RMSD, RMSF, Rg, SASA, RDF, and hydrogen bonding) to the native ligand. MM-PBSA calculations confirmed robust van der Waals and hydrophobic contributions to binding, with total binding energy at −117.144 ​± ​13.887 ​kJ/mol. These computational findings were further corroborated by prior experimental studies demonstrating campestanol's metabolic regulatory functions.</div></div><div><h3>Conclusions</h3><div>Campestanol demonstrates stable and favorable binding with PIK3CA, supporting its role as a promising candidate for further in vitro validation in metabolic and oncogenic pathway modulation. This study provides mechanistic insights into <em>Costus speciosus</em> bioactivity and strengthens the rationale for advancing campestanol as a lead compound in PI3K/AKT-targeted therapies for T2DM.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 6","pages":"Pages 401-419"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145719120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing variation among the drug-lists of 16 cities and impact on cardiovascular disease mortality: Evidence from Anhui","authors":"Anping Guo ,&nbsp;Zhenzhen Pan ,&nbsp;Haizhu Tan","doi":"10.1016/j.ipha.2025.05.002","DOIUrl":"10.1016/j.ipha.2025.05.002","url":null,"abstract":"<div><h3>Background</h3><div>This study compares the differences in city-level cardiovascular disease (CVD) drug-lists and investigates their relationship with CVD mortality rate across 16 cities in Anhui province, China.</div></div><div><h3>Methods</h3><div>Data on the usage of CVD medicines from 2016 to 2020 in hospitals across various levels in 16 cities and China's 2018 national list of essential medicines (EMs) were collected and mortality, demographic, environmental data related to CVD were analyzed. The negative binomial mixed-effects model was adopted to compare the differences. A generalized estimating equation was applied to evaluate associations between Anhui city-level drug-lists and mortality over the five years.</div></div><div><h3>Results</h3><div>The drug-lists across cities in Anhui province were short. Analysis revealed that the drug-lists of ten cities were shorter than that of the capital city, Hefei. Healthcare expenditure appeared to impact the length of drug-lists. After controlling for per capita GDP, population, widowhood rate, and beds per 1000 people, it was found that differences in drug-lists were associated with the CVD mortality rate in Anhui Province.</div></div><div><h3>Conclusion</h3><div>A shorter city-level CVD drug-list correlates with a higher CVD mortality rate, suggesting the crucial need for local health authorities to revise and establish their own list of essential CVD medicines to meet patient needs.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 6","pages":"Pages 371-377"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145719116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"International expert consensus on hospital intelligent pharmacy","authors":"Cao Li ,&nbsp;Wenshuo Jiang ,&nbsp;Aizong Shen ,&nbsp;Yilei Li ,&nbsp;Junyan Wu ,&nbsp;Hua Tao ,&nbsp;Yongqiang Tang ,&nbsp;Xiaolin Yue ,&nbsp;Alice Hao ,&nbsp;Zhigang Zhao","doi":"10.1016/j.ipha.2025.06.001","DOIUrl":"10.1016/j.ipha.2025.06.001","url":null,"abstract":"<div><div>As the rapid advancements in medical technology and increasing demands for personalized medication, Hospital Intelligent Pharmacy (HIP) integrates artificial intelligence, large-scale health data analytics, the Internet of Things (IoT), and other cutting-edge technologies to optimize end-to-end pharmaceutical supply chain processes, management, and clinical processes. In recent years, regulatory agencies such as the European Medicines Agency (EMA), the Medicines and Healthcare products Regulatory Agency (MHRA), China's National Medical Products Administration (NMPA), and the U.S. Food and Drug Administration (USFDA) have issued policies to promote intelligent pharmacy development. However, HIP still faces challenges including ambiguous definitions, absence of standardized technical protocols, and incomplete evaluation frameworks. To address these issues, international and domestic academic organizations collaboratively developed the International Expert Consensus on Hospital Intelligent Pharmacy. This consensus clarifies HIP's definition, core components, and systematic framework, providing scientific guidance for standardized implementation and clinical application of intelligent pharmacy in hospitals. Utilizing a Delphi method process, expert opinions will be collected, analyzed, and refined. The current consensus defines HIP's scope and principles, outlining a framework with 10 components: intelligent drug supply chain management, drug dispensing, prescription review, pharmacovigilance, medication therapy management, therapeutic drug monitoring, telepharmacy services, pharmacy administration, science popularization, and clinical trials. Future directions focus on 5 key areas: AI-augmented pharmacist competency development, advancing pharmaceutical scientific research, fostering intelligent pharmaceutical publications and journals, addressing ethical and legal challenges, and promoting international harmonization in pharmacy. The consensus offers critical references and exploratory pathways for HIP's global advancement.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 6","pages":"Pages 378-386"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145719118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding AI adoption in the German pharmaceutical sector: Insights from expert interviews","authors":"Raisa Aman,&nbsp;Victoria Freund","doi":"10.1016/j.ipha.2025.09.001","DOIUrl":"10.1016/j.ipha.2025.09.001","url":null,"abstract":"<div><div>The increasing complexity and competitiveness of the pharmaceutical industry are driving the need for innovative technological solutions. This article explores the use of Artificial Intelligence (AI) in the German pharmaceutical sector, with a focus on addressing key challenges related to regulation, market dynamics, internal structures, and technological capabilities. Through a combination of systematic literature review and qualitative expert interviews, the study identifies major problem areas and derives corresponding AI-based innovation potentials in departments such as research, human resources, and quality management. The findings demonstrate that a strategically guided implementation of AI can lead to substantial process improvements and foster long-term competitive advantages. To facilitate this transformation, the study concludes with actionable recommendations aimed at advancing the integration of AI beyond isolated pilot projects and towards broad, sustainable application within the industry. By combining a systematic literature review with in-depth expert interviews, this study not only provides an overview of the current state of knowledge but, thanks to its qualitative methodology, also offers new insights into the decision-making processes and perceptions of industry experts.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 6","pages":"Pages 420-438"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145719117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of artificial intelligence on remote healthcare: Enhancing patient engagement, connectivity, and overcoming challenges","authors":"Udit Chaturvedi,&nbsp;Shikha Baghel Chauhan,&nbsp;Indu Singh","doi":"10.1016/j.ipha.2024.12.003","DOIUrl":"10.1016/j.ipha.2024.12.003","url":null,"abstract":"<div><div>The incorporation of advanced telemedicine technologies is helping artificial intelligence transform remote healthcare in the enhancement of patient care, diagnostics, monitoring, and overall medical treatment. This review examines how AI has transformed virtual healthcare with regard to patient engagement and connectivity, real-time monitoring of health status, and the accuracy of diagnosis. Key applications of AI, such as AI-enabled diagnostic systems, predictive analytics, and teleconsultation platforms, are reviewed for their strengths in overcoming the limitations of the traditional models of remote healthcare. This review consists of case studies on the applications of AI in different healthcare domains, such as cardiac monitoring, diabetes management, mental health teletherapy, and dermatology. It also looks into the ethical and regulatory challenges that arise, including bias in AI, data privacy, and accountability, in a way that emphasizes the necessity for robust regulatory frameworks in safeguarding patient safety. Future directions for AI innovation include such emerging technologies as 5G, blockchain, and IoMT, among others, that “will usher in a new era of remote healthcare delivery.”</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 5","pages":"Pages 323-329"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145195982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opportunities and challenges of machine learning in anticaner drug therapies","authors":"Miao Chunlei ,&nbsp;HuangFu Rui ,&nbsp;Chen Yuan ,&nbsp;Wu Shikui ,&nbsp;Ping Yaodong","doi":"10.1016/j.ipha.2025.02.004","DOIUrl":"10.1016/j.ipha.2025.02.004","url":null,"abstract":"<div><div>Antitumor drug therapies encounter substantial costs and intricate challenges, imposing a financial strain on patients and potentially leading to serious adverse effects. These issues have prompted a shift towards personalized precision medicine, although the increased workload for clinicians limits its full implementation. Machine learning (ML) offers innovative solutions to these challenges. By effectively integrating and analysing large clinical datasets, ML can develop models to predict potential treatment-related risks for patients and optimize dosing regimens, thereby improving efficacy and reducing adverse effects. Additionally, ML can evaluate drug efficacy, providing empirical support for personalized treatments. This review highlights the research progress in ML for antitumor drug therapies and examines its crucial role in advancing personalized precision medicine. It is expected that ML will deliver more accurate, efficient, and cost-effective treatment options for patients while providing strong support for clinicians in refining treatment decisions, making it an essential tool in cancer therapy.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 5","pages":"Pages 336-341"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review on intervention of AI in pharmaceutical sector: Revolutionizing drug discovery and manufacturing","authors":"Vijeth N. Bhat ,&nbsp;Swati Bharati ,&nbsp;Chellampillai Bothiraja ,&nbsp;Jaiprakash Sangshetti ,&nbsp;Vinod Gaikwad","doi":"10.1016/j.ipha.2025.04.001","DOIUrl":"10.1016/j.ipha.2025.04.001","url":null,"abstract":"<div><div>Artificial intelligence (AI) is designed to mimic human intelligence in machines. The growth of information technology and advancement in the computing power of computers provided a great platform for progress in many pharmaceutical industry and healthcare sectors. Leading to the consolidation of the pharmaceutical, and healthcare industries with AI companies. AI is used in various departments of the pharmaceutical sector such as drug discovery, development, target identification, manufacturing process, dosage design, clinical trial design, and many more. There are several challenges and limitations of AI that must be addressed by the pharmaceutical industry before its adoption and successful integration into various processes. The present article is focused on Artificial Neural Networks in the pharmaceutical sector, Drug design and discovery, drug repurposing, research and development, pharmaceutical product development, manufacturing process, quality assurance and quality controls, and some challenges and prospects of AI.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 5","pages":"Pages 342-349"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying potentiators of adverse reactions to antiretroviral drugs in a primary care model","authors":"Joyner David Anaya Miranda ,&nbsp;Maricela Rojas Canchala ,&nbsp;Carlos Alberto Gómez Mercado","doi":"10.1016/j.ipha.2025.02.003","DOIUrl":"10.1016/j.ipha.2025.02.003","url":null,"abstract":"<div><h3>Introduction</h3><div>Adverse drug reactions (ADRs) in Antiretroviral Treatment (ART) are influenced by multiple potentiators related to the patient, the disease, the drug, the environment and medical treatment, these ADRs are highly prevalent and are identified as an important risk factor that predisposes patients to ADRs. It was considered necessary to determine the demographic, social, and clinical factors associated with ADRs from antiretrovirals in HIV-positive patients, who were treated by the specialized comprehensive care program in a primary health care model.</div></div><div><h3>Methodology</h3><div>Observational, cross-sectional, analytical, and retrospective study with a population of patients on antiretroviral therapy in a primary care program. The outcome evaluated was adverse drug reactions vs. sociodemographic, pharmacological and clinical factors. For the statistical analysis, univariate, bivariate and multivariate analyses were performed, where a multiple binary logistic regression was used for explanatory purposes.</div></div><div><h3>Results</h3><div>A total of 5406 records of patients with antiretroviral therapy were analyzed, the prevalence of ADR was 16.68%, the multivariate analysis showed that the variables that increase the probability of ADR are age, education, area of residence, pharmacological group, HDL cholesterol levels, adherence, persistence, change of two or more times of ARV and treatment time.</div></div><div><h3>Conclusion</h3><div>Antiretrovirals, as well as the risk factors that are mainly associated with the occurrence of ADRs in this study, contribute to health professionals at all levels to anticipate, identify and minimize ADR, as well as to understand the need for close follow-up and monitoring to avoid the occurrence of serious ADRs.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 5","pages":"Pages 330-335"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Development and evaluation of topical liposomal preparation of walnut oil and tamarind seed oil against ageing”","authors":"Aditya Singh ,&nbsp;Vaseem Ahamad Ansari ,&nbsp;Md Faheem Haider ,&nbsp;Farogh Ahsan ,&nbsp;Tarique Mahmood ,&nbsp;Rufaida Wasim ,&nbsp;Shubhrat Maheshwari","doi":"10.1016/j.ipha.2025.07.003","DOIUrl":"10.1016/j.ipha.2025.07.003","url":null,"abstract":"","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 5","pages":"Pages 368-370"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145196028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}