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

Biomed Pharmacother. 2025 May 3;187:118124. doi: 10.1016/j.biopha.2025.118124. Online ahead of print.

ABSTRACT

Chronic kidney disease (CKD) is characterized by kidney fibrosis involving epithelial-mesenchymal transition (EMT), and extracellular matrix (ECM) accumulation, and often leads to end-stage kidney disease (ESKD). Currently, available therapies are not uniformly effective and lead to serious adverse effects. Levosimendan (LVS), a calcium sensitizer and an inodilator, manages cardiac failure. We aimed to evaluate the renoprotective effect of LVS on unilateral ureteral obstruction (UUO)-induced CKD in male Sprague-Dawley (SD) rats and exogenous transforming growth factor-β1 (TGF-β1)-induced fibrosis in NRK-52E cells. Rats were randomly grouped as normal control (NC), sham, UUO and UUO + LVS (3 mg/kg, p.o., o.d.) for 21 days. All animals were sacrificed post-treatment, and plasma, urine and kidney specimens were utilized for biochemistry, histology, immunohistochemistry and immunoblotting. Moreover, exogenous TGF-β1 was used to stimulate kidney fibrosis in NRK-52E cells and treated with LVS (10 µM) for 48 h. The in-vitro samples were collected for cell morphology, viability, immunofluorescence and immunoblotting. LVS treatment significantly improved the kidney mass, plasma and urine creatinine, BUN, urine urea nitrogen and plasma proteins levels of TGF-β1 and fibronectin. Histology revealed a significant decrease in tubular necrosis, glomerulosclerosis and tubulointerstitial fibrosis in LVS-treated rats. Moreover, LVS treatment remarkably downregulated the levels of α-SMA, vimentin, p-Smad 2/3 and upregulated E-cadherin in UUO rats, decreased Smad 4, collagen I, β-catenin, and MMP-7-mediated ECM and increased Smurf 2 and Smad 7 in NRK-52E cells. LVS inhibits EMT and ECM turnover via TGF-β1/Smad axis modulation, highlighting the potential clinical use of LVS for CKD.

PMID:40319657 | DOI:10.1016/j.biopha.2025.118124

Angew Chem Int Ed Engl. 2025 May 3:e202425206. doi: 10.1002/anie.202425206. Online ahead of print.

ABSTRACT

New compounds targeting human malaria parasites are critical for effective malaria control and elimination. Here, we pursued the imidazoquinolinone AZD0156 (MMV1580483), a human ataxia-telangiectasia mutated (ATM) kinase inhibitor that completed Phase I clinical trials as an anticancer agent. We validated its in vitro activity against the two main forms of the Plasmodium falciparum parasite in the human host, viz. the asexual blood (symptomatic) stage and sexual gametocyte (transmission) stage. Resistance selection, cross-resistance, biochemical and conditional knockdown studies revealed that AZD0156 inhibits P. falciparum phosphatidylinositol 4-kinase type III beta (PfPI4Kβ), a clinically-validated target for the treatment of malaria. Metabolic perturbations, fixed-ratio isobolograms, killing kinetics and morphological evaluation correlated AZD0156 inhibition with other known PI4Kβ inhibitors. The compound showed favourable in vivo pharmacokinetic properties and 81% antimalarial efficacy (4 x 50 mg/kg) in a P. berghei mouse malaria infection model. Importantly, a cleaner biochemical profile was measured against human kinases (MAP4K4, MINK1) implicated in embryofoetal developmental toxicity associated with the PfPI4Kβ inhibitor MMV390048. This improved kinase selectivity profile and structural differentiation from other PI4Kβ inhibitors, together with its multistage antiplasmodial activity and favourable pharmacokinetic properties, makes AZD0156 an attractive candidate for target-based drug repositioning against malaria via a medicinal chemistry optimisation approach.

PMID:40317875 | DOI:10.1002/anie.202425206

Antiviral Res. 2025 Apr 30:106184. doi: 10.1016/j.antiviral.2025.106184. Online ahead of print.

ABSTRACT

Helicases have emerged as promising targets in antiviral drug development but remain largely undrugged. To support the focused development of viral helicase inhibitors we identified, collected, and integrated all chemogenomics data for all helicases annotated in the ChEMBL database. After thoroughly curating and enriching the data with accurate annotations we have created a derivative database of helicase inhibitors which we dubbed Heli-SMACC (Helicase-targeting SMAll Molecule Compound Collection). Heli-SMACC contains 13,597 molecules, 29 proteins, and 20,431 bioactivity entries for viral, human, and bacterial helicases. We selected 30 compounds with promising viral helicase activity and tested them in a SARS-CoV-2 NSP13 ATPase assay. Twelve compounds demonstrated ATPase inhibition and a consistent dose-response curve. While Heli-SMACC provides a rich resource for identifying candidate inhibitors, cross-species compound transferability remains a significant challenge. In particular, inhibitory activity observed against viral helicases often does not translate well to human or bacterial homologs and vice versa due to differences in binding site composition, helicase structure, and cofactor dependencies. Despite these limitations, Heli-SMACC offers a valuable starting point for structure-based optimization and target-specific inhibitor design. The Heli-SMACC database may serve as a reference for virologists and medicinal chemists working on the development of novel helicase inhibitors. Heli-SMACC is publicly available at https://smacc.mml.unc.edu.

PMID:40316178 | DOI:10.1016/j.antiviral.2025.106184

Brief Bioinform. 2025 May 1;26(3):bbaf163. doi: 10.1093/bib/bbaf163.

ABSTRACT

Host-directed antivirals offer a promising strategy for addressing the challenge of viral resistance. Virus-host interactions often trigger stage-specific metabolic reprogramming in the host, and the causal links between these interactions and virus-induced metabolic changes provide valuable insights for identifying host targets. In this study, we present a workflow for repurposing host-directed antivirals using virus-induced protein networks. These networks capture the dynamic progression of viral infection by integrating host proteins directly interacting with the virus and enzymes associated with significantly altered metabolic fluxes, identified through dual-species genome-scale metabolic models. This approach reveals numerous hub nodes as potential host targets. As a case study, 50 approved drugs with potential anti-influenza virus A (IVA) activity were identified through eight stage-specific IVA-induced protein networks, each comprising 699-899 hub nodes. Lisinopril, saxagliptin, and gliclazide were further validated for anti-IVA efficacy in vitro through assays measuring the inhibition of cytopathic effects and viral titers in A549 cells infected with IVA PR8. This workflow paves the way for the rapid repurposing of host-directed antivirals.

PMID:40315435 | DOI:10.1093/bib/bbaf163

Cornea. 2025 May 2. doi: 10.1097/ICO.0000000000003877. Online ahead of print.

NO ABSTRACT

PMID:40315261 | DOI:10.1097/ICO.0000000000003877

Front Bioinform. 2025 Apr 17;5:1522401. doi: 10.3389/fbinf.2025.1522401. eCollection 2025.

ABSTRACT

The incidence of non-alcoholic fatty liver disease (NAFLD), encompassing the more severe non-alcoholic steatohepatitis (NASH), is rising alongside the surges in diabetes and obesity. Increasing evidence indicates that NASH is responsible for a significant share of idiopathic hepatocellular carcinoma (HCC) cases, a fatal cancer with a 5-year survival rate below 22%. Biomarkers can facilitate early screening and monitoring of at-risk NAFLD/NASH patients and assist in identifying potential drug candidates for treatment. This study utilized an ensemble feature selection framework to analyze transcriptomic data, identifying biomarker genes associated with the stage-wise progression of NAFLD-related HCC. Seven machine learning algorithms were assessed for disease stage classification. Twelve feature selection methods including correlation-based techniques, mutual information-based methods, and embedded techniques were utilized to rank the top genes as features, through this approach, multiple feature selection methods were combined to yield more robust features important in this disease progression. Cox regression-based survival analysis was carried out to evaluate the biomarker potentiality of these genes. Furthermore, multiphase drug repurposing strategy and molecular docking were employed to identify potential drug candidates against these biomarkers. Among the seven machine learning models initially evaluated, DISCR resulted as the most accurate disease stage classifier. Ensemble feature selection identified ten top genes, among which eight were recognized as potential biomarkers based on survival analysis. These include genes ABAT, ABCB11, MBTPS1, and ZFP1 mostly involved in alanine and glutamate metabolism, butanoate metabolism, and ER protein processing. Through drug repurposing, 81 candidate drugs were found to be effective against these markers genes, with Diosmin, Esculin, Lapatinib, and Phenelzine as the best candidates screened through molecular docking and MMGBSA. The consensus derived from multiple methods enhances the accuracy of identifying relevant robust biomarkers for NAFLD-associated HCC. The use of these biomarkers in a multiphase drug repurposing strategy highlights potential therapeutic options for early intervention, which is essential to stop disease progression and improve outcomes.

PMID:40313868 | PMC:PMC12043677 | DOI:10.3389/fbinf.2025.1522401

Int J Neuropsychopharmacol. 2025 May 2:pyaf029. doi: 10.1093/ijnp/pyaf029. Online ahead of print.

ABSTRACT

BACKGROUND: Vortioxetine is a serotonin reuptake inhibitor and serotonin receptor modulator used for the treatment of major depressive disorder, but recent studies have also reported anticancer effects in models of glioblastoma. Given the well-established benefits of drug repositioning, we examined the pharmacological mechanism for these anticancer actions using bioinformatics and molecular docking.

METHODS: Putative molecular targets for vortioxetine were identified by searching DrugBank, GeneCards, SwissTargetPrediction, CTD, and SuperPred databases, while glioblastoma-related proteins were identified using GeneCards, OMIM, and TTD. A protein-protein interaction (PPI) network was constructed from vortioxetine targets also involved in glioblastoma to identify core (hub) targets, which were then characterized by GO and KEGG pathway enrichment analyses using DAVID. Cytoscape was utilized to generate a drug-pathway-target-disease network, and molecular docking simulations were performed to evaluate direct interactions between vortioxetine and core target proteins.

RESULTS: A total of 234 unique vortioxetine protein targets were identified. Among 234 vortioxetine targets identified, 48 were also related to glioblastoma. Topological analysis of the PPI network revealed five core targets: the serine/threonine kinase AKT1, transcription factor hypoxia-inducible factor (HIF)-1, cell adhesion molecule cadherin-E, NF-κB subunit p105, and prostaglandin-endoperoxide synthase 2. According to GO and KEGG pathway analyses, the anticancer efficacy of vortioxetine may be mediated by effects on glucose metabolism, cell migration, phosphorylation, inflammatory responses, apoptosis, and signaling via Rap1, chemical carcinogenesis-reactive oxygen species, and HIF-1. Molecular docking revealed moderately strong affinities between vortioxetine and four core targets.

CONCLUSIONS: This study suggests that vortioxetine may inhibit glioblastoma development through direct effects on multiple targets, and further emphasizes the value of bioinformatics analyses for drug repositioning.

PMID:40312983 | DOI:10.1093/ijnp/pyaf029

Prog Neuropsychopharmacol Biol Psychiatry. 2025 Apr 29:111386. doi: 10.1016/j.pnpbp.2025.111386. Online ahead of print.

ABSTRACT

The development of new drugs is a complex, expensive, and time-consuming process, often fraught with a high likelihood of failure. Amid these challenges, drug repurposing, which identifies new therapeutic applications for already existing medications, offers a more economical and time-saving approach, particularly in the challenging field of neurological and psychiatric disorders. This narrative review explores both preclinical and clinical studies to examine the potential of anesthetics such as ketamine, nitrous oxide, isoflurane, sevoflurane, propofol, dexmedetomidine, and sodium oxybate in treating central nervous system disorders. Various research highlights the potential of anesthetics to provide rapid antidepressant effects, enhance learning and memory, improve synaptic plasticity, and offer neuroprotective benefits, demonstrating promise for treating depression, post-traumatic stress disorder, cognitive decline, traumatic brain injury, and neurodegenerative disorders. Anesthetics appear to alleviate symptoms in neurological conditions, likely by modulating GABAergic and glutamatergic pathways. However, challenges such as dose-dependent neurotoxicity, variability in preclinical and clinical outcomes, as well as environmental concerns remain significant issues. Future research is essential to optimize dosing strategies, ensure long-term safety, and gain a deeper understanding of the precise mechanisms of action. The concept of anesthetics' repurposing presents a unique solution to tackle the challenges in neurological and psychiatric therapy by providing a platform for the development of new and improved therapies.

PMID:40311741 | DOI:10.1016/j.pnpbp.2025.111386

J Neuroimmune Pharmacol. 2025 May 1;20(1):49. doi: 10.1007/s11481-025-10205-6.

ABSTRACT

Current medications for seizure symptoms can reduce seizure severity but do not stop or slow their progression. These drugs often have unpleasant side effects and may not work for all patients. The search for new therapeutic targets for seizure progression can be expedited through drug repurposing, which leverages existing approved medications, ultimately reducing clinical trial costs. This study investigates the neuroprotective properties of pomalidomide, an immunomodulatory drug, in a male rat model of pentylenetetrazol-induced seizures. Pomalidomide pretreatment significantly decreased the frequency and severity of seizures and delayed their onset. It elevated glutathione peroxidase (GPX) and superoxide dismutase (SOD) levels while lowering malondialdehyde (MDA), showcasing its antioxidant effects. Furthermore, it activated the Nrf2/HO-1 signaling pathway by increasing gene expression in the hippocampus, providing neuroprotection in the CA1 and CA3 regions. These findings suggest that pomalidomide may enhance the antioxidant defense system, support the Nrf2/HO-1 pathway, and protect the hippocampus, indicating its potential for treating patients with seizures, particularly intractable ones.

PMID:40310605 | DOI:10.1007/s11481-025-10205-6

Clin Pharmacol Ther. 2025 May 1. doi: 10.1002/cpt.3682. Online ahead of print.

ABSTRACT

Although attractive for relevance to real-world scenarios, real-world data (RWD) is typically used for drug repurposing and not therapeutic target discovery. Repurposing studies have identified few effective options in neurological diseases such as the rare disease, amyotrophic lateral sclerosis (ALS), which has no disease-modifying treatments available. We previously reclassified drugs by their simulated effects on proteins downstream of drug targets and observed class-level effects in the EHR, implicating the downstream protein as the source of the effect. Here, we developed a novel ALS-focused network medicine model using data from patient samples, the public domain, and consortia. With this model, we simulated drug effects on ALS and measured class effects on overall survival in retrospective EHR studies. We observed an increased but non-significant risk of death for patients taking drugs with complement system proteins downstream of their targets and experimentally validated drug effects on complement activation. We repeated this for six protein classes, three of which, including multiple chemokine receptors, were associated with a significantly increased risk for death, suggesting that targeting proteins such as CXCR5, CXCR3, chemokine signaling generally, or neuropeptide Y (NPY) could be advantageous therapeutic targets for these patients. We expanded our analysis to the neuroinflammatory condition, myasthenia gravis, and neurodegenerative disease, Parkinson's, and recovered similar effect sizes. We demonstrated the utility of network medicine for testing novel therapeutic effects using RWD and believe this approach may accelerate target discovery in neurological diseases, addressing the critical need for new therapeutic options.

PMID:40310263 | DOI:10.1002/cpt.3682

Nucleic Acids Res. 2025 May 1:gkaf373. doi: 10.1093/nar/gkaf373. Online ahead of print.

ABSTRACT

As part of the Library of Integrated Network-Based Cellular Signatures (LINCS) NIH initiative, 248 human cell lines were profiled with the L1000 assay to measure the effect of 33 621 small molecules and 7508 single-gene CRISPR knockouts. From this massive dataset, we computed 1.678 million sets of up- and down-regulated genes. These gene sets are served for search by the LINCS L1000 Signature Search (L2S2) web server application. With L2S2, users can identify small molecules and single gene CRISPR KOs that produce gene expression profiles similar or opposite to their submitted single or up/down gene sets. L2S2 also includes a consensus search feature that ranks perturbations across all cellular contexts, time points, and concentrations. To demonstrate the utility of L2S2, we crossed the L2S2 gene sets with gene sets collected for the RummaGEO resource. The analysis identified clusters of differentially expressed genes that match drug classes, tissues, and diseases, pointing to many opportunities for drug repurposing and drug discovery. Overall, the L2S2 web server application can be used to further the development of personalized therapeutics while expanding our understanding of complex human diseases. The L2S2 web server application is available at https://l2s2.maayanlab.cloud.

PMID:40308216 | DOI:10.1093/nar/gkaf373

BMC Bioinformatics. 2025 Apr 30;26(1):117. doi: 10.1186/s12859-025-06132-1.

ABSTRACT

Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse effects. While experimental methods can accurately identify GR antagonists, they are often not cost-effective for large-scale drug discovery. Thus, computational approaches leveraging SMILES information for precise in silico identification of GR antagonists are crucial, enabling efficient and scalable drug discovery. Here, we develop a new ensemble learning approach using a multi-step stacking strategy (M3S), termed M3S-GRPred, aimed at rapidly and accurately discovering novel GR antagonists. To the best of our knowledge, M3S-GRPred is the first SMILES-based predictor designed to identify GR antagonists without the use of 3D structural information. In M3S-GRPred, we first constructed different balanced subsets using an under-sampling approach. Using these balanced subsets, we explored and evaluated heterogeneous base-classifiers trained with a variety of SMILES-based feature descriptors coupled with popular ML algorithms. Finally, M3S-GRPred was constructed by integrating probabilistic feature from the selected base-classifiers derived from a two-step feature selection technique. Our comparative experiments demonstrate that M3S-GRPred can precisely identify GR antagonists and effectively address the imbalanced dataset. Compared to traditional ML classifiers, M3S-GRPred attained superior performance in terms of both the training and independent test datasets. Additionally, M3S-GRPred was applied to identify potential GR antagonists among FDA-approved drugs confirmed through molecular docking, followed by detailed MD simulation studies for drug repurposing in Cushing's syndrome. We anticipate that M3S-GRPred will serve as an efficient screening tool for discovering novel GR antagonists from vast libraries of unknown compounds in a cost-effective manner.

PMID:40307679 | DOI:10.1186/s12859-025-06132-1

IUBMB Life. 2025 May;77(5):e70020. doi: 10.1002/iub.70020.

ABSTRACT

Chemotherapeutic treatments for breast cancer are often associated with severe toxicity due to the requirement of high concentrations of the drugs for efficacy. The combination of chemotherapy drugs along with repurposed drugs offers a promising strategy to enhance efficacy while reducing toxicity. However, the effectiveness of such combinations is likely to be hindered by improper metabolism of the drugs due to the sharing of the same metabolizing enzymes. In this study, we explored a novel approach to enhance the efficacy of Pimozide (repurposed drug) by combining it with chemotherapeutic drugs that utilize different metabolizing enzymes than Pimozide, thereby reducing metabolic load and toxicity. The Epirubicin-SAHA(Vorinostat)-Pimozide (ESP) combination emerged as highly synergistic, reducing the IC50 of Pimozide from 16.54 to 0.57 μM in MCF-7 cells and from 17.5 to 3.35 μM in MDA-MB-231 cells, representing a significant enhancement in efficacy. Mechanistic studies revealed increased intracellular reactive oxygen species (ROS) generation and activation of the intrinsic apoptosis pathway, as indicated by a 10-fold increase in the cleaved PARP levels. In MDA-MB-231 cells, there was also a 2-fold increase in p53 and a 10-fold increase in p21 expression, with a concomitant reduction in AKT signaling. Furthermore, the ESP combination reduced cancer stemness, invasiveness, fatty acid uptake, and lipid droplet accumulation, pointing to its broad impact on cancer cell survival and metabolism. These findings suggest that the ESP combination holds promise as an effective therapeutic strategy for breast cancer, with reduced toxicity and enhanced efficacy.

PMID:40305333 | DOI:10.1002/iub.70020