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

Assay Drug Dev Technol. 2024 Sep 24. doi: 10.1089/adt.2024.081. Online ahead of print.

NO ABSTRACT

PMID:39320326 | DOI:10.1089/adt.2024.081

J Mol Recognit. 2024 Sep 25:e3103. doi: 10.1002/jmr.3103. Online ahead of print.

ABSTRACT

The Kirsten Rat Sarcoma (KRAS) G12D mutant protein is a primary driver of pancreatic ductal adenocarcinoma, necessitating the identification of targeted drug molecules. Repurposing of drugs quickly finds new uses, speeding treatment development. This study employs microsecond molecular dynamics simulations to unveil the binding mechanisms of the FDA-approved MEK inhibitor trametinib with KRASG12D, providing insights for potential drug repurposing. The binding of trametinib was compared with clinical trial drug MRTX1133, which demonstrates exceptional activity against KRASG12D, for better understanding of interaction mechanism of trametinib with KRASG12D. The resulting stable MRTX1133-KRASG12D complex reduces root mean square deviation (RMSD) values, in Switch I and II domains, highlighting its potential for inhibiting KRASG12D. MRTX1133's robust interaction with Tyr64 and disruption of Tyr96-Tyr71-Arg68 network showcase its ability to mitigate the effects of the G12D mutation. In contrast, trametinib employs a distinctive binding mechanism involving P-loop, Switch I and II residues. Extended simulations to 1 μs reveal sustained network interactions with Tyr32, Thr58, and GDP, suggesting a role of trametinib in maintaining KRASG12D in an inactive state and impede the further cell signaling. The decomposition binding free energy values illustrate amino acids' contributions to binding energy, elucidating ligand-protein interactions and molecular stability. The machine learning approach reveals that van der Waals interactions among the residues play vital role in complex stability and the potential amino acids involved in drug-receptor interactions of each complex. These details provide a molecular-level understanding of drug binding mechanisms, offering essential knowledge for further drug repurposing and potential drug discovery.

PMID:39318275 | DOI:10.1002/jmr.3103

Curr Drug Targets. 2024 Sep 24. doi: 10.2174/0113894501330963240905083020. Online ahead of print.

ABSTRACT

BACKGROUND: Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds.

OBJECTIVE: This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction, providing a fresh perspective on this evolving field.

METHODS: We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literature. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript.

RESULTS: The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community.

CONCLUSION: The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process.

PMID:39318214 | DOI:10.2174/0113894501330963240905083020

Elife. 2024 Sep 24;13:RP93796. doi: 10.7554/eLife.93796.

ABSTRACT

Drugs that induce reversible slowing of metabolic and physiological processes would have great value for organ preservation, especially for organs with high susceptibility to hypoxia-reperfusion injury, such as the heart. Using whole-organism screening of metabolism, mobility, and development in Xenopus, we identified an existing drug, SNC80, that rapidly and reversibly slows biochemical and metabolic activities while preserving cell and tissue viability. Although SNC80 was developed as a delta opioid receptor activator, we discovered that its ability to slow metabolism is independent of its opioid modulating activity as a novel SNC80 analog (WB3) with almost 1000 times less delta opioid receptor binding activity is equally active. Metabolic suppression was also achieved using SNC80 in microfluidic human organs-on-chips, as well as in explanted whole porcine hearts and limbs, demonstrating the cross-species relevance of this approach and potential clinical relevance for surgical transplantation. Pharmacological induction of physiological slowing in combination with organ perfusion transport systems may offer a new therapeutic approach for tissue and organ preservation for transplantation, trauma management, and enhancing patient survival in remote and low-resource locations.

PMID:39316042 | DOI:10.7554/eLife.93796

Res Sq [Preprint]. 2024 Sep 9:rs.3.rs-4765282. doi: 10.21203/rs.3.rs-4765282/v1.

ABSTRACT

Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of the United States Food and Drug Administration (FDA) approved drugs used for GBM deliver satisfactory survival improvement. This study presents a novel computational pipeline by utilizing gene expression data analysis for GBM for drug repurposing to address the challenges in rare disease drug development, particularly focusing on GBM. The GBM Gene Expression Profile (GGEP) was constructed with multi-omics data to identify drugs with reversal gene expression to GGEP from the Integrated Network-Based Cellular Signatures (iLINCS) database. We prioritized the candidates via hierarchical clustering of their expression signatures and quantification of their reversal strength by calculating two self-defined indices based on the GGEP genes' log 2 foldchange (LFCs) that the drug candidates could induce. Among eight prioritized candidates, in-vitro experiments validated Clofarabine and Ciclopirox as highly efficacious in selectively targeting GBM cancer cells. The success of this study illustrated a promising avenue for accelerating drug development by uncovering underlying gene expression effect between drugs and diseases, which can be extended to other rare diseases and non-rare diseases.

PMID:39315277 | PMC:PMC11419258 | DOI:10.21203/rs.3.rs-4765282/v1

World J Exp Med. 2024 Sep 20;14(3):96042. doi: 10.5493/wjem.v14.i3.96042. eCollection 2024 Sep 20.

ABSTRACT

The rapidly advancing field of artificial intelligence (AI) has garnered substantial attention for its potential application in drug discovery and development. This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry. AI, encompassing machine learning algorithms, deep learning, and data analytics, offers unprecedented opportunities to streamline and enhance various stages of drug development. This opinion review delved into the current landscape of AI-driven approaches, discussing their utilization in target identification, lead optimization, and predictive modeling of pharmacokinetics and toxicity. We aimed to scrutinize the integration of large-scale omics data, electronic health records, and chemical informatics, highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies. Despite the considerable potential of AI, the review also addressed inherent challenges, including data privacy concerns, interpretability of AI models, and the need for robust validation in real-world clinical settings. Additionally, we explored ethical considerations surrounding AI-driven decision-making in drug development. This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends, presenting critical insights and addressing potential hurdles. In conclusion, this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

PMID:39312699 | PMC:PMC11372739 | DOI:10.5493/wjem.v14.i3.96042

Cancer J. 2024 Sep-Oct 01;30(5):345-351. doi: 10.1097/PPO.0000000000000746.

ABSTRACT

The development of agents for cancer prevention is a lengthy process requiring a delicate balance between the safety and tolerability of potential interventions and effectiveness in preventing future cancer. Individuals at risk for a specific cancer are frequently at risk for multiple types of cancer as well as other chronic diseases, especially ones associated with aging. Shared environmental exposures, genetic predisposition, metabolic factors, and commonalities in pathogenesis suggest opportunities for combined targeting of cancer and other chronic diseases. Examples discussed here include mechanisms shared between various cancers and obesity, diabetes, and cardiovascular disease.

PMID:39312454 | DOI:10.1097/PPO.0000000000000746

Biochem J. 2024 Sep 23:BCJ20240366. doi: 10.1042/BCJ20240366. Online ahead of print.

ABSTRACT

The pursuit of novel therapeutics is a complex and resource-intensive endeavour marked by significant challenges, including high costs and low success rates. In response, drug repositioning strategies leverage existing FDA-approved compounds to predict their efficacy across diverse diseases. Peptidyl arginine deiminase 4 (PAD4) plays a pivotal role in protein citrullination, a process implicated in the autoimmune pathogenesis of rheumatoid arthritis (RA). Targeting PAD4 has thus emerged as a promising therapeutic approach. This study employs computational and enzyme inhibition strategies to identify potential PAD4-targeting compounds from a library of FDA-approved drugs. In-silico docking analyses validated the binding interactions and orientations of screened compounds within PAD4's active site, with key residues such as ASP350, HIS471, ASP473, and CYS645 participating in crucial hydrogen bonding and van der Waals interactions. Molecular dynamics simulations further assessed the stability of top compounds exhibiting high binding affinities. Among these compounds, Saquinavir (SQV) emerged as a potent PAD4 inhibitor, demonstrating competitive inhibition with a low IC50 value of 1.21 ± 0.04 µM. In-vitro assays, including enzyme kinetics and biophysical analyses, highlighted significant changes in PAD4 conformation upon SQV binding, as confirmed by circular dichroism spectroscopy. SQV induced localized alterations in PAD4 structure, effectively occupying the catalytic pocket and inhibiting enzymatic activity. These findings underscore SQV's potential as a therapeutic candidate for RA through PAD4 inhibition. Further validation through in-vitro and in-vivo studies is essential to confirm SQV's therapeutic benefits in autoimmune diseases associated with dysregulated citrullination.

PMID:39312210 | DOI:10.1042/BCJ20240366

Acta Parasitol. 2024 Sep 23. doi: 10.1007/s11686-024-00897-w. Online ahead of print.

ABSTRACT

BACKGROUND: The rise in Plasmodium resistant strains, decreasing susceptibility to first-line combination therapies, and inadequate efficacy shown by vaccines developed to date necessitate innovative approaches to combat malaria. Drug repurposing refers to finding newer indications for existing medications that provide significant advantages over de novo drug discovery, leading to rapid treatment options. Growing evidence suggests that drugs could regulate the expression of disease-associated microRNAs (miRNAs), implying the potential of miRNAs as attractive targets of therapy for several diseases.

METHODS: We aimed to computationally predict drug-disease relationships through miRNAs for the potential repurposing of the drugs as antimalarials. To achieve this, we created a model that combines experimentally validated miRNA-drug interactions and miRNA-disease correlations, assuming that drugs will be linked to disease if they share significant miRNAs. The first step involved constructing a network of drug-drug interactions using curated drug-miRNA relations from the Pharmaco-miR and SM2miR databases. Additionally, the drug-disease relations were acquired from the comparative toxicogenomics database (CTD), and the random walk with restart (RWR) algorithm was applied to the interaction network to anticipate newer drug indications. Further, experimentally verified miRNA-disease associations were procured from the human microRNA disease database (HMDD), followed by an evaluation of the model's performance by examining case studies retrieved from the literature.

RESULTS: Topological network analysis revealed that beta-adrenergic drugs in the network that are closely linked may have a tendency to be used as antimalarials. Case studies retrieved from the literature demonstrated acceptable model performance. A few of the predicted drugs, namely, propranolol, metoprolol, epinephrine, and atenolol, have been evaluated for their association with malaria, thereby indicating the adequacy of our model and offering experimental leads for alternative drugs.

CONCLUSION: The study puts forth a computational model for forecasting potential connections between beta-adrenergic receptor targeting drugs and malaria to suggest potential for future drug repurposing. This takes into account the concept of commonly associated miRNA partners and providing a mechanistic basis for targeting diseases, elucidating the implication of miRNAs in novel drug-disease relations.

PMID:39312011 | DOI:10.1007/s11686-024-00897-w

Microbiol Spectr. 2024 Sep 23:e0073824. doi: 10.1128/spectrum.00738-24. Online ahead of print.

ABSTRACT

Biofilms formed by Escherichia coli are composed of amyloid curli and cellulose and have been shown to be linked to pathogenicity, antibiotic resistance, and chronic infections. Guanabenz acetate (GABE), an antihypertensive drug, was identified as a potential strategic repurposing drug due to its biofilm inhibitory properties following an extensive antimicrobial screening assay of 2,202 Food and Drug Administration-approved non-antibiotic agents. The results of this study provide insights into the effectiveness of GABE as a therapeutic alternative against E. coli biofilm-associated infectious diseases.

IMPORTANCE: Biofilm-associated bacterial infections are one of the major problems in medical settings. There are currently limited biofilm inhibitors available for clinical use. Guanabenz acetate, a drug used to treat high blood pressure, was found to be an effective anti-biofilm agent against Escherichia coli. Our results show that this drug can inhibit the production of cellulose and curli amyloid protein, which are the two main components of E. coli biofilms. Our findings highlight the possibility of repurposing a drug to prevent E. coli biofilm formation.

PMID:39311590 | DOI:10.1128/spectrum.00738-24

Antivir Ther. 2024 Oct;29(5):13596535241271589. doi: 10.1177/13596535241271589.

ABSTRACT

BACKGROUND: This in vitro study aimed to investigate the effect of several phenolic compounds, including doxorubicin, quercetin, and resveratrol, on HSV-1 infection.

METHODS: The cytotoxicity of the drugs was assessed on Vero cells using the MTT assay. HSV-1 was treated with the drugs, and the supernatants were collected at various time points. TCID50% and qPCR tests were conducted on the supernatants to determine viral titration post-inoculation.

RESULTS: The TCID50% assay showed significant changes in viral titration for acyclovir, doxorubicin, and quercetin at most concentrations (p-value < .05), while no significant changes were observed for resveratrol. The qPCR results demonstrated that drug-treated HSV-1 exhibited a significant reduction in DNA titers at various time points compared to non-treated HSV-1 infected Vero cells, except doxorubicin (0.2 µM) and acyclovir (5 µm). However, over time, DNA virus levels gradually increased in the drug-treated groups. Notably, at certain concentrations of doxorubicin and quercetin-treated groups, virus titer significantly declined, similar to acyclovir.

CONCLUSIONS: Our findings suggest that quercetin at concentrations of 62 and 125 µM significantly reduced HSV-1 infectivity, as well as these two concentrations of quercetin showed a significant difference in virus reduction compared with acyclovir (10 µM) at certain time points. The anti-inflammatory properties of quercetin, in contrast to acyclovir, make it a potential candidate for anti HSV-1 treatment in life-threatening conditions such as Herpes encephalitis. Additionally, doxorubicin, an anticancer drug, showed meaningful inhibition of HSV-1 at non-toxic concentrations of 2 and 8 µM, suggesting its potential interference with HSV-1 in viral-oncolytic therapy in cancer treatment.

PMID:39311585 | DOI:10.1177/13596535241271589

ACS Omega. 2024 Sep 4;9(37):38820-38831. doi: 10.1021/acsomega.4c04886. eCollection 2024 Sep 17.

ABSTRACT

This study explores the innovative use of machine learning (ML) to identify novel tryptase inhibitors from a library of FDA-approved drugs, with subsequent confirmation via molecular docking and experimental validation. Tryptase, a significant mediator in inflammatory and allergic responses, presents a therapeutic target for various inflammatory diseases. However, the development of effective tryptase inhibitors has been challenging due to the enzyme's complex activation and regulation mechanisms. Utilizing a machine learning model, we screened an extensive FDA-approved drug library to identify potential tryptase inhibitors. The predicted compounds were then subjected to molecular docking to assess their binding affinity and conformation within the tryptase active site. Experimental validation was performed using RBL-2H3 cells, a rat basophilic leukemia cell line, where the efficacy of these compounds was evaluated based on their ability to inhibit tryptase activity and suppress β-hexosaminidase activity and histamine release. Our results demonstrated that several FDA-approved drugs, including landiolol, laninamivir, and cidofovir, significantly inhibited tryptase activity. Their efficacy was comparable to that of the FDA-approved mast cell stabilizer nedocromil and the investigational agent APC-366. These findings not only underscore the potential of ML in accelerating drug repurposing but also highlight the feasibility of this approach in identifying effective tryptase inhibitors. This research contributes to the field of drug discovery, offering a novel pathway to expedite the development of therapeutics for tryptase-related pathologies.

PMID:39310179 | PMC:PMC11411685 | DOI:10.1021/acsomega.4c04886