Int J Gen Med. 2024 Sep 11;17:3975-3986. doi: 10.2147/IJGM.S467891. eCollection 2024.

ABSTRACT

Despite technological advances in science and medicine, acute lung injury (ALI) is still associated with high mortality rates in the ICU. Therefore, finding novel drugs and treatment approaches is crucial to preventing ALI. Drug repurposing is a common practice in clinical research, primarily for drugs that have previously received approval for use in patients, to investigate novel uses of drugs and therapies. One such medication is edaravone, which is a highly effective free-radical scavenger that also has anti-inflammatory, anti-apoptotic, antioxidant, and anti-fibrotic effects. Both basic and clinical studies have shown that edaravone can treat different types of lung injury through its distinct properties. Edaravone exhibits significant protective benefits and holds promising clinical treatment potential for ALI caused by diverse factors, thereby offering a novel approach to treating ALI. This study aims to provide new insights and treatment options for ALI by reviewing both basic and clinical research on the use of edaravone. The focus is on evaluating the effectiveness of edaravone in treating ALI caused by various factors.

PMID:39286534 | PMC:PMC11403130 | DOI:10.2147/IJGM.S467891

Drug Des Devel Ther. 2024 Sep 12;18:4065-4088. doi: 10.2147/DDDT.S460575. eCollection 2024.

ABSTRACT

INTRODUCTION: Infantile Hemangioma (IH) is a prevalent benign vascular tumor affecting approximately 5-10% of infants. Its underlying pathogenesis remains enigmatic, and current therapeutic approaches show limited effectiveness. Our study aimed to discover potential IH-associated therapeutics through a transcriptomic, computational drug repurposing methodology.

METHODS: Utilizing the IH-specific dataset GSE127487 from the Gene Expression Omnibus, we identified differentially expressed genes (DEGs) and conducted weighted gene coexpression network analysis (WGCNA). Subsequently, a protein-protein interaction (PPI) network was constructed to obtain the top 100 hub genes. Drug candidates were sourced from the Connectivity Map (CMap) and Comparative Toxicogenomics Database (CTD).

RESULTS: Our analysis revealed 1203 DEGs and a significant module of 1780 mRNAs strongly correlated with IH. These genes were primarily enriched in the PI3K/AKT/MTOR, RAS/MAPK, and CGMP/PKG signaling pathway. After creating a PPI network of overlapping genes, we filtered out the top 100 hub genes. Ultimately, 44 non-toxic drugs were identified through the CMap and CTD databases. Twelve molecular-targeting agents (belinostat, chir 99021, dasatinib, entinostat, panobinostat, sirolimus, sorafenib, sunitinib, thalidomide, U 0126, vorinostat, and wortmannin) may be potential candidates for IH therapy. Moreover, in vitro experiments demonstrated that entinostat, sorafenib, dasatinib, and sirolimus restricted the proliferation and migration and initiated apoptosis in HemEC cells, thereby underscoring their potential therapeutic value.

CONCLUSION: Our investigation revealed that the pathogenic mechanism underlying IH might be closely associated with the PI3K/AKT/MTOR, RAS/MAPK, and CGMP/PKG signaling pathways. Furthermore, we identified twelve molecular-targeting agents among the predicted drugs that show promise as therapeutic candidates for IH.

PMID:39286286 | PMC:PMC11404501 | DOI:10.2147/DDDT.S460575

Adv Exp Med Biol. 2024;1457:215-235. doi: 10.1007/978-3-031-61939-7_12.

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified as the novel coronavirus that caused a life-threatening viral illness (COVID-19) at the end of 2019. Within a short period of time, this virus spread leading to tremendous loss of life and economic damage. Medications to treat this virus are not yet established, and the process of implementing new strategies for medications is time-consuming. Recent clinical studies revealed the abandonment of the most promising candidates, who later became potential leads. Only through comprehensive study for safety and efficacy the medications, which have already received approval, be repurposed for use in different therapeutic purposes. Natural sources are being used arbitrarily as antiviral drugs and immunity boosters because there are no clear therapies on the horizon. It has long been known that most natural compounds have strong antiviral properties including SARS-CoV-2. Natural remedies have been demonstrated to have inhibitory effects on MERS-CoV and SARS-CoV infections. The non-structural proteins of the virus, such as PLPRO, MPRO, and RdRp, as well as structural proteins like the spike (S) protein, have been demonstrated to have a substantial binding affinity and an inhibitory effect by a variety of natural products, according to in silico research. The virus also demonstrates to be a legitimate target for therapeutic development since it makes use of the host cell's transmembrane ACE2 receptor. In this chapter, we highlight on the potential of alkaloids, phenolic and polyphenolic compounds, flavonoids, terpenoids, cardiac glycosides, and natural products from marine sources against the human coronavirus via different mode of actions. Most of the studied metabolites act either by inhibiting virus replication or by blocking the active site of the protein of the virus either in silico or ex vivo. This review serves as a topic for further study and to discover other secondary metabolites for COVID-19 management.

PMID:39283429 | DOI:10.1007/978-3-031-61939-7_12

Adv Exp Med Biol. 2024;1457:199-214. doi: 10.1007/978-3-031-61939-7_11.

ABSTRACT

The advent of COVID-19 has brought the use of computer tools to the fore in health research. In recent years, computational methods have proven to be highly effective in a variety of areas, including genomic surveillance, host range prediction, drug target identification, and vaccine development. They were also instrumental in identifying new antiviral compounds and repurposing existing therapeutics to treat COVID-19. Using computational approaches, researchers have made significant advances in understanding the molecular mechanisms of COVID-19 and have developed several promising drug candidates and vaccines. This chapter highlights the critical importance of computational drug design strategies in elucidating various aspects of COVID-19 and their contribution to advancing global drug design efforts during the pandemic. Ultimately, the use of computing tools will continue to play an essential role in health research, enabling researchers to develop innovative solutions to combat new and emerging diseases.

PMID:39283428 | DOI:10.1007/978-3-031-61939-7_11

Indian J Microbiol. 2024 Sep;64(3):1153-1214. doi: 10.1007/s12088-024-01282-x. Epub 2024 Apr 26.

ABSTRACT

Multidrug resistance is a paramount impediment to successful treatment of most hospital acquired bacterial infections. A plethora of bacterial genera exhibit differential levels of resistance to the existing antibiotics. Prevalent Uropathogenic Escherichia coli or UPEC conduce high mortality among them. Multi-Drug Resistant bacterial strains utilize precise mechanisms to bypass effects of antibiotics. This is probably due to their familiar genomic origin. In this article drug repositioning method have been utilised to target 23 enzymes of UPEC strains viz. CFT073, 536 and UTI89. 3-D drug binding motifs have been predicted using SPRITE and ASSAM servers that compare amino acid side chain similarities. From the hit results anti-viral drugs have been considered for their uniqueness and specificity. Out of 14 anti-viral drugs 3 anti-HIV drugs viz. Amprenavir, Darunavir and Saquinavir have selected for maximum binding score or drug targetability. Finally, active sites of the enzymes were analyzed using GASS-WEB for eloquent drug interference. Further analyses with the active sites of all the enzymes showed that the three selected anti-HIV drugs were very much potent to inhibit their active sites. Combination or sole application of Amprenavir, Darunavir and Saquinavir to MDR-UPEC infections may leads to cure and inhibition of mortality.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12088-024-01282-x.

PMID:39282172 | PMC:PMC11399541 | DOI:10.1007/s12088-024-01282-x

Indian J Microbiol. 2024 Sep;64(3):821-845. doi: 10.1007/s12088-024-01285-8. Epub 2024 Apr 29.

ABSTRACT

Antibiotics are the super drugs that have revolutionized modern medicine by curing many infectious diseases caused by various microbes. They efficiently inhibit the growth and multiplication of the pathogenic microbes without causing adverse effects on the host. However, prescribing suboptimal antibiotic and overuse in agriculture and animal husbandry have led to the emergence of antimicrobial resistance, one of the most serious threats to global health at present. The efficacy of a new antibiotic is high when introduced; however, a small bacterial population attains resistance gradually and eventually survives. Understanding the mode of action of these miracle drugs, as well as their interaction with targets is very complex. However, it is necessary to fulfill the constant need for novel therapeutic alternatives to address the inevitable development of resistance. Therefore, considering the need of the hour, this article has been prepared to discuss the mode of action and recent advancements in the field of antibiotics. Efforts has also been made to highlight the current scenario of antimicrobial resistance and drug repurposing as a fast-track solution to combat the issue.

PMID:39282166 | PMC:PMC11399512 | DOI:10.1007/s12088-024-01285-8

Narra J. 2024 Aug;4(2):e818. doi: 10.52225/narra.v4i2.818. Epub 2024 Jun 28.

ABSTRACT

Drug repurposing is a promising approach to identify new pharmacological indications for drugs that have already been established. However, there is still a limitation in the availability of a high-throughput in vivo preclinical system that is suitable for screening and investigating new pharmacological indications. The aim of this study was to introduce the application of Drosophila larvae as an in vivo platform to screen drug candidates with anti-aging and immunomodulatory activities. To determine whether Drosophila larvae can be utilized for assessing anti-aging and immunomodulatory activities, phenotypical and molecular assays were conducted using wildtype and mutant lines of Drosophila. The utilization of mutant lines (PGRP-LBΔ and Psh[1];;ModSP[KO]) mimics the autoinflammatory and immunodeficient conditions in humans, thereby enabling a thorough investigation of the effects of various compounds. The phenotypical assay was carried out using survival and locomotor observation in Drosophila larvae and adult flies. Meanwhile, the molecular assay was conducted using the RT-qPCR method. In vivo survival analysis revealed that caffeine was relatively safe for Drosophila larvae and exhibited the ability to extend Drosophila lifespan compared to the untreated controls, suggesting its anti-aging properties. Further analysis using the RT-qPCR method demonstrated that caffeine treatment induced transcriptional changes in the Drosophila larvae, particularly in the downstream of NF-κB and JAK-STAT pathways, two distinct immune-related pathways homologue to humans. In addition, caffeine enhanced the survival of Drosophila autoinflammatory model, further implying its immunosuppressive activity. Nevertheless, this compound had minimal to no effect on the survival of Staphylococcus aureus-infected wildtype and immunodeficient Drosophila, refuting its antibacterial and immunostimulant activities. Overall, our results suggest that the anti-aging and immunosuppressive activities of caffeine observed in Drosophila larvae align with those reported in mammalian model systems, emphasizing the suitability of Drosophila larvae as a model organism in drug repurposing endeavors, particularly for the screening of newly discovered chemical entities to assess their immunomodulatory activities before proceedings to investigations in mammalian animal models.

PMID:39280322 | PMC:PMC11391967 | DOI:10.52225/narra.v4i2.818

Evol Bioinform Online. 2024 Sep 13;20:11769343241272414. doi: 10.1177/11769343241272414. eCollection 2024.

ABSTRACT

The identification of potential interactions and relationships between diseases and drugs is significant in public health care and drug discovery. As we all know, experimenting to determine the drug-disease interactions is very expensive in both time and money. However, there are still many drug-disease associations that are still undiscovered and potential. Therefore, the development of computational methods to explore the relationship between drugs and diseases is very important and essential. Many computational methods for predicting drug-disease associations have been developed based on known interactions to learn potential interactions of unknown drug-disease pairs. In this paper, we propose 3 new main groups of meta-paths based on the heterogeneous biological network of drug-protein-disease objects. For each meta-path, we design a machine learning model, then an integrated learning method is formed by these models. We evaluated our approach on 3 standard datasets which are DrugBank, OMIM, and Gottlieb's dataset. Experimental results demonstrate that the proposed method is better than some recent methods such as EMP-SVD, LRSSL, MBiRW, MPG-DDA, SCMFDD,. . . in some measures such as AUC, AUPR, and F1-score.

PMID:39279816 | PMC:PMC11401013 | DOI:10.1177/11769343241272414

Pathol Res Pract. 2024 Sep 8;263:155589. doi: 10.1016/j.prp.2024.155589. Online ahead of print.

ABSTRACT

The 2019 widespread contagion of the human coronavirus novel type (SARS-CoV-2) led to a pandemic declaration by the World Health Organization. A daily increase in patient numbers has formed an urgent necessity to find suitable targets and treatment options for the novel coronavirus (COVID-19). Despite scientists' struggles to discover quick treatment solutions, few effective specific drugs are approved to control SARS-CoV-2 infections thoroughly. Drug repositioning or Drug repurposing and target-based approaches are promising strategies for facilitating the drug discovery process. Here, we review current in silico, in vitro, in vivo, and clinical updates regarding proposed drugs for prospective treatment options for COVID-19. Drug targets that can direct pharmaceutical sciences efforts to discover new drugs against SARS-CoV-2 are divided into two categories: Virus-based targets, for example, Spike glycoprotein and Nucleocapsid Protein, and host-based targets, for instance, inflammatory cytokines and cell receptors through which the virus infects the cell. A broad spectrum of drugs has been found to show anti-SARS-CoV-2 potential, including antiviral drugs and monoclonal antibodies, statins, anti-inflammatory agents, and herbal products.

PMID:39276508 | DOI:10.1016/j.prp.2024.155589

Cell Rep. 2024 Sep 13;43(9):114735. doi: 10.1016/j.celrep.2024.114735. Online ahead of print.

ABSTRACT

Phenotypic associations have been reported between heart failure (HF) and blood lipids (BLs), blood pressure (BP), and blood glucose (BG). However, the shared genetic etiology underlying these associations remains incompletely understood. Conducting a large-scale multi-trait association study for HF with these traits, we discovered 143 previously unreported genomic risk loci for HF. Results showed that 46, 35, and 14 colocalized loci were shared by HF with BLs, BP, and BG, respectively. Notably, the loci shared by HF with these traits rarely overlapped, indicating distinct mechanisms. The combination of gene-mapping, gene-based, and transcriptome-wide association analyses prioritized noteworthy candidate genes (such as lipoprotein lipase [LPL], G protein-coupled receptor kinase 5 [GRK5], and troponin C1, slow skeletal and cardiac type [TNNC1]) for HF. Enrichment analysis revealed that HF exhibited comparable characteristics to cardiovascular traits and metabolic traits correlated to BLs, BP, and BG. Finally, we reported drug repurposing candidates and plasma protein targets for HF. These results provide biological insights into the pathogenesis of these comorbidities of HF.

PMID:39276349 | DOI:10.1016/j.celrep.2024.114735

Daru. 2024 Sep 14. doi: 10.1007/s40199-024-00535-w. Online ahead of print.

ABSTRACT

BACKGROUND: Obesity has emerged as a major health challenge globally in the last two decades. Dysregulated fatty acid metabolism and de novo lipogenesis are prime causes for obesity development which ultimately trigger other co-morbid pathological conditions thereby risking life longevity. Fatty acid metabolism and de novo lipogenesis involve several biochemical steps both in cytosol and mitochondria. Reportedly, the high catalytically active mitochondrial carbonic anhydrases (CAVA/CAVB) regulate the intercellular depot of bicarbonate ions and catalyze the rapid carboxylation of pyruvate and acetyl-co-A to acetyl-co-A and malonate respectively, which are the precursors of fatty acid synthesis and lipogenesis. Several in vitro and in vivo investigations indicate inhibition of mitochondrial carbonic anhydrase isoforms interfere in the functioning of pyruvate, fatty acid and succinate pathways. Targeting of mitochondrial carbonic anhydrase isoforms (CAVA/CAVB) could thereby modulate gluconeogenetic as well as lipogenetic pathways and pave way for designing of novel leads in the development pipeline of anti-obesity medications.

METHODS: The present review unveils a diverse chemical space including synthetic sulphonamides, sulphamates, sulfamides and many natural bioactive molecules which selectively inhibit the mitochondrial isoform CAVA/CAVB with an emphasis on major state-of-art drug design strategies.

RESULTS: More than 60% similarity in the structural framework of the carbonic anhydrase isoforms has converged the drug design methods towards the development of isoform selective chemotypes. While the benzene sulphonamide derivatives selectively inhibit CAVA/CAVB in low nanomolar ranges depending on the substitutions on the phenyl ring, the sulpamates and sulpamides potently inhibit CAVB. The virtual screening and drug repurposing methods have also explored many non-sulphonamide chemical scaffolds which can potently inhibit CAVA.

CONCLUSION: The review could pave way for the development of novel and effective anti-obesity drugs which can modulate the energy metabolism.

PMID:39276204 | DOI:10.1007/s40199-024-00535-w

Protein Sci. 2024 Oct;33(10):e5167. doi: 10.1002/pro.5167.

ABSTRACT

Predicting the binding of ligands to the human proteome via reverse-docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off-target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet-SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off-target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety.

PMID:39276010 | DOI:10.1002/pro.5167

Molecules. 2024 Aug 26;29(17):4041. doi: 10.3390/molecules29174041.

ABSTRACT

Leishmania infantum is the vector-borne trypanosomatid parasite causing visceral leishmaniasis in the Mediterranean basin. This neglected tropical disease is treated with a limited number of obsolete drugs that are not exempt from adverse effects and whose overuse has promoted the emergence of resistant pathogens. In the search for novel antitrypanosomatid molecules that help overcome these drawbacks, drug repurposing has emerged as a good strategy. Nitroaromatic compounds have been found in drug discovery campaigns as promising antileishmanial molecules. Fexinidazole (recently introduced for the treatment of stages 1 and 2 of African trypanosomiasis), and pretomanid, which share the nitroimidazole nitroaromatic structure, have provided antileishmanial activity in different studies. In this work, we have tested the in vitro efficacy of these two nitroimidazoles to validate our 384-well high-throughput screening (HTS) platform consisting of L. infantum parasites emitting the near-infrared fluorescent protein (iRFP) as a biomarker of cell viability. These molecules showed good efficacy in both axenic and intramacrophage amastigotes and were poorly cytotoxic in RAW 264.7 and HepG2 cultures. Fexinidazole and pretomanid induced the production of ROS in axenic amastigotes but were not able to inhibit trypanothione reductase (TryR), thus suggesting that these compounds may target thiol metabolism through a different mechanism of action.

PMID:39274889 | DOI:10.3390/molecules29174041

Molecules. 2024 Aug 26;29(17):4025. doi: 10.3390/molecules29174025.

ABSTRACT

A 3D structure-based pharmacophore model built for bromodomain-containing protein 4 (BRD4) is reported here, specifically developed for investigating and identifying the key structural features of the (+)-JQ1 known inhibitor within the BRD4 binding site. Using this pharmacophore model, 273 synthesized and purchased compounds previously considered for other targets but yielding poor results were screened in a drug repositioning campaign. Subsequently, only six compounds showed potential as BRD4 binders and were subjected to further biophysical and biochemical assays. Compounds 2, 5, and 6 showed high affinity for BRD4, with IC50 values of 0.60 ± 0.25 µM, 3.46 ± 1.22 µM, and 4.66 ± 0.52 µM, respectively. Additionally, these compounds were tested against two other bromodomains, BRD3 and BRD9, and two of them showed high selectivity for BRD4. The reported 3D structure-based pharmacophore model proves to be a straightforward and useful tool for selecting novel BRD4 ligands.

PMID:39274873 | DOI:10.3390/molecules29174025

Int J Mol Sci. 2024 Sep 6;25(17):9682. doi: 10.3390/ijms25179682.

ABSTRACT

The risk behaviors underlying the most prevalent chronic noncommunicable diseases (NCDs) encompass alcohol misuse, unhealthy diets, smoking and sedentary lifestyle behaviors. These are all linked to the altered function of the mesocorticolimbic (MCL) system. As the mesocorticolimbic circuit is central to the reward pathway and is involved in risk behaviors and mental disorders, we set out to test the hypothesis that these pathologies may be approached therapeutically as a group. To address these questions, the identification of novel targets by exploiting knowledge-based, network-based and disease similarity algorithms in two major Thomson Reuters databases (MetaBase™, a database of manually annotated protein interactions and biological pathways, and IntegritySM, a unique knowledge solution integrating biological, chemical and pharmacological data) was performed. Each approach scored proteins from a particular approach-specific standpoint, followed by integration of the scores by machine learning techniques yielding an integrated score for final target prioritization. Machine learning identified characteristic patterns of the already known targets (control targets) with high accuracy (area under curve of the receiver operator curve was ~93%). The analysis resulted in a prioritized list of 250 targets for MCL disorders, many of which are well established targets for the mesocorticolimbic circuit e.g., dopamine receptors, monoamino oxidases and serotonin receptors, whereas emerging targets included DPP4, PPARG, NOS1, ACE, ARB1, CREB1, POMC and diverse voltage-gated Ca2+ channels. Our findings support the hypothesis that disorders involving the mesocorticolimbic circuit may share key molecular pathology aspects and may be causally linked to NCDs, yielding novel targets for drug repurposing and personalized medicine.

PMID:39273628 | DOI:10.3390/ijms25179682

Int J Mol Sci. 2024 Sep 6;25(17):9651. doi: 10.3390/ijms25179651.

ABSTRACT

Hu antigen R (HuR) plays a key role in regulating genes critical to the pathogenesis of diabetic nephropathy (DN). This study investigates the therapeutic potential of niclosamide (NCS) as an HuR inhibitor in DN. Uninephrectomized mice were assigned to four groups: normal control; untreated db/db mice terminated at 14 and 22 weeks, respectively; and db/db mice treated with NCS (20 mg/kg daily via i.p.) from weeks 18 to 22. Increased HuR expression was observed in diabetic kidneys from db/db mice, which was mitigated by NCS treatment. Untreated db/db mice exhibited obesity, progressive hyperglycemia, albuminuria, kidney hypertrophy and glomerular mesangial matrix expansion, increased renal production of fibronectin and a-smooth muscle actin, and decreased glomerular WT-1+-podocytes and nephrin expression. NCS treatment did not affect mouse body weight, but reduced blood glucose and HbA1c levels and halted the DN progression observed in untreated db/db mice. Renal production of inflammatory and oxidative stress markers (NF-κBp65, TNF-a, MCP-1) and urine MDA levels increased during disease progression in db/db mice but were halted by NCS treatment. Additionally, the Wnt1-signaling-pathway downstream factor, Wisp1, was identified as a key downstream mediator of HuR-dependent action and found to be markedly increased in db/db mouse kidneys, which was normalized by NCS treatment. These findings suggest that inhibition of HuR with NCS is therapeutic for DN by improving hyperglycemia, renal inflammation, and oxidative stress. The reduction in renal Wisp1 expression also contributes to its renoprotective effects. This study supports the potential of repurposing HuR inhibitors as a novel therapy for DN.

PMID:39273597 | DOI:10.3390/ijms25179651

Int J Mol Sci. 2024 Sep 4;25(17):9576. doi: 10.3390/ijms25179576.

ABSTRACT

The vast corpus of heterogeneous biomedical data stored in databases, ontologies, and terminologies presents a unique opportunity for drug design. Integrating and fusing these sources is essential to develop data representations that can be analyzed using artificial intelligence methods to generate novel drug candidates or hypotheses. Here, we propose Non-Negative Matrix Tri-Factorization as an invaluable tool for integrating and fusing data, as well as for representation learning. Additionally, we demonstrate how representations learned by Non-Negative Matrix Tri-Factorization can effectively be utilized by traditional artificial intelligence methods. While this approach is domain-agnostic and applicable to any field with vast amounts of structured and semi-structured data, we apply it specifically to computational pharmacology and drug repurposing. This field is poised to benefit significantly from artificial intelligence, particularly in personalized medicine. We conducted extensive experiments to evaluate the performance of the proposed method, yielding exciting results, particularly compared to traditional methods. Novel drug-target predictions have also been validated in the literature, further confirming their validity. Additionally, we tested our method to predict drug synergism, where constructing a classical matrix dataset is challenging. The method demonstrated great flexibility, suggesting its applicability to a wide range of tasks in drug design and discovery.

PMID:39273521 | DOI:10.3390/ijms25179576

Int J Mol Sci. 2024 Aug 30;25(17):9473. doi: 10.3390/ijms25179473.

ABSTRACT

Radiation therapy continues to be the cornerstone treatment for malignant brain tumors, the majority of which express wild-type p53. Therefore, the identification of drugs that promote the ionizing radiation (IR)-induced activation of p53 is expected to increase the efficacy of radiation therapy for these tumors. The growth inhibitory effects of CEP-1347, a known inhibitor of MDM4 expression, on malignant brain tumor cell lines expressing wild-type p53 were examined, alone or in combination with IR, by dye exclusion and/or colony formation assays. The effects of CEP-1347 on the p53 pathway, alone or in combination with IR, were examined by RT-PCR and Western blot analyses. The combination of CEP-1347 and IR activated p53 in malignant brain tumor cells and inhibited their growth more effectively than either alone. Mechanistically, CEP-1347 and IR each reduced MDM4 expression, while their combination did not result in further decreases. CEP-1347 promoted IR-induced Chk2 phosphorylation and increased p53 expression in concert with IR in a Chk2-dependent manner. The present results show, for the first time, that CEP-1347 is capable of promoting Chk2-mediated p53 activation by IR in addition to inhibiting the expression of MDM4 and, thus, CEP-1347 has potential as a radiosensitizer for malignant brain tumors expressing wild-type p53.

PMID:39273420 | DOI:10.3390/ijms25179473

Int J Mol Sci. 2024 Aug 30;25(17):9460. doi: 10.3390/ijms25179460.

ABSTRACT

As the most common and aggressive primary malignant brain tumor, glioblastoma is still lacking a satisfactory curative approach. The standard management consisting of gross total resection followed by radiotherapy and chemotherapy with temozolomide only prolongs patients' life moderately. In recent years, many therapeutics have failed to give a breakthrough in GBM treatment. In the search for new treatment solutions, we became interested in the repurposing of existing medicines, which have established safety profiles. We focused on the possible implementation of well-known drugs, metformin, and arginine. Metformin is widely used in diabetes treatment, but arginine is mainly a cardiovascular protective drug. We evaluated the effects of metformin and arginine on total DNA methylation, as well as the oxidative stress evoked by treatment with those agents. In glioblastoma cell lines, a decrease in 5-methylcytosine contents was observed with increasing drug concentration. When combined with temozolomide, both guanidines parallelly increased DNA methylation and decreased 8-oxo-deoxyguanosine contents. These effects can be explained by specific interactions of the guanidine group with m5CpG dinucleotide. We showed that metformin and arginine act on the epigenetic level, influencing the foreground and potent DNA regulatory mechanisms. Therefore, they can be used separately or in combination with temozolomide, in various stages of disease, depending on desired treatment effects.

PMID:39273414 | DOI:10.3390/ijms25179460

Int J Mol Sci. 2024 Aug 29;25(17):9392. doi: 10.3390/ijms25179392.

ABSTRACT

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer, with a high mortality rate due to the limited therapeutic options. Systemic drug treatments improve the patient's life expectancy by only a few months. Furthermore, the development of novel small molecule chemotherapeutics is time-consuming and costly. Drug repurposing has been a successful strategy for identifying and utilizing new therapeutic options for diseases with limited treatment options. This study aims to identify candidate drug molecules for HCC treatment through repurposing existing compounds, leveraging the machine learning tool MDeePred. The Open Targets Platform, UniProt, ChEMBL, and Expasy databases were used to create a dataset for drug target interaction (DTI) predictions by MDeePred. Enrichment analyses of DTIs were conducted, leading to the selection of 6 out of 380 DTIs identified by MDeePred for further analyses. The physicochemical properties, lipophilicity, water solubility, drug-likeness, and medicinal chemistry properties of the candidate compounds and approved drugs for advanced stage HCC (lenvatinib, regorafenib, and sorafenib) were analyzed in detail. Drug candidates exhibited drug-like properties and demonstrated significant target docking properties. Our findings indicated the binding efficacy of the selected drug compounds to their designated targets associated with HCC. In conclusion, we identified small molecules that can be further exploited experimentally in HCC therapeutics. Our study also demonstrated the use of the MDeePred deep learning tool in in silico drug repurposing efforts for cancer therapeutics.

PMID:39273340 | DOI:10.3390/ijms25179392