Current pharmaceutical design最新文献

{"title":"Recent Advances in Drug-in-Adhesive Transdermal Patches: Innovations in Materials, Formulations, and Delivery Mechanisms.","authors":"Sara Salatin, Ali Reza Shafiee-Kandjani, Maryam Azarfarin, Samin Hamidi","doi":"10.2174/0113816128459914260825102240","DOIUrl":"https://doi.org/10.2174/0113816128459914260825102240","url":null,"abstract":"<p><strong>Introduction/objective: </strong>This review examines recent developments in transdermal drug delivery systems, with emphasis on drug-in-adhesive patches. It aims to summarize advances in patch design, formulation strategies, and methods used to improve drug permeation and delivery performance.</p><p><strong>Methods: </strong>This narrative review was conducted through a literature search of PubMed, Scopus, and Web of Science. Search terms included combinations of 'drug-in-adhesive patch', 'pressure-sensitive adhesive transdermal', 'hydrogel adhesive', 'penetration enhancer', 'ionic liquid transdermal', and 'active pharmaceutical ingredient crystallization adhesive matrix'. Studies were selected based on relevance to drug in adhesive patch formulation, adhesive material performance, and permeation enhancement strategies, with priority given to original research articles published in peer-reviewed journals. A total of 129 references are cited in this review.</p><p><strong>Results: </strong>New adhesive materials, including acrylic-based and hydrogel-based systems, have been developed to improve skin adhesion and compatibility with diverse drug molecules. Significant progress has been made in enhancing drug permeation through chemical penetration enhancers and approaches involving ionic liquids. Key formulation challenges, such as drug crystallization within the adhesive matrix, stability, and control of release kinetics, remain active areas of investigation. Research increasingly emphasizes understanding the molecular state of drug substances within the patch to optimize delivery efficiency.</p><p><strong>Discussion: </strong>The findings demonstrate meaningful advancement in materials and formulation methods that support more effective and patient-friendly transdermal patches. These developments align with broader efforts to improve therapeutic outcomes, reduce dosing frequency, and enhance patient compliance. Persistent limitations include the need for standardized testing approaches and a deeper understanding of long-term stability and skin interactions. Future work should address these gaps while exploring novel materials and permeation strategies.</p><p><strong>Conclusion: </strong>Advances in drug-in-adhesive transdermal systems show strong potential for improving drug delivery. Continued innovation in adhesive design, permeation enhancement, and formulation science is essential for developing next-generation transdermal therapies.</p>","PeriodicalId":10845,"journal":{"name":"Current pharmaceutical design","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeting the Immune System with Nanoparticles: A Comprehensive Review on Immunomodulation.","authors":"Muhammad Saad Latif, Fahad Ali Noori, Muhammad Saqlain Mushtaq, Syed Damin Abbas Hamdani, Mustafeez Mujtaba Babar","doi":"10.2174/0113816128498072260824145100","DOIUrl":"https://doi.org/10.2174/0113816128498072260824145100","url":null,"abstract":"<p><strong>Introduction: </strong>The immune system plays a significant role in combating pathogens, maintaining homeostasis, and protecting the overall well-being of the body. Modulating the immune system to enhance or suppress the immune response is a remarkable approach against several diseases. The objective of this article is to comprehensively review the role of nanoparticles (NPs) in modulating the immune response.</p><p><strong>Methods: </strong>This review discusses and analyzes current literature on how NPs can alter the immune response, detailing the different types of nanomaterials used, their specific mechanisms of action, and their interactions with immune cells.</p><p><strong>Discussion: </strong>NPs are significantly impacting the field of immunomodulation through targeted drug delivery, effectively solving major pharmacological challenges like systemic side effects and low bioavailability. These novel therapeutic agents provide improved efficacy compared to traditional methods. Additionally, the review highlights the significant potential and ongoing applications of NPs in specialized areas, specifically in cancer immunotherapy and advanced vaccine development.</p><p><strong>Conclusion: </strong>Significant advancements in NP-based immunomodulation offer effective alternatives to conventional therapies. Challenges, including nanotoxicity, manufacturing scalability, and clinical translation, must be addressed, with AI-assisted design and personalized nanomedicine identified as significant future directions.</p>","PeriodicalId":10845,"journal":{"name":"Current pharmaceutical design","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning in Hyperlipidaemia Research: Screening and Experimental Insights into Lipid Metabolism Modulators.","authors":"Ashish Srivastava, Mohit Prajapati, Parul Srivastava, Anil Kumar Yadav, Ankita Wal, Manshi Singh, Ritik Yadav","doi":"10.2174/0113816128491240260824154011","DOIUrl":"https://doi.org/10.2174/0113816128491240260824154011","url":null,"abstract":"<p><p>Hyperlipidemia, characterized by elevated blood lipid levels, represents a major global health concern due to its strong association with cardiovascular disease, diabetes, and metabolic syndrome. While current therapies - such as statins, fibrates, bile acid sequestrants, and PCSK9 inhibitors - are effective in controlling hyperlipidemia, they are often associated with adverse effects, potential drug resistance, and suboptimal efficacy in certain patient populations. All of the above underscore the urgent need for safer and more effective therapeutic alternatives. Among the major molecular targets involved in the regulation of lipid metabolism are HMG-CoA reductase, PCSK9, peroxisome proliferator-activated receptors (PPARs), cholesteryl ester transfer protein (CETP), and nuclear receptors, including the liver X receptor (LXR) and farnesoid X receptor (FXR), which are also targets for future antihyperlipidemic drug development. Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly transformed and accelerated drug discovery by enabling the processing of vast amounts of genomic, proteomic, and chemical data. Furthermore, ML tools such as quantitative structure-activity relationship (QSAR) modelling, deep learning, random forest, and support vector machines (SVM) have proven predictive and effective in identifying novel lipid metabolism modulators, thereby enhancing the efficacy and accuracy of virtual screening. Meanwhile, molecular docking has become an integral part of structure-based drug design (SBDD), and software such as AutoDock, Glide, and GOLD have proven effective in generating accurate ligand-target docking models. Molecular docking, together with ML-based approaches, enables the identification of potent and selective drug candidates. Overall, the combination of ML and molecular docking offers an efficient and accurate platform for antihyperlipidemic drug discovery, helping to overcome the limitations of currently available therapeutic strategies.</p>","PeriodicalId":10845,"journal":{"name":"Current pharmaceutical design","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148944403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-Chain Fatty Acids and FFAR2: Modulators of the Gut-Brain Axis in Neuroinflammatory Disorders.","authors":"Harshpreet Kaur, Joy Das, Ashok Kumar Sah, Utpal Bhui, Khalimova Fariza Tursunbaevna, Nasiruddin Khan, Borony Akter, Jiniya Sharma, Navneet Khurana, Bimlesh Kumar, Neha Sharma","doi":"10.2174/0113816128460115260825084835","DOIUrl":"https://doi.org/10.2174/0113816128460115260825084835","url":null,"abstract":"<p><p>Short-chain fatty acids (SCFAs), primarily acetate, propionate, and butyrate, are key microbial metabolites generated through the fermentation of indigestible carbohydrates by gut bacteria. Beyond their local actions in the intestinal environment, these metabolites exert far-reaching systemic effects, particularly in shaping neuroimmune communication along the gut-brain axis. Their interaction with free fatty acid receptor 2 (FFAR2), expressed on intestinal epithelial cells, peripheral immune populations, and microglia, has emerged as a central mechanism linking gut microbial activity to neuroinflammatory regulation. Disrupted FFAR2 signalling and altered SCFA production are now implicated in the pathogenesis of key neuroinflammatory and neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis. In this narrative review, we synthesize current evidence suggesting that SCFAs can mediate microglial development, cytokine cross-linking, and barrier stability, as well as neuronal-glial communication via FFAR2-mediated and receptor-independent mechanisms, including HDAC inhibition. Importantly, current evidence supports three partially overlapping mechanistic frameworks: (i) direct receptor-dependent signalling, including potential FFAR2-mediated effects on microglia, although such expression and functional relevance in adult microglia remain debated; (ii) receptor-independent intracellular mechanisms, particularly histone deacetylase (HDAC) inhibition and metabolic reprogramming; and (iii) indirect peripheral immune and gut-brain regulatory pathways, involving modulation of systemic cytokine milieus, barrier integrity, and immune cell trafficking, which appear to be more consistently supported in adult in vivo models. Experimental research shows that restoring SCFA supply or activating FFAR2 can suppress neuroinflammation, increase the clearance of pathological proteins by proteases, and promote neuronal survival. Available strategies to increase endogenous SCFA production include dietary fiber consumption, prebiotics, and customized SCFA formulations, and novel synthetic FFAR2 agonists offer a pharmacological approach to targeted therapy. Nonetheless, there remains a significant degree of variability in dosing, route of delivery, bioavailability, and interindividual microbiota profiles, which restricts clinical translation. Combined, the existing knowledge base places the SCFA-FFAR2 axis as an encouraging therapeutic agent regarding altering neuroimmune responses and creating new approaches to the control of neuroinflammatory and neurodegenerative diseases.</p>","PeriodicalId":10845,"journal":{"name":"Current pharmaceutical design","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Yindan Pinggan Capsule Suppresses Hepatocellular Carcinoma Proliferation via TGR5-mediated Dual Inhibition of YAP and Hedgehog Pathways.","authors":"Yilin Hong, Wei Xi, Hanshi Guo, Jiadi Liang, Fudong Zhang, Junjie Zhang, Yong Zhang, Chundong Yu","doi":"10.2174/0113816128513472260825053944","DOIUrl":"https://doi.org/10.2174/0113816128513472260825053944","url":null,"abstract":"<p><strong>Introduction: </strong>Hepatocellular Carcinoma (HCC) remains a highly lethal malignancy with limited therapeutic options, particularly in advanced stages. Yindan Pinggan Capsule (YDPG), a Traditional Chinese Medicine (TCM) formulation with hepatoprotective properties, has shown potential in liver diseases; however, its anti-tumor effects against HCC and the underlying mechanisms remain unclear. This study aimed to evaluate the anti-tumor effects of YDPG against HCC, elucidate its molecular mechanisms, and investigate its potential synergistic effects with Sorafenib.</p><p><strong>Methods: </strong>Network pharmacology was performed to predict potential targets and pathways of YDPG against HCC. Anti-tumor effects were evaluated in vitro using CCK-8, colony formation, and flow cytometry assays, and in vivo using subcutaneous allograft, orthotopic liver tumor, and Diethylnitrosamine (DEN)/ carbon tetrachloride (CCl4)-induced HCC mouse models. RNA sequencing, RT-qPCR, Western blot, immunofluorescence, mass spectrometry, and agonist treatment assays were conducted to elucidate the underlying mechanisms.</p><p><strong>Results: </strong>Network pharmacology analysis identified cell cycle-related pathways and proliferation-associated genes, including Ccnd1 and Myc, as potential targets of YDPG. YDPG markedly inhibited HCC cell proliferation and induced G1/S phase arrest in vitro, and significantly suppressed tumor growth in mouse models in vivo. Mechanistically, YDPG activated G protein-coupled bile acid receptor 1 (TGR5), thereby inhibiting YAP nuclear translocation and suppressing Gli1 expression, ultimately downregulating the downstream oncogenic regulators c-Myc and Cyclin D1. Notably, YDPG exhibited synergistic anti-tumor effects with low-dose Sorafenib, achieving comparable efficacy to that of high-dose Sorafenib alone.</p><p><strong>Discussion: </strong>By activating TGR5 and inhibiting YAP and Hedgehog pathways, YDPG functions as a multitargeting agent with a distinct mechanism from conventional HCC therapies. Its synergy with low-dose Sorafenib offers a potential strategy to reduce targeted therapy-related toxicity without compromising efficacy, supporting the integration of mechanistically characterized TCM into conventional HCC treatment.</p><p><strong>Conclusion: </strong>These findings demonstrate that YDPG suppresses HCC progression through activation of TGR5 and simultaneous inhibition of the YAP and Hedgehog signaling pathways. Moreover, YDPG enhances the anti-tumor efficacy of Sorafenib, highlighting its translational potential as an adjuvant therapeutic strategy for HCC.</p>","PeriodicalId":10845,"journal":{"name":"Current pharmaceutical design","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148944228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preparation and Characterization of Prednisolone-Loaded Solid Lipid Nanoparticles for Potential Transdermal Application.","authors":"Sabrina Mohamed Hasnol, Meor Mohd Redzuan Meor Mohd Affandi, Wong Tin Wui, Nor Khaizan Anuar","doi":"10.2174/0113816128485438260630083009","DOIUrl":"https://doi.org/10.2174/0113816128485438260630083009","url":null,"abstract":"<p><strong>Introduction: </strong>Transdermal delivery of prednisolone using solid-lipid nanoparticles (SLN) offers a promising strategy to enhance drug administration by enabling direct transport through the skin into the systemic circulation. However, the impact of processing conditions on the physicochemical properties and release mechanisms of prednisolone‑loaded SLN remains insufficiently explored.</p><p><strong>Methods: </strong>The present study developed prednisolone-loaded SLN using an emulsification-homogenization- ultrasonication approach. Key process parameters, namely homogenization stirring speed and duration, as well as ultrasonication time, temperature, and power, were systematically refined based on particle size, polydispersity index (PDI), and zeta potential. The selected formulation was further characterized for entrapment efficiency (EE), drug loading (DL), attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, differential scanning calorimetry (DSC), transmission electron microscopy (TEM), and in vitro drug release.</p><p><strong>Results: </strong>The experimentally determined DL ranged from 8.52 ± 0.55 to 9.89 ± 1.73%, corresponding to 85-99% of the intended loading, indicating minimal processing loss. Formulation H3, selected for further characterization based on its physicochemical profile, was homogenized at 10,000 rpm for 4 minutes, followed by ultrasonication at 60°C and 60 W for 4 minutes. H3 exhibited a particle size of 319.00 ± 56.15 nm, a PDI of 0.48 ± 0.022, a zeta potential of -19.86 ± 9.56 mV, and an EE of 98.25 ± 0.49%. All formulations demonstrated sustained drug release over 24 h with no statistically significant differences observed among them (p = 0.148).</p><p><strong>Discussion: </strong>Kinetic modeling showed that both Higuchi and Korsmeyer-Peppas models adequately described the release profile of H3, suggesting heterogeneous release behavior involving overlapping diffusion- and matrix- related mechanisms. ATR-FTIR and DSC analyses indicated alterations in intermolecular interactions and lipid packing behavior following nanoparticle formation, while TEM confirmed spherical morphology and absence of visible aggregation, consistent with successful formation of the SLN system.</p><p><strong>Conclusion: </strong>Prednisolone-loaded SLN prepared using emulsification-homogenization-ultrasonication demonstrated favorable physicochemical properties and sustained release behavior governed by combined diffusion and matrix-related processes. Further studies on long-term stability, as well as ex vivo and in vivo skin permeation, are required to confirm their suitability for transdermal application.</p>","PeriodicalId":10845,"journal":{"name":"Current pharmaceutical design","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148905278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of Quinazoline Dual Inhibitors Targeting Focal Adhesion Kinase and Polo-like Kinase 1 for Metastatic Cancers: QSAR, Pharmacophore Modelling, and Molecular Docking Approaches.","authors":"Mayank Garhewal, Dipti Pal, Ajay Kumar Gupta, Gajendra Singh Thakur, Achal Mishra, Shekhar Verma, Sivakumar Annadurai, Sanmati Kumar Jain, Yogesh Vaishnav","doi":"10.2174/0113816128475011260821100240","DOIUrl":"https://doi.org/10.2174/0113816128475011260821100240","url":null,"abstract":"<p><strong>Introduction: </strong>FAK and PLK1 are regulators of oncogenic signaling and metastasis, and their coinhibition offers a synergistic anticancer strategy. This study employed computational modeling to design quinazoline-based scaffolds as potential dual FAK-PLK1 inhibitors.</p><p><strong>Methods: </strong>54 quinazoline derivatives were analyzed using 2D/3D-QSAR, pharmacophore mapping, pharmacokinetic (ADMET) profile prediction, and molecular docking (PyRx-AutoDock Vina) to identify structural features and evaluate their dual FAK-PLK1 inhibitory potential with favourable pharmacokinetics properties.</p><p><strong>Results: </strong>2D-QSAR modeling of 54 quinazoline derivatives yielded statistically significant models, with Model 2 exhibiting the best performance (r² = 0.6504, q² = 0.5704, Pred_r² = 0.5360, F = 31.63). The 3DQSAR kNN-MFA analysis produced a reasonably reliable predictive model (q² = 0.6970, Pred_r² = 0.6528), confirming the contribution of steric and electrostatic fields to dual FAK-PLK1 inhibition. ADMET predictions supported favourable pharmacokinetic and safety profiles for compounds 28 and 33. Molecular docking revealed that compounds 28 and 33 displayed the highest binding affinities toward FAK and PLK1, outperforming reference inhibitors PF-573228 and BI 2536. RMSD values (1.18-2.31 Å) indicated stable ligand conformations.</p><p><strong>Discussion: </strong>The 2D- and 3D-QSAR models showed strong predictive ability, confirming the role of steric and electrostatic factors in dual FAK-PLK1 inhibition. Docking and ADMET analyses identified compounds 28 and 33 as potent, stable, and pharmacokinetically favourable dual inhibitors.</p><p><strong>Conclusion: </strong>This integrated computational approach combining QSAR modeling, pharmacophore analysis, ADMET prediction, and molecular docking identified promising quinazoline scaffolds as potential dual FAK-PLK1 inhibitors for the treatment of metastatic cancers, such as breast, colon, and non-small cell lung cancer.</p>","PeriodicalId":10845,"journal":{"name":"Current pharmaceutical design","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148905295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Driven Precision Medicine in Alzheimer's Disease: Drug Repurposing, Digital Therapeutics and Clinical Decision Support.","authors":"Bhagawati Saxena, Neha Sisodiya, Rakhi Khabiya, Amit Pandit, Mithun Singh Rajput, Richa Gupta","doi":"10.2174/0113816128478269260818075055","DOIUrl":"https://doi.org/10.2174/0113816128478269260818075055","url":null,"abstract":"<p><p>Alzheimer's Disease (AD) is a neurodegenerative disease that causes significant clinical, social, and economic burden worldwide. Despite improvements in understanding its multifaceted pathogenesis, current treatments are mostly symptomatic and ineffective across varied patient populations. To overcome these constraints, AI-driven precision medicine allows tailored risk assessment, treatment selection, and disease monitoring. This review covers AI's role in AD precision medicine, focusing on drug repurposing, digital therapies and clinical decision support systems. Machine and deep learning models are used to predict medication response, integrate heterogeneous data sources such as genomics, transcriptomics, neuroimaging and electronic health records, and uncover pharmacogenomic treatment success factors. The paper covers AIenabled precision pharmacology, including tailored dosing algorithms, adaptive therapeutic monitoring, and adverse drug reaction prediction. Bioinformatics-based target identification, network pharmacology, graphbased AI models, virtual screening, and real-world and clinical data validation are emphasized in AI-driven medication repurposing. AI-powered digital treatments like personalized cognitive training platforms, wearable- derived digital biomarkers, virtual and mixed reality interventions, adherence monitoring, and digital twins for therapy optimization have been discussed. AI-based clinical decision support systems are also thoroughly assessed for clinical value, accuracy, and explainability in disease subtyping, trajectory prediction, and risk stratification in preclinical and prodromal AD. Despite these promises, data heterogeneity, algorithmic bias, legal barriers, and privacy concerns exist. Federated learning enables safe multi-center collaboration and hybrid AI-human approaches, and it represents the future. AI's ability to alter AD care opens the door to precision medicine paradigms that use repurposed medications, digital tools and intelligent decision-making to improve patient outcomes.</p>","PeriodicalId":10845,"journal":{"name":"Current pharmaceutical design","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148904558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microbial Metabolites as Systemic Signaling Molecules: Integrating Metabolism, Immunity, and Organ Crosstalk in Health and Disease.","authors":"Amit Kumar, Phool Chandra, Prakhar Varshney, Neetu Sachan, Mudit Kumar","doi":"10.2174/0113816128488786260819100245","DOIUrl":"https://doi.org/10.2174/0113816128488786260819100245","url":null,"abstract":"<p><p>The gut microbiota produces a wide variety of metabolites that are essential for host-microbe communication and play a critical role in regulating host physiology, metabolism, and immunity. Among the most important of these metabolites are Short-Chain Fatty Acids (SCFAs), bile acid derivatives, tryptophan metabolites, polyamines, vitamins, and polyphenol-derived compounds. These bioactive metabolites regulate energy homeostasis, glucose and lipid metabolism, intestinal barrier integrity, immune signaling, and gene expression. Moreover, they influence systemic physiological processes, including cardiovascular and neuroendocrine functions, while playing a pivotal role in regulating hepatic and adipose tissue metabolism and maintaining intestinal homeostasis. Dysbiosis-induced alterations in microbial metabolic activity have been associated with the development of several chronic diseases, including obesity, type 2 diabetes mellitus, nonalcoholic fatty liver disease, cardiovascular diseases, cancer, autoimmune disorders, and neurological conditions. Consequently, therapeutic strategies aimed at modulating microbial metabolism, such as probiotics, prebiotics, postbiotics, dietary interventions, faecal microbiota transplantation, and synthetic biology-based approaches, are being extensively investigated, with microbial metabolites emerging as promising pharmacological targets. Despite these advances, significant challenges remain regarding their mechanistic understanding, standardisation, safety, and successful translation into clinical practice. The integration of multi-omics technologies, artificial intelligence, and precision microbiome-based interventions is expected to accelerate the development of personalized therapeutic strategies and enhance the clinical applicability of microbial metabolite research.</p>","PeriodicalId":10845,"journal":{"name":"Current pharmaceutical design","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148905228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence-Driven Diagnosis, Prediction, and Management of Polycystic Ovarian Disease (PCOD): A Comprehensive Systematic Review of Machine Learning, Deep Learning, and Emerging Technologies.","authors":"Ankita Wal, Mekala Moorthy, Rakesh Verma, Ajay Kumar, Ladi Alik Kumar, Rajni Kant Panik, Rohini Karunakaran, Amin Gasmi","doi":"10.2174/0113816128453237260817113317","DOIUrl":"https://doi.org/10.2174/0113816128453237260817113317","url":null,"abstract":"<p><strong>Introduction: </strong>Polycystic Ovarian Disease (PCOD) is a complicated endocrine-metabolic disorder affecting about one-quarter of women of reproductive age in the world and a major cause of infertility. This disorder is characterised by hyperandrogenism, anovulation, insulin resistance and metabolic abnormalities that pose challenges for timely diagnosis and management. Standardised criteria and symptom variability often limit traditional diagnostic strategies.</p><p><strong>Objective: </strong>This study aims to evaluate the role of artificial intelligence (AI) technologies in enhancing the diagnosis, prediction and management of PCOD.</p><p><strong>Methods: </strong>The systematic literature review was performed following PRISMA guidelines and included studies from 2021 to 2025. We reviewed more than 140 peer-reviewed publications in the clinical, biochemical, imaging, and multi-omics domains. The review covers machine learning (ML), deep learning (DL), hybrid AI models, explainable AI (XAI), federated learning (FL), quantum machine learning (QML), Edge AI, and generative adversarial networks (GANs).</p><p><strong>Results: </strong>The results demonstrate the superior performance of ML, DL, and hybrid AI frameworks compared to conventional diagnostic methods in PCOD classification and prediction of metabolic and reproductive risks. XAI provided transparency into the model, and FL facilitated privacy-preserving sharing of data from multiple institutions. QML and integration of multi-omics showed promise for biomarker discovery. The challenges of limited datasets and real-time screening were addressed through GAN-based augmentation and Edge AI.</p><p><strong>Discussion: </strong>These findings underscore the growing clinical relevance of AI in enhancing diagnostic accuracy and facilitating personalised decision-making. However, routine clinical implementation is still hindered by limitations such as data heterogeneity and imbalance, limited external validation, and lack of standardised datasets.</p><p><strong>Conclusion: </strong>AI-based methods present enormous potential to revolutionise the diagnosis and management of PCOD by providing accurate, interpretable, and personalised care. Future work should be based on large multicentre datasets, standardised validation protocols, and development of clinically interpretable models.</p>","PeriodicalId":10845,"journal":{"name":"Current pharmaceutical design","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}