Journal of pharmaceutical analysis最新文献

{"title":"Aldolase A accelerates hepatocarcinogenesis by refactoring c-Jun transcription.","authors":"Xin Yang, Guang-Yuan Ma, Xiao-Qiang Li, Na Tang, Yang Sun, Xiao-Wei Hao, Ke-Han Wu, Yu-Bo Wang, Wen Tian, Xin Fan, Zezhi Li, Caixia Feng, Xu Chao, Yu-Fan Wang, Yao Liu, Di Li, Wei Cao","doi":"10.1016/j.jpha.2024.101169","DOIUrl":"https://doi.org/10.1016/j.jpha.2024.101169","url":null,"abstract":"<p><p>Hepatocellular carcinoma (HCC) expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming. Aldolase A (ALDOA) plays a prominent role in glycolysis; however, little is known about its role in HCC development. In the present study, we aim to explore how ALDOA is involved in HCC proliferation. HCC proliferation was markedly suppressed both <i>in vitro</i> and <i>in vivo</i> following <i>ALDOA</i> knockout, which is consistent with <i>ALDOA</i> overexpression encouraging HCC proliferation. Mechanistically, <i>ALDOA</i> knockout partially limits the glycolytic flux in HCC cells. Meanwhile, ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase; <i>ALDOA</i> knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function. A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun, and Y364S ALDOA expression failed to rescue cell proliferation in <i>ALDOA</i> deletion cells. In HCC patients, the expression level of ALDOA was correlated with the phosphorylation level of c-Jun (Thr93) and poor prognosis. Remarkably, hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models, and the knockdown of <i>A</i> <i>ldoa</i> strikingly decreased HCC development <i>in vivo</i>. Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription, opening additional avenues for anti-cancer therapies.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101169"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12284681/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710441","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}

{"title":"GPCRs identified on mitochondrial membranes: New therapeutic targets for diseases.","authors":"Yanxin Pan, Ning Ji, Lu Jiang, Yu Zhou, Xiaodong Feng, Jing Li, Xin Zeng, Jiongke Wang, Ying-Qiang Shen, Qianming Chen","doi":"10.1016/j.jpha.2024.101178","DOIUrl":"https://doi.org/10.1016/j.jpha.2024.101178","url":null,"abstract":"<p><p>G protein-coupled receptors (GPCRs) are the largest family of membrane proteins in eukaryotes, with nearly 800 genes coding for these proteins. They are involved in many physiological processes, such as light perception, taste and smell, neurotransmitter, metabolism, endocrine and exocrine, cell growth and migration. Importantly, GPCRs and their ligands are the targets of approximately one third of all marketed drugs. GPCRs are traditionally known for their role in transmitting signals from the extracellular environment to the cell's interior via the plasma membrane. However, emerging evidence suggests that GPCRs are also localized on mitochondria, where they play critical roles in modulating mitochondrial functions. These mitochondrial GPCRs (mGPCRs) can influence processes such as mitochondrial respiration, apoptosis, and reactive oxygen species (ROS) production. By interacting with mitochondrial signaling pathways, mGPCRs contribute to the regulation of energy metabolism and cell survival. Their presence on mitochondria adds a new layer of complexity to the understanding of cellular signaling, highlighting the organelle's role as not just an energy powerhouse but also a crucial hub for signal transduction. This expanding understanding of mGPCR function on mitochondria opens new avenues for research, particularly in the context of diseases where mitochondrial dysfunction plays a key role. Abnormalities in the phase conductance pathway of GPCRs located on mitochondria are closely associated with the development of systemic diseases such as cardiovascular disease, diabetes, obesity and Alzheimer's disease. In this review, we examined the various types of GPCRs identified on mitochondrial membranes and analyzed the complex relationships between mGPCRs and the pathogenesis of various diseases. We aim to provide a clearer understanding of the emerging significance of mGPCRs in health and disease, and to underscore their potential as therapeutic targets in the treatment of these conditions.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101178"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12284666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710442","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}

{"title":"Silent or low expression of <i>bla</i> _TEM and <i>bla</i> _SHV suggests potential for targeted proteomics in clinical detection of β-lactamase-related antimicrobial resistance.","authors":"Huige Wu, Wenting Dong, Xinxin Hu, Chunyang Xie, Xinyi Yang, Congran Li, Guoqing Li, Yun Lu, Xuefu You","doi":"10.1016/j.jpha.2025.101220","DOIUrl":"https://doi.org/10.1016/j.jpha.2025.101220","url":null,"abstract":"<p><p>Image 1.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101220"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710443","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}

{"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":"https://doi.org/10.1016/j.jpha.2024.101183","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101183"},"PeriodicalIF":0.0,"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}

{"title":"<i>In silico</i> prediction of p<i>K</i> _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":"<p><p>Negative logarithm of the acid dissociation constant (p<i>K</i> _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 p<i>K</i> _a prediction models currently exist, they often focus on enhancing model precision while neglecting interpretability. In this study, we present GraFp<i>K</i> _a, a p<i>K</i> _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, GraFp<i>K</i> _a also incorporates Integrated Gradients (IGs), providing a clearer visual description of the atoms significantly affecting the p<i>K</i> _a values. The high reliability and interpretability of GraFp<i>K</i> _a ensure accurate p<i>K</i> _a predictions while also facilitating a deeper understanding of the relationship between molecular structure and p<i>K</i> _a values, making it a valuable tool in the field of p<i>K</i> _a prediction.</p>","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}

{"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":"<p><p>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.</p>","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}

{"title":"Advances and challenges in drug design against dental caries: Application of <i>in silico</i> 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":"<p><p>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 <i>Streptococcus mutans,</i> 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 <i>in silico</i> approaches of drug design against dental caries, which have emerged more and more powerful complementary to biochemical attempts. Firstly, we categorize the <i>in silico</i> 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 <i>S. mutans</i>, 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.</p>","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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/.</p>","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}

{"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":"<p><p>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.</p>","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}