IEEE J Biomed Health Inform. 2024 Jan 5;PP. doi: 10.1109/JBHI.2024.3350083. Online ahead of print.

ABSTRACT

Knowledge of unintended effects of drugs is critical in assessing the risk of treatment and in drug repurposing. Although numerous existing studies predict drug-side effect presence, only four of them predict the frequency of the side effects. Unfortunately, current prediction methods (1) do not utilize drug targets, (2) do not predict well for unseen drugs, and (3) do not use multiple heterogeneous drug features. We propose a novel deep learning-based drug-side effect frequency prediction model. Our model utilized heterogeneous features such as target protein information as well as molecular graph, fingerprints, and chemical similarity to create drug embeddings simultaneously. Furthermore, the model represents drugs and side effects into a common vector space, learning the dual representation vectors of drugs and side effects, respectively. We also extended the predictive power of our model to compensate for the drugs without clear target proteins using the Adaboost method. We achieved state-of-the-art performance over the existing methods in predicting side effect frequencies, especially for unseen drugs. Ablation studies show that our model effectively combines and utilizes heterogeneous features of drugs. Moreover, we observed that, when the target information given, drugs with explicit targets resulted in better prediction than the drugs without explicit targets. The implementation is available at https://github.com/eskendrian/sider.

PMID:38241108 | DOI:10.1109/JBHI.2024.3350083

Appl Microbiol Biotechnol. 2024 Jan 19;108(1):147. doi: 10.1007/s00253-023-12884-8.

ABSTRACT

Fungal infections represent a serious global health threat. The new emerging pathogens and the spread of different forms of resistance are now hardly challenging the tools available in therapy and diagnostics. With the commonly used diagnoses, fungal identification is often slow and inaccurate, and, on the other hand, some drugs currently used as treatments are significantly affected by the decrease in susceptibility. Herein, the antifungal arsenal is critically summarized. Besides describing the old approaches and their mechanisms, advantages, and limitations, the focus is dedicated to innovative strategies which are designed, identified, and developed to take advantage of the discrepancies between fungal and host cells. Relevant pathways and their role in survival and virulence are discussed as their suitability as sources of antifungal targets. In a similar way, molecules with antifungal activity are reported as potential agents/precursors of the next generation of antimycotics. Particular attention was devoted to biotechnological entities, to their novelty and reliability, to drug repurposing and restoration, and to combinatorial applications yielding significant improvements in efficacy. KEY POINTS: • New antifungal agents and targets are needed to limit fungal morbidity and mortality. • Therapeutics and diagnostics suffer of delays in innovation and lack of targets. • Biologics, drug repurposing and combinations are the future of antifungal treatments.

PMID:38240822 | DOI:10.1007/s00253-023-12884-8

J Biomol Struct Dyn. 2024 Jan 19:1-8. doi: 10.1080/07391102.2024.2304679. Online ahead of print.

ABSTRACT

The ribosomal protein S6 kinase beta-1 (RPS6KB1), also known as p70S6 kinase, plays a crucial role in various disease-related conditions such as diabetes, obesity, and cancer. Its activity is regulated by phosphorylation events, including phosphorylation of Threonine 389 in the hydrophobic motif by the mammalian target of rapamycin complex 1 (mTORC1) and phosphorylation of Threonine 229 in the activation loop by PDK1 (phosphoinositide-dependent kinase 1). However, other phenomena connected to RPS6KB1 remain unknown. In this study, we employed virtual screening and molecular docking techniques on the molecules in the ZINC library to identify potential inhibitors. Molecular dynamics (MD) simulations and MMGBSA calculations were carried out on promising compounds to determine their binding affinity and stability. We also evaluated the drug-likeness properties of the selected compounds. A comparative study between the native RPS6KB1 structure, co-crystal ligands, and the shortlisted molecules from the ZINC dataset was carried out. The identified molecules possess significant potential for future in vitro and in vivo studies, paving the way for developing effective cancer treatments.Communicated by Ramaswamy H. Sarma.

PMID:38240100 | DOI:10.1080/07391102.2024.2304679

Front Pharmacol. 2024 Jan 4;14:1329244. doi: 10.3389/fphar.2023.1329244. eCollection 2023.

ABSTRACT

Gastrointestinal (GI) cancers comprise a significant number of cancer cases worldwide and contribute to a high percentage of cancer-related deaths. To improve survival rates of GI cancer patients, it is important to find and implement more effective therapeutic strategies with better prognoses and fewer side effects. The development of new drugs can be a lengthy and expensive process, often involving clinical trials that may fail in the early stages. One strategy to address these challenges is drug repurposing (DR). Drug repurposing is a developmental strategy that involves using existing drugs approved for other diseases and leveraging their safety and pharmacological data to explore their potential use in treating different diseases. In this paper, we outline the existing therapeutic strategies and challenges associated with GI cancers and explore DR as a promising alternative approach. We have presented an extensive review of different DR methodologies, research efforts and examples of repurposed drugs within various GI cancer types, such as colorectal, pancreatic and liver cancers. Our aim is to provide a comprehensive overview of employing the DR approach in GI cancers to inform future research endeavors and clinical trials in this field.

PMID:38239190 | PMC:PMC10794567 | DOI:10.3389/fphar.2023.1329244

J Biomol Struct Dyn. 2024 Jan 18:1-13. doi: 10.1080/07391102.2024.2305316. Online ahead of print.

ABSTRACT

In the era of targeted therapeutics, protein kinases like WEE1 have become pivotal drug targets, especially for cancer therapy. Utilizing a multi-faceted approach, our study adds fresh insights to this endeavour. We employed the t-SNE algorithm, combined with ECFP4 fingerprints, to analyse the molecular similarity between FDA-approved drugs and known clinical trial inhibitors. Our t-SNE analysis identified the closest clusters to known inhibitors and selected 11 FDA-approved drugs for further study. Using the DrugSpaceX platform, we generated analogues for these 11 FDA-approved drugs. These analogues were refined according to Lipinski's Rule of Five and Synthetic Accessibility scores, yielding 68,640 analogues for additional scrutiny. Among these, derivatives of Palbociclib and Ribociclib stood out as the most promising WEE1 inhibitors, based on docking scores and interaction patterns. Molecular dynamics simulations validated the stability of these protein-ligand interactions, particularly for DE50607359, a top-ranked Palbociclib analogue, which also met most pharmacokinetic parameters within acceptable limits. Our study uncovers new candidates for WEE1 inhibition not previously reported. With our multi-layered computational strategy, we provide a solid foundation for future experimental validation and targeted drug development in cancer therapeutics.Communicated by Ramaswamy H. Sarma.

PMID:38239070 | DOI:10.1080/07391102.2024.2305316

J Biomol Struct Dyn. 2024 Jan 18:1-19. doi: 10.1080/07391102.2024.2305697. Online ahead of print.

ABSTRACT

Six drugs (dapsone, diltiazem, timolol, rosiglitazone, mesalazine, and milnacipran) that were predicted by network-based polypharmacology approaches as potential anti-Alzheimer's drugs, have been subjected in this study for in silico and in vitro evaluation to check their potential against protein fibrillation, which is a causative factor for multiple diseases such as Alzheimer's disease, Parkinson's disease, Huntington disease, cardiac myopathy, type-II diabetes mellitus and many others. Molecular docking and thereafter molecular dynamics (MD) simulations revealed that diltiazem, rosiglitazone, and milnacipran interact with the binding residues such as Asp52, Glu35, Trp62, and Asp101, which lie within the fibrillating region of HEWL. The MM-GBSA analysis revealed -7.86, -5.05, and -10.29 kcal/mol as the binding energy of diltiazem, rosiglitazone, and milnacipran. The RMSD and RMSF calculations revealed significant stabilities of these ligands within the binding pocket of HEWL. While compared with two reported ligands inhibiting HEWL fibrillation, milnacipran depicted almost similar binding potential with one of the known ligands (Ligand binding affinity -10.66 kcal/mol) of HEWL. Furthermore, secondary structure analyses revealed notable inhibition of the secondary structural changes with our candidate ligand; especially regarding retention of the 3/10 α-helix both by DSSP simulation, Circular dichroism, and FESEM-based microscopic image analyses. Taking further into experimental validation, all three ligands inhibited fibrillation in HEWL in simulated conditions as revealed by blue shift in Congo red assay and later expressing percentage inhibition in ThioflavinT assay as well. However, dose-dependent kinetics revealed that the antifibrillatory effects of drugs are more pronounced at low protein concentrations.Communicated by Ramaswamy H. Sarma.

PMID:38239069 | DOI:10.1080/07391102.2024.2305697

J Vector Borne Dis. 2024 Jan 16. doi: 10.4103/0972-9062.393975. Online ahead of print.

ABSTRACT

BACKGROUND OBJECTIVES: The goal of this study was to see how well an AI language model called Chat Generative Pre-trained Transformer (ChatGPT) assisted healthcare personnel in selecting relevant medications for filariasis therapy. A team of medical specialists and tropical medicine experts reviewed ChatGPT's recommendations for ten hypothetical filariasis clinical situations.

METHODS: The purpose of this study was to look at the effectiveness of an AI language model called Chat Generative Pre-trained Transformer (ChatGPT) in supporting healthcare providers in picking appropriate drugs for filariasis treatment. Ten hypothetical filariasis clinical cases were submitted to ChatGPT, and its recommendations were evaluated by a panel of medical professionals and tropical medicine experts.

RESULTS: ChatGPT gave appropriate suggestions for potential medication repurposing in filariasis treatment in all ten clinical scenarios. Its drug recommendations were in line with current medical research and literature. Despite the lack of particular treatment regimens, ChatGPT's general ideas proved useful for healthcare practitioners, providing insights and updates on prospective drug repurposing tactics.

INTERPRETATION CONCLUSION: ChatGPT shows promise as a useful method for repurposing drugs in the treatment of filariasis. Its thorough and brief responses make it useful for finding possible pharmacological candidates. However, it is critical to recognize ChatGPT's limitations, such as the requirement for additional clinical information and the inability to change therapy. Further research and development are required to optimize its use in filariasis therapy settings.

PMID:38238871 | DOI:10.4103/0972-9062.393975

Front Oncol. 2024 Jan 3;13:1183766. doi: 10.3389/fonc.2023.1183766. eCollection 2023.

ABSTRACT

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies.

PMID:38234400 | PMC:PMC10792052 | DOI:10.3389/fonc.2023.1183766

J Biomol Struct Dyn. 2024 Jan 17:1-12. doi: 10.1080/07391102.2024.2304681. Online ahead of print.

ABSTRACT

Adult T-cell Lymphoma (ATL) is caused by the delta retrovirus family member known as Human T-cell Leukaemia Type I (HTLV-1). Due to the unavailability of any cure, the study gained motivation to identify some repurposed drugs against the virus. A quick and accurate method of screening licensed medications for finding a treatment for HTLV-1 is by cheminformatics drug repurposing in order to analyze a dataset of FDA approved integrase antivirals against HTLV-1 infection. To determine how the antiviral medications interacted with the important residues in the HTLV-1 integrase active regions, molecular docking modeling was used. The steady behavior of the ligands inside the active region was then confirmed by molecular dynamics for the probable receptor-drug complexes. Cabotegravir, Raltegravir and Elvitegravir had the best docking scores with the target, indicating that they can tightly bind to the HTLV-1 integrase. Moreover, MD simulation revealed that the Cabotegravir-HTLV-1, Raltegravir-HTLV-1 and Elvitegravir-HTLV-1 interactions were stable. It is obvious that more testing of these medicines in both clinical trials and experimental tests is necessary to demonstrate their efficacy against HTLV-1 infection.Communicated by Ramaswamy H. Sarma.

PMID:38234060 | DOI:10.1080/07391102.2024.2304681

Mol Cell Endocrinol. 2024 Jan 14:112159. doi: 10.1016/j.mce.2024.112159. Online ahead of print.

ABSTRACT

In hyperparathyroidism (hyperPTH), excessive amounts of PTH are secreted, interfering with calcium regulation in the body. Several drugs can control the disease's side effects, but none of them is an alternative treatment to surgery. Therefore, new drug candidates are necessary. In this study, three computationally repositioned drugs, DG 041, IMD 0354, and cucurbitacin I, are evaluated in an in vitro model of hyperPTH. First, we integrated publicly available transcriptomics datasets to propose drug candidates. Using 3D spheroids derived from a single primary hyperPTH patient, we assessed their in vitro efficacy. None of the proposed drugs affected the viability of healthy cell control (HEK293) or overactive parathyroid cells at the level of toxicity. This behavior was attributed to the non-cancerous nature of the parathyroid cells, establishing the hyperPTH disease model. Cucurbitacin I and IMD 0354 exhibited a slight inverse relationship between increased drug concentrations and cell viability, whereas DG 041 increased viability. Based on these results, further studies are needed on the mechanism of action of the repurposed drugs, including determining the effects of these drugs on cellular PTH synthesis and secretion and on the metabolic pathways that regulate PTH secretion.

PMID:38228226 | DOI:10.1016/j.mce.2024.112159

Nat Rev Rheumatol. 2024 Jan 16. doi: 10.1038/s41584-024-01076-x. Online ahead of print.

NO ABSTRACT

PMID:38228855 | DOI:10.1038/s41584-024-01076-x

Oncotarget. 2024 Jan 16;15:1-18. doi: 10.18632/oncotarget.28551.

ABSTRACT

Glioblastoma cells can restrict the DNA-damaging effects of temozolomide (TMZ) and radiation therapy (RT) using the DNA damage response (DDR) mechanism which activates cell cycle arrest and DNA repair pathways. Ataxia-telangiectasia and Rad3-Related protein (ATR) plays a pivotal role in the recognition of DNA damage induced by chemotherapy and radiation causing downstream DDR activation. Here, we investigated the activity of gartisertib, a potent ATR inhibitor, alone and in combination with TMZ and/or RT in 12 patient-derived glioblastoma cell lines. We showed that gartisertib alone potently reduced the cell viability of glioblastoma cell lines, where sensitivity was associated with the frequency of DDR mutations and higher expression of the G2 cell cycle pathway. ATR inhibition significantly enhanced cell death in combination with TMZ and RT and was shown to have higher synergy than TMZ+RT treatment. MGMT promoter unmethylated and TMZ+RT resistant glioblastoma cells were also more sensitive to gartisertib. Analysis of gene expression from gartisertib treated glioblastoma cells identified the upregulation of innate immune-related pathways. Overall, this study identifies ATR inhibition as a strategy to enhance the DNA-damaging ability of glioblastoma standard treatment, while providing preliminary evidence that ATR inhibition induces an innate immune gene signature that warrants further investigation.

PMID:38227740 | DOI:10.18632/oncotarget.28551

Mol Divers. 2024 Jan 16. doi: 10.1007/s11030-023-10784-7. Online ahead of print.

ABSTRACT

Endometrial cancer (EC) is the 6th most common cancer in women around the world. Alone in the United States (US), 66,200 new cases and 13,030 deaths are expected to occur in 2023 which needs the rapid development of potential therapies against EC. Here, a network-based drug-repurposing strategy is developed which led to the identification of 16 FDA-approved drugs potentially repurposable for EC as potential immune checkpoint inhibitors (ICIs). A network of EC-associated immune checkpoint proteins (ICPs)-induced protein interactions (P-ICP) was constructed. As a result of network analysis of P-ICP, top key target genes closely interacting with ICPs were shortlisted followed by network proximity analysis in drug-target interaction (DTI) network and pathway cross-examination which identified 115 distinct pathways of approved drugs as potential immune checkpoint inhibitors. The presented approach predicted 16 drugs to target EC-associated ICPs-induced pathways, three of which have already been used for EC and six of them possess immunomodulatory properties providing evidence of the validity of the strategy. Classification of the predicted pathways indicated that 15 drugs can be divided into two distinct pathway groups, containing 17 immune pathways and 98 metabolic pathways. In addition, drug-drug correlation analysis provided insight into finding useful drug combinations. This fair and verified analysis creates new opportunities for the quick repurposing of FDA-approved medications in clinical trials.

PMID:38227161 | DOI:10.1007/s11030-023-10784-7

J Biomol Struct Dyn. 2024 Jan 15:1-14. doi: 10.1080/07391102.2024.2302926. Online ahead of print.

ABSTRACT

The hyperactivity of urease enzymes plays a crucial role in the development of hepatic coma, hepatic encephalopathy, urolithiasis, gastric and peptic ulcers. Additionally, these enzymes adversely impact the soil's nitrogen efficiency for crop production. In the current study 100 known drugs were tested against Jack Bean urease and Proteus mirabilis urease and identified three inhibitors i.e. terbutaline (compound 1), Ketoprofen (compound 2) and norepinephrine bitartrate (compound 3). As a result, these compounds showed excellent inhibition against Jack Bean urease i.e. (IC50 = 2.1-11.3 µM), and Proteus mirabilis urease (4.8-11.9 µM). Moreover, in silico studies demonstrate maximum interactions of compounds in the enzyme's active site. Furthermore, intermolecular interactions between compounds and enzyme atoms were examined using STD-NMR spectrophotometry. In parallel, molecular dynamics simulation was carried out to study compounds dynamic behavior within the urease binding region. Urease remained stable during most of the simulation time and ligands were bound in the protein active pocket as observed from the Root mean square deviation (RMSD) and ligand RMSD analyses. Furthermore, these compounds display interactions with the crucial residues, including His492 and Asp633, in 100 ns simulations. In the binding energy analysis, norepinephrine bitartrate exhibited the highest binding energy (-76.32 kcal/mol) followed by Ketoprofen (-65.56 kcal/mol) and terbutaline (-62.15 kcal/mol), as compared to acetohydroxamic acid (-52.86 kcal/mol). The current findings highlight the potential of drug repurposing as an effective approach for identifying novel anti-urease compounds.Communicated by Ramaswamy H. Sarma.

PMID:38225797 | DOI:10.1080/07391102.2024.2302926

BMC Cancer. 2024 Jan 15;24(1):82. doi: 10.1186/s12885-023-11797-z.

ABSTRACT

BACKGROUND: Glioblastoma is the most common and most aggressive malignant primary brain tumor in adults. Glioblastoma cells synthesize and secrete large quantities of the excitatory neurotransmitter glutamate, driving epilepsy, neuronal death, tumor growth and invasion. Moreover, neuronal networks interconnect with glioblastoma cell networks through glutamatergic neuroglial synapses, activation of which induces oncogenic calcium oscillations that are propagated via gap junctions between tumor cells. The primary objective of this study is to explore the efficacy of brain-penetrating anti-glutamatergic drugs to standard chemoradiotherapy in patients with glioblastoma.

METHODS/DESIGN: GLUGLIO is a 1:1 randomized phase Ib/II, parallel-group, open-label, multicenter trial of gabapentin, sulfasalazine, memantine and chemoradiotherapy (Arm A) versus chemoradiotherapy alone (Arm B) in patients with newly diagnosed glioblastoma. Planned accrual is 120 patients. The primary endpoint is progression-free survival at 6 months. Secondary endpoints include overall and seizure-free survival, quality of life of patients and caregivers, symptom burden and cognitive functioning. Glutamate levels will be assessed longitudinally by magnetic resonance spectroscopy. Other outcomes of interest include imaging response rate, neuronal hyperexcitability determined by longitudinal electroencephalography, Karnofsky performance status as a global measure of overall performance, anticonvulsant drug use and steroid use. Tumor tissue and blood will be collected for translational research. Subgroup survival analyses by baseline parameters include segregation by age, extent of resection, Karnofsky performance status, O6-methylguanine DNA methyltransferase (MGMT) promotor methylation status, steroid intake, presence or absence of seizures, tumor volume and glutamate levels determined by MR spectroscopy. The trial is currently recruiting in seven centers in Switzerland.

TRIAL REGISTRATION: NCT05664464. Registered 23 December 2022.

PMID:38225589 | DOI:10.1186/s12885-023-11797-z

AMIA Annu Symp Proc. 2024 Jan 11;2023:559-568. eCollection 2023.

NO ABSTRACT

PMID:38222367 | PMC:PMC10785886

Biomed Res Int. 2024 Jan 6;2024:9985719. doi: 10.1155/2024/9985719. eCollection 2024.

ABSTRACT

INTRODUCTION: Alzheimer's disease (AD) is a neurodegenerative disorder with no conclusive remedy. Yohimbine, found in Rauwolfia vomitoria, may reduce brain inflammation by targeting tumour necrosis factor-alpha (TNFα), implicated in AD pathogenesis. Metoserpate, a synthetic compound, may inhibit TNFα. The study is aimed at assessing the potential utility of repurposing metoserpate for TNFα inhibition to reduce neuronal damage and inflammation in AD. The development of safe and effective treatments for AD is crucial to address the growing burden of the disease, which is projected to double over the next two decades.

METHODS: Our study repurposed an FDA-approved drug as TNFα inhibitor for AD management using structural similarity studies, molecular docking, and molecular dynamics simulations. Yohimbine was used as a reference compound. Molecular docking used SeeSAR, and molecular dynamics simulation used GROMACS.

RESULTS: Metoserpate was selected from 10 compounds similar to yohimbine based on pharmacokinetic properties and FDA approval status. Molecular docking and simulation studies showed a stable interaction between metoserpate and TNFα over 100 ns (100000 ps). This suggests a reliable and robust interaction between the protein and ligand, supporting the potential utility of repurposing metoserpate for TNFα inhibition in AD treatment.

CONCLUSION: Our study has identified metoserpate, a previously FDA-approved antihypertensive agent, as a promising candidate for inhibiting TNFα in the management of AD.

PMID:38221912 | PMC:PMC10787656 | DOI:10.1155/2024/9985719

Eur J Pharmacol. 2024 Jan 12:176330. doi: 10.1016/j.ejphar.2024.176330. Online ahead of print.

ABSTRACT

Pancreatic cancer (PC) is the most frequently occurring cancer, with few effective treatments and a 5-year survival rate of only about 11%. It is characterized by stiff interstitium and pressure on blood vessels, leading to an increased glycolytic metabolism. PFKFB3 plays an important role in glycolysis, and its products (fructose-2,6-bisphosphate), which are allosteric PFK1 activators, limit the glycolytic rate. In this study, 14 PFKFB3 inhibitors were obtained by virtually screening the FDA-approved compound library. Subsequently, the in-vitro investigations confirmed that Lomitapide and Cabozantinib S-malate exhibit the excellent potential to inhibit PFKFB3. The combined administration of Lomitapide and Gemcitabine at a certain molar ratio indicated an enhanced anti-tumor effect in Orthotopic Pancreatic Cancer (OPC) models. This investigation provides a new treatment strategy for PC therapy.

PMID:38220139 | DOI:10.1016/j.ejphar.2024.176330

Bioorg Chem. 2024 Jan 10;144:107109. doi: 10.1016/j.bioorg.2024.107109. Online ahead of print.

ABSTRACT

Herein, (-)-galiellalactone 1 congeners responsible for the nuclear factor erythroid 2-related factor 2 (Nrf2)-activating neuroprotective effects were elucidated. Additionally, novel congener-based Nrf2 activators were identified using a drug repositioning strategy. (-)-Galiellalactone, which comprises a tricyclic lactone skeleton, significantly activates antioxidant response element (ARE)-mediated transcription in neuroblastoma SH-SY5Y cells. Interestingly, two cyclohexene-truncated [3.3] bicyclic lactone analogs, which possess an exocyclic α-methylene-γ-butyrolactone moiety, exhibited higher Nrf2/ARE transcriptional activities than the parent (-)-galiellalactone. We confirmed that the cyclohexene moiety embedding the [3.3] bicyclic lactone congener does not play the essential role of (-)-galiellalactone for Nrf2/ARE activation. Nrf2/ARE activation by novel analogs resulted in the upregulation of downstream antioxidative and phase II detoxifying enzymes, heme oxygenase-1, and NAD(P)H quinone oxidoreductase 1, which are closely related to the cytoprotective effects on neurodegenerative diseases. (-)-Galiellalactone and its [3.3] bicyclic variants 3l and 3p increased the expression of antioxidant genes and exhibited neuroprotective effects against 6-hydroxydopamine-mediated neurotoxicity in the neuroblastoma SH-SY5Y cell line.

PMID:38219480 | DOI:10.1016/j.bioorg.2024.107109

Nat Comput Sci. 2021 Jan;1(1):33-41. doi: 10.1038/s43588-020-00007-6. Epub 2021 Jan 14.

ABSTRACT

Responding quickly to unknown pathogens is crucial to stop uncontrolled spread of diseases that lead to epidemics, such as the novel coronavirus, and to keep protective measures at a level that causes as little social and economic harm as possible. This can be achieved through computational approaches that significantly speed up drug discovery. A powerful approach is to restrict the search to existing drugs through drug repurposing, which can vastly accelerate the usually long approval process. In this Review, we examine a representative set of currently used computational approaches to identify repurposable drugs for COVID-19, as well as their underlying data resources. Furthermore, we compare drug candidates predicted by computational methods to drugs being assessed by clinical trials. Finally, we discuss lessons learned from the reviewed research efforts, including how to successfully connect computational approaches with experimental studies, and propose a unified drug repurposing strategy for better preparedness in the case of future outbreaks.

PMID:38217166 | DOI:10.1038/s43588-020-00007-6