Intelligent Pharmacy最新文献

Alkaloids unveiled: A comprehensive analysis of novel therapeutic properties, mechanisms, and plant-based innovations 生物碱揭牌：对新的治疗特性、机制和基于植物的创新进行全面分析

Intelligent Pharmacy Pub Date : 2025-08-01 DOI: 10.1016/j.ipha.2024.09.007

Sarvananda Letchuman , H.D.T. Madhuranga , B.L.N.K. Madhurangi , Amal D. Premarathna , Muthupandian Saravanan

{"title":"Alkaloids unveiled: A comprehensive analysis of novel therapeutic properties, mechanisms, and plant-based innovations","authors":"Sarvananda Letchuman , H.D.T. Madhuranga , B.L.N.K. Madhurangi , Amal D. Premarathna , Muthupandian Saravanan","doi":"10.1016/j.ipha.2024.09.007","DOIUrl":"10.1016/j.ipha.2024.09.007","url":null,"abstract":"<div><div>Alkaloids, naturally occurring compounds in a diverse range of plant species (<em>Coffea</em> spp., <em>Erythroxylum coca</em>, <em>Cinchona</em> spp. etc.), hold vast potential for biological, medicinal, and pharmacological applications. As the global focus shifts towards natural therapeutic agents due to their lower toxicity compared to synthetic compounds, this review takes a novel approach by examining the ecological and molecular factors influencing the medicinal properties of alkaloids. Using a comparative analysis of alkaloid potency across various plant species, we explore how environmental factors, such as soil composition and climate, impact alkaloid concentration and efficacy. Additionally, this review highlights the synergistic potential of alkaloids when combined with other phytochemicals, offering new insights into more potent, multi-compound therapeutic formulations. We documented ten key medicinal properties, including antioxidant, anti-inflammatory, and anticancer effects, and delve into the molecular pathways through which alkaloids exert these benefits. By exploring alkaloids from under-researched plant species, we aim to broaden the scope of medicinal applications, particularly within the realm of personalized medicine, where alkaloid efficacy may vary based on genetic and pathological factors. This novel perspective emphasized the need for further research to optimize alkaloid extraction methods and assess their potential in personalized and combination therapies, ultimately paving the way for more effective natural treatments.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 4","pages":"Pages 268-276"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864686","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}

引用次数: 0

Design representation of pain visualization coding 疼痛可视化编码的设计表示

Intelligent Pharmacy Pub Date : 2025-08-01 DOI: 10.1016/j.ipha.2024.11.001

Yirui Wang , Xiaoling Wu , Li Tang , Yingjie Fei , Hengyu Guo , Yujun Wang , Wei Zhao , Siqian Zheng , Bowen Sun , Xia Wang

引用次数: 0

Advancements in transdermal drug delivery systems: Enhancing medicine with pain-free and controlled drug release 经皮给药系统的进展：以无痛和药物释放控制增强药物

Intelligent Pharmacy Pub Date : 2025-08-01 DOI: 10.1016/j.ipha.2024.09.008

E. Karthikeyan , S. Sivaneswari

引用次数: 0

Ensuring data integrity: Best practices and strategies in pharmaceutical industry 确保数据完整性：制药行业的最佳实践和策略

Intelligent Pharmacy Pub Date : 2025-08-01 DOI: 10.1016/j.ipha.2024.09.010

Divya Gokulakrishnan, Sowmyalakshmi Venkataraman

{"title":"Ensuring data integrity: Best practices and strategies in pharmaceutical industry","authors":"Divya Gokulakrishnan, Sowmyalakshmi Venkataraman","doi":"10.1016/j.ipha.2024.09.010","DOIUrl":"10.1016/j.ipha.2024.09.010","url":null,"abstract":"<div><h3>Background</h3><div>The objective of this article is to examine the challenges and best strategies for incorporating data integrity in the pharmaceutical industry to ensure regulatory compliance. It highlights the importance of data governance policies, secure data handling processes, and the use of the ALCOA framework for implementing Good Documentation Practices.</div></div><div><h3>Evidence acquisition</h3><div>Using appropriate search terms such as 'Data Integrity', 'Data Integrity in Pharmaceutical Industry', and 'ALCOA principle', evidence was gathered from websites and published articles using SciFinder, Web of Science, PubMed, UGC-approved journals, and Google Scholar databases.</div></div><div><h3>Results</h3><div>The article emphasizes the importance of change management, independent data review methods, and modern technology such as electronic signatures and audit trails in pharmaceutical companies. It also points out the importance of data backup, recovery procedures, and ongoing improvement in maintaining data integrity and promoting responsibility.</div></div><div><h3>Conclusion</h3><div>The article discusses the pharmaceutical sector's procedures for ensuring data integrity throughout a product's life, promoting safe, efficient, and excellent pharmaceutical goods. It highlights the need to navigate the complex regulatory environment and emphasizes the sector's commitment to maintaining data integrity to achieve high quality standards in manufacturing and testing operations.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 4","pages":"Pages 296-303"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864688","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}

引用次数: 0

Harnessing the power of artificial intelligence in pharmaceuticals: Current trends and future prospects 在制药中利用人工智能的力量：当前趋势和未来前景

Intelligent Pharmacy Pub Date : 2025-06-01 DOI: 10.1016/j.ipha.2024.12.001

Saha Aritra , Chauhan Baghel Shikha , Singh Indu

{"title":"Harnessing the power of artificial intelligence in pharmaceuticals: Current trends and future prospects","authors":"Saha Aritra , Chauhan Baghel Shikha , Singh Indu","doi":"10.1016/j.ipha.2024.12.001","DOIUrl":"10.1016/j.ipha.2024.12.001","url":null,"abstract":"<div><div>Introduction of artificial intelligence (AI) technology in the field of pharmaceutical industry has been driven to discovery and development of drugs, also personalized medicine. In this article The review investigates systematic trends facing AI-powered transformation. AI has improved efficiency by reducing the drug development time, costs and success rates due to machine learning (ML), deep learning (DL) and natural language processing (NLP). The literature search was conducted systematically, using core scientific databases to source data-mining research studies on predictive modelling, virtual screening, and automation in AI applications. Findings here underscore the critical role that AI plays in precision medicine, as well as process optimization in manufacture, but ethical issues and privacy of data and regulations add significantly to hurdles. The study confirms that AI presents unique opportunities for developing personalized healthcare and answering global health challenges, nonetheless its adoption involves overcoming ethical and regulatory issues beautiful collaboration and agreeing to industry wide standards. The next-generation products bring hope for low-cost, patient-centric solutions indicating pharmaceutical landscape phases of the paradigm.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 3","pages":"Pages 181-192"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272262","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}

引用次数: 0

Development and evaluation of empagliflozin-loaded solid lipid nanoparticles: Pharmacokinetics and pharmacodynamics for oral delivery 恩格列净负载固体脂质纳米颗粒的开发和评价：口服给药的药代动力学和药效学

Intelligent Pharmacy Pub Date : 2025-06-01 DOI: 10.1016/j.ipha.2024.12.004

Ananda Kumar Chettupalli , Aziz Unnisa , Himabindu Peddapalli , Rajendra Kumar Jadi , Kachupally Anusha , Padmanabha Rao Amarachinta

{"title":"Development and evaluation of empagliflozin-loaded solid lipid nanoparticles: Pharmacokinetics and pharmacodynamics for oral delivery","authors":"Ananda Kumar Chettupalli , Aziz Unnisa , Himabindu Peddapalli , Rajendra Kumar Jadi , Kachupally Anusha , Padmanabha Rao Amarachinta","doi":"10.1016/j.ipha.2024.12.004","DOIUrl":"10.1016/j.ipha.2024.12.004","url":null,"abstract":"<div><div>Type 2 diabetes mellitus is frequently treated with empagliflozin (EZN), a sodium-glucose cotransporter 2 inhibitor. Solid lipid nanoparticles (SLNs) shield the drug from gastrointestinal breakdown and improve the bioavailability of lipophilic drugs. The aim of the study is to use SLNs to enhance EZN's pharmacokinetics and pharmacodynamics in the treatment of diabetes mellitus. To prepare EZN-loaded SLNs, central composite design (CCD) was employed. The optimized batch (optimized EZN-loaded SLNs) had the desired values of dependent variables Vesicle size (R1), Entrapment Efficiency (R2), and Cumulative Drug Release (CDR) (R3). This was achieved by using analysis of variance (ANOVA) to analyse independent variables such as lipid concentration (X1), surfactant concentration (X2), sonication time (X3), and homogenization speed (X4). F8 exhibited the highest drug entrapment (90.6% ± 2.8%), CDR (89.2 ± 3.6), and average particle size (98.6 ± 2.1 nm) among the 30 distinct formulated formulae (F1–F30). Based on the F-value and <em>p</em>-value, the model was determined to be significant for particle size, entrapment efficiency, and CDR. The actual values of particle size entrapment efficiency and CDR closely matched the projected values of the optimized batch. The in vitro release trials produced a burst release followed by a continuous release. When compared to the EZN solution, the relative bioavailability of EZN-loaded SLNs was 1.2 times higher, indicating superior protection against the gastrointestinal environment. In rats with streptozotocin-induced diabetes mellitus, the optimized EZN-loaded SLNs outperformed the basic drug suspension in terms of antidiabetic efficacy. One promising method for administering EZN in the treatment of diabetes mellitus is by SLNs.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 3","pages":"Pages 193-206"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272264","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}

引用次数: 0

Stratification of cephalosporins based on physicochemical and pharmacokinetic variables using multivariate statistical tools 基于物理化学和药代动力学变量使用多变量统计工具的头孢菌素分层

Intelligent Pharmacy Pub Date : 2025-06-01 DOI: 10.1016/j.ipha.2024.09.004

Carlos Alberto Escobar Angulo, Antistio Alviz Amador, Julián Javier Martínez Zambrano

{"title":"Stratification of cephalosporins based on physicochemical and pharmacokinetic variables using multivariate statistical tools","authors":"Carlos Alberto Escobar Angulo, Antistio Alviz Amador, Julián Javier Martínez Zambrano","doi":"10.1016/j.ipha.2024.09.004","DOIUrl":"10.1016/j.ipha.2024.09.004","url":null,"abstract":"<div><h3>Introduction</h3><div>Cephalosporins, a class of beta-lactam antibiotics, are commonly used in medical practice. However, their potential advantages, based on physicochemical and pharmacokinetic variables, are often overlooked. This research, proposing strategies based on multivariate statistics to stratify different cephalosporins, is a significant step towards providing the prescribing team with more rational and effective options. The potential benefits of this research are promising, as it has the potential to significantly improve the efficacy and safety of cephalosporin therapy.</div></div><div><h3>Method</h3><div>Exploratory study and review of pharmacokinetic parameters of cephalosporins. Data were extracted from DrugBank go.drugbank.com, and multivariate statistical techniques such as Pearson correlation and cluster analysis were applied. This approach allowed the identification of groupings of cephalosporins with similar characteristics, thus facilitating their rational selection in clinical practice.</div></div><div><h3>Results</h3><div>The results reveal that cefazolin, cefotetan, cefoperazone, and ceftriaxone form the conglomerate with the most favorable properties for reaching effective concentrations at the site of action due to their high solubility, high percentage of binding to plasma proteins, and adequate residence times in the organism. Solubility, protein binding, half-life, MRT, molecular weight, volume of distribution, number of interactions, and pKa are all critical factors that influence the efficacy and safety of cephalosporin therapy.</div></div><div><h3>Conclusions</h3><div>It is relevant to highlight the use of multivariate statistics as a tool for drug selection and rational use. In the present study, cefazolin, cefotetan, cefoperazone, and ceftriaxone were highlighted as the best therapeutic alternatives according to the variables selected for the study.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 3","pages":"Pages 159-166"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272368","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}

引用次数: 0

Machine learning & deep learning tools in pharmaceutical sciences: A comprehensive review 制药科学中的机器学习和深度学习工具：综合综述

Intelligent Pharmacy Pub Date : 2025-06-01 DOI: 10.1016/j.ipha.2024.11.003

Saleem Javid , Abdul Rahmanulla , Mohammed Gulzar Ahmed , Rokeya sultana , B.R. Prashantha Kumar

{"title":"Machine learning & deep learning tools in pharmaceutical sciences: A comprehensive review","authors":"Saleem Javid , Abdul Rahmanulla , Mohammed Gulzar Ahmed , Rokeya sultana , B.R. Prashantha Kumar","doi":"10.1016/j.ipha.2024.11.003","DOIUrl":"10.1016/j.ipha.2024.11.003","url":null,"abstract":"<div><div>Drug discovery and development is an important area of research for pharmaceutical industries and medicinal chemists. This classical approach demanded significant investments of time and resources to bring a single drug to market. Furthermore, the complexity and vast scale of data from genomics, proteomics, microarrays, and clinical trials present significant challenges in the drug discovery pipeline. Nevertheless, bioinformatics, pharmacoinformatics, and cheminformatics technologies have been developed thanks to breakthroughs in computational methodologies and a surge in multi-omics data, drastically shortening the time it takes to create new drugs. Large amounts of biological data stored in global databases are the building blocks for machine learning and deep learning methods. They make it easier to find patterns and models that can help find therapeutically active molecules with less time, work, and money. Machine learning and deep learning technology are vital in drug design and development. We have applied these algorithms to various drug discovery processes such as protein structure prediction, toxicity prediction, oral bioavailability prediction, de novo design of new chemical scaffolds, structure-based and ligand-based virtual screening, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, and clinical trial design. Historical evidence underscores the successful implementation of AI and deep learning in this domain. Finally, we highlight some successful machine learning or deep learning-based models employed in the drug design and development pipeline. Furthermore, there has been a notable increase in interest regarding the application of AI technology in hospital pharmacy settings, which has been discussed in this review. This review will be invaluable to medicinal and computational chemists seeking DL tools for drug discovery projects and hospital pharmacies.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 3","pages":"Pages 167-180"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272261","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}

引用次数: 0

Artificial intelligence, computational tools and robotics for drug discovery, development, and delivery 用于药物发现、开发和交付的人工智能、计算工具和机器人技术

Intelligent Pharmacy Pub Date : 2025-06-01 DOI: 10.1016/j.ipha.2025.01.001

Ayodele James Oyejide , Yemi Adekola Adekunle , Oluwatosin David Abodunrin , Ebenezer Oluwatosin Atoyebi

{"title":"Artificial intelligence, computational tools and robotics for drug discovery, development, and delivery","authors":"Ayodele James Oyejide , Yemi Adekola Adekunle , Oluwatosin David Abodunrin , Ebenezer Oluwatosin Atoyebi","doi":"10.1016/j.ipha.2025.01.001","DOIUrl":"10.1016/j.ipha.2025.01.001","url":null,"abstract":"<div><div>The integration of Artificial Intelligence (AI) and robotics into the pharmaceutical sector is rapidly transforming drug discovery, development, and delivery (D-DDD) processes. Traditional drug development is often characterized by lengthy timelines, high costs, and complex challenges associated with target identification, drug efficacy, and safety profiling. AI and robotics offer transformative solutions, bringing speed, precision, and scalability to various stages of D-DDD. In this review, we analyze cutting-edge advancements in AI-driven predictive modeling, machine learning algorithms for molecular screening, and data mining techniques that enable efficient drug target identification and toxicity prediction. We also explore robotics applications that enhance automation in high-throughput screening, compound synthesis, and patient-specific drug delivery systems. Through examining the applications, limitations, and future trends of these technologies, this review provides a comprehensive outlook on the potential of AI and robotics to streamline the drug pipeline and enable personalized therapeutic strategies. Our review reveals that the convergence of AI, robotics, and big data has potential to reshape pharmaceutical research, reduce costs, and pave the way for more accessible, effective therapies. This review thus serves as a critical resource for understanding the future trajectory of intelligent, technology-driven pharmacy and its implications for advancing healthcare.</div></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":"3 3","pages":"Pages 207-224"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272365","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}

引用次数: 0

ECLUNG consensus/guidelines development principles and methods (2024 edition) ECLUNG共识/指南制定原则和方法（2024年版）

Intelligent Pharmacy Pub Date : 2025-04-01 DOI: 10.1016/j.ipha.2024.11.004

Chunwei Xu , Yue Hao , Dong Wang , Shirong Zhang , Wenxian Wang , Qian Wang , Tangfeng Lv , Zhengbo Song , Ziming Li

引用次数: 0