Journal of pharmaceutical analysis最新文献_第2页

Silent or low expression of bla _TEM and bla _SHV suggests potential for targeted proteomics in clinical detection of β-lactamase-related antimicrobial resistance. bla TEM和bla SHV沉默或低表达提示靶向蛋白质组学在临床检测β-内酰胺酶相关抗菌素耐药性方面的潜力。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-07-01 Epub Date: 2025-01-28 DOI: 10.1016/j.jpha.2025.101220

Huige Wu, Wenting Dong, Xinxin Hu, Chunyang Xie, Xinyi Yang, Congran Li, Guoqing Li, Yun Lu, Xuefu You

引用次数: 0

The anti-hyperuricemia potential of bioactive natural products and extracts derived from traditional Chinese medicines: A review and perspective. 中药生物活性天然产物和提取物的抗高尿酸血症潜力：综述与展望。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-07-01 Epub Date: 2025-01-03 DOI: 10.1016/j.jpha.2024.101183

Yaolei Li, Zhijian Lin, Hongyu Jin, Feng Wei, Shuangcheng Ma, Bing Zhang

{"title":"The anti-hyperuricemia potential of bioactive natural products and extracts derived from traditional Chinese medicines: A review and perspective.","authors":"Yaolei Li, Zhijian Lin, Hongyu Jin, Feng Wei, Shuangcheng Ma, Bing Zhang","doi":"10.1016/j.jpha.2024.101183","DOIUrl":"10.1016/j.jpha.2024.101183","url":null,"abstract":"Hyperuricemia (HUA) and gout became typical metabolic disorders characterized by multiple pathogenic factors. Their incidence increased annually, affecting younger populations. Given that uric acid (UA) and inflammation were the primary disease mechanisms, the search for effective and low-side-effect UA-lowering and anti-inflammatory drugs became a pressing scientific priority. Traditional Chinese medicine (TCM) encompassed a rich array of theoretical and practical experience, along with a diverse range of chemical substances, making herbs or their components potential sources for therapeutic drugs. Despite the significant role that modern herbal medicines played in treating HUA and gout, the existing research literature remained fragmented, lacking comprehensive and systematic reviews. In this review, we focused on the regulation of UA and summarized the discovery of UA-lowering pharmacodynamic components or ingredients derived from herbs and formulas, as well as their multi-targeted mechanisms of action. Emphasizing this focus, we proposed that, compared to acute inflammation, low-grade inflammation may play a relatively \"unnoticed\" role in the disease process. In contrast to Western medicine, we discussed the risks and benefits of herbal medicines and their ingredients for treatment, drawing from theoretical insights and clinical practice. This review offered comprehensive perspectives on the research into anti-HUA and gout treatments using herbal medicines and their natural products. Additionally, it provided a forward-looking view on natural product discovery, the exploration of therapeutic strategies, and new drug research in this field.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101183"},"PeriodicalIF":8.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biological activity analysis of baicalin nanodrugs: Nanosizing enhances antiviral and anti-inflammatory effects in the treatment of viral pneumonia. 黄芩苷纳米药物的生物活性分析：纳米增强抗病毒和抗炎作用治疗病毒性肺炎。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-07-01 Epub Date: 2025-01-21 DOI: 10.1016/j.jpha.2025.101201

Chenqi Chang, Chang Lu, Yu Zheng, Lili Lin, XiuZhen Chen, Linwei Chen, Zhipeng Chen, Rui Chen

{"title":"Biological activity analysis of baicalin nanodrugs: Nanosizing enhances antiviral and anti-inflammatory effects in the treatment of viral pneumonia.","authors":"Chenqi Chang, Chang Lu, Yu Zheng, Lili Lin, XiuZhen Chen, Linwei Chen, Zhipeng Chen, Rui Chen","doi":"10.1016/j.jpha.2025.101201","DOIUrl":"https://doi.org/10.1016/j.jpha.2025.101201","url":null,"abstract":"Respiratory syncytial virus (RSV) is a ubiquitous respiratory virus that affects individuals of all ages; however, there is a notable lack of targeted treatments. RSV infection is associated with a range of respiratory symptoms, including bronchiolitis and pneumonia. Baicalin (BA) exhibits significant therapeutic effects against RSV infection through mechanisms of viral inhibition and anti-inflammatory action. Nonetheless, the clinical application of BA is constrained by its low solubility and bioavailability. In this study, we prepared BA nanodrugs (BA NDs) with enhanced water solubility utilizing the supramolecular self-assembled strategy, and we further conducted a comparative analysis of this pharmacological activity between free drugs and NDs of BA. Both in vitro and in vivo results demonstrated that BA NDs significantly enhanced the dual effects of viral inhibition and inflammation relief compared to free BA, attributed to prolonged lung retention, improved cellular uptake, and increased targeting affinity. Our study confirms that the nanosizing strategy, a straightforward approach to enhance drug solubility, can also increase biological activity compared to free drugs with the same content, thereby providing a potential ND for RSV treatment. This correlation analysis between the existing forms of drugs and their biological activity offers a novel perspective for research on the active ingredients of traditional Chinese medicine.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101201"},"PeriodicalIF":8.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12305573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reproducibility of the NMR-based quantitative metabolomics and HBV-caused changes in human serum lipoprotein subclasses and small metabolites. 基于核磁共振的定量代谢组学和hbv引起的人血清脂蛋白亚类和小代谢物变化的可重复性。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-07-01 Epub Date: 2024-12-31 DOI: 10.1016/j.jpha.2024.101180

Qingxia Huang, Qinsheng Chen, Xiaoxuan Yi, Huan Wang, Qi Wang, Haijuan Zhi, Junfang Wu, Dao Wen Wang, Huiru Tang

引用次数: 0

In silico prediction of pK _a values using explainable deep learning methods. 使用可解释的深度学习方法进行pK值的计算机预测。

Journal of pharmaceutical analysis Pub Date : 2025-06-01 Epub Date: 2024-12-28 DOI: 10.1016/j.jpha.2024.101174

Chen Yang, Changda Gong, Zhixing Zhang, Jiaojiao Fang, Weihua Li, Guixia Liu, Yun Tang

{"title":"In silico prediction of pK a values using explainable deep learning methods.","authors":"Chen Yang, Changda Gong, Zhixing Zhang, Jiaojiao Fang, Weihua Li, Guixia Liu, Yun Tang","doi":"10.1016/j.jpha.2024.101174","DOIUrl":"10.1016/j.jpha.2024.101174","url":null,"abstract":"Negative logarithm of the acid dissociation constant (pK a) significantly influences the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of molecules and is a crucial indicator in drug research. Given the rapid and accurate characteristics of computational methods, their role in predicting drug properties is increasingly important. Although many pK a prediction models currently exist, they often focus on enhancing model precision while neglecting interpretability. In this study, we present GraFpK a, a pK a prediction model using graph neural networks (GNNs) and molecular fingerprints. The results show that our acidic and basic models achieved mean absolute errors (MAEs) of 0.621 and 0.402, respectively, on the test set, demonstrating good predictive performance. Notably, to improve interpretability, GraFpK a also incorporates Integrated Gradients (IGs), providing a clearer visual description of the atoms significantly affecting the pK a values. The high reliability and interpretability of GraFpK a ensure accurate pK a predictions while also facilitating a deeper understanding of the relationship between molecular structure and pK a values, making it a valuable tool in the field of pK a prediction.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101174"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update. 基于人工智能的2型糖尿病治疗策略的进展：一个关键的更新。

Journal of pharmaceutical analysis Pub Date : 2025-06-01 Epub Date: 2025-04-10 DOI: 10.1016/j.jpha.2025.101305

Aniruddha Sen, Palani Selvam Mohanraj, Vijaya Laxmi, Sumel Ashique, Rajalakshimi Vasudevan, Afaf Aldahish, Anupriya Velu, Arani Das, Iman Ehsan, Anas Islam, Sabina Yasmin, Mohammad Yousuf Ansari

{"title":"Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update.","authors":"Aniruddha Sen, Palani Selvam Mohanraj, Vijaya Laxmi, Sumel Ashique, Rajalakshimi Vasudevan, Afaf Aldahish, Anupriya Velu, Arani Das, Iman Ehsan, Anas Islam, Sabina Yasmin, Mohammad Yousuf Ansari","doi":"10.1016/j.jpha.2025.101305","DOIUrl":"10.1016/j.jpha.2025.101305","url":null,"abstract":"In the unrelenting race to strive to dominate type 2 diabetes mellitus (T2DM) care better, this review paper sets out on a significant discovery trip across recent advancements in treatment and the blooming era of artificial intelligence (AI) utilities. Given the considerable global burden of T2DM, innovative therapeutic approaches to improve patient outcomes remain a public health priority. This review first provides an in-depth analysis of the current state of therapy, from novel pharmacotherapy to lifestyle interventions and new treatment methods. At the same time, the rapidly increasing role of AI in diabetes care is woven into the story, mainly targeting how insulin therapy can be modified and personalized through algorithms and predictive modelling. It leaves a deep review of their pre-existing synergies, which helps understand how collaborative opportunities will unlock the future of T2DM care. This critical role is shown by integrating recent therapeutic advances and AI with overall showcasing better screening, diagnosis, and therapeutics decision-making to outcome prediction in T2DM. The review emphasizes how AI applications in insulin therapy have transformative potential in diabetes care. These person-centred approaches to T2DM management, which are more effective and personalized than some traditional strategies, only work because of the often-hidden synergies between AI algorithms in areas such as diagnostic criteria, predictive methods, and familiar classification tools for subgroups with relevant aspects/predictors on prognosis or treatment responsiveness.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101305"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances and challenges in drug design against dental caries: Application of in silico approaches. 抗龋药物设计的进展与挑战：计算机方法的应用。

Journal of pharmaceutical analysis Pub Date : 2025-06-01 Epub Date: 2024-12-09 DOI: 10.1016/j.jpha.2024.101161

Zhongxin Chen, Xinyao Zhao, Hanyu Zheng, Yufei Wang, Linglin Zhang

{"title":"Advances and challenges in drug design against dental caries: Application of in silico approaches.","authors":"Zhongxin Chen, Xinyao Zhao, Hanyu Zheng, Yufei Wang, Linglin Zhang","doi":"10.1016/j.jpha.2024.101161","DOIUrl":"10.1016/j.jpha.2024.101161","url":null,"abstract":"Dental caries, a chronic disease characterized by tooth decay, occupies the second position in terms of disease burden and is primarily caused by cariogenic bacteria, especially Streptococcus mutans, because of its acidogenic, aciduric, and biofilm-forming capabilities. Developing novel targeted anti-virulence agents is always a focal point in caries control to overcome the limitations of conventional anti-virulence agents. The current study represents an up-to-date review of in silico approaches of drug design against dental caries, which have emerged more and more powerful complementary to biochemical attempts. Firstly, we categorize the in silico approaches into computer-aided drug design (CADD) and AI-assisted drug design (AIDD) and highlight the specific methods and models they contain respectively. Subsequently, we detail the design of anti-virulence drugs targeting single or multiple cariogenic virulence targets of S. mutans, such as glucosyltransferases (Gtfs), antigen I/II (AgI/II), sortase A (SrtA), the VicRK signal transduction system and superoxide dismutases (SODs). Finally, we outline the current opportunities and challenges encountered in this field to aid future endeavors and applications of CADD and AIDD in anti-virulence drug design.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101161"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction. 基于指纹增强层次分子图神经网络的物业预测。

Journal of pharmaceutical analysis Pub Date : 2025-06-01 Epub Date: 2025-02-20 DOI: 10.1016/j.jpha.2025.101242

Shuo Liu, Mengyun Chen, Xiaojun Yao, Huanxiang Liu

{"title":"Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction.","authors":"Shuo Liu, Mengyun Chen, Xiaojun Yao, Huanxiang Liu","doi":"10.1016/j.jpha.2025.101242","DOIUrl":"10.1016/j.jpha.2025.101242","url":null,"abstract":"Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials. Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction. However, traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules. Similarly, graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information. To address these limitations, we propose a novel fingerprint-enhanced hierarchical graph neural network (FH-GNN) for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints. The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks (D-MPNN) on a hierarchical molecular graph that integrates atomic-level, motif-level, and graph-level information along with their relationships. Additionally, we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features, creating a comprehensive molecular embedding that integrated hierarchical molecular structures with domain knowledge. Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction, validating its capability to comprehensively capture molecular information. By integrating molecular structure and chemical knowledge, FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101242"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12246612/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144628413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

druglikeFilter 1.0: An AI powered filter for collectively measuring the drug-likeness of compounds. druglikeFilter 1.0：一个人工智能驱动的过滤器，用于集体测量化合物的药物相似性。

Journal of pharmaceutical analysis Pub Date : 2025-06-01 Epub Date: 2025-04-09 DOI: 10.1016/j.jpha.2025.101298

Minjie Mou, Yintao Zhang, Yuntao Qian, Zhimeng Zhou, Yang Liao, Tianle Niu, Wei Hu, Yuanhao Chen, Ruoyu Jiang, Hongping Zhao, Haibin Dai, Yang Zhang, Tingting Fu

{"title":"druglikeFilter 1.0: An AI powered filter for collectively measuring the drug-likeness of compounds.","authors":"Minjie Mou, Yintao Zhang, Yuntao Qian, Zhimeng Zhou, Yang Liao, Tianle Niu, Wei Hu, Yuanhao Chen, Ruoyu Jiang, Hongping Zhao, Haibin Dai, Yang Zhang, Tingting Fu","doi":"10.1016/j.jpha.2025.101298","DOIUrl":"10.1016/j.jpha.2025.101298","url":null,"abstract":"Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101298"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268052/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identify drug-drug interactions via deep learning: A real world study. 通过深度学习识别药物-药物相互作用：一项真实世界的研究。

Journal of pharmaceutical analysis Pub Date : 2025-06-01 Epub Date: 2025-01-08 DOI: 10.1016/j.jpha.2025.101194

Jingyang Li, Yanpeng Zhao, Zhenting Wang, Chunyue Lei, Lianlian Wu, Yixin Zhang, Song He, Xiaochen Bo, Jian Xiao

{"title":"Identify drug-drug interactions via deep learning: A real world study.","authors":"Jingyang Li, Yanpeng Zhao, Zhenting Wang, Chunyue Lei, Lianlian Wu, Yixin Zhang, Song He, Xiaochen Bo, Jian Xiao","doi":"10.1016/j.jpha.2025.101194","DOIUrl":"10.1016/j.jpha.2025.101194","url":null,"abstract":"Identifying drug-drug interactions (DDIs) is essential to prevent adverse effects from polypharmacy. Although deep learning has advanced DDI identification, the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits. Here, we developed a Multi-Dimensional Feature Fusion model named MDFF, which integrates one-dimensional simplified molecular input line entry system sequence features, two-dimensional molecular graph features, and three-dimensional geometric features to enhance drug representations for predicting DDIs. MDFF was trained and validated on two DDI datasets, evaluated across three distinct scenarios, and compared with advanced DDI prediction models using accuracy, precision, recall, area under the curve, and F1 score metrics. MDFF achieved state-of-the-art performance across all metrics. Ablation experiments showed that integrating multi-dimensional drug features yielded the best results. More importantly, we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs. Among 12 real-world adverse drug reaction reports, the predictions of 9 reports were supported by relevant evidence. Additionally, MDFF demonstrated the ability to explain adverse DDI mechanisms, providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101194"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0