Drug Repurposing Research Articles

Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection.

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

Identification of Potential Tryptase Inhibitors from FDA-Approved Drugs Using Machine Learning, Molecular Docking, and Experimental Validation.

This study explores the innovative use of machine learning (ML) to identify novel tryptase inhibitors from a library of FDA-approved drugs, with subsequent confirmation via molecular docking and experimental validation. Tryptase, a significant mediator in inflammatory and allergic responses, presents a therapeutic target for various inflammatory diseases. However, the development of effective tryptase inhibitors has been challenging due to the enzyme's complex activation and regulation mechanisms. Utilizing a machine learning model, we screened an extensive FDA-approved drug library to identify potential tryptase inhibitors. The predicted compounds were then subjected to molecular docking to assess their binding affinity and conformation within the tryptase active site. Experimental validation was performed using RBL-2H3 cells, a rat basophilic leukemia cell line, where the efficacy of these compounds was evaluated based on their ability to inhibit tryptase activity and suppress β-hexosaminidase activity and histamine release. Our results demonstrated that several FDA-approved drugs, including landiolol, laninamivir, and cidofovir, significantly inhibited tryptase activity. Their efficacy was comparable to that of the FDA-approved mast cell stabilizer nedocromil and the investigational agent APC-366. These findings not only underscore the potential of ML in accelerating drug repurposing but also highlight the feasibility of this approach in identifying effective tryptase inhibitors. This research contributes to the field of drug discovery, offering a novel pathway to expedite the development of therapeutics for tryptase-related pathologies.

From Bench to Babies - Drug Development for Male Subfertility.

Infertility is estimated to affect more than 50 million couples around the world, with male factor accounting for half of these cases, yet there is a notable absence of effective treatment options for men, other than in-vitro fertilisation (IVF) or intracytoplasmic sperm injection (ICSI). This review considers unlicensed and empirical treatments used for male subfertility, including hormonal therapy, phosphodiesterase inhibitors, and antioxidants. Compounds generally demonstrate variable improvements in sperm function but benefits for fertility are less clear. There is a pressing need for effective treatment options for subfertile men, however, our knowledge of sperm function is limited, restricting the identification of precise treatment targets. The traditional drug discovery pathway is also notorious for its extensive resource and time requirements, often extending over decades and demanding significant financial investment. Unfortunately, a substantial number of potential therapies fail before reaching the marketplace. Furthermore, reliance on mammalian models is not possible in the drug development process for male subfertility, due to significant variability between animals and man. We review recent breakthroughs and highlight novel methods aimed at improving the effectiveness and efficiency of drug discovery for male subfertility. High-throughput screening, combinatorial chemistry, and the repurposing of established medications have great potential. These strategies offer the promise of accelerating the pace of drug development, curbing the extensive demand for resources, and, in the case of drug repurposing, diminish the demand for comprehensive pharmacokinetic and pharmacodynamic studies. As these innovative approaches are adopted, the feasibility of addressing male subfertility through scientific advancements appears to be increasingly attainable.

Innovative Strategies in Drug Repurposing to Tackle Intracellular Bacterial Pathogens.

Intracellular bacterial pathogens pose significant public health challenges due to their ability to evade immune defenses and conventional antibiotics. Drug repurposing has recently been explored as a strategy to discover new therapeutic uses for established drugs to combat these infections. Utilizing high-throughput screening, bioinformatics, and systems biology, several existing drugs have been identified with potential efficacy against intracellular bacteria. For instance, neuroleptic agents like thioridazine and antipsychotic drugs such as chlorpromazine have shown effectiveness against Staphylococcus aureus and Listeria monocytogenes. Furthermore, anticancer drugs including tamoxifen and imatinib have been repurposed to induce autophagy and inhibit bacterial growth within host cells. Statins and anti-inflammatory drugs have also demonstrated the ability to enhance host immune responses against Mycobacterium tuberculosis. The review highlights the complex mechanisms these pathogens use to resist conventional treatments, showcases successful examples of drug repurposing, and discusses the methodologies used to identify and validate these drugs. Overall, drug repurposing offers a promising approach for developing new treatments for bacterial infections, addressing the urgent need for effective antimicrobial therapies.

Maximizing Treatment Options for IBD through Drug Repurposing.

Chronic inflammation characterizes Inflammatory Bowel Disease (IBD), encompassing Crohn's Disease (CD) and Ulcerative Colitis (UC). Despite modest activity of disease in most UC patients, exacerbations occur, especially in those with severe symptoms, necessitating interventions, like colectomy. Current treatments for IBD, predominantly small molecule therapies, impose significant economic burdens. Drug repurposing offers a cost-effective alternative, leveraging existing drugs for novel therapeutic applications. This approach capitalizes on shared molecular pathways across diseases, accelerating therapeutic discovery while minimizing costs and risks. This article provides an overview of IBD and explores drug repurposing as a promising avenue for more effective and affordable treatments. Through computational and animal studies, potential drug candidates are categorized, offering insights into IBD pathogenesis and treatment strategies.

Development of a Novel Male Reproductive Toxicity Assessment Method to Predict Male-mediated Effects on the Next Generation

Less than 10% of the candidate drug compounds are associated with male reproductive toxicity. Genetic and/or epigenetic information on sperm may be crucial for fetal development. Therefore, developmental toxicity, such as paternally transmitted birth defects, is possible if genetic abnormalities in the male germ line persist and accumulate in the sperm during spermatogenesis. First, this study provides an overview of chemical and male reproductive toxicity, which may lead to developmental toxicity from the perspective of male reproduction. Second, we demonstrate methods for evaluating male reproductive toxicity to anticipate male-mediated developmental toxicity. We developed a novel staining technique for evaluating sperm quality, as well as a noninvasive imaging analysis of male reproductive toxicity. The former is a mammalian male germ cell-specific staining method using reactive blue 2 dye (RB2), as previously confirmed in human sperm, and a method for detecting the early-stage DNA fragmentation in a single nucleus from mouse spermatozoa using single-cell pulsed-field gel electrophoresis. The latter is a new, ready-to-use, and compact magnetic resonance imaging (MRI) platform utilizing a high-field permanent magnet to evaluate male reproductive toxicity. The histopathological analysis supported the suitability of the MRI platform. The present study, for the first time, revealed a rapid, noninvasive evaluation of male reproductive toxicity in vivo using compact MRI. These novel toxicity assessments can help predict male-mediated developmental toxicity, contributing to accelerated drug discovery and drug repositioning.

Management of Facial Burn Scar using Repurposed FDA-Approved Drugs

Management of Facial Burn Scar using Repurposed FDA-Approved Drugs

Optimized differential evolution and hybrid deep learning for superior drug-target binding affinity prediction

Investigating Drug-Target Interactions (DTI) is crucial for drug repositioning and discovery tasks. However, discovering DTIs through experimental approaches is time-consuming and requires substantial financial resources. To address these challenges, machine learning-based methodologies have been adopted to reduce costs and save time. Unfortunately, the effectiveness of these methods has been limited due to the binary classification approach and the lack of empirically validated negative samples. The availability of abundant DTI datasets and protein structure data has enabled the development of new approaches, such as redefining the DTI problem as a regression task. Given this context, we propose an innovative deep-learning approach to predict binding affinities between drugs and targets. Our model, named the Convolution Self-Attention Network with Attention-based Bidirectional Long Short-Term Memory Network (CSAN-BiLSTM-Att), integrates convolutional neural network (CNN) blocks with self-attention mechanisms to create an attention-based bidirectional long short-term memory (BiLSTM) model, followed by fully connected layers. Due to the model's complexity, proper hyperparameter tuning is essential. To optimize this, we employ the Differential Evolution (DE) technique to select the most suitable hyperparameters. Experimental results demonstrate that the DE-based CSAN-BiLSTM-Att model outperforms previous approaches. Specifically, the model achieved a concordance index of 0.898 and a mean square error of 0.228 on the DAVIS dataset, and a concordance value of 0.971 with a mean square error of 0.014 on the KIBA dataset.

A pseudo-label supervised graph fusion attention network for drug–target interaction prediction

Drug–target interaction (DTI) prediction can reveal new drug targets and assist in drug repositioning. It can also help identify the most potential candidate drugs for specific targets, advancing new drug discovery. Graph Convolutional Networks (GCNs) have been employed to explore potential relationships between drug–target pairs (DTPs) due to their strong learning capabilities. However, existing methods primarily rely on static graphs constructed from topological structures. These graphs may contain missing or meaningless edges, limiting the ability of GCNs to capture node embeddings. Furthermore, the lack of labels in practical DTI prediction presents a significant challenge. To address these issues, this paper introduces a pseudo-label supervised graph fusion attention network for DTI prediction (PSF-DTI). Specifically, we establish a far-neighbor graph to capture robust differential information between DTPs, compensating for the limitations of traditional topology graphs. Additionally, we create an adaptive graph to dynamically update edge information for more accurate graph structures. During training, we assign pseudo-labels to unlabeled data based on feature similarities between DTPs, mitigating the impact of label scarcity. Comparative experiments with seven state-of-the-art algorithms on public datasets demonstrate the superior performance of PSF-DTI. Extensive ablation experiments validate the effectiveness of the proposed approaches. Our findings suggest that PSF-DTI offers significant advantages in DTI prediction, providing innovative methods and perspectives for future drug discovery and repositioning.

Signature Search Polestar: a comprehensive drug repurposing method evaluation assistant for customized oncogenic signature.

The burgeoning high-throughput technologies have led to a significant surge in the scale of pharmacotranscriptomic datasets, especially for oncology. Signature search methods (SSMs), utilizing oncogenic signatures formed by differentially expressed genes through sequencing, have been instrumental in anti-cancer drug screening and identifying mechanisms of action without relying on prior knowledge. However, various studies have found that different SSMs exhibit varying performance across pharmacotranscriptomic datasets. In addition, the size of the oncogenic signature can also significantly impact the result of drug repurposing. Therefore, finding the optimal SSMs and customized oncogenic signature for a specific disease remains a challenge. To address this, we introduce Signature Search Polestar (SSP), a webserver integrating the largest pharmacotranscriptomic datasets of anti-cancer drugs from LINCS L1000 with five state-of-the-art SSMs (XSum, CMap, GSEA, ZhangScore, XCos). SSP provides three main modules: Benchmark, Robustness, and Application. Benchmark uses two indices, Area Under the Curve and Enrichment Score, based on drug annotations to evaluate SSMs at different oncogenic signature sizes. Robustness, applicable when drug annotations are insufficient, uses a performance score based on drug self-retrieval for evaluation. Application provides three screening strategies, single method, SS_all, and SS_cross, allowing users to freely utilize optimal SSMs with tailored oncogenic signature for drug repurposing. SSP is free at https://web.biotcm.net/SSP/. The current version of SSP is archived in https://doi.org/10.6084/m9.figshare.26524741.v1, allowing users to directly use or customize their own SSP webserver.

Comparable clinical advantages identification of three formulae on rheumatic disease using a modular-based network proximity approach

Ethnopharmacological relevanceHerbal formulae have been used in China for thousands of years but have unclear clinical positioning and unknown characteristic indications make it difficult to determine their specific application in various diseases, which seriously hamper their clinical value. Identifying the precise clinical positioning and clinical advantages of similar formulae for related diseases is a critical issue. Aim of this studyTo develop a methodology based on modular pharmacology to determine the clinical advantages and precise clinical position of similar formulae. Materials and methodsIn this study, we proposed a modular-based network proximity approach to explore drug repositioning and clinical advantages of three formulae, Shirebi tablets (SRB), Yuxuebi capsules (YXB), and Wangbifukang granules (WBFK), for rheumatic disease. First, we constructed a rheumatology target network, and modules were obtained using the cluster tool molecular complex detection (MCODE). Based on the modular interaction map established by a quantitative approach for inter-module coordination and its transition (IMCC), using a targeting rate (TR) matrix to identify targeted modules of three formulae. Moreover, the network proximity Z-score and Jaccard similarity coefficient were used to identify potential optimal symptomatic indications and related diseases using three formulae. At the same time, the driver genes for SRB and gouty arthritis were identified by flow centrality and shortest distance, and the epresentative driver genes were validated by in vivo experiments. Results32 modules were obtained using the MCODE method. 4, 4, and 14 characteristic targeted modules of SRB, YXB, and WBFK, respectively, were identified using a targeting rate (TR) matrix. Module 2, 16, and 19 were targeted by both SRB and WBFK. The common effects of SRB and WBFK focused on inflammatory response and innate immune response, YXB was found to be involved in the collagen catabolic process, transmembrane receptor protein serine/threonine kinase signaling pathway. Moreover, potential optimal symptomatic indications and representative related diseases were identified for three formulae: SRB was significantly associated with GA (Z = −20.26); YXB was significantly associated with AS (Z = −4.532), MI (Z = −29.11), RhFv (Z = −6.945), OA (Z = −39.97), and GA (Z = −13.03); and WBFK was significantly associated with MI (Z = −205.5), SLE (Z = −37.65), RhFv (Z = −42.45), and GA (Z = −17.24). Finally, 8 driver genes for SRB and gouty arthritis were identified,the representative driver genes TRAF6 and NFE2L1 were validated by in vivo experiments. ConclusionsThe modular-based network proximity approach proposed in this study may provide a new perspective for the precise drug repositioning and clinical advantages of similar formulae in disease treatment.

Accelerating drug discovery and repurposing by combining transcriptional signature connectivity with docking.

We present an in silico approach for drug discovery, dubbed connectivity enhanced structure activity relationship (ceSAR). Building on the landmark LINCS library of transcriptional signatures of drug-like molecules and gene knockdowns, ceSAR combines cheminformatic techniques with signature concordance analysis to connect small molecules and their targets and further assess their biophysical compatibility using molecular docking. Candidate compounds are first ranked in a target structure-independent manner, using chemical similarity to LINCS analogs that exhibit transcriptomic concordance with a target gene knockdown. Top candidates are subsequently rescored using docking simulations and machine learning-based consensus of the two approaches. Using extensive benchmarking, we show that ceSAR greatly reduces false-positive rates, while cutting run times by multiple orders of magnitude and further democratizing drug discovery pipelines. We further demonstrate the utility of ceSAR by identifying and experimentally validating inhibitors of BCL2A1, an important antiapoptotic target in melanoma and preterm birth-associated inflammation.

Clinical, Laboratory Aspects and Management of Factor X Deficiency.

Coagulation factor X (FX), originally named Stuart-Prower factor, plays a pivotal role in the coagulation cascade, activating thrombin to promote platelet plug formation and prevent excess blood loss. Genetic variants in F10 may lead to FX deficiency and to impaired coagulation. FX variants are phenotypically classified as being type I, with the concomitant reduction of FX coagulant activity and FX antigen levels or type II, corresponding to a reduction in activity with normal antigen plasma levels. Patients affected with FX deficiency tend to be one of the most seriously affected among those with rare bleeding disorders. They show a variable bleeding tendency strongly associated with FX coagulant activity levels in plasma and may present, in the severe form of the deficiency, life-threatening symptoms such as gastrointestinal and umbilical stump bleeding and intracranial hemorrhages or central nervous system bleeding. Treatment of FX deficiency was originally based on the replacement of the missing factor using fresh frozen plasma, cryoprecipitate and prothrombin complex concentrates; however, a plasma-derived concentrate, shown to be safe and effective in clinical trials, is now available. In addition, novel nonreplacement therapy such as small interference RNA, gene therapy, drug repurposing, and gene editing may also represent novel therapeutic approaches for FX deficiency, but further, much focused studies are needed before considering this emerging therapy in such patients.

Common molecular and pathophysiological underpinnings of delirium and Alzheimer’s disease: molecular signatures and therapeutic indications

BackgroundDelirium and Alzheimer's disease (AD) are common causes of cognitive dysfunction among older adults. These neurodegenerative diseases share a common and complex relationship, and can occur individually or concurrently, increasing the chance of permanent mental dysfunction. However, the common molecular pathophysiology, key proteomic biomarkers, and functional pathways are largely unknown, whereby delirium is superimposed on AD and dementia.MethodsWe employed an integrated bioinformatics and system biology analysis approach to decipher such common key proteomic signatures, pathophysiological links between delirium and AD by analyzing the gene expression data of AD-affected human brain samples and comparing them with delirium-associated proteins. The present study identified the common drug target hub-proteins examining the protein–protein interaction (PPI) and gene regulatory network analysis. The functional enrichment and pathway analysis was conducted to reveal the common pathophysiological relationship. Finally, the molecular docking and dynamic simulation was used to computationally identify and validate the potential drug target and repurposable drugs for delirium and AD.ResultsWe detected 99 shared differentially expressed genes (sDEGs) associated with AD and delirium. The sDEGs-set enrichment analysis detected the transmission across chemical synapses, neurodegeneration pathways, neuroinflammation and glutamatergic signaling pathway, oxidative stress, and BDNF signaling pathway as the most significant signaling pathways shared by delirium and AD. The disease-sDEGs interaction analysis highlighted the other disease risk factors with delirium and AD development and progression. Among the sDEGs of delirium and AD, the top 10 hub-proteins including ALB, APP, BDNF, CREB1, DLG4, GAD1, GAD2, GFAP, GRIN2B and GRIN2A were found by the PPI network analysis. Based on the maximum molecular docking binding affinities and molecular dynamic simulation (100 ns) results, the ALB and GAD2 were found as prominent drug target proteins when tacrine and donepezil were identified as potential drug candidates for delirium and AD.ConclusionThe study outlined the common key biomolecules and biological pathways shared by delirium and AD. The computationally reported potential drug molecules need a deeper investigation including clinical trials to validate their effectiveness. The outcomes from this study will help to understand the typical pathophysiological relationship between delirium and AD and flag future therapeutic development research for delirium.

Next-generation pediatric care: nanotechnology-based and AI-driven solutions for cardiovascular, respiratory, and gastrointestinal disorders.

Global pediatric healthcare reveals significant morbidity and mortality rates linked to respiratory, cardiac, and gastrointestinal disorders in children and newborns, mostly due to the complexity of therapeutic management in pediatrics and neonatology, owing to the lack of suitable dosage forms for these patients, often rendering them "therapeutic orphans". The development and application of pediatric drug formulations encounter numerous challenges, including physiological heterogeneity within age groups, limited profitability for the pharmaceutical industry, and ethical and clinical constraints. Many drugs are used unlicensed or off-label, posing a high risk of toxicity and reduced efficacy. Despite these circumstances, some regulatory changes are being performed, thus thrusting research innovation in this field. Up-to-date peer-reviewed journal articles, books, government and institutional reports, data repositories and databases were used as main data sources. Among the main strategies proposed to address the current pediatric care situation, nanotechnology is specially promising for pediatric respiratory diseases since they offer a non-invasive, versatile, tunable, site-specific drug release. Tissue engineering is in the spotlight as strategy to address pediatric cardiac diseases, together with theragnostic systems. The integration of nanotechnology and theragnostic stands poised to refine and propel nanomedicine approaches, ushering in an era of innovative and personalized drug delivery for pediatric patients. Finally, the intersection of drug repurposing and artificial intelligence tools in pediatric healthcare holds great potential. This promises not only to enhance efficiency in drug development in general, but also in the pediatric field, hopefully boosting clinical trials for this population. Despite the long road ahead, the deepening of nanotechnology, the evolution of tissue engineering, and the combination of traditional techniques with artificial intelligence are the most recently reported strategies in the specific field of pediatric therapeutics.

Multimodal enhancement of ferroptosis for synergistic cascade colorectal cancer therapy

Emerging research positions ferroptosis as a formidable anti-tumor modality for colorectal cancer (CRC). However, the current pathways to potentiate ferroptosis through a singular or dual approach have fallen short in maximizing treatment efficacy. Herein, a multimodal enhancement strategy of ferroptosis is achieved for synergistic cascade colorectal cancer therapy, via clinical-related drug repurposing and prodrug self-assembly of SN38 and sulfasalazine (SAS). In this process, the SN38 or SAS prodrug is synthesized by conjugating ferrocene via disulfide bonds to SN38 or SAS, then the two prodrugs self-assemble into a hybrid nanomedicine (FSS). FSS exhibited pronounced stability under physiological conditions, while demonstrating rapid disassembly via glutathione-responsive break of disulfide bonds in tumor microenvironments. Then, a multimodal enhancement strategy of cancer ferroptosis is achieved via the SAS-induced regulation of amino acid metabolism, SN38-resulted regulation of glutathione synthesis, accumulation of ferroptosis-related lipid signaling, and ferrocene-directed iron overload in tumors. The potent ferroptosis-based CRC anticancer effects of FSS were systematically explored and confirmed by comprehensive in vitro and in vivo analyses, including cellular experiments, subcutaneous and orthotopic tumor-bearing mouse models, and RNA-sequencing transcriptomic studies. This study lays the groundwork for the clinical translation of multimodal ferroptosis inducers, offering an effective and safe approach to cancer treatment.

SAHA potentiates the activity of repurposed drug promethazine loaded PLGA nanoparticles in triple-negative breast cancer cells.

Triple-negative breast cancer (TNBC) is considered the most aggressive form of breast cancer owing to the negative expression of targetable bioreceptors. Epithelial to mesenchymal transition (EMT) associated with metastatic abilities is its critical feature. As an attempt to target TNBC, nanotechnology was utilised to augment the effects of drug repurposing. Concerning that, a combination therapeutic module was structured with one of the aspects being a repurposed antihistamine, promethazine hydrochloride loaded PLGA nanoparticles. The as-synthesized nanoparticles were 217 nm in size and fluoresced at 522 nm, rendering them suitable for theranostic applications too. The second feature of the module was a common histone deacetylase inhibitor, suberoylanilide hydroxamic acid (SAHA), used as a form of pre-treatment. Experimental studies demonstrated efficient cellular internalisation and significant innate anti-proliferative potential. The use of SAHA sensitised the cells to the drug loaded nanoparticle treatment. Mechanistic studies showed increase in ROS generation, mitochondrial dysfunction followed by apoptosis. Investigations into protein expression also revealed reduction of mesenchymal proteins like vimentin by 1.90 fold; while increase in epithelial marker like E-Cadherin by 1.42 fold, thus indicating an altered EMT dynamics. Further findings also provided better insight into the benefits of SAHA potentiated targeting of tumor spheroids that mimic solid tumors of TNBC. Thus, this study paves the avenue to a more rational translational validation of combining nanotherapeutics with drug repurposing.

Repurposing of glatiramer acetate to treat cardiac ischemia in rodent models.

Myocardial injury may ultimately lead to adverse ventricular remodeling and development of heart failure (HF), which is a major cause of morbidity and mortality worldwide. Given the slow pace and substantial costs of developing new therapeutics, drug repurposing is an attractive alternative. Studies of many organs, including the heart, highlight the importance of the immune system in modulating injury and repair outcomes. Glatiramer acetate (GA) is an immunomodulatory drug prescribed for patients with multiple sclerosis. Here, we report that short-term GA treatment improves cardiac function and reduces scar area in a mouse model of acute myocardial infarction and a rat model of ischemic HF. We provide mechanistic evidence indicating that, in addition to its immunomodulatory functions, GA exerts beneficial pleiotropic effects, including cardiomyocyte protection and enhanced angiogenesis. Overall, these findings highlight the potential repurposing of GA as a future therapy for a myriad of heart diseases.

Free Radical Production Induced by Nitroimidazole Compounds Lead to Cell Death in Leishmania infantum Amastigotes.

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

BMS345541 is predicted as a repurposed drug for the treatment of TMZ-resistant Glioblastoma using target gene expression and virtual drug screening

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.