ArXiv [Preprint]. 2025 Jan 20:arXiv:2411.15418v2.

ABSTRACT

Virtual screening of small molecules against protein targets can accelerate drug discovery and development by predicting drug-target interactions (DTIs). However, structure-based methods like molecular docking are too slow to allow for broad proteome-scale screens, limiting their application in screening for off-target effects or new molecular mechanisms. Recently, vector-based methods using protein language models (PLMs) have emerged as a complementary approach that bypasses explicit 3D structure modeling. Here, we develop SPRINT, a vector-based approach for screening entire chemical libraries against whole proteomes for DTIs and novel mechanisms of action. SPRINT improves on prior work by using a self-attention based architecture and structure-aware PLMs to learn drug-target co-embeddings for binder prediction, search, and retrieval. SPRINT achieves SOTA enrichment factors in virtual screening on LIT-PCBA, DTI classification benchmarks, and binding affinity prediction benchmarks, while providing interpretability in the form of residue-level attention maps. In addition to being both accurate and interpretable, SPRINT is ultra-fast: querying the whole human proteome against the ENAMINE Real Database (6.7B drugs) for the 100 most likely binders per protein takes 16 minutes. SPRINT promises to enable virtual screening at an unprecedented scale, opening up new opportunities for in silico drug repurposing and development. SPRINT is available on the web as ColabScreen: https://bit.ly/colab-screen.

PMID:39975427 | PMC:PMC11838698

ACS Pharmacol Transl Sci. 2025 Jan 23;8(2):308-338. doi: 10.1021/acsptsci.4c00679. eCollection 2025 Feb 14.

ABSTRACT

Skin cancer represents a major health concern due to its rising incidence and limited treatment options. Current treatments (surgery, chemotherapy, radiotherapy, immunotherapy, and targeted therapy) often entail high costs, patient inconvenience, significant adverse effects, and limited therapeutic efficacy. The search for novel treatment options is also marked by the high capital investment and extensive development involved in the drug discovery process. In response to these challenges, repurposing existing drugs for topical application and optimizing their delivery through nanotechnology could be the answer. This innovative strategy aims to combine the advantages of the known pharmacological background of commonly used drugs to expedite therapeutic development, with nanosystem-based formulations, which among other advantages allow for improved skin permeation and retention and overall higher therapeutic efficacy and safety. The present review provides a critical analysis of repurposed drugs such as doxycycline, itraconazole, niclosamide, simvastatin, leflunomide, metformin, and celecoxib, formulated into different nanosystems, namely, nanoemulsions and nanoemulgels, nanodispersions, solid lipid nanoparticles, nanostructured lipid carriers, polymeric nanoparticles, hybrid lipid-polymer nanoparticles, hybrid electrospun nanofibrous scaffolds, liposomes and liposomal gels, ethosomes and ethosomal gels, and aspasomes, for improved outcomes in the battle against skin cancer. Enhanced antitumor effects on melanoma and nonmelanoma research models are highlighted, with some nanoparticles even showing intrinsic anticancer properties, leading to synergistic effects. The explored research findings highly evidence the potential of these approaches to complement the currently available therapeutic strategies in the hope that these treatments might one day reach the pharmaceutical market.

PMID:39974652 | PMC:PMC11833728 | DOI:10.1021/acsptsci.4c00679

In Silico Pharmacol. 2025 Feb 17;13(1):30. doi: 10.1007/s40203-025-00316-6. eCollection 2025.

ABSTRACT

In COVID-19 patients, respiratory failure was reported due to damage to the respiratory centers of the brainstem. Molecular mimicry of three brainstem pre-Botzinger complex proteins (DAB1, AIFM and SURF1) was regarded as the underlying reason for respiratory failure and the autoimmune neurological sequelae. Of the three brainstem proteins mimicked by SARS CoV-2, corresponding sequences to two of the mimicry peptides were located in the N-protein of SARS CoV-2. N-protein is important for viral RNA synthesis and genome packaging. Here, we have used molecular modeling, docking and MD simulations to discern potential drugs which can inhibit molecular mimicry of DAB1 by SARS CoV-2 and also eliminate it by interfering in genome packaging. The binding site (drug target) for molecular docking was defined as the amino acid sequence extending from position 168-185 of the N-protein which was a SLiM region and also included the mimicry hexapeptide. Molecular docking after MD simulations was used to discern probable inhibitors of the drug-target from FDA-approved neurological drugs in the Broad Institute's Drug Repurposing Hub. Our results revealed that an anti-anxiety drug afobazole qualified the ADMET parameters, formed a stable complex with the drug-target and exhibited the highest binding energy (-88.21 kJ/mol). This suggests that afobazole can be repurposed against SARS CoV-2 for disrupting molecular mimicry of human DAB1 protein and also eliminate the etiopathological agent by interfering in viral genome packaging.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-025-00316-6.

PMID:39974371 | PMC:PMC11832858 | DOI:10.1007/s40203-025-00316-6

Arterioscler Thromb Vasc Biol. 2025 Feb 20. doi: 10.1161/ATVBAHA.124.321579. Online ahead of print.

ABSTRACT

BACKGROUND: Thiol isomerases play essential and nonredundant roles in platelet activation, aggregation, and thrombus formation. Thiol isomerase inhibitors have the potential to overcome the 2 major drawbacks of current antithrombotic therapies, as they target both arterial and venous thrombosis without enhancing bleeding risks. Recently, a Food and Drug Administration-approved drug, zafirlukast (ZAF), was shown to be a promising pan-thiol isomerase inhibitor. The objective of this study is to develop analogues of ZAF with optimized thiol isomerase inhibition and antithrombotic activity.

METHODS: Thirty-five ZAF analogues were tested in an insulin turbidometric assay for thiol isomerase inhibition. Analogues were tested for platelet activation, aggregation, P-selectin expression, and laser-induced thrombosis in mice and compared with the parent compound.

RESULTS: Of the 35 analogues, 12 retained activity, with 1, compound 21, that demonstrated a greater potency than that of ZAF, 5 had a similar potency to that of ZAF, and 6 had a weaker potency. Analogues demonstrated inhibition of platelet aggregation and P-selectin expression as compared with ZAF, consistent with their potencies. ZAF and compound 21 were shown to be reversible inhibitors of thiol isomerases, and not cytotoxic to cultured, lung, liver, and kidney cells. Finally, in an in vivo assessment of thrombus formation, compound 21 was able to significantly inhibit thrombus formation without affecting bleeding times.

CONCLUSIONS: A ZAF analogue, compound 21, with properties superior to those of ZAF was synthesized, demonstrating improved inhibition of platelet activation, aggregation, and thrombus formation as compared with the parent ZAF. This approach could yield a promising clinical candidate for treatment and prophylaxis of arterial and venous thrombosis.

PMID:39973747 | DOI:10.1161/ATVBAHA.124.321579

Infect Dis (Lond). 2025 Feb 20:1-8. doi: 10.1080/23744235.2025.2468510. Online ahead of print.

ABSTRACT

BACKGROUND: Tick-borne encephalitis (TBE) is a neurological disease caused by the tick-borne encephalitis virus (TBEV). Despite available vaccines, breakthrough infections occur, some fatal.

OBJECTIVES: As no antiviral therapy for TBE is currently approved, this study evaluated the in vitro activity of already licenced remdesivir (RDV) and sofosbuvir (SOF) for possible drug repurposing against TBEV.

METHODS: TBEV was cultured in A549 cells, and the inhibitory effects of RDV (GS-5734), its parent nucleotide GS-441524, and SOF (GS-7977) were assessed.

RESULTS: After 78 h, RDV demonstrated significantly lower EC50 values than SOF (0.14 vs. 11 µM) based on TBEV RNA levels measured by RT-qPCR. RDV also had a lower mean EC50 (0.55 µM) compared to GS-441524 and SOF (>8.9 and 13.1 µM, respectively) using crystal violet staining after 5 days. After 11 passages of TBEV in the presence of RDV, emergence of virus with a higher EC50 (1.32 vs. 0.55 µM) was detected with two mutations (L3122F and Y3278F) in NS5, the viral RNA-dependent RNA polymerase (RdRp), and one substitution in envelope (E) protein (E402G). Similarly, SOF resistance appeared after 20 passages, increasing EC50 values (35.5 vs. 10 µM).

CONCLUSION: RDV exhibits potent in vitro antiviral activity against TBEV via specific targeting of the viral RdRp as confirmed by the emergence of resistance-associated double NS5 substitutions in vitro in the presence of RDV. While the potential in vivo implications of the observed RDV resistance remain to be determined, these in vitro data support further assessment of RDV for the treatment of TBEV infection.

PMID:39973341 | DOI:10.1080/23744235.2025.2468510

Brief Bioinform. 2024 Nov 22;26(1):bbaf054. doi: 10.1093/bib/bbaf054.

ABSTRACT

Combination therapy has become increasingly important for treating complex diseases which often involve multiple pathways and targets. However, experimental screening of drug combinations is costly and time-consuming. The availability of large-scale transcriptomic datasets (e.g. CMap and LINCS) from in vitro drug treatment experiments makes it possible to computationally predict drug combinations with synergistic effects. Towards this end, we developed a computational approach, termed Identification of Drug Combinations via Multi-Set Operations (iDOMO), to predict drug synergy based on multi-set operations of drug and disease gene signatures. iDOMO quantifies the synergistic effect of a pair of drugs by taking into account the combination's beneficial and detrimental effects on treating a disease. We evaluated iDOMO, in a DREAM Challenge dataset with the matched, pre- and post-treatment gene expression data and cell viability information. We further evaluated the performance of iDOMO by concordance index and Spearman correlation on predicting the Highest Single Agency (HSA) synergy scores for four most common cancer types in two large-scale drug combination databases, showing that iDOMO significantly outperformed two existing popular drug combination approaches including the Therapeutic Score and the SynergySeq Orthogonality Score. Application of iDOMO to triple-negative breast cancer (TNBC) identified drug pairs with potential synergistic effects, with the combination of trifluridine and monobenzone being the most synergistic. Our in vitro experiments confirmed that the top predicted drug combination exerted a significant synergistic effect in inhibiting TNBC cell growth. In summary, iDOMO is an effective method for the in silico screening of synergistic drug combinations and will be a valuable tool for the development of novel therapeutics for complex diseases.

PMID:39973082 | DOI:10.1093/bib/bbaf054

Nat Commun. 2025 Feb 19;16(1):1755. doi: 10.1038/s41467-025-56690-4.

ABSTRACT

Repurposed drugs provide a rich source of potential therapies for Alzheimer's disease (AD) and other neurodegenerative disorders (NDD). Repurposed drugs have information from non-clinical studies, phase 1 dosing, and safety and tolerability data collected with the original indication. Computational approaches, "omic" studies, drug databases, and electronic medical records help identify candidate therapies. Generic repurposed agents lack intellectual property protection and are rarely advanced to late-stage trials for AD/NDD. In this review we define repurposing, describe the advantages and challenges of repurposing, offer strategies for overcoming the obstacles, and describe the key contributions of repurposing to the drug development ecosystem.

PMID:39971900 | DOI:10.1038/s41467-025-56690-4

Eur J Med Chem. 2025 Feb 15;288:117385. doi: 10.1016/j.ejmech.2025.117385. Online ahead of print.

ABSTRACT

Non-alcoholic fatty liver disease (NAFLD) is implicated in steatohepatitis (NASH), liver cirrhosis (LC) to hepatocellular carcinoma (HCC), sequentially. Herein, our aim was to unravel the nuanced key components (compounds, and targets) to deter the progressive severity concerning hepatocellular diseases. We incorporated rigor bioinformatics and computational screening tools to decode effector(s) against NAFLD, NASH, LC, and HCC. The corresponding ligands of PDX1 (transcription factor of INS; one agonist), and IL6 (thirty-two antagonists) were identified by Selleckchem. Molecular docking test (MDT) revealed that PDX1- BRD7552 conformer (-12.1 kcal/mol), and IL6- Forsythoside B (-11.4 kcal/mol) conformer formed most stable complex. In parallel, DFT proposed that BRD7552, and Forsythoside B had significant chemical properties to react the targets, respectively. In conclusion, we decoded causatives of the progressive liver disease with web-based tools in drug repositioning theory. BRD7552 as PDX1 agonist, and Forsythoside B as IL6 antagonist were attributed to synergistic efficacy against NAFLD-derived HCC.

PMID:39970728 | DOI:10.1016/j.ejmech.2025.117385

Cancer Med. 2025 Feb;14(4):e70681. doi: 10.1002/cam4.70681.

ABSTRACT

BACKGROUND: Ovarian cancer (OC) is the most lethal gynecological malignancy and a major global health concern, often diagnosed at advanced stages with poor survival rates. Despite advancements in treatment, resistance to standard chemotherapy remains a critical challenge with limited treatment options available. In recent years, the role of metabolic reprogramming in OC has emerged as a key factor driving tumor progression, therapy resistance, and poor clinical outcomes.

METHODS: This review explores the intricate connections between metabolic syndrome, enhanced glycolysis, and altered lipid metabolism within OC cells, which fuel the aggressive nature of the disease. We discuss how metabolic pathways are rewired in OC to support uncontrolled cell proliferation, survival under hypoxic conditions, and evasion of cell death mechanisms, positioning metabolic alterations as central to disease progression. The review also highlights the potential of repurposed metabolic-targeting drugs, such as metformin and statins, which have shown promise in preclinical studies for their ability to disrupt these altered metabolic pathways.

CONCLUSION: Drug repurposing offers a promising strategy to overcome chemoresistance and improve patient outcomes. Future research should focus on unraveling the complex metabolic networks in OC to develop innovative, targeted therapies that can enhance treatment efficacy and patient survival.

PMID:39969135 | DOI:10.1002/cam4.70681

Expert Rev Neurother. 2025 Feb 19. doi: 10.1080/14737175.2025.2469041. Online ahead of print.

ABSTRACT

INTRODUCTION: Post-traumatic epilepsy (PTE) accounts for 10% to 20% of all symptomatic epilepsies and 5% of all forms of epilepsy, and drug resistance is reported in up to 45% of cases.

AREAS COVERED: This is a focused narrative review that discusses the available data on the current and new PTE treatments, giving particular attention to the last 10 years.

EXPERT OPINION: Despite the disappointing results of many antiseizure medications (ASMs) in preventing epileptogenicity, it is still unclear whether the early intervention could lead to different clinical phenotypes in terms, for example, of seizure severity, drug resistance and comorbidity patterns. The same applies to compounds targeting neuroinflammation, oxidative stress and neurotransmission modulation. The heterogeneity of etiologies leading to PTE has limited the investigation and implementation of specific interventions. New studies must focus on identifying common pathways and mechanisms shared by different etiological processes, identifying biomarkers, and validating animal models of epileptogenesis concerning PTE. Drug repurposing research will facilitate rapid translation into clinical research. Multitarget drug combinations will also receive increasing attention. In terms of non-pharmacological treatments, Vagus Nerve Stimulation seems to be a good option, while epilepsy surgery and Deep Brain Stimulation deserve further attention.

PMID:39968755 | DOI:10.1080/14737175.2025.2469041

Front Cell Neurosci. 2025 Feb 4;19:1559821. doi: 10.3389/fncel.2025.1559821. eCollection 2025.

NO ABSTRACT

PMID:39968391 | PMC:PMC11832465 | DOI:10.3389/fncel.2025.1559821

Heliyon. 2025 Jan 10;11(3):e41894. doi: 10.1016/j.heliyon.2025.e41894. eCollection 2025 Feb 15.

ABSTRACT

Despite the vast vaccination campaigns against SARS-CoV-2, vaccine-resistant variants have emerged, and COVID-19 is continuing to spread with the fear of emergence of new variants that are resistant to the currently available anti-viral drugs. Hence, there is an urgent need to discover potential host-directed - rather than virus-directed - therapies against COVID-19. SARS-CoV-2 enters host cells through binding of the viral spike (S)-protein to the host angiotensin-converting enzyme 2 (ACE2) receptor, rendering the viral port of entry an attractive therapeutic target. Accordingly, this study aimed to investigate FDA-approved drugs for their potential repurposing to inhibit the entry point of SARS-CoV-2. Accordingly, the FDA-approved drugs library was enrolled in docking simulations to identify drugs that bind to the Spike-ACE2 interface. The drugs list retrieved by the docking simulations was shortlisted to 19 drugs based on docking scores and safety profiles. These drugs were screened for their ability to prevent binding between ACE2 and S-protein using an ELISA-based Spike-ACE2 binding assay. Five drugs showed statistically significant inhibition of binding between ACE2 and S-protein, ranging from 4 % to 37 %. Of those five, argatroban, glimepiride and ranolazine showed potential antiviral activity at IC50 concentrations well below their CC50 assessed by the plaque assay. Their mode of antiviral action was then determined using the plaque assay with some modifications, which revealed that argatroban acted mainly through a direct virucidal mechanism, while glimepiride largely inhibited viral replication, and ranolazine exerted its antiviral impact primarily through inhibiting viral adsorption. In conclusion, this study has identified three FDA-approved drugs - argatroban, glimepiride and ranolazine - which could potentially be repurposed and used for the management of COVID-19.

PMID:39968139 | PMC:PMC11834051 | DOI:10.1016/j.heliyon.2025.e41894

Small. 2025 Feb 19:e2409126. doi: 10.1002/smll.202409126. Online ahead of print.

ABSTRACT

This study advances sustainable pharmaceutical research for endometriosis by developing in vitro 3D cell culture models of endometriotic pathophysiology that allow antifibrotic drug candidates to be tested. Fibrosis is a key aspect of endometriosis, yet current cell models to study it remain limited. This work aims to bridge the translational gap between in vitro fibrosis research and preclinical testing of non-hormonal drug candidates. When grown in a 3D matrix of sustainably produced silk protein functionalized with a fibronectin-derived cell adhesion motif (FN-silk), endometrial stromal and epithelial cells respond to transforming growth factor beta-1 (TGF-β1) in a physiological manner as probed at the messenger RNA (mRNA) level. For stromal cells, this response to TGF-β1 is not observed in spheroids, while epithelial cell spheroids behave similarly to epithelial cell FN-silk networks. Pirfenidone, an antifibrotic drug approved for the treatment of idiopathic pulmonary fibrosis, reverses TGF-β1-induced upregulation of mRNA transcripts involved in fibroblast-to-myofibroblast transdifferentiation of endometrial stromal cells in FN-silk networks, supporting pirfenidone's potential as a repurposed non-hormonal endometriosis therapy. Overall, endometrial stromal cells cultured in FN-silk networks-which are composed of a sustainably produced, fully defined FN-silk protein-recapitulate fibrotic cellular behavior with high fidelity and enable antifibrotic drug testing.

PMID:39967482 | DOI:10.1002/smll.202409126

NPJ Antimicrob Resist. 2025 Feb 18;3(1):9. doi: 10.1038/s44259-024-00070-3.

ABSTRACT

Antimicrobial resistance (AMR) poses a growing global health threat. Nanomedicine, combined with drug repurposing, may help extend the effective lifespan of current and new antimicrobials. This review, presents an Australian perspective on nanotechnology-based therapies, highlighting scientific and clinical challenges. Early consideration of the potential barriers to market access may help to accelerate research translation, regulatory approval and patient access to nano-antimicrobial (NAM) drugs for resistant pathogens, not only in Australia, but globally.

PMID:39966608 | DOI:10.1038/s44259-024-00070-3

Neurotherapeutics. 2025 Feb 17:e00553. doi: 10.1016/j.neurot.2025.e00553. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) presents significant challenges in drug discovery and development due to its complex and poorly understood pathology and etiology. Digital twins (DTs) are recently developed virtual real-time representations of physical entities that enable rapid assessment of the bidirectional interaction between the virtual and physical domains. With recent advances in artificial intelligence (AI) and the growing accumulation of multi-omics and clinical data, application of DTs in healthcare is gaining traction. Digital twin technology, in the form of multiscale virtual models of patients or organ systems, can track health status in real time with continuous feedback, thereby driving model updates that enhance clinical decision-making. Here, we posit an additional role for DTs in drug discovery, with particular utility for complex diseases like AD. In this review, we discuss salient challenges in AD drug development, including complex disease pathology and comorbidities, difficulty in early diagnosis, and the current high failure rate of clinical trials. We also review DTs and discuss potential applications for predicting AD progression, discovering biomarkers, identifying new drug targets and opportunities for drug repurposing, facilitating clinical trials, and advancing precision medicine. Despite significant hurdles in this area, such as integration and standardization of dynamic medical data and issues of data security and privacy, DTs represent a promising approach for revolutionizing drug discovery in AD.

PMID:39965994 | DOI:10.1016/j.neurot.2025.e00553

Int J Biol Macromol. 2025 Feb 16:141057. doi: 10.1016/j.ijbiomac.2025.141057. Online ahead of print.

ABSTRACT

Of all isoforms, human carbonic anhydrase II (PF00194; EC 4.2.1.1), which is mostly found in red cells, kidneys, and the eyes, plays a pivotal role in numerous physiological processes, and its dysregulation has been linked to the wide range of illnesses, such as glaucoma. Finding new inhibitors that target Carbonic anhydrase II, therefore has great potential in drug discovery. Using drug repurposing approach, this study focused on the investigation of different drugs as Carbonic anhydrase II inhibitors and their structural studies using X-ray crystallography. For this purpose, 100 different drugs were evaluated for bovine and human carbonic anhydrase II inhibitory activity. Among all, two drugs, i.e. acetohexamide (1) and levosulpiride (54) were found to be active, with IC50 = 437.0 ± 0.2 and 1128 ± 0.75 μM, respectively. Mechanistic studies suggested that both drugs are competitive inhibitors of the human carbonic anhydrase II enzyme. The X-ray crystal structure analysis revealed that acetohexamide (1) interacts via terminal acetyl group with the active site residues of the carbonic anhydrase II enzyme, and showed strong hydrogen bonding with Zn, His94, His119, and Asn67. The sulfonamide group of levosulpiride was involved in strong hydrogen bonding with Zn, His94, His119, and Thr199. From in vivo studies, we found that carbonic anhydrase activity was significantly inhibited by the intraperitoneal administration of levosulpiride for up to 5 h. Our findings provide comprehensive insights for the optimization of the pharmacological profile of these drugs, and provide avenues for the exploration of different derivatives of these drugs with enhanced efficacy and fewer adverse effects.

PMID:39965680 | DOI:10.1016/j.ijbiomac.2025.141057

J Am Med Inform Assoc. 2025 Feb 18:ocaf035. doi: 10.1093/jamia/ocaf035. Online ahead of print.

ABSTRACT

OBJECTIVE: This study addresses the significant challenges posed by emerging SARS-CoV-2 variants, particularly in developing diagnostics and therapeutics. Drug repurposing is investigated by identifying critical regulatory proteins impacted by the virus, providing rapid and effective therapeutic solutions for better disease management.

MATERIALS AND METHODS: We employed a comprehensive approach combining mathematical modeling and efficient parameter estimation to study the transient responses of regulatory proteins in both normal and virus-infected cells. Proportional-integral-derivative (PID) controllers were used to pinpoint specific protein targets for therapeutic intervention. Additionally, advanced deep learning models and molecular docking techniques were applied to analyze drug-target and drug-drug interactions, ensuring both efficacy and safety of the proposed treatments. This approach was applied to a case study focused on the cytokine storm in COVID-19, centering on Angiotensin-converting enzyme 2 (ACE2), which plays a key role in SARS-CoV-2 infection.

RESULTS: Our findings suggest that activating ACE2 presents a promising therapeutic strategy, whereas inhibiting AT1R seems less effective. Deep learning models, combined with molecular docking, identified Lomefloxacin and Fostamatinib as stable drugs with no significant thermodynamic interactions, suggesting their safe concurrent use in managing COVID-19-induced cytokine storms.

DISCUSSION: The results highlight the potential of ACE2 activation in mitigating lung injury and severe inflammation caused by SARS-CoV-2. This integrated approach accelerates the identification of safe and effective treatment options for emerging viral variants.

CONCLUSION: This framework provides an efficient method for identifying critical regulatory proteins and advancing drug repurposing, contributing to the rapid development of therapeutic strategies for COVID-19 and future global pandemics.

PMID:39965087 | DOI:10.1093/jamia/ocaf035

Front Public Health. 2025 Feb 3;13:1520467. doi: 10.3389/fpubh.2025.1520467. eCollection 2025.

ABSTRACT

Rare diseases, affecting millions globally, pose a significant healthcare burden despite impacting a small population. While approximately 70% of all rare diseases are genetic and often begin in childhood, diagnosis remains slow and only 5% have approved treatments. The UN emphasizes improved access to primary care (diagnostic and potentially therapeutic) for these patients and their families. Next-generation sequencing (NGS) offers hope for earlier and more accurate diagnoses, potentially leading to preventative measures and targeted therapies. In here, we explore the therapeutic landscape for rare diseases, analyzing drugs in development and those already approved by the European Medicines Agency (EMA). We differentiate between orphan drugs with market exclusivity and repurposed existing drugs, both crucial for patients. By analyzing market size, segmentation, and publicly available data, this comprehensive study aims to pave the way for improved understanding of the treatment landscape and a wider knowledge accessibility for rare disease patients.

PMID:39963479 | PMC:PMC11830808 | DOI:10.3389/fpubh.2025.1520467

Med Biol Eng Comput. 2025 Feb 17. doi: 10.1007/s11517-025-03325-x. Online ahead of print.

ABSTRACT

Computational drug repositioning approaches should be investigated for the identification of new treatments for Alzheimer's patients as a huge amount of omics data has been produced during pre-clinical and clinical studies. Here, we investigated a gene network in Alzheimer's patients to detect a proper therapeutic target. We screened the targets of different drugs (34,006 compounds) using data available in the Connectivity Map database. Then, we analyzed transcriptome profiles of Alzheimer's patients to discover a network of gene-drugs based on mutual information, representing an index of dependence among genes. This study identified a network consisting of 25 genes and compounds and interconnected biological processes using computational approaches. The results also highlight the diagnostic role of the 25 genes since we obtained good classification performances using a neural network model. We also suggest 12 repurposable drugs (like KU-60019, AM-630, CP55940, enflurane, ginkgolide B, linopirdine, apremilast, ibudilast, pentoxifylline, roflumilast, acitretin, and tamibarotene) interacting with 6 genes (ATM, CNR1, GLRB, KCNQ2, PDE4B, and RARA), that we linked to retrograde endocannabinoid signaling, synaptic vesicle cycle, morphine addiction, and homologous recombination.

PMID:39961913 | DOI:10.1007/s11517-025-03325-x

J Bioinform Comput Biol. 2024 Dec;22(6):2450028. doi: 10.1142/S0219720024500288. Epub 2025 Feb 1.

ABSTRACT

The prediction of drug-target affinity (DTA) is crucial for efficiently identifying potential targets for drug repurposing, thereby reducing resource wastage. In this paper, we propose a novel graph-based deep learning model for DTA that leverages adaptive structure-aware pooling for graph processing. Our approach integrates a self-attention mechanism with an enhanced graph neural network to capture the significance of each node in the graph, marking a significant advancement in graph feature extraction. Specifically, adjacent nodes in the 2D molecular graph are aggregated into clusters, with the features of these clusters weighted according to their attention scores to form the final molecular representation. In terms of model architecture, we utilize both global and hierarchical pooling, and assess the performance of the model on multiple benchmark datasets. The evaluation results on the KIBA dataset show that our model achieved the lowest mean squared error (MSE) of 0.126, which is a 0.5% reduction compared to the best-performing baseline method. Additionally, to validate the generalization capabilities of the model, we conduct comparative experiments on regression and binary classification tasks. The results demonstrate that our model outperforms previous models in both types of tasks.

PMID:39961610 | DOI:10.1142/S0219720024500288