Biomol Biomed. 2025 Mar 18. doi: 10.17305/bb.2025.12058. Online ahead of print.

ABSTRACT

Cervical cancer presents a significant healthcare challenge due to recurrent disease and drug resistance, highlighting the urgent need for novel therapeutic strategies. Network pharmacology facilitates drug repurposing by elucidating multi-target mechanisms of action. Adefovir, an acyclic nucleotide analog, has shown promising potential in cervical cancer treatment, particularly in HeLa cells. In vitro studies have demonstrated that adefovir inhibits HeLa cell proliferation by enhancing apoptosis while maintaining a low cytotoxicity profile at therapeutic concentrations, making it an attractive candidate for further exploration. A combined network pharmacology and in vitro study was conducted to investigate the molecular mechanism of adefovir against cervical cancer. Potential gene targets for adefovir and cervical cancer were predicted using database analysis. Hub targets were identified, and protein-protein interaction (PPI) networks were constructed. Molecular docking assessed adefovir's binding affinity to key targets. In vitro cytotoxic assays, including 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and crystal violet assays, were performed using 96-well plates to evaluate anti-proliferative effects in HeLa cells. Apoptosis was assessed via p53 immunocytochemistry Enzyme-Linked Immunosorbent Assay (ELISA), while Vascular Endothelial Growth Factor ELISA (VEGF ELISA) was used to measure cell proliferation. Venn analysis identified 144 common targets between adefovir and cervical cancer. Network analysis revealed key hub targets involved in oncogenic pathways. Molecular docking demonstrated strong binding between adefovir and Mitogen-Activated Protein Kinase 3 (MAPK3) and SRC proteins. In vitro, adefovir significantly suppressed HeLa cell viability, with an Inhibitory Concentration 50 (IC50) of 7.8 μM, outperforming 5-Fluorouracil (5-FU). Additionally, it induced apoptosis via p53 activation and inhibited cell proliferation through VEGF suppression. These integrated computational and experimental findings suggest that adefovir exerts multi-targeted effects against cervical cancer. Its promising preclinical efficacy warrants further investigation as a potential alternative therapy.

PMID:40105884 | DOI:10.17305/bb.2025.12058

Endocrinology. 2025 Mar 19:bqaf055. doi: 10.1210/endocr/bqaf055. Online ahead of print.

ABSTRACT

Diabetes is the most expensive chronic disease in the U.S. with over $400 billion in annual costs and it affects over 38 million Americans. While major advances in drug treatment have been made for type 2 diabetes (T2D) and the often-associated obesity, there are still no approved and effective medications targeting beta cell loss or islet dysfunction, which is one of the major underlying causes of both, type 1 diabetes (T1D) and T2D. In addition, there are no oral medications for T1D approved in the U.S. more than a hundred years after the discovery of insulin and attractive therapeutic targets are only starting to emerge. As we celebrate the 75th anniversary of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), progress is finally being made in this area with NIDDK support. This mini-review follows the discovery of thioredoxin-interacting protein inhibitors as an example of a methodical approach to identify and develop an oral beta cell treatment for T1D. It further discusses how the initial molecular findings were translated into novel clinical treatment approaches that promote the patient's own islet health and beta cell function using drug repurposing as well as new drug discovery.

PMID:40105688 | DOI:10.1210/endocr/bqaf055

Hum Genomics. 2025 Mar 19;19(1):29. doi: 10.1186/s40246-025-00733-w.

ABSTRACT

BACKGROUND: There is a vast prevalence of mental disorders, but patient responses to psychiatric medication fluctuate. As food choices and daily habits play a fundamental role in this fluctuation, integrating machine learning with network medicine can provide valuable insights into disease systems and the regulatory leverage of lifestyle in mental health.

METHODS: This study analyzed coexpression network modules of MDD and PTSD blood transcriptomic profile using modularity optimization method, the first runner-up of Disease Module Identification DREAM challenge. The top disease genes of both MDD and PTSD modules were detected using random forest model. Afterward, the regulatory signature of two predominant habitual phenotypes, diet-induced obesity and smoking, were identified. These transcription/translation regulating factors (TRFs) signals were transduced toward the two disorders' disease genes. A bipartite network of drugs that target the TRFS together with PTSD or MDD hubs was constructed.

RESULTS: The research revealed one MDD hub, the CENPJ, which is known to influence intellectual ability. This observation paves the way for additional investigations into the potential of CENPJ as a novel target for MDD therapeutic agents development. Additionally, most of the predicted PTSD hubs were associated with multiple carcinomas, of which the most notable was SHCBP1. SHCBP1 is a known risk factor for glioma, suggesting the importance of continuous monitoring of patients with PTSD to mitigate potential cancer comorbidities. The signaling network illustrated that two PTSD and three MDD biomarkers were co-regulated by habitual phenotype TRFs. 6-Prenylnaringenin and Aflibercept were identified as potential candidates for targeting the MDD and PTSD hubs: ATP6V0A1 and PIGF. However, habitual phenotype TRFs have no leverage over ATP6V0A1 and PIGF.

CONCLUSION: Combining machine learning and network biology succeeded in revealing biomarkers for two notoriously spreading disorders, MDD and PTSD. This approach offers a non-invasive diagnostic pipeline and identifies potential drug targets that could be repurposed under further investigation. These findings contribute to our understanding of the complex interplay between mental disorders, daily habits, and psychiatric interventions, thereby facilitating more targeted and personalized treatment strategies.

PMID:40102990 | DOI:10.1186/s40246-025-00733-w

Nat Rev Drug Discov. 2025 Mar 18. doi: 10.1038/s41573-025-01164-x. Online ahead of print.

ABSTRACT

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

PMID:40102635 | DOI:10.1038/s41573-025-01164-x

IEEE J Biomed Health Inform. 2025 Mar 18;PP. doi: 10.1109/JBHI.2025.3552536. Online ahead of print.

ABSTRACT

Central nervous system (CNS) diseases such as glioblastoma (GBM), multiple sclerosis (MS), and Alzheimer's disease (AD) remain challenging due to their complexity and limited treatments. Conventional drug repurposing strategies often rely on bulk RNA sequencing data, which can overlook cellular heterogeneity and mask rare but critical cell populations. Here, we introduce scDrugLink, a computational method that integrates single-cell transcriptomic data with drug targets and perturbation signatures to improve repurposing. For each cell type, scDrugLink constructs a Drug2Cell matrix based on drug targets to estimate promotion/inhibition scores and derives sensitivity/resistance scores by reverse matching signatures and disease-associated genes. These scores are then "linked," yielding robust therapeutic rankings. In our study, we present a systematic evaluation of single-cell drug repurposing methods for CNS diseases. Applied to atlas data for GBM, MS, and AD, scDrugLink surpassed three state-of-the-art methods (ASGARD, DrugReSC, and scDrugPrio), achieving area under the receiver operating characteristic curve (AUC) ranges of 0.6286-0.7242 and area under the precision-recall curve (AUPRC) ranges of 0.3412-0.5484. It also ranked top when comparing AUC and AUPRC at the level of individual cell types. Moreover, applying the "linking" principle to baseline methods boosted their performance, on average improving AUC and AUPRC by 0.0160 and 0.0244, respectively. Despite the advancements, the complexity and heterogeneity of CNS diseases, along with incomplete drug data, indicate that further improvement is necessary. We discuss these challenges and suggest directions for enhancing single-cell drug repurposing in the future.

PMID:40100675 | DOI:10.1109/JBHI.2025.3552536

IEEE Trans Neural Netw Learn Syst. 2025 Mar 18;PP. doi: 10.1109/TNNLS.2025.3547300. Online ahead of print.

ABSTRACT

Predicting protein-ligand binding affinities is a critical problem in drug discovery and design. A majority of existing methods fail to accurately characterize and exploit the geometrically invariant structures of protein-ligand complexes for predicting binding affinities. In this study, we propose Geo-protein-ligand binding affinity (PLA), a geometric equivariant graph representation learning framework with local-global structure awareness, to predict binding affinity by capturing the geometric information of protein-ligand complexes. Specifically, the local structural information of 3-D protein-ligand complexes is extracted by using an equivariant graph neural network (EGNN), which iteratively updates node representations while preserving the equivariance of coordinate transformations. Meanwhile, a graph transformer is utilized to capture long-range interactions among atoms, offering a global view that adaptively focuses on complex regions with a significant impact on binding affinities. Furthermore, the multiscale information from the two channels is integrated to enhance the predictive capability of the model. Extensive experimental studies on two benchmark datasets confirm the superior performance of Geo-PLA. Moreover, the visual interpretation of the learned protein-ligand complexes further indicates that our model offers valuable biological insights for virtual screening and drug repositioning.

PMID:40100667 | DOI:10.1109/TNNLS.2025.3547300

Mol Divers. 2025 Mar 18. doi: 10.1007/s11030-025-11131-8. Online ahead of print.

ABSTRACT

The global impact of emerging and re-emerging viral agents during epidemics and pandemics leads to serious health and economic burdens. Among the major emerging or re-emerging viruses include SARS-CoV-2, Ebola virus (EBOV), Monkeypox virus (Mpox), Hepatitis viruses, Zika virus, Avian flu, Influenza virus, Chikungunya virus (CHIKV), Dengue fever virus (DENV), West Nile virus, Rhabdovirus, Sandfly fever virus, Crimean-Congo hemorrhagic fever (CCHF) virus, and Rift Valley fever virus (RVFV). A comprehensive literature search was performed to identify existing studies, clinical trials, and reviews that discuss drug repositioning strategies for the treatment of emerging and re-emerging viral infections using databases, such as PubMed, Scholar Google, Scopus, and Web of Science. By utilizing drug repositioning, pharmaceutical companies can take advantage of a cost-effective, accelerated, and effective strategy, which in turn leads to the discovery of innovative treatment options for patients. In light of antiviral drug resistance and the high costs of developing novel antivirals, drug repositioning holds great promise for more rapid substitution of approved drugs. Main repositioned drugs have included chloroquine, ivermectin, dexamethasone, Baricitinib, tocilizumab, Mab114 (Ebanga™), ZMapp (pharming), Artesunate, imiquimod, saquinavir, capmatinib, naldemedine, Trametinib, statins, celecoxib, naproxen, metformin, ruxolitinib, nitazoxanide, gemcitabine, Dorzolamide, Midodrine, Diltiazem, zinc acetate, suramin, 5-fluorouracil, quinine, minocycline, trifluoperazine, paracetamol, berbamine, Nifedipine, and chlorpromazine. This succinct review will delve into the topic of repositioned drugs that have been utilized to combat emerging and re-emerging viral pathogens.

PMID:40100484 | DOI:10.1007/s11030-025-11131-8

Mol Pharm. 2025 Mar 18. doi: 10.1021/acs.molpharmaceut.5c00191. Online ahead of print.

ABSTRACT

Statins have been reported for diverse pleiotropic activities, including antimicrobial and antibiofilm. However, due to the limited understanding of their mode of action, none of the statins have gained approval for antimicrobial or antibiofilm applications. In a recent drug repurposing study, we observed that two statins (i.e., Simvastatin and Lovastatin) interact stably with TasA(28-261), the principal extracellular matrix protein of Bacillus subtilis, and also induce inhibition of biofilm formation. Nevertheless, the underlying mechanism remained elusive. In the present study, we examined the impact of these statins on the physiological activity of TasA(28-261), specifically its interaction with TapA(33-253) and aggregation into the amyloid-like structure using purified recombinant TasA(28-261) and TapA(33-253) in amyloid detection-specific in vitro assays (i.e., CR binding and ThT staining assays). Results revealed that both statins interfered with amyloid formation by the TasA(28-261)-TapA(33-253) complex, while neither statin inhibited amyloid formation by lysozyme, a model amyloid-forming protein. Moreover, neither statin significantly altered the expressions of terminal regulatory genes (viz, sinR, sinI) and terminal effector genes (viz, tasA, tapA, and bslA) involved in biofilm formation by B. subtilis. While the intricate interplay between Simvastatin and Lovastatin with the diverse molecular constituents of B. subtilis biofilm remains to be elucidated conclusively, the findings obtained during the present study suggest that the underlying mechanism for Simvastatin- and Lovastatin-mediated inhibition of B. subtilis biofilm formation is manifested by interfering with the aggregation and amyloid formation by TasA(28-261)-TapA(33-253). These results represent one of the first experimental evidence for the underlying mechanism of antibiofilm activity of statins and offer valuable directions for future research to harness statins as antibiofilm therapeutics.

PMID:40100146 | DOI:10.1021/acs.molpharmaceut.5c00191

ChemMedChem. 2025 Mar 18:e202500084. doi: 10.1002/cmdc.202500084. Online ahead of print.

ABSTRACT

This study explores the anti-cancer potential of N-methylated open-ring derivatives of carborane-substituted diclofenac analogs. By N-methylation, the open-chain form could be trapped and cyclization back to lactam or amidine derivatives was inhibited. A small library of carborane- and phenyl-based secondary and tertiary arylamines bearing carboxylic acid or nitrile groups was synthesized and analyzed for their COX-affinity in vitro and in silico. The compounds were further evaluated against mouse adenocarcinoma (MC38), human colorectal carcinoma (HCT116) and human colorectal adenocarcinoma (HT29) cell lines and showed potent cytotoxicity. Additional biological assessments of the mode of action were performed using flow cytometric techniques and fluorescence microscopy. The data obtained revealed a common antiproliferative effect coupled with the induction of caspase-independent apoptosis and the specific effects of the compound on the phenotype of MC38 cells, resulting in impaired cell viability of MC38 cells and satisfactory selectivity exceeding the antitumor activity of diclofenac.

PMID:40099997 | DOI:10.1002/cmdc.202500084

BMC Vet Res. 2025 Mar 17;21(1):178. doi: 10.1186/s12917-025-04616-z.

ABSTRACT

Cystic echinococcosis (CE) is a neglected zoonotic disease that causes significant economic losses in livestock and poses health risks to humans, necessitating improved diagnostic and therapeutic strategies. This study investigates CE in donkeys using a multifaceted approach that includes molecular identification, gene expression analysis, serum biochemical profiling, histopathological and immunohistochemical examination, and in vitro drug efficacy evaluation. Molecular analysis of hydatid cyst protoscolices (HC-PSCs) from infected donkey livers and lungs revealed a high similarity to Echinococcus equinus (GenBank accession: PP407081). Additionally, gene expression analysis indicated significant increases (P < 0.0001) in interleukin 1β (IL-1β) and interferon γ (IFN-γ) levels in lung and liver homogenates. Serum biochemical analysis showed elevated aspartate transaminase (AST), alkaline phosphatase (ALP), and globulin levels, alongside decreased albumin compared to non-infected controls. Histopathological examination revealed notable alterations in pulmonary and hepatic tissues associated with hydatid cyst infection. Immunohistochemical analysis showed increased expression of nuclear factor kappa B (NF-κB), tumor necrosis factor-α (TNF-α), and toll-like receptor-4 (TLR-4), indicating a robust inflammatory response. In vitro drug evaluations revealed that Paroxetine (at concentrations of 2.5, and 5 mg/mL) demonstrated the highest efficacy among repurposed drugs against HC-PSCs, resulting in the greatest cell mortality. Colmediten followed closely in effectiveness, whereas both Brufen and Ator exhibited minimal effects. This study identifies Paroxetine as a promising alternative treatment for hydatidosis and provides a framework for investigating other parasitic infections and novel therapies.

PMID:40098107 | DOI:10.1186/s12917-025-04616-z

Bioinformatics. 2025 Mar 17:btaf117. doi: 10.1093/bioinformatics/btaf117. Online ahead of print.

ABSTRACT

MOTIVATION: Identifying drug target interactions is a crucial step in drug repurposing and drug discovery. The significant increase in demand and the expensive nature for experimentally identifying drug target interactions necessitate computational tools for automated prediction and comprehension of drug target interactions. Despite recent advancements, current methods fail to fully leverage the hierarchical information in drug target interactions.

RESULTS: Here we introduce H2GnnDTI, a novel two-level hierarchical heterogeneous graph learning model to predict drug target interactions, by integrating the structures of drugs and proteins via a low-level view GNN (LGNN) and a high-level view GNN (HGNN). The hierarchical graph consists of high-level heterogeneous nodes representing drugs and proteins, connected by edges representing known DTIs. Each drug or protein node is further detailed in a low-level graph, where nodes represent molecules within each drug or amino acids within each protein, accompanied by their respective chemical descriptors. Two distinct low-level graph neural networks are first deployed to capture structural and chemical features specific to drugs and proteins from these low-level graphs. Subsequently, a high-level graph encoder is employed to comprehensively capture and merge interactive features pertaining to drugs and proteins from the high-level graph. The high-level encoder incorporates a structure and attribute information fusion module designed to explicitly integrate representations acquired from both a feature encoder and a graph encoder, facilitating consensus representation learning. Extensive experiments conducted on three benchmark datasets have shown that our proposed H2GnnDTI model consistently outperforms state-of-the-art deep learning methods.

AVAILABILITY AND IMPLEMENTATION: The codes are freely available at https://github.com/LiminLi-xjtu/H2GnnDTI.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40097269 | DOI:10.1093/bioinformatics/btaf117

Brain Res. 2025 Mar 15:149571. doi: 10.1016/j.brainres.2025.149571. Online ahead of print.

ABSTRACT

Dravet Syndrome is a severe childhood drug-resistant epilepsy. The predominant etiology of this condition is related to de novo mutations within the SCN1A gene, which codes for the alpha subunit of the NaV1.1 sodium channels. This dysfunction leads to hypoexcitability of GABAergic interneurons. In turn, the loss of electrical excitability in GABAergic interneurons leads to an imbalance of excitation over inhibition in many neural circuits. Notably, exacerbation of symptoms is observed when non-selective sodium channel blockers are administered to patients with Dravet. Recent studies in animal models of Dravet have highlighted the potential of highly specific sodium channel blockers capable of blocking other sodium channel subtypes without inhibiting NaV1.1 current and selective activators of NaV1.1 as viable therapeutic strategies for alleviating Dravet Syndrome symptoms. Here, we describe the development and validation of ligand-based machine learning models to identify ligands with inhibitory effects on sodium channel isoforms NaV1.1 and NaV1.2. These models were built based on in-house open-source routines and Mordred molecular descriptors. First, linear classifiers were inferred using a combination of feature-bagging and Forward Stepwise selection. Secondly, ensemble learning was applied to build meta-classifiers with improved predictive ability, whose performance was tested in retrospective screening experiments. After in silico validation, the models were applied to screen for drug repurposing opportunities in the DrugBank and Drug Repurposing Hub databases, to identify selective blocking agents of NaV1.2 devoid of NaV1.1 blocking activity as potential compounds for the treatment of Dravet Syndrome. Forty in silico hits were later identified in a prospective screening experiment. Four of them were acquired and submitted to experimental confirmation via patch clamp: three of these candidates, Eltrombopag, Sufugolix, and Glesatinib, showed blocking effects on NaV1.2 currents, although no subtype selectivity was observed. The different predictive abilities of the NaV1.1 and NaV1.2 models may be attributed to the different sizes of the datasets used to train and validate the respective models.

PMID:40096941 | DOI:10.1016/j.brainres.2025.149571

Biophys Chem. 2025 Mar 10;322:107434. doi: 10.1016/j.bpc.2025.107434. Online ahead of print.

ABSTRACT

Emergence of viruses cause unprecedented challenges and thus leading to wide-ranging consequences today. The world has faced massive disruptions like COVID-19 and continues to suffer in terms of public health and world economy. Fighting with this emergence of viruses and its reemergence plays a critical role in the health care industry. Identification of novel virus-drug associations is a vital step in drug discovery. Prediction and prioritization of novel virus-drug associations through computational approaches is an alternative and best choice considering the cost and risk of biological experiments. This study proposes a method, KR-AEVDA that relies on k-nearest neighbor based reliable negative sample selection and autoencoder based feature extraction to explore promising virus-drug associations for further experimental validation. The method analyzes complex relationships among drugs and viruses by investigating similarity and association data between drugs and viruses. It generates feature vectors from the similarity data, and reliable negative samples are extracted through an effective distance-based algorithm from the unlabeled samples in the dataset. Then high level features are extracted via an autoencoder and is fed to an ensemble classifier for inferring novel associations. Experimental results on three different datasets showed that KR-AEVDA reliably attained better performance than other state-of-the-art methods. Molecular docking is carried out between the top-predicted drugs and the crystal structure of the SARS-CoV-2's main protease to further validate the predictions. Case studies for SARS-CoV-2 illustrate the effectiveness of KR-AEVDA in identifying potential virus-drug associations.

PMID:40096790 | DOI:10.1016/j.bpc.2025.107434

Front Pharmacol. 2025 Feb 28;16:1449062. doi: 10.3389/fphar.2025.1449062. eCollection 2025.

ABSTRACT

BACKGROUND: Anti-hypertensive drugs have been reported to demonstrate anti-inflammatory and anti-angiogenic effects. This study aims to investigate the association between anti-hypertensive drugs and the prognosis of colorectal cancer (CRC) patients.

METHODS: Clinical data of 1134 CRC patients with hypertensions and the prescription of anti-hypertensive drugs who had undergone curative surgery in our hospital between 2005 and 2015 were retrieved. Their survival data and immune cell population in circulatory blood were compared among different types of anti-hypertensive drugs and overall CRC patients.

RESULTS: The 5-year overall survival for the antihypertensives-treated patients (65.2%) was higher than the CRC patients in Hong Kong (58.2%). Hydrochlorothiazide (HCTZ) group showed the best prognosis (79.1%) among different antihypertensive drug, particularly for advance stage or elderly patients, which are poor prognostic factors for overall CRC patients, demonstrated an obviously improved prognosis upon HCTZ treatment. Moreover, our data showed the recurrence rate was significantly lower for HCTZ group (18.3%) compared to non-HCTZ group (26.8%) and the reported rate (31%) of CRC patients in Hong Kong. Finally, patients with a lower pre-operative basophil level showed better overall and disease-free survival following HCTZ treatment.

CONCLUSION: This study demonstrated the association of HCTZ treatment with a better prognosis of CRC patients.

PMID:40093321 | PMC:PMC11906466 | DOI:10.3389/fphar.2025.1449062

medRxiv [Preprint]. 2025 Mar 4:2025.02.28.25323099. doi: 10.1101/2025.02.28.25323099.

ABSTRACT

Psychiatric disorders and symptoms are associated with differences in pain perception and sensitivity. These differences can have important implications in treating spinal degenerative disease (SDD) and chronic low-back pain (CLBP). Leveraging data from the UK Biobank (UKB) and the All of Us Research Program (AoU), we investigated the effects linking psychiatric disorders (alcohol use disorder, anxiety, attention deficit hyperactivity disorder, bipolar disorder, cannabis use disorder, depression, opioid use disorder, posttraumatic stress disorder, and schizophrenia) to SDD and CLBP. We applied multi-nominal regression models, polygenic risk scoring (PRS), and one-sample Mendelian randomization (MR) to triangulate the effects underlying the associations observed. We also performed gene ontology and drug-repurposing analyses to dissect the biology shared among mental illnesses, SDD, and CLBP. Comparing individuals affected only by SDD (UKB N=37,745, AoU N=3,477), those affected only by CLBP (UKB N=15,496, AoU N=23,325), and those affected by both conditions (UKB N=11,463, AoU N= 13,451) to controls (UKB N=337,362, AoU N= 117,162), observational and genetically informed analyses highlighted that the strongest effects across the three case groups were observed for alcohol use disorder, anxiety, depression, and posttraumatic stress disorder. Additionally, schizophrenia and its PRS appeared to have an inverse relationship with CLBP, SDD, and their comorbidity. One-sample MR highlighted a potential direct effect of internalizing disorders on the outcomes investigated that was particularly strong on SDD. Our drug-repurposing analyses identified histone deacetylase inhibitors as targeting molecular pathways shared among psychiatric disorders, SDD, and CLBP. In conclusion, these findings support that the comorbidity among psychiatric disorders, SDD, and CLBP is due to the contribution of direct effects and shared biology linking these health outcomes. These pleiotropic mechanisms together with sociocultural factors play a key role in shaping the SDD-CLBP comorbidity patterns observed across the psychopathology spectrum.

PMID:40093242 | PMC:PMC11908311 | DOI:10.1101/2025.02.28.25323099

Expert Opin Pharmacother. 2025 Mar 17. doi: 10.1080/14656566.2025.2480764. Online ahead of print.

ABSTRACT

INTRODUCTION: Pulmonary hypertension is a life-threatening condition characterized by elevated mean pulmonary arterial pressure and vascular resistance. Significant advances in diagnosis and treatment have been achieved over the 20th and 21st centuries, yet challenges remain in improving long-term outcomes.

AREAS COVERED: This review discusses the historical milestones in understanding and pharmacotherapy of the pulmonary arterial hypertension (PAH). A comprehensive literature search was conducted to explore the earliest reports of each approved medication for pulmonary hypertension, along with historical papers detailing the pathophysiological and diagnostic development. Additionally, the search aimed to identify novel therapeutic strategies, including repositioned drugs and emerging targets.

EXPERT OPINION: While current therapies, such as prostacyclin analogs and PDE5 inhibitors, improve functional capacity and hemodynamics, they face limitations, including costs, administration, and a predominantly vasodilatory approach. Additionally, the limitations of current clinical trial designs for rare diseases like pulmonary arterial hypertension hinder the evaluation of potentially effective drugs. These challenges underscore the urgent need for translational research to optimize trial methodologies, accelerating the development of new therapies. Innovative approaches, such as drug repositioning and the exploration of novel molecular targets, are critical to overcoming these barriers and ensuring timely, effective, and affordable treatment options for patients with PAH.

PMID:40091694 | DOI:10.1080/14656566.2025.2480764

Microbiol Spectr. 2025 Mar 17:e0331824. doi: 10.1128/spectrum.03318-24. Online ahead of print.

ABSTRACT

Fungal pathogens pose a serious threat to human health, with Candida and Aspergillus spp. representing some of the most significant opportunistic invaders. Aspergillus fumigatus causes aspergillosis, one of the most prevalent fungal diseases of humans. There is a limited number of drugs available to combat these infections, and antifungal drug resistance is on the rise. In this manuscript, we show 4-[4-(4-Piperidinyl) phenyl]-7-[4-(-(trifluoromethyl) phenyl]-2-naphthalenecarboxylic acid (PPTN), a highly specific antagonist of the human P2Y14 receptor, is a promising antifungal adjuvant against diverse fungal pathogens. PPTN interacts with caspofungin (CAS), ibrexafungerp, voriconazole (VOR), and amphotericin against A. fumigatus CAS- and VOR-resistant clinical isolates, and also CAS against Candida spp and Cryptococcus neoformans. The combination of PPTN and CAS increases cell death in A. fumigatus. In the model yeast Saccharomyces cerevisiae, heterozygous deletion of genes involved in chromatin remodeling results in PPTN hypersensitivity, and in A. fumigatus, PPTN can have increased fungicidal activity when combined with the histone deacetylase inhibitor trichostatin A and the DNA methyltransferase inhibitor 5-azacytidine. Finally, PPTN has reduced toxicity to human immortalized cell lineages and partially clears A. fumigatus conidia infection in A549 pulmonary epithelial cells. Our results indicate that PPTN is a novel adjuvant antifungal drug against fungal diseases caused by A. fumigatus and Candida spp.

IMPORTANCE: Invasive fungal infections have a high mortality rate, causing more deaths annually than tuberculosis or malaria. Aspergillus fumigatus is the main etiological agent of aspergillosis, one of the most prevalent and deadly fungal diseases. There are few therapeutic options for treating this disease, and treatment commonly fails due to host complications or the emergence of antifungal resistance. Drug repurposing, where existing drugs are deployed for other clinical indications, has increasingly been used in the process of drug discovery. Here, we show that 4-[4-(4-Piperidinyl) phenyl]-7-[4-(-(trifluoromethyl) phenyl]-2-naphthalenecarboxylic acid (PPTN), a highly specific antagonist of the human P2Y14 receptor, when combined with caspofungin (CAS), ibrexafungerp, voriconazole (VOR), and amphotericin can increase the fungicidal activity against not only A. fumigatus CAS- and VOR-resistant clinical isolates but also CAS against Candida spp.

PMID:40090930 | DOI:10.1128/spectrum.03318-24

J Mol Biol. 2025 Mar 13:169089. doi: 10.1016/j.jmb.2025.169089. Online ahead of print.

ABSTRACT

Dr. Donita C. Brady is the Harrison McCrea Dickson, MD, and Clifford C. Baker, MD Presidential Associate Professor of Cancer Biology at the University of Pennsylvania Perelman School of Medicine. She earned her BS in Chemistry from Radford University and her PhD in Pharmacology from UNC-Chapel Hill before completing postdoctoral training at Duke University with Dr. Christopher Counter. At Penn, Dr. Brady leads a research program pioneeringmetalloallostery, where redox-active metals regulate kinase activity. Her lab investigates the intersection of kinase signaling and copper (Cu) homeostasis, identifying Cu-dependent kinases and developing targeted therapies through drug repurposing and novel drug design. Her work has advanced our understanding of metals in nutrient signaling, energy homeostasis, and cancer metabolism. Dr. Brady has received numerous honors, including being a Pew Biomedical Scholar, a V Foundation Scholar, and the recipient of the Perelman School of Medicine's Michael S. Brown New Investigator Research Award. A dedicated advocate for diversity, equity, inclusion, and accessibility (DEIA), she has spent the past decade addressing barriers to representation in STEM. In 2021, she was appointed the inaugural Assistant Dean for Inclusion, Diversity, and Equity (IDE) in Research Training at Penn, leading efforts to foster an inclusive research environment. For these contributions, she received the 2022 Vanderbilt Basic Science Juneteenth Icon Award and the Penn Biomedical Graduate Studies Cell and Molecular Biology Graduate Group Community Service Award.

PMID:40089146 | DOI:10.1016/j.jmb.2025.169089

Drug Discov Today. 2025 Mar 13:104331. doi: 10.1016/j.drudis.2025.104331. Online ahead of print.

ABSTRACT

Ovarian cancer (OC) has poor survival statistics and increasing prevalence. One of the new options for its therapy could be overcoming platinum resistance. In this review, we have considered the idea of repositioning angiotensin-converting enzyme inhibitors (ACE-Is) as chemosensitizers. These drugs have been shown to suppress angiogenesis and OC cell migration in preclinical studies. Moreover, clinical data have shown that using ACE-Is with standard chemotherapy prolongs patient survival. Based on this rationale, we discuss the available in vitro models of OC for future studies with ACE-Is and demonstrate an in silico approach that has enabled us to select the most promising molecules: perindopril, ramipril, trandolapril and their diketopiperazine derivatives.

PMID:40089017 | DOI:10.1016/j.drudis.2025.104331

Comput Biol Med. 2025 Mar 14;189:109984. doi: 10.1016/j.compbiomed.2025.109984. Online ahead of print.

ABSTRACT

The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug development, allowing the modulation of neurotransmitter systems at the personal level. This review focuses on the neuropharmacology and regulation of neurotransmitters using predictive modeling, closed-loop neuromodulation, and precision drug design. The fusion of AI with applications such as machine learning, deep-learning, and even computational modeling allows for the real-time tracking and enhancement of biological processes within the body. An exemplary application of AI is the use of DeepMind's AlphaFold to design new GABA reuptake inhibitors for epilepsy and anxiety. Likewise, Benevolent AI and IBM Watson have fast-tracked drug repositioning for neurodegenerative conditions. Furthermore, we identified new serotonin reuptake inhibitors for depression through AI screening. In addition, the application of Deep Brain Stimulation (DBS) settings using AI for patients with Parkinson's disease and for patients with major depressive disorder (MDD) using reinforcement learning-based transcranial magnetic stimulation (TMS) leads to better treatment. This review highlights other challenges including algorithm bias, ethical concerns, and limited clinical validation. Their proposal to incorporate AI with optogenetics, CRISPR, neuroprosthesis, and other advanced technologies fosters further exploration and refinement of precision neurotherapeutic approaches. By bridging computational neuroscience with clinical applications, AI has the potential to revolutionize neuropharmacology and improve patient-specific treatment strategies. We addressed critical challenges, such as algorithmic bias and ethical concerns, by proposing bias auditing, diverse datasets, explainable AI, and regulatory frameworks as practical solutions to ensure equitable and transparent AI applications in neurotransmitter modulation.

PMID:40088712 | DOI:10.1016/j.compbiomed.2025.109984