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Efficient drug discovery relies on drug repurposing, an important and open research field. This work presents a novel factorization method and a practical comparison of different approaches for drug repurposing. First, we propose a novel tensor-matrix-tensor (TMT) formulation as a new data array method with a gradient-based factorization procedure. Additionally, this paper examines and contrasts four computational drug repurposing approaches-factorization-based methods, machine learning methods, deep learning methods, and graph neural networks-to fulfill the second purpose. We test the strategies on two datasets and assess each approach's performance, drawbacks, problems, and benefits based on results. The results demonstrate that deep learning techniques work better than other strategies and that their results might be more reliable. Ultimately, graph neural methods need to be in an inductive manner to have a reliable prediction., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background and Purpose: Drug repurposing (DR) offers a compelling alternative to traditional drug discovery's lengthy, resource-intensive process. DR is the process of identifying alternative clinical applications for pre-approved drugs as a low-risk and low-cost strategy. Computational approaches are crucial during the early hypothesis-generating stage of DR. However, 'large-scale' data retrieval remains a significant challenge. A computational workflow addressing such limitations might improve hypothesis generation, ultimately benefit patients and advance DR research., Experimental Approach: We introduce a novel computational workflow (combining free-accessible computational platforms) to provide 'proof-of-concept' of the pre-approved drug's suitability for repurposing. Three key phases are included: target fishing (via reverse pharmacophore mapping), target identification (via disease- and drug-target pathway identification) and retrospective literature and drug-like analysis (via in silico ADMET properties determination). Istradefylline is a Parkinson's disease-approved drug with literature-attributed antidepressant properties remaining unclear. Practically applied, istradefylline's antidepressant activity was assessed in the context of major depressive disorder (MDD)., Key Results: Data mining aided by target identification resulted in istradefylline potentially representing a novel antidepressant drug class. Retrieved drug targets (KYNU, MAO-B, ALOX12 and PLCB2) associated with selected MDD pathways (tryptophan metabolism and serotonergic synapse) generated a hypothesis that istradefylline increased extracellular 5-HT levels (MAO-B inhibition) and reduced inflammation (KYNU, ALOX12 and PLCB2 inhibition)., Conclusion and Implications: The practically applied workflow's generated hypothesis aligns with known experimental data, validating the effectiveness of this novel computational workflow. It is a low-risk and low-cost DR computational tool providing a bird's-eye view for exploring alternative clinical applications of pre-approved drugs., (© 2024 The Author(s). British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Diabetes and cancer are two prevalent disorders, pose significant public health challenges and contribute substantially to global mortality rates, with solely 10 million reported cancer-related deaths in 2020. This review explores the pathological association between diabetes and diverse cancer progressions, examining molecular mechanisms and potential therapeutic intersections. From altered metabolic landscapes to dysregulated signaling pathways, the intricate links are delineated, offering a comprehensive understanding of diabetes as a modulator of tumorigenesis. Cancer cells develop drug resistance through mechanisms like enhanced drug efflux, genetic mutations, and altered drug metabolism, allowing them to survive despite chemotherapeutic agent. Glucose emerges as a pivotal player in diabetes progression, and serving as a crucial energy source for cancer cells, supporting their biosynthetic needs and adaptation to diverse microenvironments. Glycation, a non-enzymatic process that produces advanced glycation end products (AGEs), has been linked to the etiology of cancer and has been shown in a number of tumor forms, such as leiomyosarcomas, adenocarcinomas, and squamous cell carcinomas. Furthermore, in aggressive and metastatic breast cancer, the receptor for AGEs (RAGE) is increased, which may increase the malignancy of the tumor. Reprogramming glucose metabolism manifests as hallmark cancer features, including accelerated cell proliferation, angiogenesis, metastasis, and evasion of apoptosis. This manuscript encapsulates the dual narrative of diabetes as a driver of cancer progression and the potential of repurposed antidiabetic drugs as formidable countermeasures. The amalgamation of mechanistic understanding and clinical trial outcomes establishes a robust foundation for further translational research and therapeutic advancements in the dynamic intersection of diabetes and cancer., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Seizure clusters may initiate a chain of events that have economic as well as clinical consequences. The potential economic consequences of seizure clusters must be weighed against the cost of medication to attenuate them. This is true both for individual patients and for society. Data needed for economic analyses include the chance that a cluster will progress to an adverse outcome, such as a need for emergency care, the costs of such an outcome, the cost of a rescue medication (RM), and the effectiveness of the RM. Indirect costs, such as lost employment for patients and caregivers, must also be considered. Several types of economic analyses can be used to determine costs and benefits of a medical intervention. There are studies comparing different RMs from an economic perspective, but there is little direct information on the costs of using an RM versus allowing clusters to run their course. However, the high expense of consequences of seizure clusters makes it likely that effective RMs will make economic as well as medical sense for many patients., (© 2022 International League Against Epilepsy.)

The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) necessitates strategies to identify prophylactic and therapeutic drug candidates to enter rapid clinical development. This is particularly true, given the uncertainty about the endurance of the immune memory induced by both previous infections or vaccines, and given the fact that the eradication of SARS-CoV-2 might be challenging to reach, given the attack rate of the virus, which would require unusually high protection by a vaccine. Here, we show how raloxifene, a selective estrogen receptor modulator with anti-inflammatory and antiviral properties, emerges as an attractive candidate entering clinical trials to test its efficacy in early-stage treatment COVID-19 patients., (© 2021. The Author(s).)

Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring., (© 2021. Springer Nature Limited.)

Morbidity and mortality from opioid use disorders (OUD) and other substance use disorders (SUD) is a major public health crisis, yet there are few medications to treat them. There is an urgency to accelerate SUD medication development. We present an integrated drug repurposing strategy that combines computational prediction, clinical corroboration using electronic health records (EHRs) of over 72.9 million patients and mechanisms of action analysis. Among top-ranked repurposed candidate drugs, tramadol, olanzapine, mirtazapine, bupropion, and atomoxetine were associated with increased odds of OUD remission (adjusted odds ratio: 1.51 [1.38-1.66], 1.90 [1.66-2.18], 1.38 [1.31-1.46], 1.37 [1.29-1.46], 1.48 [1.25-1.76], p value < 0.001, respectively). Genetic and functional analyses showed these five candidate drugs directly target multiple OUD-associated genes including BDNF, CYP2D6, OPRD1, OPRK1, OPRM1, HTR1B, POMC, SLC6A4 and OUD-associated pathways, including opioid signaling, G-protein activation, serotonin receptors, and GPCR signaling. In summary, we developed an integrated drug repurposing approach and identified five repurposed candidate drugs that might be of value for treating OUD patients, including those suffering from comorbid conditions., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited part of Springer Nature.)

Developmental and epileptic encephalopathies (DEEs) are among the most challenging of all epilepsies to manage, given the exceedingly frequent and often severe seizure types, pharmacoresistance to conventional antiseizure medications, and numerous comorbidities. During the past decade, efforts have focused on development of new treatment options for DEEs, with several recently approved in the United States or Europe, including cannabidiol as an orphan drug in Dravet and Lennox-Gastaut syndromes and everolimus as a possible antiepileptogenic and precision drug for tuberous sclerosis complex, with its impact on the mammalian target of rapamycin pathway. Furthermore, fenfluramine, an old drug, was repurposed as a novel therapy in the treatment of Dravet syndrome. The evolution of new insights into pathophysiological processes of various DEEs provides possibilities to investigate novel and repurposed drugs and to place them into the context of their role in future management of these patients. The purpose of this review is to provide an overview of these new medical treatment options for the DEEs and to discuss the clinical implications of these results for improved treatment., (© 2021 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Patients with substance use disorders (SUD) are at high risk to die by suicide. So far, the neurobiology of the suicide-SUD association has not been elucidated. This study aimed to identify potential pharmacological targets among hub genes from brain gene co-expression networks of individuals with SUD in a suicidal and non-suicidal context. Post-mortem samples from the prefrontal cortex of 79 individuals were analyzed. Individuals were classified into the following groups: suicides with SUD (n = 28), suicides without SUD (n = 23), nonsuicides with SUD (n = 9), nonsuicides without SUD (n = 19). Gene expression profiles were evaluated with the Illumina HumanHT-12 v4 array. Co-expression networks were constructed in WGCNA using the differentially expressed genes found in the comparisons: (a) suicides with and without SUD and (b) nonsuicides with and without SUD. Hub genes were selected for drug-gene interaction testing in the DGIdb database. Among drugs interacting with hub genes in suicides we found MAOA inhibitors and dextromethorphan. In the nonsuicide individuals, we found interactions with eglumegad and antipsychotics (olanzapine, clozapine, loxapine). Modafinil was found to interact with genes in both suicides and nonsuicides. These drugs represent possible candidate treatments for patients with SUD with and without suicidal behavior and their study in each context is encouraged., (© 2021 Wiley Periodicals LLC.)

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. [ABSTRACT FROM AUTHOR]

This special issue focuses on reporting on novel types of chemical probes and chemical tools, including but not limited to pharmacological modulators regardless of mechanism of action (inhibitors, activators, degraders, etc.), fluorescent probes, crosslinking probes and reagents, probes for activity-based profiling, chemoproteomic probes, etc. The focus is on reports that directly provide evidence for how these tools advance drug discovery and/or how they can be integrated into drug discovery pipelines. [Extracted from the article]

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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. [ABSTRACT FROM AUTHOR]

Medicine repurposing is a strategy to identify new uses for the existing medicines for the purpose of addressing areas of unmet medical need. This paper aims to provide horizon scanning intelligence on repurposed medicines that are evaluated by non-commercial organizations such as academia and highlights opportunities for further research to improve patient health outcomes. A scan of the clinical landscape of non-commercially sponsored repurposed medicines is routinely conducted by the NIHR Innovation Observatory (IO). This ongoing project involves a horizon scan of clinical trial registries and the IO's internal horizon scanning Medicines Innovation Database to identify potential candidate medicines used as monotherapy or in combination to treat new indications outside the scope of their licensed indication. In addition to making these data publicly available, the output also supports the NHS England Medicines Repurposing Programme. The snapshot scan reported here (trials completing April 2020-March 2023) identified a total of 528 technologies (meaning, a single product or combination of medicinal products targeting a specific indication in one or more related trials). The technologies were classified according to their characteristics and targeted therapeutic indications as well as revealing the least treated disease conditions. The candidate medicines identified in this scan could potentially receive tailored support toward adoption into practice and policy. The NIHR IO regularly provides this scan as a source of intelligence on repurposed medicines. This provides valuable insights into innovation trends, gaps, and areas of unmet clinical need., (© 2024 The Author(s). Pharmacology Research & Perspectives published by British Pharmacological Society and American Society for Pharmacology and Experimental Therapeutics and John Wiley & Sons Ltd.)

Pancreatic cancer, a malignancy notorious for its late-stage diagnosis and low patient survival rates, remains a formidable global health challenge. The currently available FDA-approved treatments for pancreatic cancer, notably chemotherapeutic agents, exhibit suboptimal efficacy, often accompanied by concerns regarding toxicity. Given the intricate nature of pancreatic cancer pathogenesis and the time-intensive nature of in silico drug discovery approaches, drug repurposing emerges as a compelling strategy to expedite the development of novel therapeutic interventions. In our study, we harnessed transcriptomic data from an exhaustive exploration of four diverse databases, ensuring a rigorous and unbiased analysis of differentially expressed genes, with a particular focus on upregulated genes associated with pancreatic cancer. Leveraging these pancreatic cancer-associated host protein targets, we employed a battery of cutting-edge bioinformatics tools, including Cytoscape STRING, GeneMANIA, Connectivity Map, and NetworkAnalyst, to identify potential small molecule drug candidates and elucidate their interactions. Subsequently, we conducted meticulous docking and redocking simulations for the selected drug-protein target pairs. This rigorous computational approach culminated in the identification of two promising broad-spectrum drug candidates against four pivotal host genes implicated in pancreatic cancer. Our findings strongly advocate for further investigation and preclinical validation of these candidates. Specifically, we propose prioritizing Dasatinib for evaluation against MMP3, MMP9, and EGFR due to their remarkable binding affinities, as well as Pioglitazone against MMP3, MMP2 and MMP9. These discoveries hold great promise in advancing the therapeutic landscape for pancreatic cancer, offering new avenues for improving patient outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Breast cancer is a substantial global health problem, and drug repurposing provides novel opportunities to address the urgent need for therapeutics. According to significant Mendelian randomization (MR) results, we identified 26 genes for overall breast cancer, 25 genes for ER+ breast cancer and 4 genes (CASP8, KCNN4, MYLK4, TNNT3) for ER- breast cancer. In order to explore the differences between 5 intrinsic subtypes, we found 29 actionable druggable genes for Luminal A breast cancer, 2 genes (IGF2 and TNNT3) for Luminal B breast cancer, 1 gene (FAAH) for Luminal B HER2 negative breast cancer, and 3 genes (CASP8, KCNN4, and TP53) for triple-negative breast cancer. After colocalization analysis, we determined OPRL1 as a prioritized target in both overall and Luminal A breast cancer. Additionally, FES and FAAH were considered prioritized targets for ER+ breast cancer. Through molecular docking, crizotinib stand out as a prioritized FES target drug repurposing opportunity with the lowest binding energy (-10.13 kJ·mol -1 ) and CCK-8 assay showed ER+ cell groups were more sensitive to crizotinib than ER- cell groups. In conclusion, OPRL1 was identified as a prioritized target for both overall and Luminal A breast cancer. Moreover, FES and FAAH were recognized as prioritized targets for ER+ breast cancer., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Liyi Zhang reports financial support was provided by Natural Science Foundation of Fujian province of China and the First Affiliated Hospital of Xiamen University for Excellent Nurturing Program. Qingmo Yang reports financial support was provided by Beijing Medical Award Foundation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

While the COVID-19 crisis is still ongoing, a new public health threat has emerged with recent outbreaks of monkeypox (mpox) infections in Africa. Mass vaccination is not currently recommended by the World Health Organization (WHO), and antiviral treatments are yet to be specifically approved for mpox, although existing FDA-approved drugs (Tecovirimat, Brincidofovir, and Cidofovir) may be used in severe cases or for immunocompromised patients. A first-line of defense is thus drug repurposing, which was heavily attempted against SARS-CoV-2 - albeit with limited success. This review focuses on nucleoside analogues as promising antiviral candidates for targeting of the viral DNA-dependent DNA polymerase. In contrast to broad-spectrum screening approaches employed for SARS-CoV-2, we emphasize the importance of understanding the structural specificity of viral polymerases for rational selection of potential candidates. By comparing DNA-dependent DNA polymerases with other viral polymerases, we highlight the unique features that influence the efficacy and selectivity of nucleoside analogues. These structural insights provide a framework for the preselection, repurposing, optimization, and design of nucleoside analogues, aiming to accelerate the development of targeted antiviral therapies for mpox and other viral infections., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Predicting drug-target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug-target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs and the sequence features of targets, employing a multi-scale convolutional neural network (MSCNN) with parallel shared-weight modules to extract features from the drug side. For the target side, it combines MSCNN with Transformer modules to capture both local and global features effectively. The extracted features are then weighted and fused, enabling comprehensive feature representation to enhance the predictive power of the model. Experimental results on the Davis dataset demonstrate that MCF-DTI achieves an AUC of 0.9746 and an AUPR of 0.9542, outperforming other state-of-the-art models. Our case study demonstrates that our model effectively validated several known drug-target relationships in lung cancer and predicted the therapeutic potential of certain preclinical compounds in treating lung cancer. These findings contribute valuable insights for subsequent drug repurposing efforts and novel drug development.

Tyrosine kinase inhibitors (TKIs) represent a pivotal class of targeted therapies in oncology, with multiple generations developed to address diverse molecular targets. Imatinib is the first TKI developed to target the BCR-ABL1 chimeric protein, which is the key driver oncogene implicated in Philadelphia chromosome-positive chronic myeloid leukemia (CML). Several second-generation tyrosine kinase inhibitors (2GTKIs), such as nilotinib, dasatinib, bosutinib, and radotinib (RTB), followed the groundbreaking introduction of imatinib. RTB occupies the unique position of being the least explored member of this class. While nilotinib, dasatinib, and bosutinib have garnered significant attention and extensive research focus, RTB remains relatively uncharted in comparison to its counterparts. Fundamental drug characteristics, such as the pharmacokinetic and pharmacodynamic properties of RTB, remain unavailable in existing sources. Compared to other 2GTKIs, RTB has been less utilized in combinatorial drug studies, and no investigations have been reported on its effects on solid tumors to date. However, the effects of RTB have been studied in acute myeloid leukemia (AML), multiple myeloma (MM), Parkinson's disease, and idiopathic pulmonary fibrosis (IPF). Although RTB has been investigated in some conditions, these studies are still in their preliminary stages and are comparatively lesser than studies on other 2GTKIs. This review is the first attempt that extensively presents a compilation of data on RTB and describes its therapeutic potential against blood and solid tumors. Further investigations on RTB could expand its chemotherapeutic usage in various solid tumors and enhance the possibility of drug repurposing in cancer therapy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Ataxia Telangiectasia (A-T) is a rare, autosomal recessive genetic disorder characterized by a variety of symptoms, including progressive neurodegeneration, telangiectasia, immunodeficiency, and an increased susceptibility to cancer. It is caused by bi-allelic mutations impacting a gene encoding a serine/threonine kinase ATM (Ataxia Telangiectasia Mutated), which plays a crucial role in DNA repair and maintenance of genomic stability. The disorder primarily affects the nervous system, leading to a range of neurological issues, including cerebellar ataxia. The cause of neurodegeneration due to mutations in ATM is still an area of investigation, and currently there is no known treatment to slow down or stop the progression of the neurological problems. In this collaboration of the A-T Children's Project (ATCP) with Charles River Discovery, we successfully developed a high-throughput assay using induced pluripotent stem cells (iPSC) from A-T donors to measure DNA damage response (DDR). By measuring the changes in levels of activated phosphorylated CHK2 (p-CHK2), which is a downstream signaling event of ATM, we were able to identify compounds that restore this response in the DDR pathway in A-T derived patient cells. Over 6,000 compounds from small molecule drug repurposing libraries were subsequently screened in the assay developed, leading to identification of several promising in vitro hits. Using the assay developed and the identified hits opens avenues to investigate potential therapeutics for A-T., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Brad Margus reports a relationship with AT Childrens Project that includes: board membership. Jennifer Thornton reports a relationship with AT Childrens Project that includes: employment and non-financial support. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

The substance P (SP) and neurokinin-1 receptor (NK-1R) axis is crucial in numerous pathological processes, including inflammation, stress responses, pain perception, and vomiting. Consequently, aprepitant, an NK-1R blocker, is used as an antiemetic in chemotherapy, including the use of doxorubicin (DOX), but whether aprepitant can also assuage DOX-mediated chemobrain remains to be unveiled. Here, we scrutinized the potential neuroprotective effect and underlying mechanisms of aprepitant using DOX-induced chemobrain model, where rats were allocated into 4 groups (control, aprepitant, DOX, and DOX+ aprepitant). Cognitive deficits were assessed through behavioral tests and hippocampal structural alterations were determined by H&E and toluidine blue staining. Biochemical measurements were performed using ELISA, real-time quantitative PCR, western blotting, and immunohistochemical methods. Aprepitant improved cognitive responses, and hippocampal morphology, enhancing the presence of intact neurons. At the molecular tier, aprepitant significantly reduced hippocampal contents of SP and the inflammatory markers NF-κB and IL-1β. Additionally, it signified its antioxidant and antiapoptotic capacities by downregulating cleaved caspase-3 protein expression and curbing the content of malondialdehyde but boosted those of glutathione and Bcl-2. Aprepitant also downregulated the expression of miR-146a and turned off the endoplasmic reticulum (ER) stress cascade PERK/eIF-2α/ATF-4/CHOP. To recapitulate, aprepitant demonstrates a neuroprotective effect against DOX-mediated chemobrain by alleviating inflammatory, oxidative, and apoptotic responses, partly by reducing SP, ER stress, and miR-146a. These findings not only underscore the potential of aprepitant as a neuroprotective agent but also offer new understanding of the mechanisms behind chemobrain, leading to better therapeutic strategies for cancer patients., Competing Interests: Declaration of competing interest The corresponding author on behalf of the other coauthors declares having no conflict of interest that can influence the submitted work. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The growing prevalence of MDR and XDR bacterial pathogens is posing a critical threat to global health. Traditional antibiotic development paths have encountered significant challenges and are drying up thus necessitating innovative approaches. Drug repurposing, which involves identifying new therapeutic applications for existing drugs, offers a promising alternative to combat resistant pathogens. By leveraging pre-existing safety and efficacy data, drug repurposing accelerates the development of new antimicrobial therapy regimes. This review explores the potential of repurposing existing FDA approved drugs against the ESKAPE and other clinically relevant bacterial pathogens and delves into the identification of suitable drug candidates, their mechanisms of action, and the potential for combination therapies. It also describes clinical trials and patent protection of repurposed drugs, offering perspectives on this evolving realm of therapeutic interventions against drug resistance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Breast cancer has been one of the supreme causes of cancer-related deaths among women worldwide. To make the case even more compounded, due to innate or acquired causes, cancer cells often develop resistance against the available chemotherapy or monotargeted treatments. This resistance is concomitant with increased activation of the MAPK (mitogen-activated protein kinase) signaling pathway. This study simultaneously targets three imperative intermediates in this pathway using molecular docking and real-time simulation. Docking was performed via the integrated AutoDock Vina 1.1.2 & 1.2.5 of the PyRx software, while the Discovery Studio (BIOVIA) v24.1.0.23298 was utilized to conduct the simulation. The aim is to investigate the therapeutic prospects of known potential inhibitors of the targeted intermediates and repurposable drugs to comprehend the effectiveness of targeting these trinodes simultaneously. The target points were deemed to be PDPK1 (3-phosphoinositide-dependent protein kinase 1), ERK1/2 (extracellular signal-related protein kinases 1/2), and mTOR (mammalian target of Rapamycin). Our study reveals that out of the candidate inhibitors chosen for each node, MP7 exhibited the most superior binding affinities for all three: -10.918 kcal/mol for PDPK1, -10.224 kcal/mol for ERK1, -10.134 kcal/mol for ERK2, and -9.2 kcal/mol for mTOR (via AutoDock Vina 1, .2.5). Some scores with MP7 were often higher than the available single-targeted drugs for different nodes in the MAPK pathway. Additionally, a total of 1867 repurposed analgesic, antibiotic, and antiparasitic drugs, including Zavegepant (-13.399 kcal/mol for PDPK1), Adozelesin (-11.74 kcal/mol for mTOR) and Modoflaner (-11.29 kcal/mol for PDPK1), showed promising binding energetics while targeting our triad points than other compounds used. This approach prompts for mitigating not only breast cancer but other elusive diseases as well, with state-of-the-art multitargeted therapies coupled with bioinformatic strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Autophagy is a critical cellular process for degrading damaged organelles and proteins under stressful conditions and has casually been shown to contribute to tumor survival and drug resistance. Sequestosome-1 (SQSTM1/p62) is an autophagy receptor that interacts with its binding partners via the LC3-interacting region (LIR). The p62 protein has been a highly researched target for its critical role in selective autophagy. In this study, we aimed to identify FDA-approved drugs that bind to the LIR motif of p62 and inhibit its LIR function, which could be useful targets for modulating autophagy. To this, the homology model of the p62 protein was predicted using biological data, and docking analysis was performed using Molegro Virtual Docker and PyRx softwares. We further assessed the toxicity profile of the drugs using the ProTox-II server and performed dynamics simulations on the effective candidate drugs identified. The results revealed that the kanamycin, velpatasvir, verteporfin, and temoporfin significantly decreased the binding of LIR to the p62 protein. Finally, we experimentally confirmed that Kanamycin can inhibit autophagy-associated acidic vesicular formation in breast cancer MCF-7 and MDA-MB 231 cells. These repositioned drugs may represent novel autophagy modulators in clinical management, warranting further investigation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study employed a drug repositioning strategy to discover novel PD-L1 small molecule inhibitors. 3D-QSAR pharmacophore models were establishedand subsequently validated through various means to select a robust model, Hypo-1, suitable for virtual screening. Hypo1 was used toscreen a library of 7,475 compounds from the Drugbank database, leading to the identification of 283 molecules following molecular docking with PD-L1.19 compounds underwent HTRF assays, with 15 showing varying degrees of inhibition of the PD-1/PD-L1 interaction. Compounds2202,2204,2207, and2208were further confirmed to bind to PD-L1 using SPR experiments. Among them, compound2204(Daclatasvir, K D  = 11.4 μM) showeda higher affinity for human PD-L1 than the control compound BMS-1. In the HepG2/Jurkat cell co-culture model, Daclatasvir effectively activated Jurkat cells to kill HepG2 cells. In the mouse H22 hepatocellular tumor model, Daclatasvir significantly inhibited tumor growth (TGI = 53.4 % at a dose of 100 mg/kg). Its anti-tumor effect was more pronounced when combined with Lenvatinib (TGI = 85.1 %). Flow cytometry analysis of splenocytes and tumor cells indicated that Daclatasvir activated the immune system in both models. In summary, Daclatasvir was identified as a novel PD-L1small molecule inhibitor., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The treatment of status epilepticus (SE) has changed little in the last 20 years, largely because of the high risks and costs of new drug development for SE. Moreover, SE poses specific challenges to drug development, such as patient diversity, logistical hurdles, and the need for acute treatment strategies that differ from chronic seizure prevention. This has reduced the appetite of industry to develop new drugs in this area. Drug repurposing is an attractive approach to address this unmet need. It offers significant advantages, including reduced development time, lower costs, and higher success rates, compared to novel drug development. Here I demonstrate how novel methods integrating biological knowledge and computational methods can be applied to drug repurposing in status epilepticus. Biological approaches focus on addressing mechanisms underlying drug resistance in SE (using for example ketamine, tacrolimus and safinamide) and longer-term consequences (using for example omaveloxolone, celecoxib and losartan). Additionally, artificial intelligence platforms, such as ChatGPT, can rapidly generate promising drug lists, while in silico methods can analyze gene expression changes to predict molecular targets. Combining AI and in silico approaches has identified several candidate drugs, including metformin, sirolimus and riluzole, for SE treatment. Despite the promise of repurposing, challenges remain, such as intellectual property issues and regulatory barriers. Nonetheless, drug repurposing presents a viable solution to the high costs and slow progress of traditional drug development for SE. This paper is based on a presentation made at the 9th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures, in April 2024., Competing Interests: Declaration of competing interest MCW has received honoraria or consulting fees from Bioquest, Eisai, UCB pharma, Angelini, Seer and EpilepsyGtx. MCW has shares in EpilpesyGtx, an epilepsy gene therapy company., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Alzheimer's disease (AD) is the most common neurodegenerative disease, characterized by memory loss, speech and motor defects, personality changes, and psychological disorders. The exact cause of AD remains unclear. Current treatments focus on maintaining neurotransmitter levels or targeting β-amyloid (Aβ) protein, but these only alleviate symptoms and do not reverse the disease. Developing new drugs is time-consuming, costly, and has a high failure rate. Utilizing multi-omics for drug repositioning has emerged as a new strategy. Based on transcriptomic perturbation data of over 40,000 drugs in human cells from the LINCS-L1000 database, our study employed the Jaccard index and hypergeometric distribution test for reverse transcriptional feature matching analysis, identifying Givinostat as a potential treatment for AD. Our research found that Givinostat improved cognitive behavior and brain pathology in models and enhanced hippocampal synaptic plasticity. Transcriptome sequencing revealed increased expression of mitochondrial respiratory chain complex proteins in the brains of APP/PS1 mice after Givinostat treatment. Functionally, Givinostat restored mitochondrial membrane potential, reduced reactive oxygen species, and increased ATP content in Aβ-induced HT22 cells. Additionally, it improved mitochondrial morphology and quantity in the hippocampus of APP/PS1 mice and enhanced brain glucose metabolic activity. These effects are linked to Givinostat promoting mitochondrial biogenesis and improving mitochondrial function. In summary, Givinostat offers a promising new strategy for AD treatment by targeting mitochondrial dysfunction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Breast cancer, a leading cause of cancer-related mortality in women, is characterized by genomic instability and aberrant gene expression, often influenced by noncanonical nucleic acid structures such as G-quadruplexes (G4s). These structures, commonly found in the promoter regions and 5'-untranslated RNA sequences of several oncogenes, play crucial roles in regulating transcription and translation. Stabilizing these G4 structures offers a promising therapeutic strategy for targeting key oncogenic pathways. In this study, we employed a drug repurposing approach to identify FDA-approved drugs capable of binding and stabilizing G4s in breast cancer-related genes. Using ligand-based virtual screening and biophysical methods, we identified several promising compounds, such as azelastine, belotecan, and irinotecan, as effective G4 binders, with significant antiproliferative effects in breast cancer cell lines. Notably, belotecan and irinotecan exhibited a synergistic mechanism, combining G4 stabilization with their established topoisomerase I inhibition activity to enhance cytotoxicity in cancer cells. Our findings support the therapeutic potential of G4 stabilization in breast cancer, validate drug repurposing as an efficient strategy to identify G4-targeting drugs, and highlight how combining G4 stabilization with other established drug activities may improve anticancer efficacy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Masson SAS. All rights reserved.)

Tuberculosis (TB) is one of the leading causes of death due to infectious disease. It is a treatable disease; however, conventional treatment requires a lengthy treatment regimen with severe side effects, resulting in poor compliance among TB patients. Intermittent drug use, the non-compliance of patients, and prescription errors, among other factors, have led to the emergence of multidrug-resistant TB, while the mismanagement of multidrug-resistant TB (MDR-TB) has eventually led to the development of extensively drug-resistant tuberculosis (XDR-TB). Thus, there is an urgent need for new drug development, but due to the enormous expenses and time required (up to 20 years) for new drug research and development, new therapeutic approaches to TB are required. Host-directed therapies (HDT) could be a most attractive strategy, as they target the host defense processes instead of the microbe and thereby may prevent the alarming rise of MDR- and XDR-TB. This paper reviews the progress in HDT for the treatment of TB using repurposed drugs which have been investigated in clinical trials (completed or ongoing) and plant-derived natural products that are in clinical or preclinical trial stages. Additionally, this review describes the existing challenges to the development and future research directions in the implementation of HDT.

Drug repositioning, which involves identifying new therapeutic indications for approved drugs, is pivotal in accelerating drug discovery. Recently, to mitigate the effect of label sparsity on inferring potential drug-disease associations (DDAs), graph contrastive learning (GCL) has emerged as a promising paradigm to supplement high-quality self-supervised signals through designing auxiliary tasks, then transfer shareable knowledge to main task, i.e. DDA prediction. However, existing approaches still encounter two limitations. The first is how to generate augmented views for fully capturing higher-order interaction semantics. The second is the optimization imbalance issue between auxiliary and main tasks. In this paper, we propose a novel heterogeneous Graph Contrastive learning method with Gradient Balance for DDA prediction, namely GCGB. To handle the first challenge, a fusion view is introduced to integrate both semantic views (drug and disease similarity networks) and interaction view (heterogeneous biomedical network). Next, inter-view contrastive learning auxiliary tasks are designed to contrast the fusion view with semantic and interaction views, respectively. For the second challenge, we adaptively adjust the gradient of GCL auxiliary tasks from the perspective of gradient direction and magnitude for better guiding parameter update toward main task. Extensive experiments conducted on three benchmarks under 10-fold cross-validation demonstrate the model effectiveness., (© The Author(s) 2024. Published by Oxford University Press.)

Aim: The current study explores niclosamide's neuroprotective potential in an animal model of autism spectrum disorder (ASD) and goes further to understand how the ERK/MAPK signaling pathway is thought to contribute to this activity., Methods: In order to create an autism-like phenotype in rats, 4 μl of 1 M PPA was infused intracerebroventricularly. The oral treatment with niclosamide (50 and 100 mg/kg) and risperidone (1 mg/kg) (used as standard) was given from 3rd to 30th day. Between the 14th and 28th day, behavioral assessments were made for sociability, stereotypy, anxiety, depression, novelty preference, repetitive behavior, and perseverative behavior. The animals were euthanized on the 29th day, and oxidative stress markers were assessed in the brain homogenate. The levels of neuroinflammatory cytokines such as TNF-α, IL-6, NF-κB, IFN-γ and glutamate were estimated using ELISA kits. To assess the involvement of the ERK/MAPK signaling pathway, levels of Nrf2 and ERK2 were also measured., Key Findings: Niclosamide therapy significantly restored behavioral, biochemical, neurological, and molecular impairments. Hence, niclosamide could be a potential neurotherapeutic candidate for further studies for use in ASD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Motivation: The greatest risk factor for many non-communicable diseases is aging. Studies on model organisms have demonstrated that genetic and chemical perturbation alterations can lengthen longevity and overall health. However, finding longevity-enhancing medications and their related targets is difficult., Method: In this work, we designed a novel drug repurposing model by identifying the interaction between aging-related genes or targets and drugs similar to aging disease. Each disease is associated with certain specific genetic factors for the occurrence of that disease. The factors include gene expression, pathway, miRNA, and degree of genes in the protein-protein interaction network. In this paper, we aim to find the drugs that prolong the life span of humans with their aging-related targets using the above-mentioned factors. In addition, the contribution or importance of each factor may vary among drugs and targets. Therefore, we designed a novel multi-layer random walk-based network representation learning model including node and edge weight to learn the features of drugs and targets respectively., Result: The performance of the proposed model is demonstrated using k-fold cross-validation (k = 5). This model achieved better performance with scores of 0.93 and 0.91 for precision and recall respectively. The drugs identified by the system are evaluated to be potential candidates for aging since the degree of interaction between the potential drugs and their gene sets are high. In addition, the genes that are interacting with drugs produce the same biological functions. Hence the life span of the human will be increased or prolonged., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Five New or Repurposed Drugs (NRDs) were approved in the last decade for treatment of multi-drug resistant tuberculosis: bedaquiline, clofazimine, linezolid, delamanid, and pretomanid. Unfortunately, resistance to these drugs emerged faster than anticipated, potentially due to preexisting resistance in naïve strains. Previous investigations into the rapid emergence have mostly included short variants. For the first time, we utilize de novo-assembled genomes, and systematically include Structural Variations (SV) and heterogeneity to comprehensively study this rapid emergence. We show high prevalence of preexisting resistance, identify novel markers of resistance, and lay the foundation for preventing preexisting resistance in future drug development., Methods: First, a systematic literature review revealed 313 NRD resistance variants in 13 genes. Next, 409 globally diverse clinical isolates collected prior to the drugs' programmatic use (308 were multidrug resistant, 106 had de novo assembled genomes) were utilized to study the 13 genes comprehensively for conventional, structural, and heterogeneous variants., Findings: We identified 5 previously reported and 67 novel putative NRD resistance variants. These variants were 2 promoter mutations (in 8/409 isolates), 13 frameshifts (21/409), 6 SVs (9/409), 35 heterogeneous frameshifts (32/409) and 11 heterogeneous SVs (12/106). Delamanid and pretomanid resistance mutations were most prevalent (48/409), while linezolid resistance mutations were least prevalent (8/409)., Interpretation: Preexisting mutations implicated in resistance to at least one NRD was highly prevalent (85/409, 21 %). This was mostly caused by loss-of-function mutations in genes responsible for prodrug activation and efflux pump regulation. These preexisting mutations may have emerged through a bet-hedging strategy, or through cross-resistance with non-tuberculosis drugs such as metronidazole. Future drugs that could be resisted through loss-of-function in non-essential genes may suffer from preexisting resistance. The methods used here for comprehensive preexisting resistance assessment (especially SVs and heterogeneity) may mitigate this risk during early-stage drug development., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Faramarz Valafar reports financial support was provided by National Institute of Allergy and Infectious Diseases. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier GmbH. All rights reserved.)

Glioblastoma (GBM) is one of the most aggressive and fatal cancers, for which Temozolomide (TMZ) chemo drug is commonly used for its treatment. However, patients gradually develop resistance to this drug, leading to tumor relapse. In our previous study, we have identified lncRNAs that regulate chemoresistance through the competing endogenous RNA (ceRNA) mechanism. In this study, we tried to find FDA-approved drugs against the target proteins of these ceRNA networks through drug repurposing using differential gene expression profiles, which could be used to nullify the effect of lncRNAs and promote the sensitivity of TMZ in GBM. We performed molecular docking and simulation studies of predicted repurposed drugs and their targets. Among the predicted repurposed drugs, we found BMS345541 has a higher binding affinity towards its target protein - FOXG1, making it a more stable complex with FOXG1-DNA. The ADMET analysis of this drug BMS345541 shows a higher half-life and lower cytotoxicity level than other predicted repurposed drugs. Hence, we conjecture that this could be a better drug for increasing the sensitivity of TMZ for treating GBM patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Globally, colorectal cancer is a major health problem that ranks in third place in terms of occurrence and second in terms of mortality worldwide. New cases increase annually, with the absence of effective therapies, especially for metastatic colorectal cancer, emphasizing the need for novel therapeutic approaches. Although conventional treatments are commonly used in oncotherapy, their success rate is low, which leads to the exploration of novel technologies. Recent efforts have focused on developing safe and efficient cancer nanocarriers. With their nanoscale properties, nanocarriers have the potential to utilize internal metabolic modifications amid cancer and healthy cells. Drug repurposing is an emerging strategy in cancer management as it is a faster, cheaper, and safer method than conventional drug development. However, most repurposed drugs are characterized by low-key pharmacokinetic characteristics, such as poor aqueous solubility, permeability, retention, and bioavailability. Nanoparticles formulations and delivery have expanded over the past few decades, creating opportunities for drug repurposing and promises as an advanced cancer modality. This review provides a concise and updated overview of colorectal cancer treatment regimens and their therapeutic limitations. Furthermore, the chemotherapeutic effect of various FDA-approved medications, including statins, non-steroidal anti-inflammatory drugs, antidiabetic and anthelmintic agents, and their significance in colorectal cancer management. Along with the role of various nanocarrier systems in achieving the desired therapeutic outcomes of employing these redefined drugs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Repurposing antidiabetic drugs for the treatment of Alzheimer's disease (AD) has emerged as a promising therapeutic strategy. This review examines the potential of repurposing antidiabetic drugs for AD treatment, focusing on preclinical evidence, clinical trials, and observational studies. In addition, the review aims to explore challenges and opportunities in repurposing antidiabetic drugs for AD, emphasizing the importance of well-designed clinical trials that consider patient selection criteria, refined outcome measures, adverse effects, and combination therapies to enhance therapeutic efficacy. Preclinical evidence suggests that glucagon-like peptide-1 (GLP-1) analogs, dipeptidyl peptidase-4 (DPP4) inhibitors, metformin, thiazolidinediones, and sodium-glucose co-transporter-2 (SGLT2) inhibitors exhibit neuroprotective effects in AD preclinical models. In preclinical studies, antidiabetic drugs have demonstrated neuroprotective effects by reducing amyloid beta (Aβ) plaques, tau hyperphosphorylation, neuroinflammation, and cognitive impairment. Antidiabetic drug classes, notably GLP-1 analogs and SGLT2 inhibitors, and a reduced risk of dementia in patients with diabetes mellitus. While the evidence for DPP4 inhibitors is mixed, some studies suggest a potential protective effect. On the other hand, alpha-glucosidase inhibitors (AGIs) and sulfonylureas may potentially increase the risk, especially in those experiencing recurrent hypoglycemic events. Repurposing antidiabetic drugs for AD is a promising therapeutic strategy, but challenges such as disease heterogeneity, limited biomarkers, and benefits versus risk evaluation need to be addressed. Ongoing clinical trials in mild cognitive impairment (MCI) and early AD patients without diabetes will be crucial in determining the clinical efficacy and safety of the antidiabetic drugs, paving the way for potential treatments for AD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Leishmaniasis is a disease caused by the parasite Leishmania donovani affecting populations belonging to developing countries. The present study explores drug repurposing as an innovative strategy to identify new uses for approved clinical drugs, reducing the time and cost required for drug discovery. The three-dimensional structure of Leishmania donovani Sterol C-24 methyltransferase (LdSMT) was modeled and 1615 FDA-approved drugs from the ZINC database were computationally screened to identify the potent leads. Fulvestrant, docetaxel, indocyanine green, and iohexol were shortlisted as potential leads with the highest binding affinity and fitness scores for the concerned pathogenic receptor. Molecular dynamic simulation studies showed that the macromolecular complexes of indocyanine green and iohexol with LdSMT remained stable throughout the simulation and can be further evaluated experimentally for developing an effective drug. The proposed leads have further demonstrated promising safety profiles during cytotoxicity analysis on the J774.A1 macrophage cell line. Mechanistic analysis with these two drugs also revealed significant morphological alterations in the parasite, along with reduced intracellular parasitic load. Overall, this study demonstrates the potential of drug repurposing in identifying new treatments for leishmaniasis and other diseases affecting developing countries, highlighting the importance of considering approved clinical drugs for new applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Despite the WHO's recommended treatment regimen, challenges such as patient non-adherence and the emergence of drug-resistant strains persist with TB claiming 1.5 million lives annually. In this study, we propose a novel approach by targeting the DNA replication-machinery of M.tb through drug-repurposing. The β2-Sliding clamp (DnaN), a key component of this complex, emerges as a potentially vulnerable target due to its distinct structure and lack of human homology. Leveraging TBVS, we screened ∼2600 FDA-approved drugs, identifying five potential DnaN inhibitors, by employing computational studies, including molecular-docking and molecular-dynamics simulations. The shortlisted compounds were subjected to in-vitro and ex-vivo studies, evaluating their anti-mycobacterial potential. Notably, Dicoumarol, Paromomycin, and Posaconazole exhibited anti-TB properties with a MIC value of 6.25, 3.12 and 50 μg/ml respectively, with Dicoumarol and Paromomycin, demonstrating efficacy in reducing live M.tb within macrophages. Biophysical analyses confirmed the strong binding-affinity of DnaN drug complexes, validating our in-silico predictions. Moreover, RNA-Seq data revealed the upregulation of proteins associated with DNA repair and replication mechanisms upon Paromomycin treatment. This study explores repurposing FDA-approved drugs to target TB via the mycobacterial DNA replication-machinery, showing promising inhibitory effects. It sets the stage for further clinical research, demonstrating the potential of drug repurposing in TB treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)