Cancers (Basel). 2024 Sep 3;16(17):3068. doi: 10.3390/cancers16173068.

ABSTRACT

SWI/SNF (SWItch/Sucrose Non-Fermentable) is the most frequently mutated chromatin-remodelling complex in human malignancy, with over 20% of tumours having a mutation in a SWI/SNF complex member. Mutations in specific SWI/SNF complex members are characteristic of rare chemoresistant ovarian cancer histopathological subtypes. Somatic mutations in ARID1A, encoding one of the mutually exclusive DNA-binding subunits of SWI/SNF, occur in 42-67% of ovarian clear cell carcinomas (OCCC). The concomitant somatic or germline mutation and epigenetic silencing of the mutually exclusive ATPase subunits SMARCA4 and SMARCA2, respectively, occurs in Small cell carcinoma of the ovary, hypercalcaemic type (SCCOHT), with SMARCA4 mutation reported in 69-100% of SCCOHT cases and SMARCA2 silencing seen 86-100% of the time. Somatic ARID1A mutations also occur in endometrioid ovarian cancer (EnOC), as well as in the chronic benign condition endometriosis, possibly as precursors to the development of the endometriosis-associated cancers OCCC and EnOC. Mutation of the ARID1A paralogue ARID1B can also occur in both OCCC and SCCOHT. Mutations in other SWI/SNF complex members, including SMARCA2, SMARCB1 and SMARCC1, occur rarely in either OCCC or SCCOHT. Abrogated SWI/SNF raises opportunities for pharmacological inhibition, including the use of DNA damage repair inhibitors, kinase and epigenetic inhibitors, as well as immune checkpoint blockade.

PMID:39272926 | DOI:10.3390/cancers16173068

J Cell Mol Med. 2024 Sep;28(17):e70085. doi: 10.1111/jcmm.70085.

ABSTRACT

Acute myeloid leukaemia (AML) is a highly heterogeneous disease, which lead to various findings in transcriptomic research. This study addresses these challenges by integrating 34 datasets, including 26 control groups, 6 prognostic datasets and 2 single-cell RNA sequencing (scRNA-seq) datasets to identify 10,000 AML-related genes (ARGs). We focused on genes with low variability and high consistency and successfully discovered 191 AML signatures (ASs). Leveraging machine learning techniques, specifically the XGBoost model and our custom framework, we classified AML subtypes with both scRNA-seq and bulk RNA-seq data, complementing the ELN2022 classification approach. Our research also identified promising treatments for AML through drug repurposing, with solasonine showing potential efficacy for high-risk AML patients, supported by molecular docking and transcriptomic analyses. To enhance reproducibility and customizability, we developed CSAMLdb, a user-friendly database platform. It facilitates the reuse and personalized analysis of nearly all results obtained in this research, including single-gene prognostics, multi-gene scoring, enrichment analysis, machine learning risk assessment, drug repositioning analysis and literature abstract named entity recognition. CSAMLdb is available at http://www.csamldb.com.

PMID:39267259 | DOI:10.1111/jcmm.70085

Am J Epidemiol. 2024 Sep 11:kwae355. doi: 10.1093/aje/kwae355. Online ahead of print.

ABSTRACT

Most drug repurposing studies using real-world data focused on validating, instead of generating, hypotheses. We used tree-based scan statistics to generate repurposing hypotheses for sodium-glucose cotransporter-2 inhibitors (SGLT2i). We used an active-comparator, new-user design to create a 1:1 propensity-score matched cohort of SGLT2i and dipeptidyl peptidase-4 inhibitors (DPP4i) initiators in the MerativeTM MarketScan® Research Databases. Tree-based scan statistics were estimated across an ICD-10-CM-based hierarchical outcome tree using incident outcomes identified from hospital and outpatient diagnoses. We used an adjusted P≤0.01 as the threshold for statistical alert to prioritize associations for evaluation as repurposing signals. We varied the analyses by tree size, scanning level, and clinical settings for outcomes. There were 80,510 matched SGLT2i-DPP4i initiator pairs with 215,333 outcomes among SGLT2i initiators and 223,428 outcomes among DPP4i initiators. There were 18 prioritized associations, which included chronic kidney disease (P=0.0001), an expected signal, and anemia (P=0.0001). Heart failure (P=0.0167), another expected signal, was identified slightly beyond the statistical alert threshold. Narrowing the outcome tree, scanning at different tree levels, and including outcomes from different clinical settings influenced the scan statistics. We identified signals aligning with recently approved indications of SGLT2i, plus potential repurposing signals supported by existing evidence but requiring future validation.

PMID:39270669 | DOI:10.1093/aje/kwae355

Front Pharmacol. 2024 Aug 29;15:1448319. doi: 10.3389/fphar.2024.1448319. eCollection 2024.

ABSTRACT

OBJECTIVE: Addressing the rising prevalence of pain disorders and limitations of current analgesics, our study explores repurposing antihypertensive drugs for pain management, inspired by the link between hypertension and pain. We leverage a drug-target Mendelian Randomization (MR) approach to explore their dual benefits and establish causal connections.

METHODS: A comprehensive compilation of antihypertensive drug classes was undertaken through British National Formulary, with their target genes identified using the DrugBank database. Relevant single nucleotide polymorphisms (SNPs) associated with these targets were selected from published genomic studies on systolic blood pressure (SBP) as genetic instruments. These SNPs were validated through MR against acute coronary artery disease (CAD) to ensure genes not linked to CAD were excluded from acting as proxies for antihypertensive drugs. An MR analysis of 29 pain-related outcomes was conducted using the FinnGen R10 database employing the selected and validated genetic instruments. We utilized the Inverse Variance Weighted (IVW) method for primary analysis, applying Bonferroni correction to control type I error. IVW's multiplicative random effects (MRE) addressed heterogeneity, and MR-PRESSO managed pleiotropy, ensuring accurate causal inference.

RESULTS: Our analysis differentiates strong and suggestive evidence in linking antihypertensive drugs to pain disorder risks. Strong evidence was found for adrenergic neuron blockers increasing migraine without aura risk, loop diuretics reducing panniculitis, and vasodilator antihypertensives lowering limb pain risk. Suggestive evidence suggests alpha-adrenoceptor blockers might increase migraine risk, while beta-adrenoceptor blockers could lower radiculopathy risk. Adrenergic neuron blockers also show a potential protective effect against coxarthrosis (hip osteoarthritis) and increased femgenpain risk (pain and other conditions related to female genital organs and menstrual cycle). Additionally, suggestive links were found between vasodilator antihypertensives and reduced radiculopathy risk, and both alpha-adrenoceptor blockers and renin inhibitors possibly decreasing dorsalgianas risk (unspecified dorsalgia). These findings highlight the intricate effects of antihypertensive drugs on pain disorders, underlining the need for further research.

CONCLUSION: The findings indicate that antihypertensive medications may exert varied effects on pain management, suggesting a repurposing potential for treating specific pain disorders. The results advocate for further research to confirm these associations and to explore underlying mechanisms, to optimize pain management practices.

PMID:39268473 | PMC:PMC11390634 | DOI:10.3389/fphar.2024.1448319

Front Pharmacol. 2024 Aug 29;15:1472396. doi: 10.3389/fphar.2024.1472396. eCollection 2024.

ABSTRACT

Many drugs can act on multiple targets or disease pathways, regardless of their original purpose. Drug repurposing involves reevaluating existing compounds for new medical uses. This can include repositioning approved drugs, redeveloping unapproved drugs, or repurposing any chemical, nutraceutical, or biotherapeutic product for new applications. Traditional drug development is slow, expensive, and has high failure rates. Drug repurposing can speed up the process, costing less and saving time. This approach can save 6-7 years of early-stage research time. Drug repurposing benefits from existing compounds with optimized structures and approved for clinical use with associated structure-activity relationship publications, supporting the development of new effective compounds. Drug repurposes can now utilize advanced in silico screening enabled by artificial intelligence (AI) and sophisticated tissue and organ-level in vitro models. These models more accurately replicate human physiology and improve the selection of existing drugs for further pre-clinical testing and, eventually, clinical trials for new indications. This mini-review discusses some examples of drug repurposing and novel strategies for further development of compounds for targets of the arachidonic acid cascade. In particular, we will delve into the prospect of repurposing antiplatelet agents for cancer prevention and addressing the emerging noncanonical functionalities of 5-lipoxygenase, potentially for leukemia therapy.

PMID:39268466 | PMC:PMC11390530 | DOI:10.3389/fphar.2024.1472396

Front Pharmacol. 2024 Aug 29;15:1439289. doi: 10.3389/fphar.2024.1439289. eCollection 2024.

ABSTRACT

BACKGROUND: Osteoarthritis (OA) can lead to chronic joint pain, and currently there are no methods available for complete cure. Utilizing the Gene Expression Omnibus (GEO) database for bioinformatics analysis combined with Mendelian randomization (MR) has been widely employed for drug repurposing and discovery of novel therapeutic targets. Therefore, our research focus is to identify new diagnostic markers and improved drug target sites.

METHODS: Gene expression data from different tissues of synovial membrane, cartilage and subchondral bone were collected through GEO data to screen out differential genes. Two-sample MR Analysis was used to estimate the causal effect of expression quantitative trait loci (eQTL) on OA. Through the intersection of the two, core genes were obtained, which were further screened by bioinformatics analysis for in vitro and in vivo molecular experimental verification. Finally, drug prediction and molecular docking further verified the medicinal value of drug targets.

RESULTS: In the joint analysis utilizing the GEO database and MR approach, five genes exhibited significance across both analytical methods. These genes were subjected to bioinformatics analysis, revealing their close association with immunological functions. Further refinement identified two core genes (ARL4C and GAPDH), whose expression levels were found to decrease in OA pathology and exhibited a protective effect in the MR analysis, thus demonstrating consistent trends. Support from in vitro and in vivo molecular experiments was also obtained, while molecular docking revealed favorable interactions between the drugs and proteins, in line with existing structural data.

CONCLUSION: This study identified potential diagnostic biomarkers and drug targets for OA through the utilization of the GEO database and MR analysis. The findings suggest that the ARL4C and GAPDH genes may serve as therapeutic targets, offering promise for personalized treatment of OA.

PMID:39268462 | PMC:PMC11390638 | DOI:10.3389/fphar.2024.1439289

Front Oncol. 2024 Aug 29;14:1442737. doi: 10.3389/fonc.2024.1442737. eCollection 2024.

ABSTRACT

BACKGROUND: Pancreatic cancer (PC), sometimes referred to as pancreatic ductal adenocarcinoma (PDAC), is a major cause of global mortality from cancer. Pancreatic cancer is a very aggressive and devastating kind of cancer, characterized by limited options for therapy and low possibilities of survival. Sulforaphane (SFN), a naturally occurring sulfur-containing compound, is believed to possess anti-inflammatory, anti-obesity, and anti-cancer characteristics.

OBJECTIVE: However, efficient preventative and treatment measures are essential and SFN has been studied for its ability to suppress pancreatic cancer cell proliferation and induce apoptosis.

METHODS: Here, SFN induced cytotoxicity and apoptosis in PDAC cell lines such as MIA PaCa-2 and PANC-1 cells, as evaluated by cytotoxicity, colony formation, western blot analysis, fluorescence-activated cell sorting (FACS), reactive oxygen species (ROS) detection, caspase-3 activity assay, immunofluorescence assay, and mitochondrial membrane potential assay.

RESULTS: In MIA PaCa-2 and PANC-1 cells, SFN inhibited cell survival and proliferation in a dose-dependent manner. The activation of caspase zymogens results in cleaved PARP and cleaved caspase-3, which is associated with an accumulation in the sub G1 phase. Furthermore, SFN increased ROS level and γH2A.X expression while decreasing mitochondrial membrane potential (ΔΨm). Notably, the ROS scavenger N-Acetyl-L-cysteine (NAC) was shown to reverse SFN-induced cytotoxicity and ROS level. Subsequently, SFN-induced cell cycle arrest and apoptosis induction as a Trojan horse to eliminate pancreatic cancer cells via ROS-mediated pathways were used to inhibit pancreatic cancer cells.

CONCLUSION: Collectively, our data demonstrates that SFN-induced cell death follows the apoptosis pathway, making it a viable target for therapeutic interventions against pancreatic cancer.

PMID:39267822 | PMC:PMC11390404 | DOI:10.3389/fonc.2024.1442737

J Alzheimers Dis. 2024 Sep 12. doi: 10.3233/JAD-240235. Online ahead of print.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that is the most common form of dementia in the elderly. The drugs currently used to treat AD only have limited effects and are not able to cure the disease. Drug repositioning has increasingly become a promising approach to find potential drugs for diseases like AD.

OBJECTIVE: To screen potential drug candidates for AD based on the relationship between risk genes of AD and drugs.

METHODS: We collected the risk genes of AD and retrieved the information of known drugs from DrugBank. Then, the AD-related genes and the targets of each drug were mapped to the human protein-protein interaction network (PPIN) to represent AD and the drugs on the network. The network distances between each drug and AD were calculated to screen the drugs proximal to AD-related genes on PPIN, and the screened drug candidates were further analyzed by molecular docking and molecular dynamics simulations.

RESULTS: We compiled a list of 714 genes associated with AD. From 5,833 drugs used for human diseases, we identified 1,044 drugs that could be potentially used to treat AD. Then, amyloid-β (Aβ) protein, the key molecule involved in the pathogenesis of AD was selected as the target to further screen drugs that may inhibit Aβ aggregation by molecular docking. We found that ergotamine and RAF-265 could bind stably with Aβ. In further analysis by molecular dynamics simulations, both drugs exhibited reasonable stability.

CONCLUSIONS: Our work indicated that ergotamine and RAF-265 may be potential candidates for treating AD.

PMID:39269834 | DOI:10.3233/JAD-240235

J Chem Theory Comput. 2024 Sep 13. doi: 10.1021/acs.jctc.4c00663. Online ahead of print.

ABSTRACT

The continuous emergence of novel infectious diseases poses a significant threat to global public health security, necessitating the development of small-molecule inhibitors that directly target pathogens. The RNA-dependent RNA polymerase (RdRp) and main protease (Mpro) of SARS-CoV-2 have been validated as potential key antiviral drug targets for the treatment of COVID-19. However, the conventional new drug R&D cycle takes 10-15 years, failing to meet the urgent needs during epidemics. Here, we propose a general multimodal deep learning framework for drug repurposing, MMFA-DTA, to enable rapid virtual screening of known drugs and significantly improve discovery efficiency. By extracting graph topological and sequence features from both small molecules and proteins, we design attention mechanisms to achieve dynamic fusion across modalities. Results demonstrate the superior performance of MMFA-DTA in drug-target affinity prediction over several state-of-the-art baseline methods on Davis and KIBA data sets, validating the benefits of heterogeneous information integration for representation learning and interaction modeling. Further fine-tuning on COVID-19-relevant bioactivity data enhances model predictions for critical SARS-CoV-2 enzymes. Case studies screening the FDA-approved drug library successfully identify etacrynic acid as the potential lead compound against both RdRp and Mpro. Molecular dynamics simulations further confirm the stability and binding affinity of etacrynic acid to these targets. This study proves the great potential and advantages of deep learning and drug repurposing strategies in supporting antiviral drug discovery. The proposed general and rapid response computational framework holds significance for preparedness against future public health events.

PMID:39269697 | DOI:10.1021/acs.jctc.4c00663

J Transl Med. 2024 Sep 12;22(1):839. doi: 10.1186/s12967-024-05624-7.

ABSTRACT

BACKGROUND: Myocardial fibrosis, a hallmark of heart disease, is closely associated with macrophages, yet the genetic pathophysiology remains incompletely understood. In this study, we utilized integrated single-cell transcriptomics and bulk RNA-seq analysis to investigate the relationship between macrophages and myocardial fibrosis across omics integration.

METHODS: We examined and curated existing single-cell data from dilated cardiomyopathy (DCM), ischemic cardiomyopathy (ICM), myocardial infarction (MI), and heart failure (HF), and analyzed the integrated data using cell communication, transcription factor identification, high dimensional weighted gene co-expression network analysis (hdWGCNA), and functional enrichment to elucidate the drivers of macrophage polarization and the macrophage-to-myofibroblast transition (MMT). Additionally, we assessed the accuracy of single-cell data from the perspective of driving factors, cell typing, anti-fibrosis performance of left ventricular assist device (LVAD). Candidate drugs were screened using L1000FWD.

RESULTS: All four heart diseases exhibit myocardial fibrosis, with only MI showing an increase in macrophage proportions. Macrophages participate in myocardial fibrosis through various fibrogenic molecules, especially evident in DCM and MI. Abnormal RNA metabolism and dysregulated transcription are significant drivers of macrophage-mediated fibrosis. Furthermore, profibrotic macrophages exhibit M1 polarization and increased MMT. In HF patients, those responding to LVAD therapy showed a significant decrease in driver gene expression, M1 polarization, and MMT. Drug repurposing identified cinobufagin as a potential therapeutic agent.

CONCLUSION: Using integrated single-cell transcriptomics, we identified the drivers of macrophage-mediated myocardial fibrosis in four heart diseases and confirmed the therapeutic effect of LVAD on improving HF with single-cell accuracy, providing novel insights into the diagnosis and treatment of myocardial fibrosis.

PMID:39267173 | DOI:10.1186/s12967-024-05624-7

FEBS Lett. 2024 Sep 12. doi: 10.1002/1873-3468.15016. Online ahead of print.

ABSTRACT

Drug repurposing has emerged as an effective strategy against infectious diseases such as visceral leishmaniasis. Here, we evaluated four FDA-approved drugs-valrubicin, ciclesonide, deflazacort, and telithromycin-for their anti-leishmanial activity on Leishmania donovani parasites, especially their ability to target the enzyme glutathione synthetase (LdGS), which enables parasite survival under oxidative stress in host macrophages. Valrubicin and ciclesonide exhibited superior inhibitory effects compared to deflazacort and telithromycin, inhibiting the promastigotes at very low concentrations, with IC50 values of 1.09 ± 0.09 μm and 2.09 ± 0.09 μm, respectively. Subsequent testing on amastigotes revealed the IC50 values of 1.74 ± 0.05 μm and 3.32 ± 0.21 μm for valrubicin and ciclesonide, respectively. Molecular and cellular level analysis further elucidated the mechanisms underlying the anti-leishmanial activity of valrubicin and ciclesonide.

PMID:39266470 | DOI:10.1002/1873-3468.15016

Arch Biochem Biophys. 2024 Sep 10:110150. doi: 10.1016/j.abb.2024.110150. Online ahead of print.

ABSTRACT

Cancer is the leading cause of death worldwide and is often associated with tumor relapse even after chemotherapeutics. This reveals malignancy is a complex process, and high-throughput omics strategies in recent years have contributed significantly in decoding the molecular mechanisms of these complex events in cancer. Further, the omics studies yield a large volume of cancer-specific molecular signatures that promote the discovery of cancer therapy drugs by a method termed signature-based drug repurposing. The drug repurposing method identifies new uses for approved drugs beyond their intended initial therapeutic use, and there are several approaches to it. In this review, we discuss signature-based drug repurposing in cancer, how cancer omics have revolutionized this method of drug discovery, and how one can use the cancer signature data for repurposed drug identification by providing a step-by-step procedural handout. This modern approach maximizes the use of existing therapeutic agents for cancer therapy or combination therapy to overcome chemotherapeutics resistance, making it a pragmatic and efficient alternative to traditional resource-intensive and time-consuming methods.

PMID:39265695 | DOI:10.1016/j.abb.2024.110150

PLoS Comput Biol. 2024 Sep 12;20(9):e1012417. doi: 10.1371/journal.pcbi.1012417. Online ahead of print.

ABSTRACT

BACKGROUND: In the modern era, the growth of scientific literature presents a daunting challenge for researchers to keep informed of advancements across multiple disciplines.

OBJECTIVE: We apply natural language processing (NLP) and embedding learning concepts to design PubDigest, a tool that combs PubMed literature, aiming to pinpoint potential drugs that could be repurposed.

METHODS: Using NLP, especially term associations through word embeddings, we explored unrecognized relationships between drugs and diseases. To illustrate the utility of PubDigest, we focused on chronic thromboembolic pulmonary hypertension (CTEPH), a rare disease with an overall limited number of scientific publications.

RESULTS: Our literature analysis identified key clinical features linked to CTEPH by applying term frequency-inverse document frequency (TF-IDF) scoring, a technique measuring a term's significance in a text corpus. This allowed us to map related diseases. One standout was venous thrombosis (VT), which showed strong semantic links with CTEPH. Looking deeper, we discovered potential repurposing candidates for CTEPH through large-scale neural network-based contextualization of literature and predictive modeling on both the CTEPH and the VT literature corpora to find novel, yet unrecognized associations between the two diseases. Alongside the anti-thrombotic agent caplacizumab, benzofuran derivatives were an intriguing find. In particular, the benzofuran derivative amiodarone displayed potential anti-thrombotic properties in the literature. Our in vitro tests confirmed amiodarone's ability to reduce platelet aggregation significantly by 68% (p = 0.02). However, real-world clinical data indicated that CTEPH patients receiving amiodarone treatment faced a significant 15.9% higher mortality risk (p<0.001).

CONCLUSIONS: While NLP offers an innovative approach to interpreting scientific literature, especially for drug repurposing, it is crucial to combine it with complementary methods like in vitro testing and real-world evidence. Our exploration with benzofuran derivatives and CTEPH underscores this point. Thus, blending NLP with hands-on experiments and real-world clinical data can pave the way for faster and safer drug repurposing approaches, especially for rare diseases like CTEPH.

PMID:39264975 | DOI:10.1371/journal.pcbi.1012417

PLoS One. 2024 Sep 12;19(9):e0299342. doi: 10.1371/journal.pone.0299342. eCollection 2024.

ABSTRACT

Monkeypox (MPXV) is one of the infectious viruses which caused morbidity and mortality problems in these years. Despite its danger to public health, there is no approved drug to stand and handle MPXV. On the other hand, drug repurposing is a promising screening method for the low-cost introduction of approved drugs for emerging diseases and viruses which utilizes computational methods. Therefore, drug repurposing is a promising approach to suggesting approved drugs for the MPXV. This paper proposes a computational framework for MPXV antiviral prediction. To do this, we have generated a new virus-antiviral dataset. Moreover, we applied several machine learning and one deep learning method for virus-antiviral prediction. The suggested drugs by the learning methods have been investigated using docking studies. The target protein structure is modeled using homology modeling and, then, refined and validated. To the best of our knowledge, this work is the first work to study deep learning methods for the prediction of MPXV antivirals. The screening results confirm that Tilorone, Valacyclovir, Ribavirin, Favipiravir, and Baloxavir marboxil are effective drugs for MPXV treatment.

PMID:39264896 | DOI:10.1371/journal.pone.0299342

Transl Lung Cancer Res. 2024 Aug 31;13(8):1780-1793. doi: 10.21037/tlcr-24-65. Epub 2024 Aug 28.

ABSTRACT

BACKGROUND: Lung cancer is responsible for most cancer-related deaths, and non-small cell lung cancer (NSCLC) accounts for the majority of cases. Targeted therapy has made promising advancements in systemic treatment for NSCLC over the last two decades, but inadequate drug targets with clinically proven survival benefits limit its universal application in clinical practice compared to chemotherapy and immunotherapy. There is an urgent need to explore new drug targets to expand the beneficiary group. This study aims to identify druggable genes and to predict the efficacy and prognostic value of the corresponding targeted drugs in NSCLC.

METHODS: Two-sample mendelian randomization (MR) of druggable genes was performed to predict the efficacy of their corresponding targeted therapy for NSCLC. Subsequent sensitivity analyses were performed to assess potential confounders. Accessible RNA sequencing data were incorporated for subsequent verifications, and Kaplan-Meier survival curves of different gene expressions were used to explore the prognostic value of candidate druggable genes.

RESULTS: MR screening encompassing 4,863 expression quantitative trait loci (eQTL) and 1,072 protein quantitative trait loci (pQTL, with 453 proteins overlapping) were performed. Seven candidate druggable genes were identified, including CD33, ENG, ICOSLG and IL18R1 for lung adenocarcinoma, and VSIR, FSTL1 and TIMP2 for lung squamous cell carcinoma. The results were validated by further transcriptomic investigations.

CONCLUSIONS: Drugs targeting genetically supported genomes are considerably more likely to yield promising efficacy and succeed in clinical trials. We provide compelling genetic evidence to prioritize drug development for NSCLC.

PMID:39263038 | PMC:PMC11384480 | DOI:10.21037/tlcr-24-65

Int Rev Immunol. 2024 Sep 11:1-14. doi: 10.1080/08830185.2024.2402023. Online ahead of print.

ABSTRACT

The occurrence of incidents involving radiation-combined burn injuries (RCBI) poses a significant risk to public health. Understanding the immunological and physiological responses associated with such injuries is crucial for developing care triage to counter the mortality that occurs due to the synergistic effects of radiation and burn injuries. The core focus of this narrative review lies in unraveling the immune response against RCBI. Langerhans cells, mast cells, keratinocytes, and fibroblasts, which induce innate immunity, have been explored for their response to radiation, burns, and combined injuries. In the case of adaptive immune response, exploring behavioral changes in T regulatory (Treg) cells, T helper cells (Th1, Th2, and Th17), and immunoglobulin results in delayed healing compared to burn and radiation injury. The review also includes the function of complement system components such as neutrophils, acute phase proteins (CRP, C3, and C5), and cytokines for their role in RCBI. Combined insults resulting in a reduction in the cell population of immune cells display variation in response based on radiation doses, burn injury types, and their intrinsic radiosensitivity. The lack of approved countermeasures against RCBI poses a significant challenge. Drug repurposing might help to balance immune cell alteration, resulting in fast recovery and decreasing mortality, which gives it clinical significance for its implication on the site of such incidence. However, the exact immune response in RCBI remains insufficiently explored in pre-clinical and clinical stages, which might be due to the non-availability of in vitro models, standard animal models, or human subjects, warranting further research.

PMID:39262163 | DOI:10.1080/08830185.2024.2402023

J Pak Med Assoc. 2024 Mar;74(3 (Supple-3)):S64-S81. doi: 10.47391/JPMA.S3.GNO-08.

ABSTRACT

Low- and middle-income countries (LMICs) have historically been under-represented in clinical trials, leading to a disparity in evidence-based recommendations for the management of neurooncological conditions. To address this knowledge gap, we conducted a scoping review to assess the current literature on clinical trials in neuro-oncology from LMICs. The eligibility criteria for inclusion in this review included clinical trials registered and conducted with human subjects, with available English language text or translation, and focussed on neuro-oncological cases. The literature search strategy captured 408 articles, of which 61 met these criteria, with a significant number of randomised controlled trials from specific LMICs. The review found that LMIC clinical trials have contributed significantly to understanding surgical, chemotherapeutic, and radiation therapy interventions for brain tumours, paediatric cancers, and the repurposing of drugs as new targets in neuro-oncology. These findings highlight the potential for expanding clinical trials research in neuro-oncology in LMICs, which may significantly impact global understanding and management of these conditions, particularly from diverse populations from the global south.

PMID:39262066 | DOI:10.47391/JPMA.S3.GNO-08

Chin Med. 2024 Sep 11;19(1):124. doi: 10.1186/s13020-024-00994-y.

ABSTRACT

BACKGROUND: Drug repositioning has the potential to reduce costs and accelerate the rate of drug development, with highly promising applications. Currently, the development of artificial intelligence has provided the field with fast and efficient computing power. Nevertheless, the repositioning of traditional Chinese medicine (TCM) is still in its infancy, and the establishment of a reasonable and effective research method is a pressing issue that requires urgent attention. The use of graph neural network (GNN) to compute the similarity between TCM prescriptions to develop a method for finding their new indications is an innovative attempt.

METHODS: This paper focused on traditional Chinese medicine prescriptions containing ephedra, with 20 prescriptions for treating external cough and asthma taken as target prescriptions. The remaining 67 prescriptions containing ephedra were taken as to-be-matched prescriptions. Furthermore, a multitude of data pertaining to the prescriptions, including diseases, disease targets, symptoms, and various types of information on herbs, was gathered from a diverse array of literature sources, such as Chinese medicine databases. Then, cosine similarity and Jaccard coefficient were calculated to characterize the similarity between prescriptions using graph convolutional network (GCN) with a self-supervised learning method, such as deep graph infomax (DGI).

RESULTS: A total of 1340 values were obtained for each of the two calculation indicators. A total of 68 prescription pairs were identified after screening with 0.77 as the threshold for cosine similarity. Following the removal of false positive results, 12 prescription pairs were deemed to have further research value. A total of 5 prescription pairs were screened using a threshold of 0.50 for the Jaccard coefficient. However, the specific results did not exhibit significant value for further use, which may be attributed to the excessive variety of information in the dataset.

CONCLUSIONS: The proposed method can provide reference for finding new indications of target prescriptions by quantifying the similarity between prescriptions. It is expected to offer new insights for developing a scientific and systematic research methodology for traditional Chinese medicine repositioning.

PMID:39261848 | DOI:10.1186/s13020-024-00994-y

Sci Rep. 2024 Sep 11;14(1):21282. doi: 10.1038/s41598-024-72136-1.

ABSTRACT

Visceral cestodiases, like cysticercoses and echinococcoses, are caused by cystic larvae from parasites of the Cestoda class and are endemic or hyperendemic in many areas of the world. Current therapeutic approaches for these diseases are complex and present limitations and risks. Therefore, new safer and more effective treatments are urgently needed. The Niemann-Pick C1 (NPC1) protein is a cholesterol transporter that, based on genomic data, would be the solely responsible for cholesterol uptake in cestodes. Considering that human NPC1L1 is a known target of ezetimibe, used in the treatment of hypercholesterolemia, it has the potential for repurposing for the treatment of visceral cestodiases. Here, phylogenetic, selective pressure and structural in silico analyses were carried out to assess NPC1 evolutive and structural conservation, especially between cestode and human orthologs. Two NPC1 orthologs were identified in cestode species (NPC1A and NPC1B), which likely underwent functional divergence, leading to the loss of cholesterol transport capacity in NPC1A. Comparative interaction analyses performed by molecular docking of ezetimibe with human NPC1L1 and cestode NPC1B pointed out to similarities that consolidate the idea of cestode NPC1B as a target for the repurposing of ezetimibe as a drug for the treatment of visceral cestodiases.

PMID:39261546 | DOI:10.1038/s41598-024-72136-1

Sci Rep. 2024 Sep 11;14(1):21258. doi: 10.1038/s41598-024-71922-1.

ABSTRACT

Parkinson's disease is the second most frequent neurodegenerative disease, and its severity is increasing with extended life expectancy. Most of current treatments provide symptomatic relief; however, disease progression is not inhibited. There are multiple trials for treatments that target the causes of the disease but they were flawed. The mechanisms underlying neurodegenerative diseases are intricate, and understanding the interplay among the biological elements involved is crucial. These relationships can be effectively analyzed through biological networks, and the application of network-based analyses in the context of neurodegenerative disease treatment has gained considerable attention. Moreover, considering the significance differences in interactions between biological elements within the network is important. Therefore, we introduce a novel biological pathway based edge-weighted network construction method for drug repurposing in Parkinson's disease. The interaction found in multiple Parkinson's disease-related pathways is more significant than other interactions, and this significance is reflected in the network edge weights. Using the edge-weighted network construction method, we found a significant difference in the efficacy between known and unknown Parkinson's disease drugs. The method predicts drug-disease interactions more accurately than approaches that do not consider the significance differences among interactions, and the paths between the drug and disease within the network correspond to the drug's mechanism of action. In summary, we propose a network-based drug repurposing method using the biological pathway based edge-weighted network. Using this methodology, researchers can find novel drug candidates for the parkinson's disease and their mechanism of actions.

PMID:39261542 | DOI:10.1038/s41598-024-71922-1