Journal of pharmaceutical analysis最新文献

{"title":"Lipidome atlas of human myometrium reveals distinctive lipid signatures associated with adenomyosis: Combination of high-coverage lipidomics and mass spectrometry imaging.","authors":"Shuo Liang, Jialin Liu, Maokun Liao, Dandan Liang, Yiyi Gong, Bo Zhang, Nan Zhao, Wei Song, Honghui Shi","doi":"10.1016/j.jpha.2025.101197","DOIUrl":"https://doi.org/10.1016/j.jpha.2025.101197","url":null,"abstract":"<p><p>Adenomyosis is a common gynecological disease characterized by the invasion of endometrial glands and stroma into the myometrium of uterus, the pathological mechanism of which remains unclear yet. Disturbed lipid metabolism extensively affects abnormal cell proliferation and invasion in various diseases. However, the lipidome signature of human myometrium, which could be crucial in the development of adenomyosis, remains unknown. In this study, we generated the first lipidome profiling of human myometrium using a high-coverage and quantitative lipidomics approach based on ultra-performance liquid chromatography (UPLC) coupled with triple quadrupole (QqQ)-mass spectrometry (MS). A total of 317 lipid species were successfully quantified in the myometrial tissues from women with (<i>n</i> = 38) or without (<i>n</i> = 65) adenomyosis who underwent hysterectomy at Peking Union Medical College Hospital (Bejing, China). Up to 83 lipid species showed significant alternations in content between the two groups. These lipid aberrations involved multiple metabolic pathways, and emphasized inflammation, cell migration, and immune dysregulation upon adenomyosis. Moreover, receiver operating characteristic (ROC) curve analysis found that the combination of five lipid species could accurately distinguished the myometrial samples from women with and without adenomyosis with an area under the curve (AUC) of 0.906. Desorption electrospray ionization MS imaging (MSI) further underscored the heterogeneous distributions of these lipid markers in the adenomyosis lesion and adjacent myometrial tissue. Collectively, these results extremely improved our understanding on the molecular basis of adenomyosis, and could shed light on developing potential biomarkers and new therapeutic directions for adenomyosis.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 9","pages":"101197"},"PeriodicalIF":8.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12495062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234517","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":"Pathogenesis and treatment strategies for infectious keratitis: Exploring antibiotics, antimicrobial peptides, nanotechnology, and emerging therapies.","authors":"Man Yu, Ling Li, Yijun Liu, Ting Wang, Huan Li, Chen Shi, Xiaoxin Guo, Weijia Wu, Chengzi Gan, Mingze Li, Jiaxu Hong, Kai Dong, Bo Gong","doi":"10.1016/j.jpha.2025.101250","DOIUrl":"https://doi.org/10.1016/j.jpha.2025.101250","url":null,"abstract":"<p><p>Infectious keratitis (IK) is a leading cause of blindness worldwide, primarily resulting from improper contact lens use, trauma, and a compromised immune response. The pathogenic microorganisms responsible for IK include bacteria, fungi, viruses, and Acanthamoeba. This review examines standard therapeutic agents for treating IK, including broad-spectrum empiric antibiotics for bacterial keratitis (BK), antifungals such as voriconazole and natamycin for fungal infections, and antiviral nucleoside analogues for viral keratitis (VK). Additionally, this review discusses therapeutic agents, such as polyhexamethylene biguanide (PHMB), for the treatment of Acanthamoeba keratitis (AK). The review also addresses emerging drugs and the challenges associated with their clinical application, including anti-biofilm agents that combat drug resistance and nuclear factor kappa-B (NF-κB) pathway-targeted therapies to mitigate inflammation. Furthermore, methods of Photodynamic Antimicrobial Therapy (PDAT) are explored. This review underscores the importance of integrating novel and traditional therapies to tackle drug resistance and enhance drug delivery, with the goal of advancing treatment strategies for IK.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 9","pages":"101250"},"PeriodicalIF":8.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12495054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234660","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":"Mechanistic insights into honey-boiled detoxification of ChuanWu: A study on alkaloid transformation and supramolecular aggregation.","authors":"Yu Zheng, Nina Wei, Chang Lu, Weidong Li, Xiaobin Jia, Linwei Chen, Rui Chen, Zhipeng Chen","doi":"10.1016/j.jpha.2025.101205","DOIUrl":"https://doi.org/10.1016/j.jpha.2025.101205","url":null,"abstract":"<p><p>ChuanWu (CW), the dried mother root of <i>Aconitum carmichaelii</i> Debx., is a well-known traditional Chinese medicine (TCM) recognized for its potent efficacy but inherent toxicity, primarily due to its alkaloid content. Traditional and modern detoxification methods for CW include proper processing, rational compatibility, and specialized decoction techniques, among which honey-boiled CW is particularly distinctive. However, research on the detoxification mechanism of honey-boiled CW remains limited. This study investigated this mechanism by analyzing alkaloid transformation and supramolecular aggregation. Honey-boiled and water-boiled CW preparations were compared. Ultra-high-performance liquid chromatography-tandem mass spectrometry was used to analyze CW alkaloids, specifically diester alkaloids (DDAs), monoester alkaloids (MDAs), and non-esterified diterpenoid alkaloids (NDAs). Transmission electron microscopy was employed to observe and identify supramolecular aggregates in the honey-boiled CW decoction. <i>In vivo</i> absorption of water-boiled, honey-boiled, and NADES-boiled CW was compared. Median lethal dose (LD₅₀) tests assessed toxicity, including hepatotoxicity and nephrotoxicity. In vitro experiments evaluated the safety, anti-inflammatory, and analgesic effects of CW-medicated serum on RAW264.7 cells, with in vivo validation in mice. Results showed that honey promoted the conversion of highly toxic DDAs to less toxic MDAs and prevented MDAs from hydrolyzing into NDAs. Honey-boiled CW formed approximately 250 nm supramolecular aggregates that encapsulated MDAs, inhibiting their conversion to NDAs. These encapsulated MDAs acted as a stable delivery system with higher bioavailability than free benzoylmesaconine. Subsequent mouse experiments confirmed that honey-boiled CW significantly increased the LD₅₀ of CW while reducing hepatotoxicity and nephrotoxicity. Additionally, honey-boiled CW significantly improved cell safety and enhanced anti-inflammatory and analgesic effects. Our findings reveal that honey-boiled CW exhibits a potent detoxification mechanism by influencing alkaloid transformation and facilitating the formation of supramolecular aggregates. This study lays the groundwork for developing detoxification or synergistic strategies within honey-boiled TCM.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 9","pages":"101205"},"PeriodicalIF":8.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12493136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234574","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":"ZFP36 promotes ferroptosis and mitochondrial dysfunction and inhibits malignant progression in osteosarcoma by regulating the E2F1/ATF4 axis.","authors":"Shiyue Qin, Hongyang Kong, Lei Jiang","doi":"10.1016/j.jpha.2025.101228","DOIUrl":"https://doi.org/10.1016/j.jpha.2025.101228","url":null,"abstract":"<p><p>Zinc finger protein 36 (ZFP36) was found to be downregulated in osteosarcoma (OS) tumor tissues. We aimed to investigate the roles and mechanisms of ZFP36 in ferroptosis regulation during OS development. Two Gene Expression Omnibus (GEO) datasets showed that ZFP36 was a differentially expressed gene (DEG) in OS. Western blot and immunohistochemistry results showed that ZFP36 was downregulated in OS tumors and cell lines. ZFP36 overexpression plasmids and small interfering RNAs (siRNAs) were respectively transfected into OS cells. ZFP36 overexpression restrained proliferation, migration, and invasion in MG63 and U2OS cells, while ZFP36 knockdown displayed the opposite results. Moreover, ZFP36 overexpression increased the levels of intracellular Fe²⁺, reactive oxygen species (ROS), and malondialdehyde (MDA), and decreased the levels of glutathione (GSH), glutathione peroxidase 4 (GPX4), and solute carrier family 7 member 11 (SLC7A11). ZFP36 overexpression disturbed mitochondrial membrane potential (MMP) and mitochondrial morphology in OS cells. However, ZFP36 knockdown had the opposite results. Mechanistic studies indicated that ZFP36 promoted E2F transcription factor 1 (E2F1) messenger RNA (mRNA) degradation by binding to the AU-rich elements (AREs) within E2F1 3' untranslated region (3'UTR) in OS cells. E2F1 overexpression abrogated the effects of ZFP36 overexpression on malignant progression, ferroptosis, and mitochondrial dysfunction in OS cells. Furthermore, E2F1 promoted the transcription activation of activating transcription factor 4 (ATF4) by binding to ATF4 promoter. E2F1 knockdown inhibited malignant progression, and promoted ferroptosis and mitochondrial dysfunction in OS cells, which was abrogated by ATF4 overexpression. Additionally, MG63 cells transfected with lentivirus ZFP36 overexpression vector (Lv-ZFP36) were injected into nude mice and tumor growth was monitored. ZFP36 overexpression significantly suppressed OS tumor growth under <i>in vivo</i> settings. In conclusion, ZFP36 overexpression promoted ferroptosis and mitochondrial dysfunction and inhibited malignant progression in OS by regulating the E2F1/ATF4 axis. We may provide the promising ZFP36 target for OS treatment.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 9","pages":"101228"},"PeriodicalIF":8.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12495438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234665","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":"Trace component fishing strategy based on offline two-dimensional liquid chromatography combined with PRDX3-surface plasmon resonance for <i>Uncaria</i> alkaloids.","authors":"Hui Ni, Zijia Zhang, Ye Lu, Yaowen Liu, Yang Zhou, Wenyong Wu, Xinqin Kong, Liling Shen, Sihan Chen, Huali Long, Cheng Luo, Hao Zhang, Jinjun Hou, Wanying Wu","doi":"10.1016/j.jpha.2025.101244","DOIUrl":"https://doi.org/10.1016/j.jpha.2025.101244","url":null,"abstract":"<p><p>The rapid screening of bioactive constituents within traditional Chinese medicine (TCM) presents a significant challenge to researchers. Prevailing strategies for the screening of active components in TCM often overlook trace components owing to their concealment by more abundant constituents. To address this limitation, a fishing strategy based on offline two-dimensional liquid chromatography (2D-LC) combined with surface plasmon resonance (SPR) was utilized to screen bioactive trace components targeting peroxiredoxin 3 (PRDX3), using <i>Uncaria</i> alkaloids (UAs) as a case study. Initially, an orthogonal preparative offline 2D-LC system combining a positively charged C₁₈ column and a conventional C₁₈ column under disparate mobile phase conditions was constructed. To fully reveal the trace alkaloids, 13 2D fractions of UAs were prepared, and their components were characterized using mass spectrometry (MS). Subsequently, employing PRDX3 as the targeting protein, a SPR-based screening approach was established and rigorously validated with geissoschizine methyl ether (GSM) serving as a positive control for binding. Employing this refined strategy, 29 candidate binding alkaloids were fished from the 13 2D fractions. Notably, combining offline 2D-LC with SPR increased the yield of candidate binding components from 10 to 29 when compared to SPR-based screening alone. Subsequent binding affinity assays confirmed that PRDX3 was a direct binding target for the 12 fished alkaloids, with isovallesiachotamine (IV), corynoxeine <i>N</i>-oxide (CO-N), and cadambine (CAD) demonstrating the highest affinity for PRDX3. Their interactions were further validated through molecular docking analysis. Subsequent intracellular H₂O₂ measurement assays and transfection experiments confirmed that these three trace alkaloids enhanced PRDX3-mediated H₂O₂ clearance. In conclusion, this study introduced an innovative strategy for the identification of active trace components in TCM. This approach holds promise for accelerating research on medicinal components within this field.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 9","pages":"101244"},"PeriodicalIF":8.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234611","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":"Integrating explainable deep learning with multi-omics for screening progressive diagnostic biomarkers of hepatocellular carcinoma covering the \"inflammation-cancer\" transformation.","authors":"Saiyu Li, Yiwen Zhang, Lifang Guan, Yijing Dong, Mingzhe Zhang, Qian Zhang, Huarong Xu, Wei Xiao, Zhenzhong Wang, Yan Cui, Qing Li","doi":"10.1016/j.jpha.2025.101253","DOIUrl":"10.1016/j.jpha.2025.101253","url":null,"abstract":"<p><p>Image 1.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 9","pages":"101253"},"PeriodicalIF":8.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12410188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145017037","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":"LocPro: A deep learning-based prediction of protein subcellular localization for promoting multi-directional pharmaceutical research.","authors":"Yintao Zhang, Lingyan Zheng, Nanxin You, Wei Hu, Wanghao Jiang, Mingkun Lu, Hangwei Xu, Haibin Dai, Tingting Fu, Ying Zhou","doi":"10.1016/j.jpha.2025.101255","DOIUrl":"10.1016/j.jpha.2025.101255","url":null,"abstract":"<p><p>Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101255"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12363569/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984923","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 future of pharmaceuticals: Artificial intelligence in drug discovery and development.","authors":"Chen Fu, Qiuchen Chen","doi":"10.1016/j.jpha.2025.101248","DOIUrl":"10.1016/j.jpha.2025.101248","url":null,"abstract":"<p><p>Artificial Intelligence (AI) is revolutionizing traditional drug discovery and development models by seamlessly integrating data, computational power, and algorithms. This synergy enhances the efficiency, accuracy, and success rates of drug research, shortens development timelines, and reduces costs. Coupled with machine learning (ML) and deep learning (DL), AI has demonstrated significant advancements across various domains, including drug characterization, target discovery and validation, small molecule drug design, and the acceleration of clinical trials. Through molecular generation techniques, AI facilitates the creation of novel drug molecules, predicting their properties and activities, while virtual screening (VS) optimizes drug candidates. Additionally, AI enhances clinical trial efficiency by predicting outcomes, designing trials, and enabling drug repositioning. However, AI's application in drug development faces challenges, including the need for robust data-sharing mechanisms and the establishment of more comprehensive intellectual property protections for algorithms. AI-driven pharmaceutical companies must also integrate biological sciences and algorithms effectively, ensuring the successful fusion of wet and dry laboratory experiments. Despite these challenges, the potential of AI in drug development remains undeniable. As AI technology evolves and these barriers are addressed, AI-driven therapeutics are poised for a broader and more impactful future in the pharmaceutical industry.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101248"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12391800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984938","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":"Spatial metabolomics combined with machine learning in colon cancer diagnosis research.","authors":"Ling Weng, Huanhuan Wang, Chunxiang Zhai, Qi Wang, Yanyan Guo, Ziyi Zhong, Chenying Ma, Jing Wang","doi":"10.1016/j.jpha.2025.101367","DOIUrl":"10.1016/j.jpha.2025.101367","url":null,"abstract":"<p><p>Image 1.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101367"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12398819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984970","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":"Quantifying compatibility mechanisms in traditional Chinese medicine with interpretable graph neural networks.","authors":"Jingqi Zeng, Xiaobin Jia","doi":"10.1016/j.jpha.2025.101342","DOIUrl":"10.1016/j.jpha.2025.101342","url":null,"abstract":"<p><p>Traditional Chinese medicine (TCM) features complex compatibility mechanisms involving multi-component, multi-target, and multi-pathway interactions. This study presents an interpretable graph artificial intelligence (GraphAI) framework to quantify such mechanisms in Chinese herbal formulas (CHFs). A multidimensional TCM knowledge graph (TCM-MKG; https://zenodo.org/records/13763953) was constructed, integrating seven standardized modules: TCM terminology, Chinese patent medicines (CPMs), Chinese herbal pieces (CHPs), pharmacognostic origins (POs), chemical compounds, biological targets, and diseases. A neighbor-diffusion strategy was used to address the sparsity of compound-target associations, increasing target coverage from 12.0% to 98.7%. Graph neural networks (GNNs) with attention mechanisms were applied to 6,080 CHFs, modeled as graphs with CHPs as nodes. To embed domain-specific semantics, virtual nodes medicinal properties, i.e., therapeutic nature, flavor, and meridian tropism, were introduced, enabling interpretable modeling of inter-CHP relationships. The model quantitatively captured classical compatibility roles such as \"monarch-minister-assistant-guide,\" and uncovered TCM etiological types derived from diagnostic and efficacy patterns. Model validation using 215 CHFs used for coronavirus disease 2019 (COVID-19) management highlighted <i>Radix Astragali</i>-<i>Rhizoma Phragmitis</i> as a high-attention herb pair. Mass spectrometry (MS) and target prediction identified three active compounds, i.e., methylinissolin-3-<i>O</i>-glucoside, corydalin, and pingbeinine, which converge on pathways such as neuroactive ligand-receptor interaction, xenobiotic response, and neuronal function, supporting their neuroimmune and detoxification potential. Given their high safety and dietary compatibility, this herb pair may offer therapeutic value for managing long COVID-19. All data and code are openly available (https://github.com/ZENGJingqi/GraphAI-for-TCM), providing a scalable and interpretable platform for TCM mechanism research and discovery of bioactive herbal constituents.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101342"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12398817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985002","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}