Int J Mol Sci. 2024 Sep 10;25(18):9781. doi: 10.3390/ijms25189781.

ABSTRACT

Previously, we have demonstrated that amiodarone (AM), a widely used antiarrhythmic drug, and its major metabolite desethylamiodarone (DEA) both affect several mitochondrial processes in isolated heart and liver mitochondria. Also, we have established DEA's antitumor properties in various cancer cell lines and in a rodent metastasis model. In the present study, we compared AM's and DEA's mitochondrial and antineoplastic effects in a human triple-negative breast cancer (TNBC) cell line. Both compounds reduced viability in monolayer and sphere cultures and the invasive growth of the MDA-MB-231 TNBC line by inducing apoptosis. They lowered mitochondrial trans-membrane potential, increased Ca2+ influx, induced mitochondrial permeability transition, and promoted mitochondrial fragmentation. In accordance with their mitochondrial effects, both substances massively decreased overall, and even to a greater extent, mitochondrial ATP production decreased, as determined using a Seahorse live cell respirometer. In all these effects, DEA was more effective than AM, indicating that DEA may have higher potential in the therapy of TNBC than its parent compound.

PMID:39337269 | DOI:10.3390/ijms25189781

Antibiotics (Basel). 2024 Sep 2;13(9):834. doi: 10.3390/antibiotics13090834.

ABSTRACT

Intracellular bacterial pathogens pose significant public health challenges due to their ability to evade immune defenses and conventional antibiotics. Drug repurposing has recently been explored as a strategy to discover new therapeutic uses for established drugs to combat these infections. Utilizing high-throughput screening, bioinformatics, and systems biology, several existing drugs have been identified with potential efficacy against intracellular bacteria. For instance, neuroleptic agents like thioridazine and antipsychotic drugs such as chlorpromazine have shown effectiveness against Staphylococcus aureus and Listeria monocytogenes. Furthermore, anticancer drugs including tamoxifen and imatinib have been repurposed to induce autophagy and inhibit bacterial growth within host cells. Statins and anti-inflammatory drugs have also demonstrated the ability to enhance host immune responses against Mycobacterium tuberculosis. The review highlights the complex mechanisms these pathogens use to resist conventional treatments, showcases successful examples of drug repurposing, and discusses the methodologies used to identify and validate these drugs. Overall, drug repurposing offers a promising approach for developing new treatments for bacterial infections, addressing the urgent need for effective antimicrobial therapies.

PMID:39335008 | DOI:10.3390/antibiotics13090834

Mol Biomed. 2024 Sep 28;5(1):40. doi: 10.1186/s43556-024-00204-z.

ABSTRACT

Drug repurposing in cancer taps into the capabilities of existing drugs, initially designed for other ailments, as potential cancer treatments. It offers several advantages over traditional drug discovery, including reduced costs, reduced development timelines, and a lower risk of adverse effects. However, not all drug classes align seamlessly with a patient's condition or long-term usage. Hence, repurposing of chronically used drugs presents a more attractive option. On the other hand, metabolic reprogramming being an important hallmark of cancer paves the metabolic regulators as possible cancer therapeutics. This review emphasizes the importance and offers current insights into the repurposing of antidiabetic drugs, including metformin, sulfonylureas, sodium-glucose cotransporter 2 (SGLT2) inhibitors, dipeptidyl peptidase 4 (DPP-4) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1RAs), thiazolidinediones (TZD), and α-glucosidase inhibitors, against various types of cancers. Antidiabetic drugs, regulating metabolic pathways have gained considerable attention in cancer research. The literature reveals a complex relationship between antidiabetic drugs and cancer risk. Among the antidiabetic drugs, metformin may possess anti-cancer properties, potentially reducing cancer cell proliferation, inducing apoptosis, and enhancing cancer cell sensitivity to chemotherapy. However, other antidiabetic drugs have revealed heterogeneous responses. Sulfonylureas and TZDs have not demonstrated consistent anti-cancer activity, while SGLT2 inhibitors and DPP-4 inhibitors have shown some potential benefits. GLP-1RAs have raised concerns due to possible associations with an increased risk of certain cancers. This review highlights that further research is warranted to elucidate the mechanisms underlying the potential anti-cancer effects of these drugs and to establish their efficacy and safety in clinical settings.

PMID:39333445 | DOI:10.1186/s43556-024-00204-z

Antiviral Res. 2024 Sep 25:106012. doi: 10.1016/j.antiviral.2024.106012. Online ahead of print.

ABSTRACT

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to the global pandemic of Coronavirus Disease 2019 (COVID-19), underscoring the urgency for effective antiviral drugs. Despite the development of different vaccination strategies, the search for specific antiviral compounds remains crucial. Here, we combine machine learning (ML) techniques with in vitro validation to efficiently identify potential antiviral compounds. We overcome the limited amount of SARS-CoV-2 data available for ML using various techniques, supplemented with data from diverse biomedical assays, which enables end-to-end training of a deep neural network architecture. We use its predictions to identify and prioritize compounds for in vitro testing. Two top-hit compounds, PKI-179 and MTI-31, originally identified as Pi3K-mTORC1/2 pathway inhibitors, exhibit significant antiviral activity against SARS-CoV-2 at low micromolar doses. Notably, both compounds outperform the well-known mTOR inhibitor rapamycin. Furthermore, PKI-179 and MTI-31 demonstrate broad-spectrum antiviral activity against SARS-CoV-2 variants of concern and other coronaviruses. In a physiologically relevant model, both compounds show antiviral effects in primary human airway epithelial (HAE) cultures derived from healthy donors cultured in an air-liquid interface (ALI). This study highlights the potential of ML combined with in vitro testing to expedite drug discovery, emphasizing the adaptability of AI-driven approaches across different viruses, thereby contributing to pandemic preparedness.

PMID:39332537 | DOI:10.1016/j.antiviral.2024.106012

Arch Med Res. 2024 Sep 26;56(1):103084. doi: 10.1016/j.arcmed.2024.103084. Online ahead of print.

ABSTRACT

Chagas disease (CD) is a parasitic infection caused by the protozoan Trypanosoma cruzi (Kinetoplastida, Trypanosomatidae). Benznidazole (Bz) has a limited ability to interfere with the pathogenicity of the parasite, which manages to overcome host defenses. This study aimed to conduct a systematic literature review and meta-analysis to understand and describe the drugs and their combinations, as well as new promising compounds used in the treatment of CD in Brazil. This study was registered in the Open Science Framework (OSF) and the International Prospective Register of Systematic Reviews, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Searches were performed in the electronic scientific databases PubMed, LILACS, SciELO, and BVS. Searches were conducted using descriptors cataloged in the Health Sciences Descriptors (DeCS) and Medical Subject Headings (MeSH), in Portuguese, English, and Spanish. Of the 26 articles included in this systematic review and meta-analysis, 16 were related to drug combinations, and nine described new inhibitors of parasitic molecules. Despite high heterogeneity (I² = 92%), studies that evaluated the combination of Bz with other treatments for CD had an overall grouped cure rate of 74% (95% CI 54-94%). Only one study presented drug repositioning by monotherapy. Thus, drug combinations offer quick and accessible solutions for CD treatment, acting against resistant strains of T. cruzi. Certainly, the introduction of these promising compounds into the pharmaceutical market is distant, and the adoption of prophylactic measures is recommended as a barrier to the increasing number of CD cases.

PMID:39332069 | DOI:10.1016/j.arcmed.2024.103084

Medicina (B Aires). 2024 Sep;84 Suppl 3:9-14.

ABSTRACT

Rare diseases are characterized by low prevalence and high complexity, affecting millions globally. Although technologies like massive sequencing improve diagnose, therapeutic options remain largely symptomatic or palliative, with few curative treatments approved. In the context of rare diseases, especially genetic neurodevelopmental disorders, therapy development faces obstacles such as phenotypic variability, diverse molecular mechanisms, and complexities in assessing neurodevelopment in natural history and clinical trials. Current strategies include drug repositioning, biomarker development, and a multilateral approach in seeking solutions, offering hope. This work reviews various strategies in developing therapies, from gene therapy and epigenetic therapies to identifying biological targets.

PMID:39331769

PeerJ. 2024 Sep 23;12:e17809. doi: 10.7717/peerj.17809. eCollection 2024.

ABSTRACT

BACKGROUND: The growing concern of antibiotic-resistant microbial strains worldwide has prompted the need for alternative methods to combat microbial resistance. Biofilm formation poses a significant challenge to antibiotic efficiency due to the difficulty of penetrating antibiotics through the sticky microbial aggregates. Drug repurposing is an innovative technique that aims to expand the use of non-antibiotic medications to address this issue. The primary objective of this study was to evaluate the antimicrobial properties of Diltiazem HCl, a 1,5-benzothiazepine Ca2 + channel blocker commonly used as an antihypertensive agent, against four pathogenic bacteria and three pathogenic yeasts, as well as its antiviral activity against the Coxsackie B4 virus (CoxB4).

METHODS: To assess the antifungal and antibacterial activities of Diltiazem HCl, the well diffusion method was employed, while crystal violet staining was used to determine the anti-biofilm activity. The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) colorimetric assay was utilized to evaluate the antiviral activity of Diltiazem HCl against the CoxB4 virus.

RESULTS: This study revealed that Diltiazem HCl exhibited noticeable antimicrobial properties against Gram-positive bacteria, demonstrating the highest inhibition of Staphylococcus epidermidis, followed by Staphylococcus aureus. It effectively reduced the formation of biofilms by 95.1% and 90.7% for S. epidermidis, and S. aureus, respectively. Additionally, the antiviral activity of Diltiazem HCl was found to be potent against the CoxB4 virus, with an IC50 of 35.8 ± 0.54 μg mL-1 compared to the reference antiviral Acyclovir (IC50 42.71 ± 0.43 μg mL-1).

CONCLUSION: This study suggests that Diltiazem HCl, in addition to its antihypertensive effect, may also be a potential treatment option for infections caused by Gram-positive bacteria and the CoxB4 viruses, providing an additional off-target effect for Diltiazem HCl.

PMID:39329140 | PMC:PMC11426324 | DOI:10.7717/peerj.17809

J Biomed Inform. 2024 Sep 24:104730. doi: 10.1016/j.jbi.2024.104730. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop the FuseLinker, a novel link prediction framework for biomedical knowledge graphs (BKGs), which fully exploits the graph's structural, textual and domain knowledge information. We evaluated the utility of FuseLinker in the graph-based drug repurposing task through detailed case studies.

METHODS: FuseLinker leverages fused pre-trained text embedding and domain knowledge embedding to enhance the graph neural network (GNN)-based link prediction model tailored for BKGs. This framework includes three parts: a) obtain text embeddings for BKGs using embedding-visible large language models (LLMs), b) learn the representations of medical ontology as domain knowledge information by employing the Poincaré graph embedding method, and c) fuse these embeddings and further learn the graph structure representations of BKGs by applying a GNN-based link prediction model. We evaluated FuseLinker against traditional knowledge graph embedding models and a conventional GNN-based link prediction model across four public BKG datasets. Additionally, we examined the impact of using different embedding-visible LLMs on FuseLinker's performance. Finally, we investigated FuseLinker's ability to generate medical hypotheses through two drug repurposing case studies for Sorafenib and Parkinson's disease.

RESULTS: By comparing FuseLinker with baseline models on four BKGs, our method demonstrates superior performance. The Mean Reciprocal Rank (MRR) and Area Under receiver operating characteristic Curve (AUROC) for KEGG50k, Hetionet, SuppKG and ADInt are 0.965 and 0.987, 0.541 and 0.903, 0.781 ad 0.928, and 0.788 and 0.890, respectively.

CONCLUSION: Our study demonstrates that FuseLinker is an effective novel link prediction framework that integrates multiple graph information and shows significant potential for practical applications in biomedical and clinical tasks. Source code and data are available at https://github.com/YKXia0/FuseLinker.

PMID:39326691 | DOI:10.1016/j.jbi.2024.104730

J Mol Graph Model. 2024 Sep 24;133:108873. doi: 10.1016/j.jmgm.2024.108873. Online ahead of print.

ABSTRACT

The outbreak of pneumonia caused by the novel coronavirus (SARS-CoV-2) has presented a challenge to public health. The identification and development of effective antiviral drugs is essential. The main protease (3CLpro) plays an important role in the viral replication of SARS-CoV-2 and is considered to be an effective therapeutic target. In this study, according to the principle of drug repurposing, a variety of antiviral drugs commonly used were studied by molecular docking and molecular dynamics (MD) simulations to obtain potential inhibitors of main proteases. 24 antiviral drugs were docked with 5 potential action sites of 3CLpro, and the drugs with high binding strength were further simulated by MD and the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations. The results showed that the drugs with high flexibility could bind to 3CLpro better than those with low flexibility. The interaction mechanism between antiviral drugs and main protease was analyzed in detail by calculating the root mean square displacement (RMSD), root mean square fluctuation (RMSF) and interaction residues properties. The results showed that the six drugs with high flexibility (Remdesivir, Simnotrelvir, Sofosbuvir, Ledipasvir, Indinavir and Raltegravir) had strong binding strength with 3CLpro, and the last four antiviral drugs can be used as potential candidates for main protease inhibitors.

PMID:39326254 | DOI:10.1016/j.jmgm.2024.108873

PLoS One. 2024 Sep 26;19(9):e0307913. doi: 10.1371/journal.pone.0307913. eCollection 2024.

ABSTRACT

Acinetobacter baumannii is a worldwide Gram-negative bacterium with a high resistance rate, responsible for a broad spectrum of hospital-acquired infections. A computational chemogenomics framework was applied to investigate the repurposing of approved drugs to target A. baumannii. This comprehensive approach involved compiling and preparing proteomic data, identifying homologous proteins in drug-target databases, evaluating the evolutionary conservation of targets, and conducting molecular docking studies and in vitro assays. Seven drugs were selected for experimental assays. Among them, tavaborole exhibited the most promising antimicrobial activity with a minimum inhibitory concentration (MIC) value of 2 μg/ml, potent activity against several clinically relevant strains, and robust efficacy against biofilms from multidrug-resistant strains at a concentration of 16 μg/ml. Molecular docking studies elucidated the binding modes of tavaborole in the editing and active domains of leucyl-tRNA synthetase, providing insights into its structural basis for antimicrobial activity. Tavaborole shows promise as an antimicrobial agent for combating A. baumannii infections and warrants further investigation in preclinical studies.

PMID:39325805 | DOI:10.1371/journal.pone.0307913

Brief Bioinform. 2024 Sep 23;25(6):bbae461. doi: 10.1093/bib/bbae461.

ABSTRACT

Drug repurposing has emerged as a effective and efficient strategy to identify new treatments for a variety of diseases. One of the most effective approaches for discovering potential new drug candidates involves the utilization of Knowledge Graphs (KGs). This review comprehensively explores some of the most prominent KGs, detailing their structure, data sources, and how they facilitate the repurposing of drugs. In addition to KGs, this paper delves into various artificial intelligence techniques that enhance the process of drug repurposing. These methods not only accelerate the identification of viable drug candidates but also improve the precision of predictions by leveraging complex datasets and advanced algorithms. Furthermore, the importance of explainability in drug repurposing is emphasized. Explainability methods are crucial as they provide insights into the reasoning behind AI-generated predictions, thereby increasing the trustworthiness and transparency of the repurposing process. We will discuss several techniques that can be employed to validate these predictions, ensuring that they are both reliable and understandable.

PMID:39325460 | DOI:10.1093/bib/bbae461

Interdiscip Sci. 2024 Sep 26. doi: 10.1007/s12539-024-00654-7. Online ahead of print.

ABSTRACT

Computational drug repositioning, through predicting drug-disease associations (DDA), offers significant potential for discovering new drug indications. Current methods incorporate graph neural networks (GNN) on drug-disease heterogeneous networks to predict DDAs, achieving notable performances compared to traditional machine learning and matrix factorization approaches. However, these methods depend heavily on network topology, hampered by incomplete and noisy network data, and overlook the wealth of biomedical knowledge available. Correspondingly, large language models (LLMs) excel in graph search and relational reasoning, which can possibly enhance the integration of comprehensive biomedical knowledge into drug and disease profiles. In this study, we first investigate the contribution of LLM-inferred knowledge representation in drug repositioning and DDA prediction. A zero-shot prompting template was designed for LLM to extract high-quality knowledge descriptions for drug and disease entities, followed by embedding generation from language models to transform the discrete text to continual numerical representation. Then, we proposed LLM-DDA with three different model architectures (LLM-DDANode Feat, LLM-DDADual GNN, LLM-DDAGNN-AE) to investigate the best fusion mode for LLM-based embeddings. Extensive experiments on four DDA benchmarks show that, LLM-DDAGNN-AE achieved the optimal performance compared to 11 baselines with the overall relative improvement in AUPR of 23.22%, F1-Score of 17.20%, and precision of 25.35%. Meanwhile, selected case studies of involving Prednisone and Allergic Rhinitis highlighted the model's capability to identify reliable DDAs and knowledge descriptions, supported by existing literature. This study showcases the utility of LLMs in drug repositioning with its generality and applicability in other biomedical relation prediction tasks.

PMID:39325266 | DOI:10.1007/s12539-024-00654-7

Front Mol Biosci. 2024 Sep 11;11:1491755. doi: 10.3389/fmolb.2024.1491755. eCollection 2024.

ABSTRACT

[This retracts the article DOI: 10.3389/fmolb.2022.860525.].

PMID:39324114 | PMC:PMC11423356 | DOI:10.3389/fmolb.2024.1491755

Front Genet. 2024 Sep 11;15:1481705. doi: 10.3389/fgene.2024.1481705. eCollection 2024.

NO ABSTRACT

PMID:39323868 | PMC:PMC11422220 | DOI:10.3389/fgene.2024.1481705

Cureus. 2024 Aug 25;16(8):e67776. doi: 10.7759/cureus.67776. eCollection 2024 Aug.

ABSTRACT

Despite extensive research directed at preventive and treatment strategies, breast cancer remains the leading cause of cancer-related mortality among women. This necessitates the development of a new medication aimed at increasing patient survival and quality of life. A new drug's development from the ground up can cost billions of dollars and take up to ten or more years. Because much of the required safety and pharmacokinetic data are already available from earlier trials, repurposing medications usually results in lower costs and shorter turnaround times. Many antiretroviral medications target biological pathways and enzymes associated with cancer, which becomes an ideal option for repurposing as anticancer medications. Efavirenz is an antiretroviral medication that targets molecular pathways implicated in the growth of breast cancer, such as LINE-1 (long interspersed nuclear elements-1) suppression, hence lowering the proliferation of breast cancer cells and exhibiting anti-cancer properties. Additionally, it suppresses the fatty acid synthase gene and other important genes related to fat metabolism, impairing mitochondrial activity and making cancer cells deprived of energy. Efavirenz also inhibits cancer-initiating stem cells, promotes differentiation, and prevents recurrence. Additionally, efavirenz promotes oxidative damage by the formation of superoxide in cancer cells. In addition to its anti-cancer properties, efavirenz has the advantage of being a well-established and relatively inexpensive medication with a favorable safety profile. If proven effective, efavirenz could offer a cost-effective therapeutic option, which is an intriguing direction that warrants further investigation.

PMID:39323697 | PMC:PMC11422744 | DOI:10.7759/cureus.67776

Curr Genomics. 2024;25(5):323-333. doi: 10.2174/0113892029281602240422052210. Epub 2024 May 16.

ABSTRACT

Fungal infections are considered a great threat to human life and are associated with high mortality and morbidity, especially in immunocompromised individuals. Fungal pathogens employ various defense mechanisms to evade the host immune system, which causes severe infections. The available repertoire of drugs for the treatment of fungal infections includes azoles, allylamines, polyenes, echinocandins, and antimetabolites. However, the development of multidrug and pandrug resistance to available antimycotic drugs increases the need to develop better treatment approaches. In this new era of -omics, bioinformatics has expanded options for treating fungal infections. This review emphasizes how bioinformatics complements the emerging strategies, including advancements in drug delivery systems, combination therapies, drug repurposing, epitope-based vaccine design, RNA-based therapeutics, and the role of gut-microbiome interactions to combat anti-fungal resistance. In particular, we focused on computational methods that can be useful to obtain potent hits, and that too in a short period.

PMID:39323620 | PMC:PMC11420568 | DOI:10.2174/0113892029281602240422052210

CNS Neurol Disord Drug Targets. 2024 Sep 25. doi: 10.2174/0118715273329531240911075309. Online ahead of print.

ABSTRACT

Repurposing drugs (DR) has become a viable approach to hasten the search for cures for neurodegenerative diseases (NDs). This review examines different off-target and on-target drug discovery techniques and how they might be used to find possible treatments for non-diagnostic depressions. Off-target strategies look at the known or unknown side effects of currently approved drugs for repositioning, whereas on-target strategies connect disease pathways to targets that can be treated with drugs. The review highlights the potential of experimental and computational methodologies, such as machine learning, proteomic techniques, network and genomics-based approaches, and in silico screening, in uncovering new drug-disease correlations. It also looks at difficulties and failed attempts at drug repurposing for NDs, highlighting the necessity of exact and standardised procedures to increase success rates. This review's objectives are to address the purpose of drug repurposing in human disorders, particularly neurological diseases, and to provide an overview of repurposing candidates that are presently undergoing clinical trials for neurological conditions, along with any possible causes and early findings. We then include a list of drug repurposing strategies, restrictions, and difficulties for upcoming research.

PMID:39323347 | DOI:10.2174/0118715273329531240911075309

Curr Cancer Drug Targets. 2024 Sep 25. doi: 10.2174/0115680096325879240815105227. Online ahead of print.

ABSTRACT

BACKGROUND: Cost-effective management of Urinary Bladder Cancer (UBC) is an unmet need.

AIMS: Our study aims to demonstrate the efficacy of a drug repurposing strategy by using disulfiram (DSF) and copper gluconate (Cu) as an add-on treatment combination to traditional GC-based chemother-apy against N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN)-induced UBC mice (C57J) model.

METHODS: Male C57BL/6J mice were given 0.05% BBN in drinking water ad libitum, and tumour for-mation was verified by histological and physical evaluation. Animals were subsequently divided into eight groups and received treatment with different drug combinations. Control animals received only ve-hicle (DMSO). At the end of the treatment schedule, the bladder tumour was excised and further used to check the expression (mRNA and protein) of ALDH1 isoenzymes using qRT-PCR, western blot, and IHC methods. Autophagy induction was assessed by quantifying the expression of LC3B and SQSTM1/p62 proteins through IHC. Biochemical analysis of superoxide dismutase (SOD), reduced glutathione (GSH), and lipid peroxidation levels in the freshly isolated tumours was performed to check the alterations in the antioxidant system caused by combination treatment.

RESULTS: We observed significant induction of an invasive form of bladder cancer in the mice after nine-teen weeks of BBN exposure. The animals began exhibiting early indications of inflammatory alterations as early as the sixth week following BBN treatment. Furthermore, the wet bladder weight and overall tu-mour burden were significantly decreased (p< 0.0001) by DSF-Cu co-treatment in addition to the GC-based chemotherapy. Real-time PCR analysis revealed that treatment with disulfiram and copper glu-conate significantly decreased (p<0.0001) the mRNA expression of ALDH1 isoenzymes. Comparing the triple drug combination group (GC+DSF-Cu) to the untreated mice, a significant rise in LC3B puncta (p<0.0001) and a decrease in P62/SQSTM1 (p=0.0002) were noted, indicating the induction of autophagy flux in the add-on group. When GC+DSF-Cu treated mice were compared to the untreated tumour group, a substantial decrease in ALDH1/2 protein expression was observed (p= 0.0029 in IHC and p<0.0001 in western blot). Lipid peroxidation was significantly higher (p<0.0001) in the triple drug combination group than in untreated mice. There was a simultaneous decrease in reduced glutathione (GSH) and en-zyme superoxide dismutase (SOD) levels (p<0.0001), which strongly suggests the generation of reactive oxygen species and induction of ferroptotic cell death in the add-on therapy group. Additionally, in both IHC and western blot assays, ALDH1A3 expression was found to be significantly increased (p=0.0033, <0.0001 respectively) in GC+DSF-Cu treated mice relative to the untreated group, suggesting a potential connection between the ferroptosis pathway and ALDH1A3 overexpression.

CONCLUSION: It was found that disulfiram with copper treatment inhibits bladder tumour growth through ferroptosis-mediated ROS induction, which further activates the process of autophagy. Our results prove that DSF-Cu can be an effective add-on therapy along with the standard chemotherapy drugs for the treatment of UBC.

PMID:39323342 | DOI:10.2174/0115680096325879240815105227

Nat Med. 2024 Sep 25. doi: 10.1038/s41591-024-03233-x. Online ahead of print.

ABSTRACT

Drug repurposing-identifying new therapeutic uses for approved drugs-is often a serendipitous and opportunistic endeavour to expand the use of drugs for new diseases. The clinical utility of drug-repurposing artificial intelligence (AI) models remains limited because these models focus narrowly on diseases for which some drugs already exist. Here we introduce TxGNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs. Trained on a medical knowledge graph, TxGNN uses a graph neural network and metric learning module to rank drugs as potential indications and contraindications for 17,080 diseases. When benchmarked against 8 methods, TxGNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, TxGNN's Explainer module offers transparent insights into multi-hop medical knowledge paths that form TxGNN's predictive rationales. Human evaluation of TxGNN's Explainer showed that TxGNN's predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of TxGNN's new predictions align well with off-label prescriptions that clinicians previously made in a large healthcare system. TxGNN's drug-repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.

PMID:39322717 | DOI:10.1038/s41591-024-03233-x

J Chem Inf Model. 2024 Sep 25. doi: 10.1021/acs.jcim.4c00595. Online ahead of print.

ABSTRACT

Characterizing the kinome selectivity profiles of kinase inhibitors is essential in the early stages of novel small-molecule drug discovery. This characterization is critical for interpreting potential adverse events caused by off-target polypharmacology effects and provides unique pharmacological insights for drug repurposing development of existing kinase inhibitor drugs. However, experimental profiling of whole kinome selectivity is still time-consuming and resource-demanding. Here, we report a deep learning classification model using an in-house built data set of inhibitors against 191 well-representative kinases constructed based on a novel strategy by systematically cleaning and integrating six public data sets. This model, a multitask deep neural network, predicts the kinome selectivity profiles of compounds with novel structures. The model demonstrates excellent predictive performance, with auROC, prc-AUC, Accuracy, and Binary_cross_entropy of 0.95, 0.92, 0.90, and 0.37, respectively. It also performs well in a priori testing for inhibitors targeting different categories of proteins from internal compound collections, significantly improving over similar models on data sets from practical application scenarios. Integrated to subsequent machine learning-enhanced virtual screening workflow, novel CDK2 kinase inhibitors with potent kinase inhibitory activity and excellent kinome selectivity profiles are successfully identified. Additionally, we developed a free online web server, KinomePro-DL, to predict the kinome selectivity profiles and kinome-wide polypharmacology effects of small molecules (available on kinomepro-dl.pharmablock.com). Uniquely, our model allows users to quickly fine-tune it with their own training data sets, enhancing both prediction accuracy and robustness.

PMID:39320984 | DOI:10.1021/acs.jcim.4c00595