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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).)

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.)

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]

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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.)

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.)

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.)

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.)

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.)

Drug repurposing has emerged as a effective and efficient strategy to identify new treatments for a variety of diseases. One of the most effective approaches for discovering potential new drug candidates involves the utilization of Knowledge Graphs (KGs). This review comprehensively explores some of the most prominent KGs, detailing their structure, data sources, and how they facilitate the repurposing of drugs. In addition to KGs, this paper delves into various artificial intelligence techniques that enhance the process of drug repurposing. These methods not only accelerate the identification of viable drug candidates but also improve the precision of predictions by leveraging complex datasets and advanced algorithms. Furthermore, the importance of explainability in drug repurposing is emphasized. Explainability methods are crucial as they provide insights into the reasoning behind AI-generated predictions, thereby increasing the trustworthiness and transparency of the repurposing process. We will discuss several techniques that can be employed to validate these predictions, ensuring that they are both reliable and understandable., (© The Author(s) 2024. Published by Oxford University Press.)

The global mortality rate has been significantly impacted by the COVID-19 pandemic, caused by the SARS CoV-2 virus. Although the pursuit for a potent antiviral is still in progress, experimental therapies based on repurposing of existing drugs is being attempted. One important therapeutic target for COVID-19 is the main protease (Mpro) that cleaves the viral polyprotein in its replication process. Recently minocycline, an antimycobacterium drug, has been successfully implemented for the treatment of COVID-19 patients. But it's mode of action is still far from clear. Furthermore, it remains unresolved whether alternative antimycobacterium drugs can effectively regulate SARS CoV-2 by inhibiting the enzymatic activity of Mpro. To comprehend these facets, eight well-established antimycobacterium drugs were put through molecular docking experiments. Four of the antimycobacterium drugs (minocycline, rifampicin, clofazimine and ofloxacin) were selected by comparing their binding affinities towards Mpro. All of the four drugs interacted with both the catalytic residues of Mpro (His41 and Cys145). Additionally, molecular dynamics experiments demonstrated that the Mpro-minocyline complex has enhanced stability, experiences reduced conformational fluctuations and greater compactness than other three Mpro-antimycobacterium and Mpro-N3/lopinavir complexes. This research furnishes evidences for implementation of minocycline against SARS CoV-2. In addition, our findings also indicate other three antimycobacterium/antituberculosis drugs (rifampicin, clofazimine and ofloxacin) could potentially be evaluated for COVID-19 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 B.V. All rights reserved.)

Breast cancer is one of the most prevalent malignancies among women. Enhancing the prognosis is an effective approach to enhance the survival rate of breast cancer. Cuproptosis, a copper-dependent programmed cell death process, has been associated with patient prognosis. Inducing cuproptosis is a promising approach for therapy. However, there is currently no anti-breast cancer drug that induces cuproptosis. In this study, we repositioned the clinical drug fluphenazine as a potential agent for breast cancer treatment by inducing cuproptosis. Firstly, we utilized the Cancer Genome Atlas (TCGA) database and Connectivity Map (CMap) database to identify 22 potential compounds with anti-breast cancer activity through inducing cuproptosis. Subsequently, our findings demonstrated that fluphenazine effectively suppressed the viability of MCF-7 cells. Fluphenazine also significantly inhibited the viability of triple negative breast cancer cells MDA-MB-453 and MDA-MB-231. Furthermore, our study revealed that fluphenazine significantly down-regulated the expression of potential prognostic biomarkers associated with cuproptosis, increased copper ion levels, and reduced intracellular pyruvate accumulation. Additionally, it up-regulated the expression of FDX1 at both the mRNA and protein levels, which has been reported to play a crucial role in the induction of cuproptosis. These findings suggest that fluphenazine has the potential to be used as an anti-breast cancer drug by inducing cuproptosis. Therefore, this research provides an insight for the development of novel cuproptosis-dependent anti-cancer agents., 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 Masson SAS.. All rights reserved.)

Drug repositioning, the discovery of new therapeutic uses for existing drugs, is increasingly gaining attention as a cost-effective and high-yield drug discovery strategy. Existing methods integrate diverse biological information into heterogeneous networks, providing a comprehensive framework for understanding complex drug–disease associations, which also will introduces noise into the data and affect the performance of the model. In this paper, first, a novel drug repositioning method, namely DVGEDR, is proposed, which generates two subgraphs of the target drug–disease pair to fuse biological information and integrate drug–disease associations from two distinct perspectives: drug–disease heterogeneous network and similarity networks. Next, a Multiple Attention Graph encoder (MAGencoder) module is designed to learn subgraph features and explore relationships between entities, which also improve the interpretability of the model. Finally, a graph enhancement mechanism is devised to improve the perception of critical information of model, enabling the model to flexibly process different graph structures. Performance comparisons with baseline models on three public datasets validate the state-of-the-art performance of DVGEDR in the field of drug repositioning. In case study, DVGEDR identifies 10 new candidate drugs for breast cancer and COVID-19, demonstrating not only superior performance in experimental settings but also potential therapeutic advantages in clinical environments. Furthermore, we select two sets of instances and further analyzed the attention distribution of the different nodes in the subgraph to explain the decision process of the model. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance is among the greatest threats to public health globally, and drug repurposing strategies may be advantageous to addressing this problem. Mefloquine, a drug traditionally used to treat malaria, has emerged as a promising antibiotic adjuvant, due to its ability to enhance the effectiveness of conventional antibiotics against resistant bacterial strains. In this paper, we first outline the enhancement properties of mefloquine and its mechanisms of action as an adjuvant antibiotic against multidrug-resistant bacteria. Mefloquine exhibits synergistic bacteriostatic effects when combined with colistin, β-lactams, antituberculosis drugs, quinolones, and linezolid. Potential mechanisms underlying its synergistic effects include inhibition of antibiotic efflux, disruption of bacterial cell membrane integrity, and disturbance of biofilm formation. In addition, we explore the bacteriostatic effects of several mefloquine derivatives against Mycobacterium tuberculosis and some fungi. Further, we summarize the findings of recent studies on other aspects of mefloquine activity, including its antiviral and antitumor effects. Finally, the advantages and challenges of mefloquine use as an antibiotic adjuvant in combination with antibiotics for bacterial inhibition are discussed. Overall, mefloquine shows excellent potential as an antibiotic adjuvant therapy against multidrug-resistant bacteria and is a promising candidate for combination therapy; however, further studies are needed to fully elucidate its mechanism of action and address the challenges associated with its clinical application. [ABSTRACT FROM AUTHOR]

Background: Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently needed., Results: This paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease associations., Conclusions: The comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor for identifying unobserved disease-drug associations., (© 2024. The Author(s).)

Naegleria fowleri, known as the brain-eating amoeba, is the pathogen that causes the primary amoebic meningoencephalitis (PAM), a severe neurodegenerative disease with a fatality rate exceeding 95%. Moreover, PAM cases commonly involved previous activities in warm freshwater bodies that allow amoebae-containing water through the nasal passages. Hence, awareness among healthcare professionals and the general public are the key to contribute to a higher and faster number of diagnoses worldwide. Current treatment options for PAM, such as amphotericin B and miltefosine, are limited by potential cytotoxic effects. In this context, the repurposing of existing compounds has emerged as a promising strategy. In this study, the evaluation of the COVID Box which contains 160 compounds demonstrated significant in vitro amoebicidal activity against two type strains of N. fowleri. From these compounds, terconazole, clemastine, ABT-239 and PD-144418 showed a higher selectivity against the parasite compared to the remaining products. In addition, programmed cell death assays were conducted with these four compounds, unveiling compatible metabolic events in treated amoebae. These compounds exhibited chromatin condensation and alterations in cell membrane permeability, indicating their potential to induce programmed cell death. Assessment of mitochondrial membrane potential disruption and a significant reduction in ATP production emphasized the impact of these compounds on the mitochondria, with the identification of increased ROS production underscoring their potential as effective treatment options. This study emphasizes the potential of the mentioned COVID Box compounds against N. fowleri, providing a path for enhanced PAM therapies., 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 Ltd.. All rights reserved.)

Shigellosis, induced by Shigella flexneri, constitutes a significant health burden in developing nations, particularly impacting socioeconomically disadvantaged communities. Designated as the second most prevalent cause of diarrheal illness by the World Health Organization (WHO), it precipitates an estimated 212,000 fatalities annually. Within the spectrum of S. flexneri strains, serotype X is notably pervasive and resilient, yet its comprehensive characterization remains deficient. The present investigation endeavors to discern potential pharmacological targets and repurpose existing drug compounds against S. flexneri serotype X. Employing the framework of subtractive genomics, the study interrogates the reference genome of S. flexneri Serotype X (strain 2,002,017; UP000001884) to delineate its proteome into categories of non-homologous, non-paralogous, essential, virulent, and resistant constituents, thereby facilitating the identification of therapeutic targets. Subsequently, a screening of approximately 9000 compounds from the FDA library against the identified drug target aims to delineate efficacious agents for combating S. flexneri serotype X infections. The application of subtractive genomics methodology yields prognostic insights, unveiling non-paralogous proteins (n = 4122), non-homologues (n = 1803), essential (n = 1246), drug-like (n = 389), resistant (n = 167), alongside 42 virulent proteins within the reference proteome. This iterative process culminates in the identification of Serine O-acetyltransferase as a viable drug target. Subsequent virtual screening endeavors to unearth FDA-approved medicinal compounds capable of inhibiting Serine O-acetyltransferase. Noteworthy candidates such as DB12983, DB15085, DB16098, DB16185, and DB16262 emerge, exhibiting potential for mitigating S. flexneri Serotype X. Despite the auspicious findings, diligent scrutiny is imperative to ascertain the efficacy and safety profile of the proposed drug candidates vis-à-vis S. flexneri., 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 B.V. All rights reserved.)

Antiviral medicines to treat COVID-19 are still scarce. Porphyrins and porphyrin derivatives (PDs) usually present broad-spectrum antiviral activity with low risk of resistance development. In fact, some PDs are clinically approved to be used in anti-cancer photodynamic therapy and repurposing clinically approved PDs might be an alternative to treat COVID-19. Here, we characterize the ability of temoporfin, verteporfin, talaporfin and redaporfin to inactivate SARS-CoV-2 infectious particles. PDs light-dependent and -independent effect on SARS-CoV-2 infectivity were evaluated. PDs photoactivation successfully inactivated SARS-CoV-2 with very low concentrations and light dose. However, only temoporfin and verteporfin inactivated SARS-CoV-2 in the dark, being verteporfin the most effective. PDs treatment reduced viral load in infected Caco-2 cells, while not inducing cytotoxicity. Furthermore, light-independent treatment with temoporfin and verteporfin act on early stages of viral infection. Using lipid vehicles as membrane models, we characterized PDs interaction to the viral envelope. Verteporfin presented the lowest IC50 for viral inactivation and the highest partition coefficients (K p ) towards lipid bilayers. Curiously, although temoporfin and redaporfin presented similar K p s, redaporfin did not present light-independent antiviral activity, and only temoporfin and verteporfin caused lipid membrane disorder. In fact, redaporfin is located closer to the bilayer surface, while temoporfin and verteporfin are located closer to the centre. Our results suggest that viral envelope affinity, with penetration and destabilization of the lipid bilayer, seems critical to mediate PDs antiviral activity. Altogether, these findings open new avenues for the off-label application of temoporfin and verteporfin in the systemic treatment of COVID-19., 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 Masson SAS.. All rights reserved.)

Drug repurposing is an effective method to reduce the time and cost of drug development. Computational drug repurposing can quickly screen out the most likely associations from large biological databases to achieve effective drug repurposing. However, building a comprehensive model that integrates drugs, proteins, and diseases for drug repurposing remains challenging. This study proposes a drug repurposing method based on the ternary heterogeneous graph attention network (DRTerHGAT). DRTerHGAT designs a novel protein feature extraction process consisting of a large-scale protein language model and a multi-task autoencoder, so that protein features can be extracted accurately and efficiently from amino acid sequences. The ternary heterogeneous graph of drug-protein-disease comprehensively considering the relationships among the three types of nodes, including three homogeneous and three heterogeneous relationships. Based on the graph and the extracted protein features, the deep features of the drugs and the diseases are extracted by graph convolutional networks (GCN) and heterogeneous graph node attention networks (HGNA). In the experiments, DRTerHGAT is proven superior to existing advanced methods and DRTerHGAT variants. DRTerHGAT's powerful ability for drug repurposing is also demonstrated in Alzheimer's disease., 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 lesson from many studies investigating the efficacy of targeted therapy in glioblastoma (GBM) showed that a future perspective should be focused on combining multiple target treatments. Our research aimed to assess the efficacy of drug combinations against glioblastoma stem cells (GSCs). Patient-derived cells U3042, U3009, and U3039 were obtained from the Human Glioblastoma Cell Culture resource. Additionally, the study was conducted on a GBM commercial U251 cell line. Gene expression analysis related to receptor tyrosine kinases (RTKs), stem cell markers and genes associated with significant molecular targets was performed, and selected proteins encoded by these genes were assessed using the immunofluorescence and flow cytometry methods. The cytotoxicity studies were preceded by analyzing the expression of specific proteins that serve as targets for selected drugs. The cytotoxicity study using the MTS assay was conducted to evaluate the effects of selected drugs/candidates in monotherapy and combinations. The most cytotoxic compounds for U3042 cells were Disulfiram combined with Copper gluconate (DSF/Cu), Dacomitinib, and Foretinib with IC 50 values of 52.37 nM, 4.38 µM, and 4.54 µM after 24 h incubation, respectively. Interactions were assessed using SynergyFinder Plus software. The analysis enabled the identification of the most effective drug combinations against patient-derived GSCs. Our findings indicate that the most promising drug combinations are Dacomitinib and Foretinib, Dacomitinib and DSF/Cu, and Foretinib and AZD3759. Since most tested combinations have not been previously examined against glioblastoma stem-like cells, these results can shed new light on designing the therapeutic approach to target the GSC population., 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 Masson SAS.. All rights reserved.)

Sarcopenia is a major public health concern among older adults, leading to disabilities, falls, fractures, and mortality. This study aimed to elucidate the pathophysiological mechanisms of sarcopenia and identify potential therapeutic targets using systems biology approaches. RNA-seq data from muscle biopsies of 24 sarcopenic and 29 healthy individuals from a previous cohort were analysed. Differential expression, gene set enrichment, gene co-expression network, and topology analyses were conducted to identify target genes implicated in sarcopenia pathogenesis, resulting in the selection of 6 hub genes (PDHX, AGL, SEMA6C, CASQ1, MYORG, and CCDC69). A drug repurposing approach was then employed to identify new pharmacological treatment options for sarcopenia (clofibric-acid, troglitazone, withaferin-a, palbociclib, MG-132, bortezomib). Finally, validation experiments in muscle cell line (C2C12) revealed MG-132 and troglitazone as promising candidates for sarcopenia treatment. Our approach, based on systems biology and drug repositioning, provides insight into the molecular mechanisms of sarcopenia and offers potential new treatment options using existing 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 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Repurposing generic drugs as new treatments for life-threatening diseases such as cancer is an exciting yet largely overlooked opportunity due to a lack of market-driven incentives. Nonprofit organizations and other non-manufacturers have been ramping up efforts to repurpose widely available generic drugs and rapidly expand affordable treatment options for patients. However, these nonmanufacturers find it difficult to obtain regulatory approval in the U.S. Without a straightforward path for approval and updating drug labeling, non-manufacturers have relied on off-label use of repurposed drugs. This limits the broad clinical adoption of these drugs and patient access. In this paper, we explore the regulatory landscape for repurposing of small molecule generic drugs within the U.S. We describe case studies of repurposed drugs that have been successfully incorporated into clinical treatment guidelines for cancer without regulatory approval. To encourage greater adoption of generic drugs in clinical practice-that is, to encourage the repurposing of these drugs-we examine existing Food and Drug Administration (FDA) pathways for approval of new uses or indications for generic drugs. We show how non-manufacturers, who are generally more active in generic drug repurposing than manufacturers, could utilize existing regulatory authorities and pathways, and we describe the challenges they face. We propose an extension of the existing 505(b)(2) new drug application (NDA) approval pathway, called a "labeling-only" 505(b)(2) NDA, that would enable non-manufacturers to seek approval of new indications for well-established small molecule drugs when multiple generic products are already available. It would not require new chemistry, manufacturing, and controls (CMC) data or introducing new drug products into the marketplace. This pathway would unlock innovation broadly and enable patients to benefit from the enormous potential of low-cost generic drugs. [ABSTRACT FROM AUTHOR]