A brief review on the lessons learned from COVID-19 on drug discovery and research.

COVID-19 pandemic has taught many lessons regarding drug discovery and development. This review covers these aspects of drug discovery and research for COVID-19 which might be used as a tool for future. It summarizes the positives such as progresses in antiviral drug discovery, drug repurposing, adaptations of clinical trial and its regulations, as well as the negative points such as the need to develop more collaboration among stakeholders and future directions. It also discusses the benefits and limitations of finding new indications for existing drugs, and the lessons learned regarding rigorous and robust clinical trials, pharmacokinetic/pharmacodynamic modelling, as well as combination therapy. The pandemic has also revealed some gaps regarding global collaboration and coordination, data sharing and transparency and equitable distribution. Finally, the review enumerates the future directions and implications of drug discovery and research for COVID-19 and other infectious diseases such as preparedness and resilience, interdisciplinary and integrative approaches, diversity and inclusion, and personalized and precision medicine.

Drug repurposing against antibiotic resistant bacterial pathogens.

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.

MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction.

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients.

Randomized phase 2 study of valproic acid combined with simvastatin and gemcitabine/nab-paclitaxel-based regimens in untreated metastatic pancreatic adenocarcinoma patients: the VESPA trial study protocol.

BACKGROUND: Metastatic pancreatic ductal adenocarcinoma (mPDAC) patients have very poor prognosis highlighting the urgent need of novel treatments. In this regard, repurposing non-oncology already-approved drugs might be an attractive strategy to offer more-effective treatment easily tested in clinical trials. Accumulating evidence suggests that epigenetic deregulation is a hallmark of cancer contributing to treatment resistance in several solid tumors, including PDAC. Histone deacetylase inhibitors (HDACi) are epigenetic drugs we have investigated preclinically and clinically as anticancer agents. Valproic acid (VPA) is a generic low-cost anticonvulsant and mood stabilizer with HDAC inhibitory activity, and anticancer properties also demonstrated in PDAC models. Statins use was reported to be associated with lower mortality risk in patients with pancreatic cancer and statins have been shown to have a direct antitumor effect when used alone or in combination therapy. We recently showed capability of VPA/Simvastatin (SIM) combination to potentiate the antitumor activity of gemcitabine/nab-paclitaxel in vitro and in vivo PDAC preclinical models. METHODS/DESIGN: VESPA is a patient-centric open label randomized multicenter phase-II investigator-initiated trial, evaluating the feasibility, safety, and efficacy of VPA/SIM plus first line gemcitabine/nab-paclitaxel-based regimens (AG or PAXG) (experimental arm) versus chemotherapy alone (standard arm) in mPDAC patients. The study involves Italian and Spanish oncology centers and includes an initial 6-patients safety run-in-phase. A sample size of 240 patients (120 for each arm) was calculated under the hypothesis that the addition of VPA/SIM to gemcitabine and nab-paclitaxel-based regimens may extend progression free survival from 6 to 9 months in the experimental arm. Secondary endpoints are overall survival, response rate, disease control rate, duration of response, CA 19.9 reduction, toxicity, and quality of life. The study includes a patient engagement plan and complementary biomarkers studies on tumor and blood samples. CONCLUSIONS: VESPA is the first trial evaluating efficacy and safety of two repurposed drugs in oncology such as VPA and SIM, in combination with standard chemotherapy, with the aim of improving mPDAC survival. The study is ongoing. Enrollment started in June 2023 and a total of 63 patients have been enrolled as of June 2024. TRIAL REGISTRATION: EudraCT number: 2022-004154-63; ClinicalTrials.gov identifier NCT05821556, posted 2023/04/20.

FDA-approved antivirals ledipasvir and daclatasvir downregulate the Src-EPHA2-Akt oncogenic pathway in colorectal and triple-negative breast cancer cells.

Direct-acting antivirals ledipasvir (LDV) and daclatasvir (DCV) are widely used as part of combination therapies to treat Hepatitis C infections. Here we show that these compounds inhibit the proliferation, invasion, and colony formation of triple-negative MDA-MB-231 breast cancer cells, SRC-transduced SW620 colon cancer cells and SRC- transduced NIH3T3 fibroblasts. DCV also inhibits the expression of PDL-1, which is responsible for resistance to immunotherapy in breast cancer cells. The demonstrated low toxicity in many Hepatitis C patients suggests LDV and DCV could be used in combination therapies for cancer patients. At the molecular level, these direct-acting antivirals inhibit the phosphorylation of Akt and the ephrin type A receptor 2 (EPHA2) by destabilizing a Src-EPHA2 complex, although they do not affect the general kinase activity of Src. Thus, LDV and DCV could be effective drugs for Src-associated cancers without the inherent toxicity of classical Src inhibitors.

Barriers and framework conditions for the market entry of second-life lithium-ion batteries from electric vehicles.

Transition to circular economy for lithium-ion batteries used in electric vehicles requires integrating multiple stages of the value cycle. However, strategies aimed at extending the lifetime of batteries are not yet sufficiently considered within the European battery industry, particularly regarding repurposing. Using second-life lithium-ion batteries (SLBs) before subsequent recycling can offer several advantages, such as the development of sustainable business models, the reduction of emissions, and alignment with UN Sustainable Development Goals 7, 12, and 13. Using expert and problem-centred interviews along with an exploratory workshop, this study guides stakeholders in the battery sector by illustrating the necessary changes for a more holistic circular economy. Moreover, an extended political, economic, social, technological, environmental, legal, and additionally safety-related (PESSTEL) analysis approach is carried out, which has not yet been used in this context. In this process, barriers, as well as necessary institutional framework conditions and organisational requirements for a successful market entry of SLB applications are investigated. Among others, key barriers relate to the competition with first-life applications and safety concerns. SLBs require high manual labour costs for repurposing, along with expenses for expired warranties and re-certifications. Ownership structures in traditional business models often result in SLBs and their corresponding usage data staying under the control of the manufacturers. Market viability, however, requires a level playing field for both first-life and second-life operators as well as circular battery and data-sharing business models. Gathering data on the ageing performance and performing improved safety testing according to test protocols facilitates the reliable assessment of SLBs.

Alpha-1 antitrypsin inhibits Clostridium botulinum C2 toxin, Corynebacterium diphtheriae diphtheria toxin and B. anthracis fusion toxin.

The bacterium Clostridium botulinum, well-known for producing botulinum neurotoxins, which cause the severe paralytic illness known as botulism, produces C2 toxin, a binary AB-toxin with ADP-ribosyltranferase activity. C2 toxin possesses two separate protein components, an enzymatically active A-component C2I and the binding and translocation B-component C2II. After proteolytic activation of C2II to C2IIa, the heptameric structure binds C2I and is taken up via receptor-mediated endocytosis into the target cells. Due to acidification of endosomes, the C2IIa/C2I complex undergoes conformational changes and consequently C2IIa forms a pore into the endosomal membrane and C2I can translocate into the cytoplasm, where it ADP-ribosylates G-actin, a key component of the cytoskeleton. This modification disrupts the actin cytoskeleton, resulting in the collapse of cytoskeleton and ultimately cell death. Here, we show that the serine-protease inhibitor α1-antitrypsin (α1AT) which we identified previously from a hemofiltrate library screen for PT from Bordetella pertussis is a multitoxin inhibitor. α1AT inhibits intoxication of cells with C2 toxin via inhibition of binding to cells and inhibition of enzyme activity of C2I. Moreover, diphtheria toxin and an anthrax fusion toxin are inhibited by α1AT. Since α1AT is commercially available as a drug for treatment of the α1AT deficiency, it could be repurposed for treatment of toxin-mediated diseases.

In silico comparative analysis of cestode and human NPC1 provides insights for ezetimibe repurposing to visceral cestodiases treatment.

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

Drug Repurposing Approach to Identify Candidate Drug Molecules for Hepatocellular Carcinoma.

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

CEP-1347 Boosts Chk2-Mediated p53 Activation by Ionizing Radiation to Inhibit the Growth of Malignant Brain Tumor Cells.

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

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.

Unlocking the potential of signature-based drug repurposing for anticancer drug discovery.

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

Repurposing antiparasitic N,N'-aliphatic diamine derivatives as promising antimycobacterial agents.

In previous studies, we demonstrated the potent activity of a library of 25 N,N'-disubstituted diamines (NNDDA) toward Trypanosomatid and Apicomplexa parasites. Considering the structure similarity between this collection and SQ109, an antituberculosis compound, and its compelling antiparasitic properties, we aimed to repurpose this library for tuberculosis treatment. We assayed this collection against Mycobacterium tuberculosis H37Rv and M. avium, obtaining several compounds with MIC values below 10 µM. The most active analogs were also evaluated against M. smegmatis, a non-pathogenic species, and the non-tuberculosis mycobacteria M. abscessus, M. kansasii, and M. fortuitum. 3c stands out as the lead mycobacterial compound of the collection, with potent activity against M. tuberculosis (minimal inhibitory concentration [MIC] = 3.4 µM) and moderate activity against M. smegmatis, M. kansasii, and M. fortuitum (all with MIC values of 26.8 µM). To unravel the mechanism of action, we employed the web-based platform Polypharmacology Browser 2 (PPB2), obtaining carbonic anhydrases as potential drug targets. Nevertheless, none of the compounds displayed experimental inhibition. In summary, our study confirms the validity of the repurposing approach and underscores the antimycobacterial potential of NNDDA compounds, especially the analog 3c, setting a stepping stone for further studies.

High-throughput screening identifies ibuprofen as an sEV PD-L1 inhibitor for synergistic cancer immunotherapy.

Programmed death-ligand 1 (PD-L1) on tumor-derived small extracellular vesicles (sEVs) limits therapeutic effectiveness by interacting with the PD-1 receptor on host immune cells. Targeting the secretion of sEV PD-L1 has emerged as a promising strategy to enhance immunotherapy. However, the lack of small-molecule inhibitors poses a challenge for clinical translation. In this study, we developed a target and phenotype dual-driven high-throughput screening strategy that combined virtual screening with nanoflow-based experimental verification. We identified ibuprofen (IBP) as a novel inhibitor that effectively targeted sEV PD-L1 secretion. IBP disrupted the biogenesis and secretion of PD-L1+ sEVs in tumor cells by physically interacting with a critical regulator of sEV biogenesis, hepatocyte growth factor-regulated tyrosine kinase substrate. Notably, the mechanism of action of IBP is distinct from its commonly known targets, cyclooxygenases. Administration of IBP stimulated antitumor immunity and enhanced the efficacy of anti-PD-1 therapy in melanoma and oral squamous cell carcinoma mouse models. To address potential adverse effects, we further developed an IBP gel for topical application, which demonstrated remarkable therapeutic efficacy when combined with anti-PD-1 treatment. The discovery of this specific small inhibitor provides a promising avenue for establishing durable, systemic antitumor immunity.

Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.

BACKGROUND: Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. OBJECTIVE: This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction, providing a fresh perspective on this evolving field. METHODS: We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literature. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. RESULTS: The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. CONCLUSION: The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process.

Anti-aging and immunomodulatory role of caffeine in Drosophila larvae.

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

Discovery of therapeutic targets in cancer using chromatin accessibility and transcriptomic data.

Most cancer types lack targeted therapeutic options, and when first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA sequencing (RNA-seq) on patient tissue in a high-throughput manner. Here, we present a computational approach that leverages these datasets to identify drug targets based on tumor lineage. We constructed gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained with three-dimensional genomic data for enhancer-to-promoter contacts. Next, we identified the key transcription factors (TFs) in these networks, which are used to find therapeutic vulnerabilities, by direct targeting of either TFs or the proteins that they interact with. We validated four candidates identified for neuroendocrine, liver, and renal cancers, which have a dismal prognosis with current therapeutic options.

Shared etiology of Mendelian and complex disease supports drug discovery.

BACKGROUND: Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease. METHODS: In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations. RESULTS: Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated. CONCLUSIONS: Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

Antiviral potential of phenolic compounds against HSV-1: In-vitro study.

BACKGROUND: This in vitro study aimed to investigate the effect of several phenolic compounds, including doxorubicin, quercetin, and resveratrol, on HSV-1 infection. METHODS: The cytotoxicity of the drugs was assessed on Vero cells using the MTT assay. HSV-1 was treated with the drugs, and the supernatants were collected at various time points. TCID50% and qPCR tests were conducted on the supernatants to determine viral titration post-inoculation. RESULTS: The TCID50% assay showed significant changes in viral titration for acyclovir, doxorubicin, and quercetin at most concentrations (p-value < .05), while no significant changes were observed for resveratrol. The qPCR results demonstrated that drug-treated HSV-1 exhibited a significant reduction in DNA titers at various time points compared to non-treated HSV-1 infected Vero cells, except doxorubicin (0.2 µM) and acyclovir (5 µm). However, over time, DNA virus levels gradually increased in the drug-treated groups. Notably, at certain concentrations of doxorubicin and quercetin-treated groups, virus titer significantly declined, similar to acyclovir. CONCLUSIONS: Our findings suggest that quercetin at concentrations of 62 and 125 µM significantly reduced HSV-1 infectivity, as well as these two concentrations of quercetin showed a significant difference in virus reduction compared with acyclovir (10 µM) at certain time points. The anti-inflammatory properties of quercetin, in contrast to acyclovir, make it a potential candidate for anti HSV-1 treatment in life-threatening conditions such as Herpes encephalitis. Additionally, doxorubicin, an anticancer drug, showed meaningful inhibition of HSV-1 at non-toxic concentrations of 2 and 8 µM, suggesting its potential interference with HSV-1 in viral-oncolytic therapy in cancer treatment.

Guanabenz acetate, an antihypertensive drug repurposed as an inhibitor of Escherichia coli biofilm.

Biofilms formed by Escherichia coli are composed of amyloid curli and cellulose and have been shown to be linked to pathogenicity, antibiotic resistance, and chronic infections. Guanabenz acetate (GABE), an antihypertensive drug, was identified as a potential strategic repurposing drug due to its biofilm inhibitory properties following an extensive antimicrobial screening assay of 2,202 Food and Drug Administration-approved non-antibiotic agents. The results of this study provide insights into the effectiveness of GABE as a therapeutic alternative against E. coli biofilm-associated infectious diseases. IMPORTANCE: Biofilm-associated bacterial infections are one of the major problems in medical settings. There are currently limited biofilm inhibitors available for clinical use. Guanabenz acetate, a drug used to treat high blood pressure, was found to be an effective anti-biofilm agent against Escherichia coli. Our results show that this drug can inhibit the production of cellulose and curli amyloid protein, which are the two main components of E. coli biofilms. Our findings highlight the possibility of repurposing a drug to prevent E. coli biofilm formation.