Review of Drug Repositioning Approaches and Resources

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

GETdb: A comprehensive database for genetic and evolutionary features of drug targets

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.

Drug repositioning based on residual attention network and free multiscale adversarial training

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.

Efficient Deep Model Ensemble Framework for Drug-Target Interaction Prediction

Accurate prediction of Drug-Target Interactions (DTI) is crucial for drug development. Current state-of-the-art deep learning methods have significantly advanced the field; however, these methods exhibit limitations in predictive performance and the propensity for false negatives. Therefore, we propose EADTN, a simple and efficient ensemble model. We have designed an innovative feature adaptation technique to automatically extract local weights of drugs and targets, and we utilize clustering-enhanced parameter fine-tuning to overcome the issue of false negatives, thereby enhancing its reliability in drug discovery. Based on EADTN, we also propose a Shapley value-based method for identifying key drug substructures, effectively enhancing the model's interpretability. Additionally, we utilized EADTN to reveal potential interactions between NQO1 targets and the drugs SIRT-IN-1 and LY2183240, which were subsequently validated through wet-lab experiments. Experimental evidence demonstrates that EADTN consistently outperforms existing best-performing models across various data sets, promising significant benefits in fields such as drug repositioning.

Exploring novel microbial metabolites and drugs for inhibiting Clostridioides difficile

Clostridioides difficile is an enteric pathogen that can cause a range of illnesses from mild diarrhea to pseudomembranous colitis and even death. This pathogen often takes advantage of microbial dysbiosis provoked by antibiotic use. With the increasing incidence and severity of infections, coupled with high recurrence rates, there is an urgent need to identify innovative therapies that can preserve the healthy state of the gut microbiota. In this study, we screened a microbial metabolite library against C. difficile. From a collection of 527 metabolites, we identified 18 compounds with no previously identified antimicrobial activity and metabolites that exhibited potent activity against C. difficile growth. Of these 18 hits, five drugs and three metabolites displayed the most potent anti-C. difficile activity and were subsequently assessed against 20 clinical isolates of C. difficile. These potent agents included ecteinascidin 770 (minimum inhibitory concentration against 50% of isolates [MIC50] ≤0.06 µg/mL); 8-hydroxyquinoline derivatives, such as broxyquinoline and choloroquinaldol (MIC50 = 0.125 µg/mL); ionomycin calcium salt, carbadox, and robenidine hydrochloride (MIC50 = 1 µg/mL); and dronedarone and milbemycin oxime (MIC50 = 4 µg/mL). Unlike vancomycin and fidaxomicin, which are the standard-of-care anti-C. difficile antibiotics, most of these metabolites showed robust bactericidal activity within 2-8 h with minimal impact on the growth of representative members of the normal gut microbiota. These results suggest that the drugs and microbial metabolite scaffolds may offer alternative avenues to address unmet needs in C. difficile disease prevention and treatment.

IMPORTANCE

The most frequent infection associated with hospital settings is Clostridioides difficile, which can cause fatal diarrhea and severe colitis, toxic megacolon, sepsis, and leaky gut. Those who have taken antibiotics for other illnesses that affect the gut's healthy microbiota are more susceptible to C. difficile infection (CDI). Recently, some reports showed higher recurrence rates and resistance to anti-C. difficile, which may compromise the efficacy of CDI treatment. Our study is significant because it is anticipated to discover novel microbial metabolites and drugs with microbial origins that are safe for the intestinal flora, effective against C. difficile, and reduce the risk of recurrence associated with CDI.

Novel drug design and repurposing: An opportunity to improve translational research in cardiovascular diseases?

Drug repurposing is defined as the use of approved therapeutic drugs for indications different from those for which they were originally designed. Repositioning diminishes both the time and cost for drug development by omitting the discovery stage, the analysis of absorption, distribution, metabolism, and excretion routes, as well as the studies of the biochemical and physiological effects of a new compound. Besides, drug repurposing takes advantage of the increased bioinformatics knowledge and availability of big data biology. There are many examples of drugs with repurposed indications evaluated in in vitro studies, and in pharmacological, preclinical, or retrospective clinical analyses. Here, we briefly review some of the experimental strategies and technical advances that may improve translational research in cardiovascular diseases. We also describe exhaustive research from basic science to clinical studies that culminated in the final approval of new drugs and provide examples of successful drug repurposing in the field of cardiology.

DrugRepoBank: a comprehensive database and discovery platform for accelerating drug repositioning

In recent years, drug repositioning has emerged as a promising alternative to the time-consuming, expensive and risky process of developing new drugs for diseases. However, the current database for drug repositioning faces several issues, including insufficient data volume, restricted data types, algorithm inaccuracies resulting from the neglect of multidimensional or heterogeneous data, a lack of systematic organization of literature data associated with drug repositioning, limited analytical capabilities and user-unfriendly webpage interfaces. Hence, we have established the first all-encompassing database called DrugRepoBank, consisting of two main modules: the 'Literature' module and the 'Prediction' module. The 'Literature' module serves as the largest repository of literature-supported drug repositioning data with experimental evidence, encompassing 169 repositioned drugs from 134 articles from 1 January 2000 to 1 July 2023. The 'Prediction' module employs 18 efficient algorithms, including similarity-based, artificial-intelligence-based, signature-based and network-based methods to predict repositioned drug candidates. The DrugRepoBank features an interactive and user-friendly web interface and offers comprehensive functionalities such as bioinformatics analysis of disease signatures. When users provide information about a drug, target or disease of interest, DrugRepoBank offers new indications and targets for the drug, proposes new drugs that bind to the target or suggests potential drugs for the queried disease. Additionally, it provides basic information about drugs, targets or diseases, along with supporting literature. We utilize three case studies to demonstrate the feasibility and effectiveness of predictively repositioned drugs within DrugRepoBank. The establishment of the DrugRepoBank database will significantly accelerate the pace of drug repositioning. Database URL:  https://awi.cuhk.edu.cn/DrugRepoBank.

In Silico Drug Repurposing Against PSMB8 as a Potential Target for Acute Myeloid Leukemia Treatment

PSMB8 emerges as a prominent gene associated with cancer survival, yet its potential therapeutic role in acute myeloid leukemia (AML) remains unexplored within the existing literature. The principal aim of this study is to systematically screen an expansive library of molecular entities, curated from various databases to identify the prospective inhibitory agents with an affinity for PSMB8. A comprehensive assortment of molecular compounds obtained from the ZINC15 database was subjected to molecular docking simulations with PSMB8 by using the AutoDock tool in PyRx (version 0.9.9) to elucidate binding affinities. Following the docking simulations, a select subset of molecules underwent further investigation through comprehensive ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis employing AdmetSar and SwissADME tools. Finally, RMSD, RMSF, Rg, and H bond analyses were conducted via GROMACS to determine the best conformationally dynamic molecule that represents the candidate agent for the study. Following rigorous evaluation, Adozelesin, Fiduxosin, and Rimegepant have been singled out based on considerations encompassing bioavailability scores, compliance with filter criteria, and acute oral toxicity levels. Additionally, ligand interaction analysis indicates that Adozelesin and Fiduxosin exhibit an augmented propensity for hydrogen bond formation, a factor recognized for its facilitative role in protein-ligand interactions. After final analyses, we report that Fiduxosin may offer a treatment possibility by reversing the low survival rates caused by PSMB8 high activation in AML. This study represents a strategic attempt to repurpose readily available pharmaceutical agents, potentially obviating the need for de novo drug development, and thereby offering promising avenues for therapeutic intervention in specific diseases.

Strategies of Pharmacological Repositioning for the Treatment of Medically Relevant Mycoses

Fungal infections represent a worldwide concern for public health, due to their prevalence and significant increase in cases each year. Among the most frequent mycoses are those caused by members of the genera Candida, Cryptococcus, Aspergillus, Histoplasma, Pneumocystis, Mucor, and Sporothrix, which have been treated for years with conventional antifungal drugs, such as flucytosine, azoles, polyenes, and echinocandins. However, these microorganisms have acquired the ability to evade the mechanisms of action of these drugs, thus hindering their treatment. Among the most common evasion mechanisms are alterations in sterol biosynthesis, modifications of drug transport through the cell wall and membrane, alterations of drug targets, phenotypic plasticity, horizontal gene transfer, and chromosomal aneuploidies. Taking into account these problems, some research groups have sought new therapeutic alternatives based on drug repositioning. Through repositioning, it is possible to use existing pharmacological compounds for which their mechanism of action is already established for other diseases, and thus exploit their potential antifungal activity. The advantage offered by these drugs is that they may be less prone to resistance. In this article, a comprehensive review was carried out to highlight the most relevant repositioning drugs to treat fungal infections. These include antibiotics, antivirals, anthelmintics, statins, and anti-inflammatory drugs.

One Molecule, Many Faces: Repositioning Cardiovascular Agents for Advanced Wound Healing

Chronic wound treatments pose a challenge for healthcare worldwide, particularly for the people in developed countries. Chronic wounds significantly impair quality of life, especially among the elderly. Current research is devoted to novel approaches to wound care by repositioning cardiovascular agents for topical wound treatment. The emerging field of medicinal products' repurposing, which involves redirecting existing pharmaceuticals to new therapeutic uses, is a promising strategy. Recent studies suggest that medicinal products such as sartans, beta-blockers, and statins have unexplored potential, exhibiting multifaceted pharmacological properties that extend beyond their primary indications. The purpose of this review is to analyze the current state of knowledge on the repositioning of cardiovascular agents' use and their molecular mechanisms in the context of wound healing.

Efficacy of Trametinib in Alleviating Cisplatin-Induced Acute Kidney Injury: Inhibition of Inflammation, Oxidative Stress, and Tubular Cell Death in a Mouse Model

Cisplatin, a platinum-based chemotherapeutic, is effective against various solid tumors, but its use is often limited by its nephrotoxic effects. This study evaluated the protective effects of trametinib, an FDA-approved selective inhibitor of mitogen-activated protein kinase kinase 1/2 (MEK1/2), against cisplatin-induced acute kidney injury (AKI) in mice. The experimental design included four groups, control, trametinib, cisplatin, and a combination of cisplatin and trametinib, each consisting of eight mice. Cisplatin was administered intraperitoneally at a dose of 20 mg/kg to induce kidney injury, while trametinib was administered via oral gavage at 3 mg/kg daily for three days. Assessments were conducted 72 h after cisplatin administration. Our results demonstrate that trametinib significantly reduces the phosphorylation of MEK1/2 and extracellular signal-regulated kinase 1/2 (ERK1/2), mitigated renal dysfunction, and ameliorated histopathological abnormalities. Additionally, trametinib significantly decreased macrophage infiltration and the expression of pro-inflammatory cytokines in the kidneys. It also lowered lipid peroxidation by-products, restored the reduced glutathione/oxidized glutathione ratio, and downregulated NADPH oxidase 4. Furthermore, trametinib significantly inhibited both apoptosis and necroptosis in the kidneys. In conclusion, our data underscore the potential of trametinib as a therapeutic agent for cisplatin-induced AKI, highlighting its role in reducing inflammation, oxidative stress, and tubular cell death.

In silico approaches for drug repurposing in oncology: a scoping review

Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.

DTKGIN: Predicting drug-target interactions based on knowledge graph and intent graph

Knowledge graph intent graph attention mechanism Predicting drug-target interactions (DTIs) plays a crucial role in drug discovery and drug development. Considering the high cost and risk of biological experiments, developing computational approaches to explore the interactions between drugs and targets can effectively reduce the time and cost of drug development. Recently, many methods have made significant progress in predicting DTIs. However, existing approaches still suffer from the high sparsity of DTI datasets and the cold start problem. In this paper, we develop a new model to predict drug-target interactions via a knowledge graph and intent graph named DTKGIN. Our method can effectively capture biological environment information for targets and drugs by mining their associated relations in the knowledge graph and considering drug-target interactions at a fine-grained level in the intent graph. DTKGIN learns the representation of drugs and targets from the knowledge graph and the intent graph. Then the probabilities of interactions between drugs and targets are obtained through the inner product of the representation of drugs and targets. Experimental results show that our proposed method outperforms other state-of-the-art methods in 10-fold cross-validation, especially in cold-start experimental settings. Furthermore, the case studies demonstrate the effectiveness of DTKGIN in predicting potential drug-target interactions. The code is available on GitHub: https://github.com/Royluoyi123/DTKGIN.

Drug Repurposing: Exploring Potential Anti-Cancer Strategies by Targeting Cancer Signalling Pathways

The repurposing of previously clinically approved drugs as an alternative therapeutic approach to treating disease has gained significant attention in recent years. A multitude of studies have demonstrated various and successful therapeutic interventions with these drugs in a wide range of neoplastic diseases, including multiple myeloma, leukaemia, glioblastoma, and colon cancer. Drug repurposing has been widely encouraged due to the known efficacy, safety, and convenience of already established drugs, allowing the bypass of the long and difficult road of lead optimization and drug development. Repurposing drugs in cancer therapy is an exciting prospect due to the ability of these drugs to successfully target cancer-associated genes, often dysregulated in oncogenic signalling pathways, amongst which are the classical cancer signalling pathways; WNT (wingless-related integration type) and Hippo signalling. These pathways play a fundamental role in controlling organ size, tissue homeostasis, cell proliferation, and apoptosis, all hallmarks of cancer initiation and progression. Prolonged dysregulation of these pathways has been found to promote uncontrolled cellular growth and malignant transformation, contributing to carcinogenesis and ultimately leading to malignancy. However, the translation of cancer signalling pathways and potential targeted therapies in cancer treatment faces ongoing challenges due to the pleiotropic nature of cancer cells, contributing to resistance and an increased rate of incomplete remission in patients. This review provides analyses of a range of potential anti-cancer compounds in drug repurposing. It unravels the current understanding of the molecular rationale for repurposing these drugs and their potential for targeting key oncogenic signalling pathways.

Understanding the interactions between repurposed drugs sertindole and temoporfin with receptor for advanced glycation endproducts: Therapeutic implications in cancer and metabolic diseases

CONTEXT

In the pursuit of novel therapeutic possibilities, repurposing existing drugs has gained prominence as an efficient strategy. The findings from our study highlight the potential of repurposed drugs as promising candidates against receptor for advanced glycation endproducts (RAGE) that offer therapeutic implications in cancer, neurodegenerative conditions and metabolic syndromes. Through careful analyses of binding affinities and interaction patterns, we identified a few promising candidates, ultimately focusing on sertindole and temoporfin. These candidates exhibited exceptional binding affinities, efficacy, and specificity within the RAGE binding pocket. Notably, they displayed a pronounced propensity to interact with the active site of RAGE. Our investigation further revealed that sertindole and temoporfin possess desirable pharmacological properties that highlighted them as attractive candidates for targeted drug development. Overall, our integrated computational approach provides a comprehensive understanding of the interactions between repurposed drugs, sertindole and temoporfin and RAGE that pave the way for future experimental validation and drug development endeavors.

METHODS

We present an integrated approach utilizing molecular docking and extensive molecular dynamics (MD) simulations to evaluate the potential of FDA-approved drugs, sourced from DrugBank, against RAGE. To gain deeper insights into the binding mechanisms of the elucidated candidate repurposed drugs, sertindole and temoporfin with RAGE, we conducted extensive all-atom MD simulations, spanning 500 nanoseconds (ns). These simulations elucidated the conformational dynamics and stability of the RAGE-sertindole and RAGE-temoporfin complexes.

Prediction of drug-protein interaction based on dual channel neural networks with attention mechanism

The precise identification of drug-protein inter action (DPI) can significantly speed up the drug discovery process. Bioassay methods are time-consuming and expensive to screen for each pair of drug proteins. Machine-learning-based methods cannot accurately predict a large number of DPIs. Compared with traditional computing methods, deep learning methods need less domain knowledge and have strong data learning ability. In this study, we construct a DPI prediction model based on dual channel neural networks with an efficient path attention mechanism, called DCA-DPI. The drug molecular graph and protein sequence are used as the data input of the model, and the residual graph neural network and the residual convolution network are used to learn the feature representation of the drug and protein, respectively, to obtain the feature vector of the drug and the hidden vector of protein. To get a more accurate protein feature vector, the weighted sum of the hidden vector of protein is applied using the neural attention mechanism. In the end, drug and protein vectors are concatenated and input into the full connection layer for classification. In order to evaluate the performance of DCA-DPI, three widely used public data, Human, C.elegans and DUD-E, are used in the experiment. The evaluation metrics values in the experiment are superior to other relevant methods. Experiments show that our model is efficient for DPI prediction.

DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm

Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease-drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method's feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease-drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases.

Deciphering the mechanisms and interactions of the endocrine disruptor bisphenol A and its analogs with the androgen receptor

Bisphenol A (BPA) and its various forms used as BPA alternatives in industries are recognized toxic compounds and antiandrogenic endocrine disruptors. These chemicals are widespread in the environment and frequently detected in biological samples. Concerns exist about their impact on hormones, disrupting natural biological processes in humans, together with their negative impacts on the environment and biotic life. This study aims to characterize the interaction between BPA analogs and the androgen receptor (AR) and the effect on the receptor's normal activity. To achieve this goal, molecular docking was conducted with BPA and its analogs and dihydrotestosterone (DHT) as a reference ligand. Four BPA analogs exhibited higher affinity (-10.2 to -8.7 kcal/mol) for AR compared to BPA (-8.6 kcal/mol), displaying distinct interaction patterns. Interestingly, DHT (-11.0 kcal/mol) shared a binding pattern with BPA. ADMET analysis of the top 10 compounds, followed by molecular dynamics simulations, revealed toxicity and dynamic behavior. Experimental studies demonstrated that only BPA disrupts DHT-induced AR dimerization, thereby affecting AR's function due to its binding nature. This similarity to DHT was observed during computational analysis. These findings emphasize the importance of targeted strategies to mitigate BPA toxicity, offering crucial insights for interventions in human health and environmental well-being.

In vitro and in silico investigation of FDA-approved drugs to be repurposed against Alzheimer's disease

Alzheimer's disease (AD), one of the main causes of dementia, is a neurodegenerative disorder. Cholinesterase inhibitors are used in the treatment of AD, but prolonged use of these drugs can lead to serious side effects. Drug repurposing is an approach that aims to reveal the effectiveness of drugs in different diseases beyond their clinical uses. In this work, we investigated in vitro and in silico inhibitory effects of 11 different drugs on cholinesterases. The results showed that trimebutine, theophylline, and levamisole had the highest acetylcholinesterase inhibitory actions among the tested drugs, and these drugs inhibited by 68.70 ± 0.46, 53.25 ± 3.40, and 44.03 ± 1.20%, respectively at 1000 µM. In addition, these drugs are bound to acetylcholinesterase via competitive manner. Molecular modeling predicted good fitness in acetylcholinesterase active site for these drugs and possible central nervous system action for trimebutine. All of these results demonstrated that trimebutine was determined to be the drug with the highest potential for use in AD.

Drug repurposing for cancer therapy

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

Drug Repurposing: A Leading Strategy for New Threats and Targets

Less than 6% of rare illnesses have an appropriate treatment option. Repurposed medications for new indications are a cost-effective and time-saving strategy that results in excellent success rates, which may significantly lower the risk associated with therapeutic development for rare illnesses. It is becoming a realistic alternative to repurposing "conventional" medications to treat joint and rare diseases considering the significant failure rates, high expenses, and sluggish stride of innovative medication advancement. This is due to delisted compounds, cheaper research fees, and faster development time frames. Repurposed drug competitors have been developed using strategic decisions based on data analysis, interpretation, and investigational approaches, but technical and regulatory restrictions must also be considered. Combining experimental and computational methodologies generates innovative new medicinal applications. It is a one-of-a-kind strategy for repurposing human-safe pharmaceuticals to treat uncommon and difficult-to-treat ailments. It is a very effective method for discovering and creating novel medications. Several pharmaceutical firms have developed novel therapies by repositioning old medications. Repurposing drugs is practical, cost-effective, and speedy and generally involves lower risks when compared to developing a new drug from the beginning.

Approaches of pre-clinical and clinical trials of repurposed drug

Medications that are currently on the market and have proven therapeutic usage can have new therapeutic indications discovered through a process called drug repurposing, which is also called drug repositioning. This approach presents a viable method for drug developers and pharmaceutical companies to discern novel targets for FDA-approved medications. Drug repurposing presents several advantages, including reduced time consumption, lower costs, and diminished risk of failure. Sildenafil, commonly known as Viagra, serves as a notable illustration of a repurposed pharmaceutical agent, initially developed and introduced to the market as an antianginal medication. However, in the current context, its application has been redirected towards serving as a pharmaceutical intervention for the treatment of erectile dysfunction. Comparably, a multitude of pharmaceutical agents exist that have demonstrated efficacy in repurposing for therapeutic management of various clinical conditions. Focusing on the historical use of repurposed pharmaceuticals and their present state of application in disease therapies, this chapter seeks to offer a thorough review of drug repurposing methodologies. Furthermore, the rules and regulations that control the repurposing of drugs will be covered in detail in this chapter.

Andrographolide: A Diterpenoid from Cymbopogon schoenanthus Identified as a New Hit Compound against Trypanosoma cruzi Using Machine Learning and Experimental Approaches

American Trypanosomiasis, also known as Chagas disease, is caused by the protozoan Trypanosoma cruzi and exhibits limited options for treatment. Natural products offer various structurally complex metabolites with biological activities, including those with anti-T. cruzi potential. The discovery and development of prototypes based on natural products frequently display multiple phases that could be facilitated by machine learning techniques to provide a fast and efficient method for selecting new hit candidates. Using Random Forest and k-Nearest Neighbors, two models were constructed to predict the biological activity of natural products from plants against intracellular amastigotes of T. cruzi. The diterpenoid andrographolide was identified from a virtual screening as a promising hit compound. Hereafter, it was isolated from Cymbopogon schoenanthus and chemically characterized by spectral data analysis. Andrographolide was evaluated against trypomastigote and amastigote forms of T. cruzi, showing IC50 values of 29.4 and 2.9 μM, respectively, while the standard drug benznidazole displayed IC50 values of 17.7 and 5.0 μM, respectively. Additionally, the isolated compound exhibited a reduced cytotoxicity (CC50 = 92.8 μM) against mammalian cells and afforded a selectivity index (SI) of 32, similar to that of benznidazole (SI = 39). From the in silico analyses, we can conclude that andrographolide fulfills many requirements implemented by DNDi to be a hit compound. Therefore, this work successfully obtained machine learning models capable of predicting the activity of compounds against intracellular forms of T. cruzi.

Proteome-Based Investigation Identified Potential Drug Repurposable Small Molecules Against Monkeypox Disease

Monkeypox Virus (MPXV), the causative agent of Monkeypox (MPX) disease, is an emerging zoonotic pathogen spreading in different endemic and non-endemic nations and creating outbreaks. MPX treatment mainly includes Cidofovir and Tecovirimat but they have several side effects and solely depending on these drugs may promote the emergence of drug-resistant variants. Hence, new drugs are required to control the spread of the disease. In this study, we explored the MPXV proteome to suggest repurposable drugs. DrugBank screening revealed drugs such as Brinzolamide, Dorzolamide, Methazolamide, Zidovudine, Gemcitabine, Hydroxyurea, Fludarabine, and Tecovirimat as controls. Structural analogs of these compounds were extracted from ChEMBL Database. After Molecular docking and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET)-based screening, we identified Zidovudine (binding affinity-5.9 kcal/mol) and a Harmala alkaloid (2S,4R)-4-(9H-Pyrido[3,4-b]indol-1-yl)-1,2,4-butanetriol (binding affinity - 6.6 kcal/mol) against L2R receptor (Thymidine Kinase). Moreover, Fludarabine (binding affinity - 6.4 kcal/mol) and 5'-Dehydroadenosine (binding affinity - 6.4 kcal/mol) can strongly interact with the I4L receptor (Ribonucleotide reductase large subunit R1). Molecular Dynamics (MD) simulations suggest all of these compounds can change the C-alpha backbone, residue mobility, compactness, and solvent accessible surface area of L2R and I4L. Our results strongly suggest that these drug repurposing small molecules are worth exploring in vivo and in vitro for clinical applications.

Computational drug repurposing for primary hyperparathyroidism

In hyperparathyroidism (hyperPTH), excessive amounts of PTH are secreted, interfering with calcium regulation in the body. Several drugs can control the disease's side effects, but none of them is an alternative treatment to surgery. Therefore, new drug candidates are necessary. In this study, three computationally repositioned drugs, DG 041, IMD 0354, and cucurbitacin I, are evaluated in an in vitro model of hyperPTH. First, we integrated publicly available transcriptomics datasets to propose drug candidates. Using 3D spheroids derived from a single primary hyperPTH patient, we assessed their in vitro efficacy. None of the proposed drugs affected the viability of healthy cell control (HEK293) or overactive parathyroid cells at the level of toxicity. This behavior was attributed to the non-cancerous nature of the parathyroid cells, establishing the hyperPTH disease model. Cucurbitacin I and IMD 0354 exhibited a slight inverse relationship between increased drug concentrations and cell viability, whereas DG 041 increased viability. Based on these results, further studies are needed on the mechanism of action of the repurposed drugs, including determining the effects of these drugs on cellular PTH synthesis and secretion and on the metabolic pathways that regulate PTH secretion.

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer

BACKGROUND

The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health.

METHODS

In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116).

RESULTS

According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29).

CONCLUSIONS

Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

Computational identification of natural senotherapeutic compounds that mimic dasatinib based on gene expression data

The major risk factor for chronic disease is chronological age, and age-related chronic diseases account for the majority of deaths worldwide. Targeting senescent cells that accumulate in disease-related tissues presents a strategy to reduce disease burden and to increase healthspan. The senolytic combination of the tyrosine-kinase inhibitor dasatinib and the flavonol quercetin is frequently used in clinical trials aiming to eliminate senescent cells. Here, our goal was to computationally identify natural senotherapeutic repurposing candidates that may substitute dasatinib based on their similarity in gene expression effects. The natural senolytic piperlongumine (a compound found in long pepper), and the natural senomorphics parthenolide, phloretin and curcumin (found in various edible plants) were identified as potential substitutes of dasatinib. The gene expression changes underlying the repositioning highlight apoptosis-related genes and pathways. The four compounds, and in particular the top-runner piperlongumine, may be combined with quercetin to obtain natural formulas emulating the dasatinib + quercetin formula.

SADR: Self-Supervised Graph Learning With Adaptive Denoising for Drug Repositioning

Traditional drug development is often high-risk and time-consuming. A promising alternative is to reuse or relocate approved drugs. Recently, some methods based on graph representation learning have started to be used for drug repositioning. These models learn the low dimensional embeddings of drug and disease nodes from the drug-disease interaction network to predict the potential association between drugs and diseases. However, these methods have strict requirements for the dataset, and if the dataset is sparse, the performance of these methods will be severely affected. At the same time, these methods have poor robustness to noise in the dataset. In response to the above challenges, we propose a drug repositioning model based on self-supervised graph learning with adptive denoising, called SADR. SADR uses data augmentation and contrastive learning strategies to learn feature representations of nodes, which can effectively solve the problems caused by sparse datasets. SADR includes an adaptive denoising training (ADT) component that can effectively identify noisy data during the training process and remove the impact of noise on the model. We have conducted comprehensive experiments on three datasets and have achieved better prediction accuracy compared to multiple baseline models. At the same time, we propose the top 10 new predictive approved drugs for treating two diseases. This demonstrates the ability of our model to identify potential drug candidates for disease indications.

An alternative method to establish an early acute ocular toxoplasmosis model for experimental tests

PURPOSE

To provide a simple alternative acute ocular toxoplasmosis model with great reproducibility for experimental tests that demand monitoring of the ocular lesion.

METHODS

ME49-wt and ME49-GFP tachyzoites from cell culture were used to infect male C57BL6 mice by intraperitoneal injection. B1 expression by real-time polymerase chain reaction (qPCR) assay was used to detect the presence of T. gondii in ocular tissue at the beginning of the infection. Fluorescence microscopy and histopathology analysis were carried out to assess the evolution of the acute infection up to 20 days in both eyes of infected mice.

RESULTS

All mice infected with the 104 tachyzoites showed B1 expression in the retina of both eyes, in the RPE (retinal pigment epithelium), and choroid structures, after 5 days of infection. Tachyzoites of the ME49-GFP strain were easily detected by fluorescence microscopy in the retina tissue of mice after 5 days post-infection. After 20 days, mice inflammatory cell infiltrates and a disorganized morphology of the retinal laminar architecture were observed.

CONCLUSION

Infection of C57BL6 mice via intraperitoneal with 104 tachyzoites of the ME49-GFP strain from cell culture is a suitable model for acute ocular toxoplasmosis. This model has great reproducibility in establishing the ocular lesion since day 5 post-infection. This model can be suitable for experimental tests of chemotherapy and the investigation of the role of the immune response on the development of uveitis.

In Silico Investigation of Novel Compounds as Inhibitors of Acetylcholinesterase Enzyme for the Treatment of Alzheimer's Diseases

Alzheimer's disease (AD) is a "progressive, neurodegenerative disease that occurs when nerve cells in the brain die." There are only 4 drugs approved by the United States Food and Drug Administration (FDA). Three (donepezil, rivastigmine, and galantamine) out of these four drugs are anticholinesterase inhibitors, while the fourth one memantine is an N-methyl-D-aspartate (NMDA) receptor inhibitor. Currently, two immunotherapy drugs that target amyloid protein (donanemab and lecanemab) are being considered for the treatment of Alzheimer's disease at an early stage. All these drug molecules are still not the complete answer to the treatment of Alzheimer's disease. A recent report from the Office of National Statistics showed that AD is the leading cause of death in 2022. Therefore, there is an urgency to develop more drugs that can treat AD. Based on this urgency, we aim to investigate how bioactive and already approved drugs could be repurposed for inhibiting the anticholinesterase enzyme using computational studies. To achieve this, the data science tool-Python coding was compiled on Jupyter Notebook to mine bioactive compounds from the ChEMBL database. The most bioactive compounds obtained were further investigated using Molecular Operating Environment (MOE) software to carry out molecular docking and ligand analysis, and this was followed by molecular dynamics simulation production at 35 ns using GROMACS 2022.4 on Archer 2 machine. The molecular dynamic analysis was carried out using HeroMDanalysis software. Data mining of the ChEMBL database was carried out for lipase inhibitors, and this gave CHEMBL-ID 1240685, a peptide molecule, the most active compound at the time of data mining. Further literature studies gave Zoladex an FDA-approved drug for the treatment of breast cancer as another compound of interest. The in silico studies were carried out against the anticholinesterase enzyme using two FDA-approved drugs donepezil and galantamine as a template for comparing the in silico activities of the repurposed drugs. A very useful receptor for this study was PDB-1DX6, a cocrystallized galantamine inhibitor of acetylcholinesterase enzyme. The molecular docking analysis (using ligand interactions) and molecular dynamic analysis (root mean square deviation (RMSD) and root mean square fluctuation (RMSF)) showed that the two peptide molecules CHEMBL-1240685 and Zoladex gave the best binding energy and stability when compared to the FDA-approved drugs (donepezil and galantamine). Finally, further literature studies revealed that Zoladex affects memory reduction; therefore, it was dropped as a possible repurposed drug. Our research showed that CHEMBL-1240685 is a potential compound that could be investigated for the inhibition of anticholinesterase enzyme and might be another drug molecule that could be used to treat Alzheimer's disease.

5-aminosalicylic acid suppresses osteoarthritis through the OSCAR-PPARγ axis

Osteoarthritis (OA) is a progressive and irreversible degenerative joint disease that is characterized by cartilage destruction, osteophyte formation, subchondral bone remodeling, and synovitis. Despite affecting millions of patients, effective and safe disease-modifying osteoarthritis drugs are lacking. Here we reveal an unexpected role for the small molecule 5-aminosalicylic acid (5-ASA), which is used as an anti-inflammatory drug in ulcerative colitis. We show that 5-ASA competes with extracellular-matrix collagen-II to bind to osteoclast-associated receptor (OSCAR) on chondrocytes. Intra-articular 5-ASA injections ameliorate OA generated by surgery-induced medial-meniscus destabilization in male mice. Significantly, this effect is also observed when 5-ASA was administered well after OA onset. Moreover, mice with DMM-induced OA that are treated with 5-ASA at weeks 8-11 and sacrificed at week 12 have thicker cartilage than untreated mice that were sacrificed at week 8. Mechanistically, 5-ASA reverses OSCAR-mediated transcriptional repression of PPARγ in articular chondrocytes, thereby suppressing COX-2-related inflammation. It also improves chondrogenesis, strongly downregulates ECM catabolism, and promotes ECM anabolism. Our results suggest that 5-ASA could serve as a DMOAD.

Drug repurposing: databases and pipelines

The concept of drug repurposing is focused on the repositioning of drug molecules that have already undergone safety trials. There are different strategies for drug repurposing. Network-based strategy focuses on the evaluation of drug combinations in a molecular environment with multi-target hits and analysis of drug interactions. Implementation of any in silico strategy requires several databases and pipelines for executing the process of shortlisting appropriate drugs.

The Application of Artificial Intelligence and Drug Repositioning for the Identification of Fibroblast Growth Factor Receptor Inhibitors: A Review

Artificial intelligence talks about modeling intelligent behavior through a computer with the least human involvement. Drug repositioning techniques based on artificial intelligence accelerate the research process and decrease the cost of experimental studies. Dysregulation of fibroblast growth factor (FGF) receptors as the tyrosine kinase family of receptors plays a vital role in a wide range of malignancies. Because of their functional significance, they were considered promising drug targets for the therapy of various cancers. This review has summarized small molecules capable of inhibiting FGF receptors that progressed using artificial intelligence and repositioning drugs examined in clinical trials associated with cancer therapy. This review is based on a literature search in PubMed, Web of Science, Scopus EMBASE, and Google Scholar databases to gather the necessary information in each chapter by employing keywords like artificial intelligence, computational drug design, drug repositioning, and FGF receptor inhibitors. To achieve this goal, a spacious literature review of human studies in these fields-published over the last 20 decades-was performed. According to published reports, nonselective FGF receptor inhibitors can be used for cancer management, and multitarget kinase inhibitors are the first drug class approved due to more advanced clinical studies. For example, AZD4547 and BGJ398 are gradually entering the consumption cycle and are good options as combined treatments. Artificial intelligence and drug repositioning methods can help preselect suitable drug targets more successfully for future inhibition of carcinogenicity.

CCL-DTI: contributing the contrastive loss in drug-target interaction prediction

BACKGROUND

The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module.

RESULTS

In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets.

CONCLUSIONS

Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches.

Identifying patterns to uncover the importance of biological pathways on known drug repurposing scenarios

BACKGROUND

Drug repurposing plays a significant role in providing effective treatments for certain diseases faster and more cost-effectively. Successful repurposing cases are mostly supported by a classical paradigm that stems from de novo drug development. This paradigm is based on the "one-drug-one-target-one-disease" idea. It consists of designing drugs specifically for a single disease and its drug's gene target. In this article, we investigated the use of biological pathways as potential elements to achieve effective drug repurposing.

METHODS

Considering a total of 4214 successful cases of drug repurposing, we identified cases in which biological pathways serve as the underlying basis for successful repurposing, referred to as DREBIOP. Once the repurposing cases based on pathways were identified, we studied their inherent patterns by considering the different biological elements associated with this dataset, as well as the pathways involved in these cases. Furthermore, we obtained gene-disease association values to demonstrate the diminished significance of the drug's gene target in these repurposing cases. To achieve this, we compared the values obtained for the DREBIOP set with the overall association values found in DISNET, as well as with the drug's target gene (DREGE) based repurposing cases using the Mann-Whitney U Test.

RESULTS

A collection of drug repurposing cases, known as DREBIOP, was identified as a result. DREBIOP cases exhibit distinct characteristics compared with DREGE cases. Notably, DREBIOP cases are associated with a higher number of biological pathways, with Vitamin D Metabolism and ACE inhibitors being the most prominent pathways. Additionally, it was observed that the association values of GDAs in DREBIOP cases were significantly lower than those in DREGE cases (p-value < 0.05).

CONCLUSIONS

Biological pathways assume a pivotal role in drug repurposing cases. This investigation successfully revealed patterns that distinguish drug repurposing instances associated with biological pathways. These identified patterns can be applied to any known repurposing case, enabling the detection of pathway-based repurposing scenarios or the classical paradigm.

Drug Repurposing Using FDA Adverse Event Reporting System (FAERS) Database

Drug repurposing is an emerging approach to reassigning existing pre-approved therapies for new indications. The FDA Adverse Event Reporting System (FAERS) is a large database of over 28 million adverse event reports submitted by medical providers, patients, and drug manufacturers and provides extensive drug safety signal data. In this review, four common drug repurposing strategies using FAERS are described, including inverse signal detection for a single disease, drug-drug interactions that mitigate a target ADE, identifying drug-ADE pairs with opposing gene perturbation signatures and identifying drug-drug pairs with congruent gene perturbation signatures. The purpose of this review is to provide an overview of these different approaches using existing successful applications in the literature. With the fast expansion of adverse drug event reports, FAERS-based drug repurposing represents a promising strategy for discovering new uses for existing therapies.

Revealing anti-fungal potential of plant-derived bioactive therapeutics in targeting secreted aspartyl proteinase (SAP) of Candida albicans: a molecular dynamics approach

Candida species have established themselves as a major source of nosocomial infections. Increased expression of secreted aspartyl proteinases (SAP5) plays a crucial role in the pathogenesis of Candida species. Phytotherapeutics continue to serve as a viable resource for discovering novel antifungal agents. Hence the main aim of the present investigation is to explore the possible inhibitory role of the selected bioactive molecules against the SAP5 enzyme of C. albicans using in silico approach. Molecular docking and dynamic simulations were utilized to predict the binding affinity of the lead molecules using the AutoDock and Gromacs in-silico screening tools. Results of preliminary docking simulations show that the compounds hesperidin, vitexin, berberine, adhatodine, piperine, and chlorogenic acid exhibit significant interactions with the core catalytic residues of the target protein. The best binding ligands (hesperidin, vitexin, fluconazole) were subjected to molecular dynamics (MD) and essential dynamics of the trajectories. Results of the MD simulation confirm that the ligand-protein complexes became more stable from 20 ns until 100 ns. The calculated residue-level contributions to the interaction energy along a steady simulation trajectory of all three hits (hesperidin (-132.720 kJ/mol), vitexin (-83.963 kJ/mol) and fluconazole (-98.864 kJ/mol)) ensure greater stability of the leads near the catalytic region. Essential dynamics of PCA and DCCM analysis signifies that the binding of hesperidin and vitexin created a more structurally stable environment in the protein target. The overall outcomes of this study clearly emphasize that the bioactive therapeutics found in medicinal herbs may have remarkable scope in managing Candida infection.

Identification of new drug candidates against Trichomonas gallinae using high-throughput screening

Trichomonas gallinae is a protozoan parasite that is the causative agent of trichomoniasis, and infects captive and wild bird species throughout the world. Although metronidazole has been the drug of choice against trichomoniasis for decades, most Trichomonas gallinae strains have developed resistance. Therefore, drugs with new modes of action or targets are urgently needed. Here, we report the development and application of a cell-based CCK-8 method for the high-throughput screening and identification of new inhibitors of Trichomonas gallinae as a beginning point for the development of new treatments for trichomoniasis. We performed the high-throughput screening of 173 anti-parasitic compounds, and found 16 compounds that were potentially effective against Trichomonas gallinae. By measuring the median inhibitory concentration (IC50) and median cytotoxic concentration (CC50), we identified 3 potentially safe and effective compounds against Trichomonas gallinae: anisomycin, fumagillin, and MG132. In conclusion, this research successfully established a high-throughput screening method for compounds and identified 3 new safe and effective compounds against Trichomonas gallinae, providing a new treatment scheme for trichomoniasis.

Medicines for Malaria Venture Pandemic Box In Vitro Screening Identifies Compounds Highly Active against the Tachyzoite Stage of Toxoplasma gondii

Toxoplasmosis is a disease that causes high mortality in immunocompromised individuals, such as AIDS patients, and sequelae in congenitally infected newborns. Despite its great medical importance, there are few treatments available and these are associated with adverse events and resistance. In this work, after screening the drugs present in the Medicines for Malaria Venture Pandemic Box, we found new hits with anti-Toxoplasma gondii activity. Through our analysis, we selected twenty-three drugs or drug-like compounds that inhibited the proliferation of T. gondii tachyzoites in vitro by more than 50% at a concentration of 1 µM after seven days of treatment. Nineteen of these compounds have never been reported active before against T. gondii. Inhibitory curves showed that most of these drugs were able to inhibit parasite replication with IC50 values on the nanomolar scale. To better understand the unprecedented effect of seven compounds against T. gondii tachyzoites, an ultrastructural analysis was carried out using transmission electron microscopy. Treatment with 0.25 µM verdinexor, 3 nM MMV1580844, and 0.25 µM MMV019724 induced extensive vacuolization, complete ultrastructural disorganization, and lytic effects in the parasite, respectively, and all of them showed alterations in the division process. Treatment with 1 µM Eberconazole, 0.5 µM MMV1593541, 1 µM MMV642550, 1 µM RWJ-67657, and 1 µM URMC-099-C also caused extensive vacuolization in the parasite. The activity of these drugs against intracellular tachyzoites supports the idea that the drugs selected in the Pandemic Box could be potential future drugs for the treatment of acute toxoplasmosis.

Use of Three-Dimensional Cell Culture Models in Drug Assays for Anti-Cancer Agents in Oral Cancer: Protocol for a Scoping Review

Advances in the development of pharmacological treatment in oral cancer require tumor models capable of simulating the complex biology of the tumor microenvironment. The spread of three-dimensional models has changed the scenery of in vitro cell culture techniques, contributing to translational oncology. Still, the full extent of their application in preclinical drug trials is yet to be understood. Therefore, the present scoping review protocol was established to screen the literature on using three-dimensional cell culture models in drug-testing assays in the context of oral cancer. This scoping review will be conducted based on the guidelines established by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review guidelines (PRISMA-ScR). We will search the PubMed/Medline, Web of Science, Scopus, and Embase databases, as well as the gray literature, including peer-reviewed research articles involving 3D models applied to drug-assessment assays in oral cancer published from 1 March 2013 until 1 March 2023. Data will be charted, and findings will be described according to the predetermined questions of interest. We will present these findings in a narrative manner.

Integration of Deep Learning and Sequential Metabolism to Rapidly Screen Dipeptidyl Peptidase (DPP)-IV Inhibitors from Gardenia jasminoides Ellis

Traditional Chinese medicine (TCM) possesses unique advantages in the management of blood glucose and lipids. However, there is still a significant gap in the exploration of its pharmacologically active components. Integrated strategies encompassing deep-learning prediction models and active validation based on absorbable ingredients can greatly improve the identification rate and screening efficiency in TCM. In this study, the affinity prediction of 11,549 compounds from the traditional Chinese medicine system's pharmacology database (TCMSP) with dipeptidyl peptidase-IV (DPP-IV) based on a deep-learning model was firstly conducted. With the results, Gardenia jasminoides Ellis (GJE), a food medicine with homologous properties, was selected as a model drug. The absorbed components of GJE were subsequently identified through in vivo intestinal perfusion and oral administration. As a result, a total of 38 prototypical absorbed components of GJE were identified. These components were analyzed to determine their absorption patterns after intestinal, hepatic, and systemic metabolism. Virtual docking and DPP-IV enzyme activity experiments were further conducted to validate the inhibitory effects and potential binding sites of the common constituents of deep learning and sequential metabolism. The results showed a significant DPP-IV inhibitory activity (IC50 53 ± 0.63 μg/mL) of the iridoid glycosides' potent fractions, which is a novel finding. Genipin 1-gentiobioside was screened as a promising new DPP-IV inhibitor in GJE. These findings highlight the potential of this innovative approach for the rapid screening of active ingredients in TCM and provide insights into the molecular mechanisms underlying the anti-diabetic activity of GJE.

Drug Repurposing for Spinal Cord Injury: Progress Towards Therapeutic Intervention for Primary Factors and Secondary Complications

Spinal cord injury (SCI) encompasses a plethora of complex mechanisms like the involvement of major cell death pathways, neurodegeneration of spinal cord neurons, overexpression of glutaminergic transmission and inflammation cascade, along with different co-morbidities like neuropathic pain, urinary and sexual dysfunction, respiratory and cardiac failures, making it one of the leading causes of morbidity and mortality globally. Corticosteroids such as methylprednisolone and dexamethasone, and non-steroidal anti-inflammatory drugs such as naproxen, aspirin and ibuprofen are the first-line treatment options for SCI, inhibiting primary and secondary progression by preventing inflammation and action of reactive oxygen species. However, they are constrained by a short effective drug administration window and their pharmacological action being limited to symptomatic relief of the secondary effects related to spinal cord injury only. Although post-injury rehabilitation treatments may enable functional recovery, they take a long time to show results. Drug repurposing might be an innovative method for expanding therapy alternatives, utilising drugs that are already approved by various esteemed federal agencies throughout the world. Reutilising a drug molecule to treat SCI can eliminate the need for expensive and lengthy drug discovery processes and pave the way for new therapeutic approaches in SCI. This review summarises marketed drugs that could be repurposed based on their safety and efficacy data. We also discuss their mechanisms of action and provide a list of repurposed drugs under clinical trials for SCI therapy.

Repositioning of HMG-CoA Reductase Inhibitors as Adjuvants in the Modulation of Efflux Pump-Mediated Bacterial and Tumor Resistance

Efflux pump (EP)-mediated multidrug resistance (MDR) seems ubiquitous in bacterial infections and neoplastic diseases. The diversity and lack of specificity of these efflux mechanisms raise a great obstacle in developing drugs that modulate efflux pumps. Since developing novel chemotherapeutic drugs requires large investments, drug repurposing offers a new approach that can provide alternatives as adjuvants in treating resistant microbial infections and progressive cancerous diseases. Hydroxy-methyl-glutaryl coenzyme-A (HMG-CoA) reductase inhibitors, also known as statins, are promising agents in this respect. Originally, statins were used in the therapy of dyslipidemia and for the prevention of cardiovascular diseases; however, extensive research has recently been performed to elucidate the functions of statins in bacterial infections and cancers. The mevalonate pathway is essential in the posttranslational modification of proteins related to vital eukaryotic cell functions. In this article, a comparative review is given about the possible role of HMG-CoA reductase inhibitors in managing diseases of bacterial and neoplastic origin. Molecular research and clinical studies have proven the justification of statins in this field. Further well-designed clinical trials are urged to clarify the significance of the contribution of statins to the lower risk of disease progression in bacterial infections and cancerous diseases.

Repurposing approved non-oncology drugs for cancer therapy: a comprehensive review of mechanisms, efficacy, and clinical prospects

Cancer poses a significant global health challenge, with predictions of increasing prevalence in the coming years due to limited prevention, late diagnosis, and inadequate success with current therapies. In addition, the high cost of new anti-cancer drugs creates barriers in meeting the medical needs of cancer patients, especially in developing countries. The lengthy and costly process of developing novel drugs further hinders drug discovery and clinical implementation. Therefore, there has been a growing interest in repurposing approved drugs for other diseases to address the urgent need for effective cancer treatments. The aim of this comprehensive review is to provide an overview of the potential of approved non-oncology drugs as therapeutic options for cancer treatment. These drugs come from various chemotherapeutic classes, including antimalarials, antibiotics, antivirals, anti-inflammatory drugs, and antifungals, and have demonstrated significant antiproliferative, pro-apoptotic, immunomodulatory, and antimetastatic properties. A systematic review of the literature was conducted to identify relevant studies on the repurposing of approved non-oncology drugs for cancer therapy. Various electronic databases, such as PubMed, Scopus, and Google Scholar, were searched using appropriate keywords. Studies focusing on the therapeutic potential, mechanisms of action, efficacy, and clinical prospects of repurposed drugs in cancer treatment were included in the analysis. The review highlights the promising outcomes of repurposing approved non-oncology drugs for cancer therapy. Drugs belonging to different therapeutic classes have demonstrated notable antitumor effects, including inhibiting cell proliferation, promoting apoptosis, modulating the immune response, and suppressing metastasis. These findings suggest the potential of these repurposed drugs as effective therapeutic approaches in cancer treatment. Repurposing approved non-oncology drugs provides a promising strategy for addressing the urgent need for effective and accessible cancer treatments. The diverse classes of repurposed drugs, with their demonstrated antiproliferative, pro-apoptotic, immunomodulatory, and antimetastatic properties, offer new avenues for cancer therapy. Further research and clinical trials are warranted to explore the full potential of these repurposed drugs and optimize their use in treating various cancer types. Repurposing approved drugs can significantly expedite the process of identifying effective treatments and improve patient outcomes in a cost-effective manner.

Drug repurposing based on the similarity gene expression signatures to explore for potential indications of quercetin: a case study of multiple sclerosis

Quercetin (QR) is a natural flavonol compound widely distributed in the plant kingdom with extensive pharmacological effects. To find the potential clinical indications of QR, 156 differentially expressed genes (DEGs) regulated by QR were obtained from the Gene Expression Omnibus database, and new potential pharmacological effects and clinical indications of QR were repurposed by integrating compounds with similar gene perturbation signatures and associated-disease signatures to QR based on the Connectivity Map and Coexpedia platforms. The results suggested QR has mainly potential therapeutic effects on multiple sclerosis (MS), osteoarthritis, type 2 diabetes mellitus, and acute leukemia. Then, MS was selected for subsequent animal experiments as a representative potential indication, and it found that QR significantly delays the onset time of classical MS model animal mice and ameliorates the inflammatory infiltration and demyelination in the central nervous system. Combined with network pharmacology technology, the therapeutic mechanism of QR on MS was further demonstrated to be related to the inhibition of the expression of inflammatory cytokines (TNF-α, IL-6, IL-1β, IFN-γ, IL-17A, and IL-2) related to TNF-α/TNFR1 signaling pathway. In conclusion, this study expanded the clinical indications of QR and preliminarily confirmed the therapeutic effect and potential mechanism of QR on MS.

Droplet Digital PCR Is a Novel Screening Method Identifying Potential Cardiac G-Protein-Coupled Receptors as Candidate Pharmacological Targets in a Rat Model of Pressure-Overload-Induced Cardiac Dysfunction

The identification of novel drug targets is needed to improve the outcomes of heart failure (HF). G-protein-coupled receptors (GPCRs) represent the largest family of targets for already approved drugs, thus providing an opportunity for drug repurposing. Here, we aimed (i) to investigate the differential expressions of 288 cardiac GPCRs via droplet digital PCR (ddPCR) and bulk RNA sequencing (RNAseq) in a rat model of left ventricular pressure-overload; (ii) to compare RNAseq findings with those of ddPCR; and (iii) to screen and test for novel, translatable GPCR drug targets in HF. Male Wistar rats subjected to transverse aortic constriction (TAC, n = 5) showed significant systolic dysfunction vs. sham operated animals (SHAM, n = 5) via echocardiography. In TAC vs. SHAM hearts, RNAseq identified 69, and ddPCR identified 27 significantly differentially expressed GPCR mRNAs, 8 of which were identified using both methods, thus showing a correlation between the two methods. Of these, Prostaglandin-F2α-receptor (Ptgfr) was further investigated and localized on cardiomyocytes and fibroblasts in murine hearts via RNA-Scope. Antagonizing Ptgfr via AL-8810 reverted angiotensin-II-induced cardiomyocyte hypertrophy in vitro. In conclusion, using ddPCR as a novel screening method, we were able to identify GPCR targets in HF. We also show that the antagonism of Ptgfr could be a novel target in HF by alleviating cardiomyocyte hypertrophy.

ACP-Dnnel: anti-coronavirus peptides' prediction based on deep neural network ensemble learning

The ongoing COVID-19 pandemic has caused dramatic loss of human life. There is an urgent need for safe and efficient anti-coronavirus infection drugs. Anti-coronavirus peptides (ACovPs) can inhibit coronavirus infection. With high-efficiency, low-toxicity, and broad-spectrum inhibitory effects on coronaviruses, they are promising candidates to be developed into a new type of anti-coronavirus drug. Experiment is the traditional way of ACovPs' identification, which is less efficient and more expensive. With the accumulation of experimental data on ACovPs, computational prediction provides a cheaper and faster way to find anti-coronavirus peptides' candidates. In this study, we ensemble several state-of-the-art machine learning methodologies to build nine classification models for the prediction of ACovPs. These models were pre-trained using deep neural networks, and the performance of our ensemble model, ACP-Dnnel, was evaluated across three datasets and independent dataset. We followed Chou's 5-step rules. (1) we constructed the benchmark datasets data1, data2, and data3 for training and testing, and introduced the independent validation dataset ACVP-M; (2) we analyzed the peptides sequence composition feature of the benchmark dataset; (3) we constructed the ACP-Dnnel model with deep convolutional neural network (DCNN) merged the bi-directional long short-term memory (BiLSTM) as the base model for pre-training to extract the features embedded in the benchmark dataset, and then, nine classification algorithms were introduced to ensemble together for classification prediction and voting together; (4) tenfold cross-validation was introduced during the training process, and the final model performance was evaluated; (5) finally, we constructed a user-friendly web server accessible to the public at http://150.158.148.228:5000/ . The highest accuracy (ACC) of ACP-Dnnel reaches 97%, and the Matthew's correlation coefficient (MCC) value exceeds 0.9. On three different datasets, its average accuracy is 96.0%. After the latest independent dataset validation, ACP-Dnnel improved at MCC, SP, and ACC values 6.2%, 7.5% and 6.3% greater, respectively. It is suggested that ACP-Dnnel can be helpful for the laboratory identification of ACovPs, speeding up the anti-coronavirus peptide drug discovery and development. We constructed the web server of anti-coronavirus peptides' prediction and it is available at http://150.158.148.228:5000/ .

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.

Sertaconazole-repurposed nanoplatform enhances lung cancer therapy via CD44-targeted drug delivery

BACKGROUND

Lung cancer is one of the most frequent causes of cancer-related deaths worldwide. Drug repurposing and nano-drug delivery systems are attracting considerable attention for improving anti-cancer therapy. Sertaconazole (STZ), an antifungal agent, has been reported to exhibit cytotoxicity against both normal and tumor cells, and its medical use is limited by its poor solubility. In order to overcome such shortcomings, we prepared a drug-repurposed nanoplatform to enhance the anti-tumor efficiency.

METHODS

Nanoplatform was prepared by thin film dispersion. Drug release studies and uptake studies were measured in vitro. Subsequently, we verified the tumor inhibition mechanisms of HTS NPs through apoptosis assay, immunoblotting and reactive oxygen species (ROS) detection analyses. Antitumor activity was evaluated on an established xenograft lung cancer model in vivo.

RESULTS

Our nanoplatform improved the solubility of sertaconazole and increased its accumulation in tumor cells. Mechanistically, HTS NPs was dependent on ROS-mediated apoptosis and pro-apoptotic autophagy to achieve their excellent anti-tumor effects. Furthermore, HTS NPs also showed strong inhibitory ability in nude mouse xenograft models without significant side effects.

CONCLUSIONS

Our results suggest that sertaconazole-repurposed nanoplatform provides an effective strategy for lung cancer treatment.