Journal of pharmaceutical analysis最新文献

{"title":"HyPepTox-Fuse: An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors.","authors":"Duong Thanh Tran, Nhat Truong Pham, Nguyen Doan Hieu Nguyen, Leyi Wei, Balachandran Manavalan","doi":"10.1016/j.jpha.2025.101410","DOIUrl":"10.1016/j.jpha.2025.101410","url":null,"abstract":"<p><p>Peptide-based therapeutics hold great promise for the treatment of various diseases; however, their clinical application is often hindered by toxicity challenges. The accurate prediction of peptide toxicity is crucial for designing safe peptide-based therapeutics. While traditional experimental approaches are time-consuming and expensive, computational methods have emerged as viable alternatives, including similarity-based and machine learning (ML)-/deep learning (DL)-based methods. However, existing methods often struggle with robustness and generalizability. To address these challenges, we propose HyPepTox-Fuse, a novel framework that fuses protein language model (PLM)-based embeddings with conventional descriptors. HyPepTox-Fuse integrates ensemble PLM-based embeddings to achieve richer peptide representations by leveraging a cross-modal multi-head attention mechanism and Transformer architecture. A robust feature ranking and selection pipeline further refines conventional descriptors, thus enhancing prediction performance. Our framework outperforms state-of-the-art methods in cross-validation and independent evaluations, offering a scalable and reliable tool for peptide toxicity prediction. Moreover, we conducted a case study to validate the robustness and generalizability of HyPepTox-Fuse, highlighting its effectiveness in enhancing model performance. Furthermore, the HyPepTox-Fuse server is freely accessible at https://balalab-skku.org/HyPepTox-Fuse/ and the source code is publicly available at https://github.com/cbbl-skku-org/HyPepTox-Fuse/. The study thus presents an intuitive platform for predicting peptide toxicity and supports reproducibility through openly available datasets.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101410"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115852","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":"Prioritization of potential drug targets for diabetic kidney disease using integrative omics data mining and causal inference.","authors":"Junyu Zhang, Jie Peng, Chaolun Yu, Yu Ning, Wenhui Lin, Mingxing Ni, Qiang Xie, Chuan Yang, Huiying Liang, Miao Lin","doi":"10.1016/j.jpha.2025.101265","DOIUrl":"10.1016/j.jpha.2025.101265","url":null,"abstract":"<p><p>Diabetic kidney disease (DKD) with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression. Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials. In this study, we integrated large-scale plasma proteomics, genetic-driven causal inference, and experimental validation to identify prioritized targets for DKD using the UK Biobank (UKB) and FinnGen cohorts. Among 2844 diabetic patients (528 with DKD), we identified 37 targets significantly associated with incident DKD, supported by both observational and causal evidence. Of these, 22% (8/37) of the potential targets are currently under investigation for DKD or other diseases. Our prospective study confirmed that higher levels of three prioritized targets-insulin-like growth factor binding protein 4 (IGFBP4), family with sequence similarity 3 member C (FAM3C), and prostaglandin D2 synthase (PTGDS)-were associated with a 4.35, 3.51, and 3.57-fold increased likelihood of developing DKD, respectively. In addition, population-level protein-altering variants (PAVs) analysis and <i>in vitro</i> experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD, through the classic NLR family pyrin domain containing 3 (NLRP3)-caspase-1-gasdermin D (GSDMD) apoptotic axis. Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101265"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115899","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 integration of machine learning into traditional Chinese medicine.","authors":"Yanfeng Hong, Sisi Zhu, Yuhong Liu, Chao Tian, Hongquan Xu, Gongxing Chen, Lin Tao, Tian Xie","doi":"10.1016/j.jpha.2024.101157","DOIUrl":"10.1016/j.jpha.2024.101157","url":null,"abstract":"<p><p>Traditional Chinese medicine (TCM) is an ancient medical system distinctive and effective in treating cancer, depression, coronavirus disease 2019 (COVID-19), and other diseases. However, the relatively abstract diagnostic methods of TCM lack objective measurement, and the complex mechanisms of action are difficult to comprehend, which hinders the application and internationalization of TCM. Recently, while breakthroughs have been made in utilizing methods such as network pharmacology and virtual screening for TCM research, the rise of machine learning (ML) has significantly enhanced their integration with TCM. This article introduces representative methodological cases in quality control, mechanism research, diagnosis, and treatment processes of TCM, revealing the potential applications of ML technology in TCM. Furthermore, the challenges faced by ML in TCM applications are summarized, and future directions are discussed.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101157"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877694","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":"Repurposing drugs for the human dopamine transporter through WHALES descriptors-based virtual screening and bioactivity evaluation.","authors":"Ding Luo, Zhou Sha, Junli Mao, Jialing Liu, Yue Zhou, Haibo Wu, Weiwei Xue","doi":"10.1016/j.jpha.2025.101368","DOIUrl":"10.1016/j.jpha.2025.101368","url":null,"abstract":"<p><p>Computational approaches, encompassing both physics-based and machine learning (ML) methodologies, have gained substantial traction in drug repurposing efforts targeting specific therapeutic entities. The human dopamine (DA) transporter (hDAT) is the primary therapeutic target of numerous psychiatric medications. However, traditional hDAT-targeting drugs, which interact with the primary binding site, encounter significant limitations, including addictive potential and stimulant effects. In this study, we propose an integrated workflow combining virtual screening based on weighted holistic atom localization and entity shape (WHALES) descriptors with <i>in vitro</i> experimental validation to repurpose novel hDAT-targeting drugs. Initially, WHALES descriptors facilitated a similarity search, employing four benztropine-like atypical inhibitors known to bind hDAT's allosteric site as templates. Consequently, from a compound library of 4,921 marketed and clinically tested drugs, we identified 27 candidate atypical inhibitors. Subsequently, ADMETlab was employed to predict the pharmacokinetic and toxicological properties of these candidates, while induced-fit docking (IFD) was performed to estimate their binding affinities. Six compounds were selected for <i>in vitro</i> assessments of neurotransmitter reuptake inhibitory activities. Among these, three exhibited significant inhibitory potency, with half maximal inhibitory concentration (IC₅₀) values of 0.753 μM, 0.542 μM, and 1.210 μM, respectively. Finally, molecular dynamics (MD) simulations and end-point binding free energy analyses were conducted to elucidate and confirm the inhibitory mechanisms of the repurposed drugs against hDAT in its inward-open conformation. In conclusion, our study not only identifies promising active compounds as potential atypical inhibitors for novel therapeutic drug development targeting hDAT but also validates the effectiveness of our integrated computational and experimental workflow for drug repurposing.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101368"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12398837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984963","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":"Predicting cardiotoxicity in drug development: A deep learning approach.","authors":"Kaifeng Liu, Huizi Cui, Xiangyu Yu, Wannan Li, Weiwei Han","doi":"10.1016/j.jpha.2025.101263","DOIUrl":"10.1016/j.jpha.2025.101263","url":null,"abstract":"<p><p>Cardiotoxicity is a critical issue in drug development that poses serious health risks, including potentially fatal arrhythmias. The human ether-à-go-go related gene (hERG) potassium channel, as one of the primary targets of cardiotoxicity, has garnered widespread attention. Traditional cardiotoxicity testing methods are expensive and time-consuming, making computational virtual screening a suitable alternative. In this study, we employed machine learning techniques utilizing molecular fingerprints and descriptors to predict the cardiotoxicity of compounds, with the aim of improving prediction accuracy and efficiency. We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms, including Gaussian naive Bayes (NB), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), eXtreme gradient boosting (XGBoost), and Transformer models, to build predictive models. Our models demonstrated advanced predictive performance. The best machine learning model, XGBoost Morgan, achieved an accuracy (ACC) value of 0.84, and the deep learning model, Transformer_Morgan, achieved the best ACC value of 0.85, showing a high ability to distinguish between toxic and non-toxic compounds. On an external independent validation set, it achieved the best area under the curve (AUC) value of 0.93, surpassing ADMETlab3.0, Cardpred, and CardioDPi. In addition, we explored the integration of molecular descriptors and fingerprints to enhance model performance and found that ensemble methods, such as voting and stacking, provided slight improvements in model stability. Furthermore, the SHapley Additive exPlanations (SHAP) explanations revealed the relationship between benzene rings, fluorine-containing groups, NH groups, oxygen in ether groups, and cardiotoxicity, highlighting the importance of these features. This study not only improved the predictive accuracy of cardiotoxicity models but also promoted a more reliable and scientifically interpretable method for drug safety assessment. Using computational methods, this study facilitates a more efficient drug development process, reduces costs, and improves the safety of new drug candidates, ultimately benefiting medical and public health.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101263"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115914","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":"GPT2-ICC: A data-driven approach for accurate ion channel identification using pre-trained large language models.","authors":"Zihan Zhou, Yang Yu, Chengji Yang, Leyan Cao, Shaoying Zhang, Junnan Li, Yingnan Zhang, Huayun Han, Guoliang Shi, Qiansen Zhang, Juwen Shen, Huaiyu Yang","doi":"10.1016/j.jpha.2025.101302","DOIUrl":"10.1016/j.jpha.2025.101302","url":null,"abstract":"<p><p>Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces. Here we have developed a deep learning algorithm, GPT2 Ion Channel Classifier (GPT2-ICC), which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins. GPT2-ICC integrates representation learning with a large language model (LLM)-based classifier, enabling highly accurate identification of potential ion channels. Several potential ion channels were predicated from the unannotated human proteome, further demonstrating GPT2-ICC's generalization ability. This study marks a significant advancement in artificial-intelligence-driven ion channel research, highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data. Moreover, it provides a valuable computational tool for uncovering previously uncharacterized ion channels.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101302"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409385/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145017034","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":"Development and application of chiral separation technology based on chiral metal-organic frameworks.","authors":"Gege Zhu, Li Ge, Xinyu Li, Bing Niu, Qin Chen, Dan Zhong, Xiaodong Sun","doi":"10.1016/j.jpha.2024.101176","DOIUrl":"10.1016/j.jpha.2024.101176","url":null,"abstract":"<p><p>Chirality is not only a natural phenomenon but also a bridge between chemistry and life sciences. An effective way to obtain a single enantiomer is through racemates resolution. Recent literature shows that chiral metal-organic frameworks (CMOFs) have many applications in various fields because of their diverse topologies and functionalities. This review outlines the design idea and summarizes the latest synthesis strategies and applications of CMOFs. It highlights key advances and issues in the separation domain. In conclusion, the review provides perspectives on the challenges and prospective advancements of CMOFs materials and CMOFs-based separation technologies.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101176"},"PeriodicalIF":8.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12305593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746714","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":"Disorder of phospholipid metabolism in the renal cortex and medulla contributes to acute tubular necrosis in mice after cantharidin exposure using integrative lipidomics and spatial metabolomics.","authors":"Tianmu He, Kexin Lin, Lijuan Xiong, Wen Zhang, Huan Zhang, Cancan Duan, Xiaofei Li, Jianyong Zhang","doi":"10.1016/j.jpha.2025.101210","DOIUrl":"10.1016/j.jpha.2025.101210","url":null,"abstract":"<p><p>Cantharidin (CTD), a natural compound used to treat multiple tumors in the clinic setting, has been limited due to acute kidney injury (AKI). However, the major cause of AKI and its underlying mechanism remain to be elucidated. Serum creatinine (SCr) and blood urea nitrogen (BUN) were detected through pathological evaluation after CTD (1.5 mg/kg) oral gavage in mice in 3 days. Kidney lipidomics based on ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to investigate lipids disorder after CTD exposure in mice. Then, spatial metabolomics based on matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) was used to detect the kidney spatial distribution of lipids. Integrative analysis was performed to reveal the spatial lipid disorder mechanism and verify key lipids <i>in vitro</i>. The results showed that the levels of SCr and BUN were increased, and tubular necrosis was observed in mouse kidneys, resulting in acute tubular necrosis (ATN) in CTD-induced AKI. Then, lipidomics results revealed that after CTD exposure, 232 differential lipid metabolites and 11 pathways including glycerophospholipid (GP) and sphingolipid (SL) metabolism were disrupted. Spatial metabolomics revealed that 55 spatial differential lipid metabolites and nine metabolic pathways were disturbed. Subsequently, integrative analysis found that GP metabolism was stimulated in the renal cortex and medulla, whereas SL metabolism was inhibited in the renal cortex. Up-regulated lysophosphatidylcholine (LysoPC) (18:2(9Z,12Z)), LysoPC (16:0/0:0), glycerophosphocholine, and down-regulated sphingomyelin (SM) (d18:0/16:0), SM (d18:1/24:0), and SM (d42:1) were key differential lipids. Among them, LysoPC (16:0/0:0) was increased in the CTD group at 1.1196 μg/mL, which aggravated CTD-induced ATN in human kidney-2 (HK<b>-</b>2) cells. LysoPC acyltransferase was inhibited and choline phosphotransferase 1 (CEPT1) was activated after CTD intervention in mice and in HK<b>-</b>2 cells. CTD induces ATN, resulting in AKI, by activating GP metabolism and inhibiting SL metabolism in the renal cortex and medulla, LysoPC (16:0/0:0), LysoPC acyltransferase, and CEPT1 may be the therapeutic targets.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101210"},"PeriodicalIF":8.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12304689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746715","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":"Effect of cholesterol on distribution, cell uptake, and protein corona of lipid microspheres at sites of cardiovascular inflammatory injury.","authors":"Lingyan Li, Xingjie Wu, Qianqian Guo, Yu'e Wang, Zhiyong He, Guangqiong Zhang, Shaobo Liu, Liping Shu, Babu Gajendran, Ying Chen, Xiangchun Shen, Ling Tao","doi":"10.1016/j.jpha.2024.101182","DOIUrl":"10.1016/j.jpha.2024.101182","url":null,"abstract":"<p><p>Cholesterol (CH) plays a crucial role in enhancing the membrane stability of drug delivery systems (DDS). However, its association with conditions such as hyperlipidemia often leads to criticism, overshadowing its influence on the biological effects of formulations. In this study, we reevaluated the delivery effect of CH using widely applied lipid microspheres (LM) as a model DDS. We conducted comprehensive investigations into the impact of CH on the distribution, cell uptake, and protein corona (PC) of LM at sites of cardiovascular inflammatory injury. The results demonstrated that moderate CH promoted the accumulation of LM at inflamed cardiac and vascular sites without exacerbating damage while partially mitigating pathological damage. Then, the slow cellular uptake rate observed for CH@LM contributed to a prolonged duration of drug efficacy. Network pharmacology and molecular docking analyses revealed that CH depended on LM and exerted its biological effects by modulating peroxisome proliferator-activated receptor gamma (PPAR-γ) expression in vascular endothelial cells and estrogen receptor alpha (ERα) protein levels in myocardial cells, thereby enhancing LM uptake at cardiovascular inflammation sites. Proteomics analysis unveiled a serum adsorption pattern for CH@LM under inflammatory conditions showing significant adsorption with CH metabolism-related apolipoprotein family members such as apolipoprotein A-V (Apoa5); this may be a major contributing factor to their prolonged circulation <i>in vivo</i> and explains why CH enhances the distribution of LM at cardiovascular inflammatory injury sites. It should be noted that changes in cell types and physiological environments can also influence the biological behavior of formulations. The findings enhance the conceptualization of CH and LM delivery, providing novel strategies for investigating prescription factors' bioactivity.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101182"},"PeriodicalIF":8.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12305578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746716","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":"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":"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":8.9,"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}