J Clin Endocrinol Metab. 2025 May 8:dgaf276. doi: 10.1210/clinem/dgaf276. Online ahead of print.

ABSTRACT

BACKGROUND: Observational studies have shown association between polycystic ovary syndrome (PCOS) and asthma-related traits. However, whether this association is genetically driven or arises from observational biases remains unclear.

METHODS: This study integrated data from 10,074 PCOS cases and asthma-related traits obtained from UK Biobank and FinnGen cohorts. Global and local genetic architectures were examined using pleiotropic analysis under the composite null hypothesis, Functional Mapping and Annotation of Genetic Associations, and fine-mapping credible set analysis. Drug database mining was employed to identify pleiotropic genes as potential therapeutic targets. Tissue and cell enrichment analyses were conducted to uncover shared biological mechanisms.

RESULTS: We identified 3 novel significant genetic loci for asthma subtypes (2 for allergic asthma and 1 for childhood asthma). A positive overall genetic correlation between PCOS and asthma-related traits was observed. We discovered 5 pleiotropic causal regions encompassing 13 genes, with ERBB3 emerging as a potential central gene contributing to the shared pathophysiology of PCOS and asthma-related traits. Additionally, drug repositioning analysis suggested anakinra and artenimol as potential therapeutic candidates for PCOS and asthma comorbidity. Linkage disequilibrium score for the specific expression of genes analysis, along with transcriptome-wide association study, further identified gene expression patterns at the tissue/cell level in hypothalamo-pituitary, exocrine/endocrine, respiratory, and urogenital systems.

CONCLUSIONS: Our findings provide novel insights into the genetic basis and biological processes underlying the association between PCOS and asthma-related traits, warranting evaluation of whether PCOS-specific asthma risk assessment could improve clinical outcomes.

PMID:40339110 | DOI:10.1210/clinem/dgaf276

J Clin Invest. 2025 May 8:e181394. doi: 10.1172/JCI181394. Online ahead of print.

ABSTRACT

The activated JAK2/STAT pathway is characteristic of myeloproliferative neoplasms (MPNs). Pleckstrin-2 (PLEK2) signalosome is downstream of the JAK2/STAT5 pathway and plays an important role in MPN development. The detailed molecular composition of this signalosome is unclear. Here, we revealed peptidylprolyl isomerase-like 2 (PPIL2) as a critical component of the complex in regulating human and murine erythropoiesis. PPIL2 was a direct target of STAT5 and was upregulated in MPN patients and a Jak2V617F MPN mouse model. Mechanistically, PPIL2 interacted with and catalyzed p53 polyubiquitination and proteasome-mediated degradation to promote cell growth. Ppil2 deficiency, or inhibition by cyclosporin A, led to a marked upregulation of p53 in vivo and ameliorated myeloproliferative phenotypes in Jak2V617F mice. Cyclosporin A also markedly reduced JAK2 mutated erythroid and myeloid proliferation in an induced pluripotent stem cell-derived human bone marrow organoid model. Our findings revealed PPIL2 as a critical component of the PLEK2 signalosome in driving MPN pathogenesis through negatively regulating p53, thus providing a target and an opportunity for drug repurposing by using cyclosporin A to treat MPNs.

PMID:40338661 | DOI:10.1172/JCI181394

Curr Med Chem. 2025 May 7. doi: 10.2174/0109298673367163250417065816. Online ahead of print.

ABSTRACT

BACKGROUND: Developing new COVID-19 antivirals requires understanding viral proteins, oxidative stress, and drug repositioning. Safety assessments of organochalcogen molecules derived from AZT in Caenorhabditis elegans offer promising prospects for new treatments.

OBJECTIVE: In this work, we evaluated the safety and antioxidant effect of eight organochalcogen AZT-derivatives using the free-living nematode C. elegans through chronic exposure [48h]. In addition, we used in silico computational modelling analyses to predict protein targets for these compounds.

METHODS: This study used survival, litter size, brood size as toxicological and safety parameters, subcellular localization of DAF-16, expression of SOD-3 and GST-4, and ROS levels to evaluate the antioxidant effects and target prediction by similarity set approach [SEA], protein-protein interaction [PPI] network analysis, and comparative phylogenetic analysis to predict protein targets for these compounds.

RESULTS: The molecules were safe at concentrations of 1-500 μM. AZT, R3a, and R3f promoted DAF-16 nuclear translocation without affecting SOD-3 levels. R3f reduced GST-4 levels, while R3a increased ROS levels. In silico analyses identified 16 human protein targets of AZT and its derivatives, linked to nucleotide metabolism, DNA replication, and anti-inflammatory pathways, showing high homology to C. elegans.

CONCLUSION: We hypothesize that Se and Te atom insertion may alter pharmacological properties by modulating DAF-16, GST-4, and ROS-related pathways. in silico data suggest these derivatives are promising for antiviral activity, targeting nucleotide metabolism and DNA replication while also potentially modulating the anti-inflammatory response, an appealing feature for COVID-19 treatment.

PMID:40337965 | DOI:10.2174/0109298673367163250417065816

Nucleic Acids Res. 2025 May 8:gkaf399. doi: 10.1093/nar/gkaf399. Online ahead of print.

ABSTRACT

Enzymes with buried active sites utilize molecular tunnels to exchange substrates, products, and solvent molecules with the surface. These transport mechanisms are crucial for protein function and influence various properties. As proteins are inherently dynamic, their tunnels also vary structurally. Understanding these dynamics is essential for elucidating structure-function relationships, drug discovery, and bioengineering. Caver Web 2.0 is a user-friendly web server that retains all Caver Web 1.0 functionalities while introducing key improvements: (i) generation of dynamic ensembles via automated molecular dynamics with YASARA, (ii) analysis of dynamic tunnels with CAVER 3.0, (iii) prediction of ligand trajectories in multiple snapshots with CaverDock 1.2, and (iv) customizable ligand libraries for virtual screening. Users can assess protein flexibility, identify and characterize tunnels, and predict ligand trajectories and energy profiles in both static and dynamic structures. Additionally, the platform supports virtual screening with FDA/EMA-approved drugs and user-defined datasets. Caver Web 2.0 is a versatile tool for biological research, protein engineering, and drug discovery, aiding the identification of strong inhibitors or new substrates to bind to the active sites or tunnels, and supporting drug repurposing efforts. The server is freely accessible at https://loschmidt.chemi.muni.cz/caverweb.

PMID:40337920 | DOI:10.1093/nar/gkaf399

Int J Biol Macromol. 2025 May 5:143959. doi: 10.1016/j.ijbiomac.2025.143959. Online ahead of print.

ABSTRACT

Drug repositioning refers to new medical application exploration for existing drugs. Heparins, beyond their well-known anticoagulant properties widely used in clinics, present the capacity to carry biosignal molecules that is responsible for other properties such as anti-inflammatory, angiogenesis. Thus, heparins interaction with different biosignal molecules such as cytokines and growth factors have recently drawn attention and have promoted heparin repositioning as an active excipient with useful applications as drug-delivery systems and biomaterial-based tissue engineering scaffolds. Indeed, biomaterial heparinization can further help in their formulation such as in self-assembled heparin-based hydrogels or nanoparticles, and improve their biocompatibility. Moreover, the capacity of heparin to carry biosignal molecules enables the direct functionalization of heparinized biomaterial for tissue engineering. Both heparin characteristics namely the biosignal molecule carrying and biomaterial heparinization are reviewed here along their combination for biomaterial functionalization in tissue engineering applications.

PMID:40334894 | DOI:10.1016/j.ijbiomac.2025.143959

PLoS One. 2025 May 7;20(5):e0322546. doi: 10.1371/journal.pone.0322546. eCollection 2025.

ABSTRACT

Proteins are fundamental biomolecules composed of one or more chains of amino acids. They are essential for all living organisms, contributing to various biological functions and regulatory processes. Alterations in protein structures and functions are closely linked to diseases, emphasizing the need for in-depth study. A thorough understanding of these associations is crucial for developing targeted and more effective therapeutic strategies.Computational analyses of biomedical data facilitate the identification of specific patterns in proteins associated with diseases, providing novel insights into their biological roles. This study introduces a computational approach designed to detect relevant sequence patterns within proteins. These patterns, characterized by specific amino acid arrangements, can be critical for protein functionality. The proposed methodology was applied to proteins targeted by drugs used in lung cancer treatment, a disease that remains the leading cause of cancer-related mortality worldwide. Given that non-small cell lung cancer represents 85-90% of all lung cancer cases, it was selected as the primary focus of this study.Significant sequence patterns were identified, establishing connections between drug-target proteins and proteins associated with lung cancer. Based on these findings, a novel computational framework was developed to extend this pattern-based analysis to proteins linked to other diseases. By employing this approach, relationships between lung cancer drug-target proteins and proteins associated with four additional cancer types were uncovered. These associations, characterized by shared amino acid sequence features, suggest potential opportunities for drug repurposing. Furthermore, validation through an extensive literature review confirmed biological links between lung cancer drug-target proteins and proteins related to other malignancies, reinforcing the potential of this methodology for identifying new therapeutic applications.

PMID:40334012 | DOI:10.1371/journal.pone.0322546

Molecules. 2025 Apr 13;30(8):1736. doi: 10.3390/molecules30081736.

ABSTRACT

Alzheimer's disease (AD) affects an increasing number of people as the human population ages. The main pathological feature of AD, amyloid plaques, consists of the key protein amyloid-β and other co-deposited proteins. These co-deposited proteins and their protein interactors could hold some additional functional insights into AD pathophysiology. For this work, proteins found on amyloid plaques were collected from the AmyCo database. A protein-protein and protein-drug interaction network was constructed with data from the IntAct and DrugBank databases, respectively. In total, there were 12 proteins co-deposited on amyloid plaques that reportedly interact with 513 other proteins and are targets of 72 drugs. These drugs were shown to be almost entirely distinct from the panel of drugs currently approved by the FDA for AD and their corresponding protein targets. In conclusion, this work demonstrates the potential for drug repurposing of drugs that target proteins found in amyloid plaques.

PMID:40333680 | DOI:10.3390/molecules30081736

Int J Mol Sci. 2025 Apr 16;26(8):3761. doi: 10.3390/ijms26083761.

ABSTRACT

Novel drug discovery and repositioning remain critical challenges in biomedical research, requiring accurate prediction of drug-target interactions (DTIs). We propose the CPDP framework, which builds upon existing biomedical representation models and integrates contrastive learning with multi-dimensional representations of proteins and drugs to predict DTIs. By aligning the representation space, CPDP enables GNN-based methods to achieve zero-shot learning capabilities, allowing for accurate predictions of unseen drug data. This approach enhances DTI prediction performance, particularly for novel drugs not included in the BioHNs dataset. Experimental results demonstrate CPDP's high accuracy and strong generalization ability in predicting novel biological entities while maintaining effectiveness for traditional drug repositioning tasks.

PMID:40332398 | DOI:10.3390/ijms26083761

BMC Chem. 2025 May 6;19(1):119. doi: 10.1186/s13065-025-01468-4.

ABSTRACT

Trichinellosis represents great public health and economic problems worldwide. Moreover, the development of parasitic resistance against conventional anthelminthic treatment led to the urgent search for new therapeutic strategies, including drug repurposing. Bisphosphonates have been used to inhibit the growth of many parasites and have also emerged as promising candidates for the treatment of cryptosporidiosis and amoebic liver abscess. Alendronate is a second-generation bisphosphonate that is widely used for the treatment and prevention of osteoporosis. Till date, there is not enough data on the effect of this drug on Trichinella spiralis and it is unknown whether the regular use of this drug in osteoporotic patients may alter the course of the infection. ALN showed a significant lethal effect on both adult worms and juveniles, with severe tegumental damage in the form of fissures in the cuticle, widening of the hypodermal gland, and flattening of the cuticular annulation, ending with the appearance of multiple vesicles and large cauliflower masses. Molecular docking outcomes unveiled the potential inhibition of ALN against T. spiralis surface proteins (i.e., Ts-SP, Ts-PPase, Ts-MAPRC2, Ts-TS, Ts-MIF, etc.), with promising results confirmed its ability to defeat T. spiralis via targeting its surface proteins. Moreover, molecular dynamics simulation, through the analysis of RMSD, RMSF, RG, SASA and cluster analysis, proved the prolonged effective inhibition of ALN on T. spiralis inorganic pyrophosphatase, as an essential surface protein required for molting and developmental process of intestinal larval stages. Thus, ALN might be a valuable drug candidate for the treatment of trichinellosis and warrant further investigation in animal models of disease.

PMID:40329381 | DOI:10.1186/s13065-025-01468-4

Naunyn Schmiedebergs Arch Pharmacol. 2025 May 6. doi: 10.1007/s00210-025-04212-w. Online ahead of print.

ABSTRACT

Inflammatory bowel disease (IBD) is a chronic inflammatory condition of the gastrointestinal tract with a multifactorial etiology. Given the limitations and adverse effects of current therapies, there is a need for novel therapeutic approaches. Drug repurposing presents a promising opportunity to utilize medications with known safety and pharmacological profiles for alternative colitis treatment. Emerging evidence suggests the renin-angiotensin system (RAS) plays a significant role in the colitis pathophysiology. Angiotensin-converting enzyme (ACE) inhibitors may offer therapeutic potential by modulating pro-inflammatory cytokines and reducing oxidative stress. This study aims to evaluate the efficacy of lisinopril (LIS) in a 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis model in Wistar rats. Colitis was induced in Wistar rats via a single intracolonic TNBS dose (100 mg/kg). Treatment groups received oral interventions for 5 days: 5-aminosalicylic acid (5-ASA; 25.5 mg/kg), LIS (10 mg/kg), or LIS (20 mg/kg). Efficacy was evaluated using the disease activity score rate (DASR), colon/body weight ratio (CBWR), and colon length, diameter, and pH. Colonic tissue was analyzed macroscopically and histopathologically. Inflammatory biomarkers interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), oxidative stress markers glutathione (GSH), and malondialdehyde (MDA), as well as C-reactive protein (CRP) and complete blood count (CBC), were measured. LIS significantly reduced colitis severity, decreasing DASR and CBWR, while restoring colon dimensions and pH. LIS showed potent anti-colitic effects by suppressing TNF-α and IL-6 levels, reducing MDA, and increasing GSH. LIS restored RBC and WBC levels while normalizing CRP and hemoglobin levels. Histopathological and macroscopic analyses confirmed colonic protection with minimal detrimental effects on the stomach and liver. LIS, particularly at 20 mg/kg, exhibited dose-dependent anti-inflammatory, antioxidant, and tissue-protective effects, showing promise as a therapeutic agent for colitis treatment.

PMID:40328912 | DOI:10.1007/s00210-025-04212-w

PLoS One. 2025 May 6;20(5):e0319865. doi: 10.1371/journal.pone.0319865. eCollection 2025.

ABSTRACT

Rare diseases affect more than 30 million individuals, with the majority facing limited treatment options, elevating the urgency to innovative therapeutic solutions. Addressing these medical challenges necessitates an exploration of novel treatment modalities. Among these, drug repurposing emerges as a promising avenue, offering both potential and risk mitigation. To achieve this goal, we primarily focused on developing predictive models that harness cutting-edge computational techniques to uncover latent relationships between gene targets and chemical compounds towards drug repurposing. Building upon our previous investigation, where we successfully identified gene targets for compounds from the Tox21 in vitro assays, our endeavor expanded to a systematic prediction of potential targets for drug repurposing employing machine learning models built on diverse algorithms such as Support Vector Classifier, K-Nearest Neighbors, Random Forest, and Extreme Gradient Boosting. These models were trained on comprehensive biological activity profile data to predict the relationship between 143 gene targets and over 6000 compounds. Our models demonstrated high accuracy (>0.75), with predictions further validated by using public experimental datasets. Furthermore, several findings were evaluated via case studies. By elucidating these connections, we aim to streamline the drug repurposing process, ultimately catalyzing the discovery of more effective therapeutic interventions for rare diseases.

PMID:40327632 | DOI:10.1371/journal.pone.0319865

IEEE J Biomed Health Inform. 2025 May 6;PP. doi: 10.1109/JBHI.2025.3567452. Online ahead of print.

ABSTRACT

Drug-Gene Interaction (DGI) is crucial for drug discovery and personalized medicine. The continuous development of genomics and drug repositioning has brought increasing attention to the complex relations between drugs and genes. However, traditional biological experiments are time-consuming and costly, which makes it challenging to efficiently explore the multi-relational interactions between drugs and genes. Therefore, computational approaches aim to develop efficient schemes for predicting drug-gene relations to reduce the search space and experimental costs. Existing computational methods often suffer from data scarcity and poor generalization, which pose significant challenges for practical applications. To address these issues, we propose a novel multi-relation DGI prediction method based on molecular structure-driving and high-low-order attention denoising framework. Our approach captures molecular structural information through both atom and bond channels with a drug feature encoder. For network structure, we enhance both high- and low-order channels: the low-order channel leverages graph convolutional networks, while the high-order channel employs hypergraph-based message propagation. Additionally, we adopt consistency information loss and inter-channel attention mechanism to refine high- and low-order features. Experimental results on three drug-gene datasets demonstrate the superior performance of our model, particularly on sparse datasets DrugBank and DGIdb, with F1 improvements of 4.06% and 5.67%, respectively. Our implementations will be publicly available at: https://github.com/jianruichen/D-MAC.

PMID:40327470 | DOI:10.1109/JBHI.2025.3567452

Naunyn Schmiedebergs Arch Pharmacol. 2025 May 6. doi: 10.1007/s00210-025-04233-5. Online ahead of print.

ABSTRACT

Since the onset of the COVID-19 pandemic in March 2020, researchers worldwide have sought effective drugs to prevent and manage SARS-CoV-2 and its spectrum of symptoms. Ivermectin, originally developed as an anthelmintic for controlling parasitic infections in humans and animals, has drawn attention based on the hypothesis that it inhibits viral replication. In Austria, ivermectin usage peaked in November 2021, following promotion by the right-wing Freedom Party of Austria (FPÖ) as an alternative treatment to vaccination, resonating strongly within anti-vaccine and skeptical communities. The topic is also very present in the United States of America due to the re-election of D. Trump as US President and the designation of R. Kennedy as the United States' Secretary of Health and Human Services. To critically examine the controversial use of ivermectin for COVID-19 and publication trends during the pandemic, this study analysed all publications listed in PubMed from 1 January 2020 to 31 December 2022 using the keywords 'ivermectin' and 'COVID-19', resulting in a dataset of 353 publications. These publications were assessed for scientific quality, methodological rigour and bias, with particular focus on the influence of social and political dynamics on publication practices, as well as the prevalence of preprints, citation trends and the role of funding sources. Our study shows that many highly cited studies on ivermectin display methodological weaknesses and data gaps, contributing to the propagation of hypotheses lacking substantial empirical support. This analysis underscores the necessity of rigorous quality control during crises and highlights the long-term risks posed to scientific databases and public health by methodologically deficient research.

PMID:40327060 | DOI:10.1007/s00210-025-04233-5

ACS Infect Dis. 2025 May 6. doi: 10.1021/acsinfecdis.4c00962. Online ahead of print.

ABSTRACT

The emergence of drug-resistant malarial parasites has been a growing challenge to medical science to safeguard public health in the malaria-endemic regions of the globe. With time, the parasite develops newer resistance mechanisms to defunct the drug's action one after another. Genetic mutation is the prime weapon parasites rely upon to initiate the resistance mechanism in a case-specific manner, following various strategies such as structural changes in the target protein, metabolic alterations, and tweaking the drug-transported channels. In order to combat these resistances, different approaches have evolved among these developing inhibitors against critical parasite enzymes and metabolic pathways, combinatorial/hybrid drug therapies, exploring new drug targets and analogues of existing drugs, use of resistance-reversal agents, drug-repurposing, gene blocking/altering using RNA interference and CRISPR/Cas systems are prominent. However, the effectiveness of these approaches needs to be earnestly monitored for better management of the disease, which demands the development of a reliable diagnosis technique. Several methodologies have been investigated in search of a suitable diagnosis technique, such as in vivo, in vitro, ex vivo drug efficacy studies, and molecular techniques. A parallel effort to transform the efficient method into an inexpensive and portable diagnosis tool for rapid screening of drug resistance malaria among masses in the societal landscape is advocated. This review gives an insight into the historical perspectives of drug-resistant malaria and the recent developments in malaria diagnosis and antimalarial drug discovery. Efforts have been made to update recent strategies formulated to combat and diagnose drug-resistant malaria. Finally, a concluding remark with a future perspective on the subject has been forwarded.

PMID:40326084 | DOI:10.1021/acsinfecdis.4c00962

JAMA Netw Open. 2025 May 1;8(5):e258339. doi: 10.1001/jamanetworkopen.2025.8339.

NO ABSTRACT

PMID:40323606 | DOI:10.1001/jamanetworkopen.2025.8339

JAMA Netw Open. 2025 May 1;8(5):e258330. doi: 10.1001/jamanetworkopen.2025.8330.

ABSTRACT

IMPORTANCE: Treatments are urgently needed for the more than 9500 rare diseases with no US Food and Drug Administration-approved therapies. Although repurposing can be less time- and cost-intensive compared with novel drug development, hurdles have impeded systematic repurposing. Rare disease nonprofit organizations (RDNPs) are well-positioned to overcome barriers and have spearheaded rare disease repurposing efforts for decades. However, no comprehensive data are available on the state of rare disease repurposing or features of successful efforts.

OBJECTIVE: To characterize the state of rare disease drug repurposing, identify factors associated with successful outcomes, and share thematic insights using the interactive Repurposing of All Drugs, Mapping All Paths (ROADMAP) Project web tool.

DESIGN, SETTING, AND PARTICIPANTS: The ROADMAP study was a qualitative study using a mixed-methods analysis of US-based RDNP leaders and their stakeholders, including a national survey and semistructured interviews of RDNP leaders, conducted from September 29, 2021, to January 6, 2022. Surveys and interviews revealed themes associated with RDNP strategies, timelines, and support mechanisms. Data were analyzed from January 22, 2024, to April 23, 2024.

MAIN OUTCOMES AND MEASURES: The primary survey outcome was the repurposing project stage (abandoned, early, clinical, late, or successful). Qualitative outcomes included themes characterizing repurposing experiences. Two random forest models of drug- and disease- specific as well as organization-specific variables were used to evaluate factor importance toward inferring the project stage. Orthogonal significance testing was conducted using Spearman rank correlation, and P values in each model were corrected for multiple hypothesis testing using a Benjamini-Hochberg procedure.

RESULTS: Representative organizations submitted survey responses, including 147 of 698 potential US-based RDNPs. The median RDNP age was 10 years (IQR, 5-20 years), and the median annual revenue was $355 390 (IQR, $90 028-$946 108). Among 34 leaders who were interviewed, representing 25 RDNPs, 23 were female (67.6%), and the RDNPs had a median age of 15 years (IQR, 6-19 years) and a median revenue of $670 719 (IQR, $193 587-$1 830 890). Among the surveyed RDNPs, 58 of 138 (42.0%) specifically identifying their involvement in repurposing supported repurposing projects, and 94 drugs were in various stages of repurposing, of which 23 met success criteria (5 with US Food and Drug Administration approval and 18 with off-label use with subjective benefit). Survey factors associated with successful outcomes included nonprofit-supported patient recruitment into trials (Gini importance, 3.90; ρ = 0.50; adjusted P < .001) and provision of nonfinancial research support (Gini importance, 0.69; ρ = 0.33; adjusted P = .02). Interview themes were synthesized into a 5-stage repurposing framework with roadblocks and recommendations that included (1) enabling drug repurposing, (2) identifying a drug therapy, (3) validating a drug therapy, (4) clinical use and testing, and (5) reaching an optimal end point for clinical practice.

CONCLUSIONS AND RELEVANCE: The findings of this qualitative study of RDNP repurposing suggest that several opportunities were associated with successful outcomes and can be considered to optimize systematic repurposing among RDNPs, external collaborators, and policymakers with the use of an interactive tool showcasing insights to facilitate data-driven drug repurposing.

PMID:40323602 | DOI:10.1001/jamanetworkopen.2025.8330

Naunyn Schmiedebergs Arch Pharmacol. 2025 May 5. doi: 10.1007/s00210-025-04155-2. Online ahead of print.

ABSTRACT

The modulation of gut microbiota presents promising therapeutic possibilities for various health conditions, ranging from gastrointestinal infections to neurodegenerative and mental health disorders. Among the available interventions, gut microbiota modulators (GMMs) such as probiotics and prebiotics have demonstrated significant potential in infection prevention and neuroprotection. Despite these encouraging findings, the clinical application of GMMs remains challenging due to safety concerns and inconsistent effectiveness across diverse patient populations. These factors create substantial barriers to the widespread adoption of microbiota-based therapies in clinical practice. To overcome these challenges and fully leverage the therapeutic potential of microbiota modulation, this review explores the feasibility of repurposing GMMs for managing multiple health disorders. A broad spectrum of microbiota-targeted strategies is examined, including dietary modifications, fecal microbiota transplantation, bacteriophage therapy, microbiome engineering, and immune system modulation. A particularly innovative approach involves integrating GMMs with pharmaceutical delivery systems to enhance therapeutic efficacy while mitigating potential adverse effects. This integrative strategy underscores the pivotal role of the gut microbiome in health and disease, supporting the development of precision medicine tailored to individual patient needs. By combining GMMs with targeted delivery mechanisms, this approach not only improves treatment effectiveness but also addresses critical concerns regarding safety and patient variability. Furthermore, this review outlines future research directions within the rapidly evolving field of microbiota modulation, emphasizing the necessity of comprehensive clinical trials and long-term safety evaluations. By critically assessing both the challenges and opportunities associated with microbiota-based interventions, this study provides a strategic framework for translating experimental research into viable clinical applications. A holistic approach to gut microbiota modulation has the potential to redefine treatment paradigms, offering personalized therapeutic strategies for a wide range of disorders and advancing the broader field of precision medicine.

PMID:40323507 | DOI:10.1007/s00210-025-04155-2

Perspect Clin Res. 2025 Apr-Jun;16(2):61-68. doi: 10.4103/picr.picr_70_24. Epub 2024 Sep 10.

ABSTRACT

Drug repurposing, also known as drug repositioning or reprofiling, involves identifying new therapeutic uses for existing drugs beyond their original indications. Historical examples include sildenafil citrate transitioning to an erectile dysfunction treatment and thalidomide shifting from a sedative to an immunomodulatory agent. Advocates tout its potential to address unmet medical needs by expediting development, reducing costs, and using drugs with established safety profiles. However, concerns exist regarding specificity for new indications, safety, and regulatory exploitation. Ethical considerations include equitable access, informed consent when using drugs off-label, and transparency. Recent advancements include artificial intelligence (AI) applications, network pharmacology, and omics technologies. Clinical trials explore repurposed drugs' efficacy, with regulatory agencies facilitating approval. Challenges include intellectual property protection, drug target specificity, trial design complexities, and funding limitations. Ethical challenges encompass patient autonomy, potential conflicts of interest due to financial incentives for industries, and resource allocation. Future directions involve precision medicine, AI, and global collaboration. In conclusion, drug repurposing offers a promising pathway for therapeutic innovation but requires careful consideration of its complexities and ethical implications to maximize benefits and minimize risks.

PMID:40322475 | PMC:PMC12048090 | DOI:10.4103/picr.picr_70_24

Expert Opin Drug Deliv. 2025 May 5. doi: 10.1080/17425247.2025.2502022. Online ahead of print.

ABSTRACT

INTRODUCTION: Artificial intelligence (AI) has emerged as a transformative force in nanomedicine. revolutionizing drug delivery, diagnostics, and personalized treatment. While nanomedicine offers precise targeted drug delivery and reduced toxic effects, its clinical translation is hindered by biological complexity, unpredictable in vivo behavior, and inefficient trial-and-error approaches.

AREAS COVERED: This review covers the application of AI and Machine Learning (ML) across the nanomedicine development pipeline, starting from drug and target identification to nanoparticle design, toxicity prediction, and personalized dosing. Different AI/ML models like QSAR, MTK-QSBER, and Alchemite, along with data sources and high-throughput screening methods, have been explored. Real-world applications are critically discussed, including AI-assisted drug repurposing, controlled-release formulations, and cancer-specific delivery systems.

EXPERT OPINION: AI has emerged as an essential component in designing next-generation nanomedicine. Efficiently handling multidimensional datasets, optimizing formulations, and personalizing treatment regimens, it has sped up the innovation process. However, challenges like data heterogeneity, model transparency, and regulatory gaps remain. Addressing these hurdles through interdisciplinary efforts and emerging innovations like explainable AI and federated learning will pave the way for the clinical translation of AI-driven nanomedicine.

PMID:40321117 | DOI:10.1080/17425247.2025.2502022

J Biomol Struct Dyn. 2025 May 4:1-12. doi: 10.1080/07391102.2025.2497448. Online ahead of print.

ABSTRACT

Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra that leads to bradykinesia and rest tremors. While the molecular mechanisms underlying PD are not fully understood, rising evidence shows neuroinflammation as a key factor in dopaminergic neuron damage. The soluble epoxide hydrolase (sEH) has appeared as a key player in neuroinflammation associated with PD which represents itself as a promising drug target. Here, we employed a structure-based virtual screening methodology using repurposed drugs from the DrugBank database to identify high-affinity potential inhibitors of sEH. Results showed that two hit molecules, Fluspirilene and Penfluridol, demonstrated appreciable docking potential and specificity toward the sEH active site. These molecules exhibited favorable pharmacological properties and formed critical interactions with residues essential for sEH activity. Further, all-atom molecular dynamics (MD) simulations followed by principal component analysis and free energy landscape were carried out which provide deeper insights into the conformational stability and interaction mechanisms of sEH in complex with Fluspirilene and Penfluridol. The simulation results indicated that the interaction of sEH with Fluspirilene and Penfluridol contributed to the stabilization of its structure throughout the MD trajectories of 500 ns. These findings collectively suggest that Fluspirilene and Penfluridol hold potential as repurposed leads for the development of sEH inhibitors, which offer therapeutic implications for combating PD and other associated conditions.

PMID:40320778 | DOI:10.1080/07391102.2025.2497448