RepurposeDrugs: an interactive web-portal and predictive platform for repurposing mono- and combination therapies.

RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications and clinical trials across 25 indication categories, including neoplasms and cardiovascular conditions. The current version comprises 4314 compounds (approved, terminated or investigational) and 161 drug combinations linked to 1756 indications/conditions, totaling 28 148 drug-disease pairs. By leveraging data on both approved and failed indications, RepurposeDrugs provides ML-based predictions for the approval potential of new drug-disease indications, both for mono- and combinatorial therapies, demonstrating high predictive accuracy in cross-validation. The validity of the ML predictor is validated through a number of real-world case studies, demonstrating its predictive power to accurately identify repurposing candidates with a high likelihood of future approval. To our knowledge, RepurposeDrugs web-portal is the first integrative database and ML-based predictor for interactive exploration and prediction of both single-drug and combination approval likelihood across indications. Given its broad coverage of indication areas and therapeutic options, we expect it accelerates many future drug repurposing projects.

Repurposing effect of cardiovascular-metabolic drug to increase lifespan: a systematic review of animal studies and current clinical trial progress.

With the increase in life expectancy, aging has emerged as a significant health concern. Due to its various mechanisms of action, cardiometabolic drugs are often repurposed for other indications, including aging. This systematic review analyzed and highlighted the repositioning potential of cardiometabolic drugs to increase lifespan as an aging parameter in animal studies and supplemented by information from current clinical trial registries. Systematic searching in animal studies was performed based on PICO: "animal," "cardiometabolic drug," and "lifespan." All clinical trial registries were also searched from the WHO International Clinical Trial Registry Platform (ICTRP). Analysis of 49 animal trials and 10 clinical trial registries show that various cardiovascular and metabolic drugs have the potential to target lifespan. Metformin, acarbose, and aspirin are the three most studied drugs in animal trials. Aspirin and acarbose are the promising ones, whereas metformin exhibits various results. In clinical trial registries, metformin, omega-3 fatty acid, acarbose, and atorvastatin are currently cardiometabolic drugs that are repurposed to target aging. Published clinical trial results show great potential for omega-3 and metformin in healthspan. Systematic Review Registration: crd.york.ac.uk/prospero/display_record.php?RecordID=457358, identifier: CRD42023457358.

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.

Activation of the PGE2-EP2 pathway as a potential drug target for treating eosinophilic rhinosinusitis.

Current treatments of eosinophilic chronic rhinosinusitis (ECRS) involve corticosteroids with various adverse effects and costly therapies such as dupilumab, highlighting the need for improved treatments. However, because of the lack of a proper mouse ECRS model that recapitulates human ECRS, molecular mechanisms underlying this disease are incompletely understood. ECRS is often associated with aspirin-induced asthma, suggesting that dysregulation of lipid mediators in the nasal mucosa may underlie ECRS pathology. We herein found that the expression of microsomal PGE synthase-1 (encoded by PTGES) was significantly lower in the nasal mucosa of ECRS patients than that of non-ECRS subjects. Histological, transcriptional, and lipidomics analyses of Ptges-deficient mice revealed that defective PGE2 biosynthesis facilitated eosinophil recruitment into the nasal mucosa, elevated expression of type-2 cytokines and chemokines, and increased pro-allergic and decreased anti-allergic lipid mediators following challenges with Aspergillus protease and ovalbumin. A nasal spray containing agonists for the PGE2 receptor EP2 or EP4, including omidenepag isopropyl that has been clinically used for treatment of glaucoma, markedly reduced intranasal eosinophil infiltration in Ptges-deficient mice. These results suggest that the present model using Ptges-deficient mice is more relevant to human ECRS than are previously reported models and that eosinophilic inflammation in the nasal mucosa can be efficiently blocked by activation of the PGE2-EP2 pathway. Furthermore, our findings suggest that drug repositioning of omidenepag isopropyl may be useful for treatment of patients with ECRS.

Drug repurposing for obsessive-compulsive disorder using deep learning-based binding affinity prediction models.

Obsessive-compulsive disorder (OCD) is a chronic psychiatric disease in which patients suffer from obsessions compelling them to engage in specific rituals as a temporary measure to alleviate stress. In this study, deep learning-based methods were used to build three models which predict the likelihood of a molecule interacting with three biological targets relevant to OCD, SERT, D2, and NMDA. Then, an ensemble model based on those models was created which underwent external validation on a large drug database using random sampling. Finally, case studies of molecules exhibiting high scores underwent bibliographic validation showcasing that good performance in the ensemble model can indicate connection with OCD pathophysiology, suggesting that it can be used to screen molecule databases for drug-repurposing purposes.

Doxorubicin as a Drug Repurposing for Disruption of α-Chymotrypsinogen-A Aggregates.

Protein conformation is affected by interaction of several small molecules resulting either stabilization or disruption depending on the nature of the molecules. In our earlier communication, Hg2+ was known to disrupt the native structure of α-Cgn A leading to aggregation (Ansari, N.K., Rais, A. & Naeem, A. Methotrexate for Drug Repurposing as an Anti-Aggregatory Agent to Mercuric Treated α-Chymotrypsinogen-A. Protein J (2024). https://doi.org/10.1007/s10930-024-10187-z ). Accumulation of ß-rich aggregates in the living system is found to be linked with copious number of disorders. Here, we have investigated the effect of varying concentration of doxorubicin (DOX) i.e. 0-100 µM on the preformed aggregates of α-Cgn A upon incubation with 120 µM Hg2+. The decrease in the intrinsic fluorescence and enzyme activity with respect to increase in the Hg2+ concentration substantiate the formation of aggregates. The DOX showed the dose dependent decrease in the ThT fluorescence, turbidity and RLS measurements endorsing the dissolution of aggregates which were consistent with red shift in ANS, confirming the breakdown of aggregates. The α-Cgn A has 30% α-helical content which decreases to 3% in presence of Hg2+. DOX increased the α-helicity to 28% confirming its anti-aggregatory potential. The SEM validates the formation of aggregates with Hg2+ and their dissolution upon incubation with the DOX. Hemolysis assay checked the cytotoxicity of α-Cgn A aggregates. Docking revealed that the DOX interacted Lys203, Cys201, Cys136, Ser159, Leu10, Trp207, Val137 and Thr134 of α-Cgn A through hydrophobic interactions and Gly133, Thr135 and Lys202 forms hydrogen bonds.

Antihypertensive Drugs for the Prevention of Atrial Fibrillation: A Drug Target Mendelian Randomization Study.

BACKGROUND: We investigated the potential impact of antihypertensive drugs for atrial fibrillation (AF) prevention through a drug target Mendelian randomization study to avoid the potential limitations of clinical studies. METHODS: Validated published single-nucleotide polymorphisms (SNPs) that mimic the action of 12 antihypertensive drug classes, including alpha-adrenoceptor blockers, adrenergic neuron blockers, angiotensin-converting enzyme inhibitors, angiotensin-II receptor blockers, beta-adrenoceptor blockers, centrally acting antihypertensive drugs, calcium channel blockers, loop diuretics, potassium-sparing diuretics and mineralocorticoid receptor antagonists, renin inhibitors, thiazides and related diuretic agents, and vasodilators were used. We estimated, via their corresponding gene and protein targets, the downstream effect of these drug classes to prevent AF via systolic blood pressure using 2-sample Mendelian randomization analyses. The SNPs were extracted from 2 European genome-wide association studies for the drug classes (n=317 754; n=757 601) and 1 European genome-wide association study for AF (n=1 030 836). RESULTS: Drug target Mendelian randomization analyses supported the significant preventive causal effects of lowering systolic blood pressure per 10 mm Hg via alpha-adrenoceptor blockers (n=11 SNPs; odds ratio [OR], 0.34 [95% CI, 0.21-0.56]; P=2.74×10-05), beta-adrenoceptor blockers (n=17 SNPs; OR, 0.52 [95% CI, 0.35-0.78]; P=1.62×10-03), calcium channel blockers (n=49 SNPs; OR, 0.50 [95% CI, 0.36-0.70]; P=4.51×10-05), vasodilators (n=19 SNPs; OR, 0.53 [95% CI, 0.34-0.84]; P=7.03×10-03), and all 12 antihypertensive drug classes combined (n=158 SNPs; OR, 0.64 [95% CI, 0.54-0.77]; P=8.50×10-07) on AF risk. CONCLUSIONS: Our results indicated that lowering systolic blood pressure via protein targets of various antihypertensive drugs seems promising for AF prevention. Our findings inform future clinical trials and have implications for repurposing antihypertensive drugs for AF prevention.

International regulatory and publicly-funded initiatives to advance drug repurposing.

Introduction: Although drug repurposing holds great potential in addressing unmet needs, successful practical implementation is challenging and has been less widespread than anticipated. Regulators may play a critical role in addressing this, and recent years have seen the conception of regulator-initiated and publicly-funded repurposing initiatives, with significant regulator involvement. Methods: International regulators and public funders (n = 8) were interviewed to obtain insight in how repurposing can be advanced from a regulatory perspective. Transcripts were thematically analyzed. Results: Most initiatives employed a broad concept of repurposing. While patient access was the main focus, label extension remained the gold standard. Commonly perceived barriers were a lack of regulatory expertise, limited downstream drug development, insufficient financial incentives, inadequate awareness of challenges, and poor collaboration. Ways for regulators to facilitate repurposing include early and accessible involvement fostering education, collaboration, and awareness. Increased stakeholder engagement, including internationally, was recommended. Legislative changes may enable the current repurposing ecosystem to evolve. Discussion: Regulators may play a central role in advancing repurposing by reconsidering their responsibilities within the current regulatory framework, both in mitigating repurposing pitfalls and actively encouraging repurposing initiatives by industry and non-traditional drug developers.

Multitargeted polypharmacotherapy for cancer treatment. theoretical concepts and proposals.

INTRODUCTION: The pharmacological treatment of cancer has evolved from cytotoxic to molecular targeted therapy. The median survival gains of 124 drugs approved by the FDA from 2003 to 2021 is 2.8 months. Targeted therapy is based on the somatic mutation theory, which has some paradoxes and limitations. While efforts of targeted therapy must continue, we must study newer approaches that could advance therapy and affordability for patients. AREAS COVERED: This work briefly overviews how cancer therapy has evolved from cytotoxic chemotherapy to current molecular-targeted therapy. The limitations of the one-target, one-drug approach considering cancer as a robust system and the basis for multitargeting approach with polypharmacotherapy using repurposing drugs. EXPERT OPINION: Multitargeted polypharmacotherapy for cancer with repurposed drugs should be systematically investigated in preclinical and clinical studies. Remarkably, most of these proposed drugs already have a long history in the clinical setting, and their safety is known. In principle, the risk of their simultaneous administration should not be greater than that of a first-in-human phase I study as long as the protocol is developed with strict vigilance to detect early possible side effects from their potential interactions. Research on cancer therapy should go beyond the prevailing paradigm targeted therapy.

Computational drug discovery approaches identify mebendazole as a candidate treatment for autosomal dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is a rare genetic disorder characterised by numerous renal cysts, the progressive expansion of which can impact kidney function and lead eventually to renal failure. Tolvaptan is the only disease-modifying drug approved for the treatment of ADPKD, however its poor side effect and safety profile necessitates the need for the development of new therapeutics in this area. Using a combination of transcriptomic and machine learning computational drug discovery tools, we predicted that a number of existing drugs could have utility in the treatment of ADPKD, and subsequently validated several of these drug predictions in established models of disease. We determined that the anthelmintic mebendazole was a potent anti-cystic agent in human cellular and in vivo models of ADPKD, and is likely acting through the inhibition of microtubule polymerisation and protein kinase activity. These findings demonstrate the utility of combining computational approaches to identify and understand potential new treatments for traditionally underserved rare diseases.

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.

Inhibition of Clostridioides difficile toxins TcdA and TcdB by the amiodarone derivative dronedarone.

The dreaded nosocomial pathogen Clostridioides difficile causes diarrhea and severe inflammation of the colon, especially after the use of certain antibiotics. The bacterium releases two deleterious toxins, TcdA and TcdB, into the gut, which are mainly responsible for the symptoms of C. difficile-associated diseases (CDADs). Both toxins are capable of entering independently into various host cells, e.g., intestinal epithelial cells, where they mono-O-glucosylate and inactivate Rho and/or Ras GTPases, important molecular switches for various cellular functions. We have shown recently that the cellular uptake of the Clostridioides difficile toxins TcdA and TcdB (TcdA/B) is inhibited by the licensed class III antiarrhythmic drug amiodarone (Schumacher et al. in Gut Microbes 15(2):2256695, 2023). Mechanistically, amiodarone delays the cellular uptake of both toxins into target cells most likely by lowering membrane cholesterol levels and by interfering with membrane insertion and/or pore formation of TcdA/B. However, serious side effects, such as thyroid dysfunction and severe pulmonary fibrosis, limit the clinical use of amiodarone in patients with C. difficile infection (CDI). For that reason, we aimed to test whether dronedarone, an amiodarone derivative with a more favorable side effect profile, is also capable of inhibiting TcdA/B. To this end, we tested in vitro with various methods the impact of dronedarone on the intoxication of Vero and CaCo-2 cells with TcdA/B. Importantly, preincubation of both cell lines with dronedarone for 1 h at concentrations in the low micromolar range rendered the cells less sensitive toward TcdA/B-induced Rac1 glucosylation, collapse of the actin cytoskeleton, cell rounding, and cytopathic effects, respectively. Our study points toward the possibility of repurposing the already approved drug dronedarone as the preferable safer-to-use alternative to amiodarone for inhibiting TcdA/B in the (supportive) therapy of CDADs.

Improving the treatment of bacterial infections caused by multidrug-resistant bacteria through drug repositioning.

Drug repurposing (repositioning) is a dynamically-developing area in the search for effective therapy of infectious diseases. Repositioning existing drugs with a well-known pharmacological and toxicological profile is an attractive method for quickly discovering new therapeutic indications. The off-label use of drugs for infectious diseases requires much less capital and time, and can hasten progress in the development of new antimicrobial drugs, including antibiotics. The use of drug repositioning in searching for new therapeutic options has brought promising results for many viral infectious diseases, such as Ebola, ZIKA, Dengue, and HCV. This review describes the most favorable results for repositioned drugs for the treatment of bacterial infections. It comprises publications from various databases including PubMed and Web of Science published from 2015 to 2023. The following search keywords/strings were used: drug repositioning and/or repurposing and/or antibacterial activity and/or infectious diseases. Treatment options for infections caused by multidrug-resistant bacteria were taken into account, including methicillin-resistant staphylococci, multidrug-resistant Mycobacterium tuberculosis, or carbapenem-resistant bacteria from the Enterobacteriaceae family. It analyses the safety profiles of the included drugs and their synergistic combinations with antibiotics and discusses the potential of antibacterial drugs with antiparasitic, anticancer, antipsychotic effects, and those used in metabolic diseases. Drug repositioning may be an effective response to public health threats related to the spread of multidrug-resistant bacterial strains and the growing antibiotic resistance of microorganisms.

TATA-Binding Protein-Based Virtual Screening of FDA Drugs Identified New Anti-Giardiasis Agents.

Parasitic diseases, predominantly prevalent in developing countries, are increasingly spreading to high-income nations due to shifting migration patterns. The World Health Organization (WHO) estimates approximately 300 million annual cases of giardiasis. The emergence of drug resistance and associated side effects necessitates urgent research to address this growing health concern. In this study, we evaluated over eleven thousand pharmacological compounds sourced from the FDA database to assess their impact on the TATA-binding protein (TBP) of the early diverging protist Giardia lamblia, which holds medical significance. We identified a selection of potential pharmacological compounds for combating this parasitic disease through in silico analysis, employing molecular modeling techniques such as homology modeling, molecular docking, and molecular dynamics simulations. Notably, our findings highlight compounds DB07352 and DB08399 as promising candidates for inhibiting the TBP of Giardia lamblia. Also, these compounds and DB15584 demonstrated high efficacy against trophozoites in vitro. In summary, this study identifies compounds with the potential to combat giardiasis, offering the prospect of specific therapies and providing a robust foundation for future research.

Integrated systems biology analysis of acute lymphoblastic leukemia: unveiling molecular signatures and drug repurposing opportunities.

Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by aberrant proliferation and accumulation of lymphoid precursor cells within the bone marrow. The tyrosine kinase inhibitor (TKI), imatinib mesylate, has played a significant role in the treatment of Philadelphia chromosome-positive ALL (Ph + ALL). However, the achievement of durable and sustained therapeutic success remains a challenge due to the development of TKI resistance during the clinical course.The primary objective of this investigation is to propose a novel and efficacious treatment approach through drug repositioning, targeting ALL and its Ph + subtype by identifying and addressing differentially expressed genes (DEGs). This study involves a comprehensive analysis of transcriptome datasets pertaining to ALL and Ph + ALL in order to identify DEGs associated with the progression of these diseases to identify possible repurposable drugs that target identified hub proteins.The outcomes of this research have unveiled 698 disease-related DEGs for ALL and 100 for Ph + ALL. Furthermore, a subset of drugs, specifically glipizide for Ph + ALL, and maytansine and isoprenaline for ALL, have been identified as potential candidates for therapeutic intervention. Subsequently, cytotoxicity assessments were performed to confirm the in vitro cytotoxic effects of these selected drugs on both ALL and Ph + ALL cell lines.In conclusion, this study offers a promising avenue for the management of ALL and Ph + ALL through drug repurposed drugs. Further investigations are necessary to elucidate the mechanisms underlying cell death, and clinical trials are recommended to validate the promising results obtained through drug repositioning strategies.

Repositioning of antiarrhythmics for prostate cancer treatment: a novel strategy to reprogram cancer-associated fibroblasts towards a tumor-suppressive phenotype.

BACKGROUND: Cancer-associated fibroblasts (CAFs) play a significant role in fueling prostate cancer (PCa) progression by interacting with tumor cells. A previous gene expression analysis revealed that CAFs up-regulate genes coding for voltage-gated cation channels, as compared to normal prostate fibroblasts (NPFs). In this study, we explored the impact of antiarrhythmic drugs, known cation channel inhibitors, on the activated state of CAFs and their interaction with PCa cells. METHODS: The effect of antiarrhythmic treatment on CAF activated phenotype was assessed in terms of cell morphology and fibroblast activation markers. CAF contractility and migration were evaluated by 3D gel collagen contraction and scratch assays, respectively. The ability of antiarrhythmics to impair CAF-PCa cell interplay was investigated in CAF-PCa cell co-cultures by assessing tumor cell growth and expression of epithelial-to-mesenchymal transition (EMT) markers. The effect on in vivo tumor growth was assessed by subcutaneously injecting PCa cells in SCID mice and intratumorally administering the medium of antiarrhythmic-treated CAFs or in co-injection experiments, where antiarrhythmic-treated CAFs were co-injected with PCa cells. RESULTS: Activated fibroblasts show increased membrane conductance for potassium, sodium and calcium, consistently with the mRNA and protein content analysis. Antiarrhythmics modulate the expression of fibroblast activation markers. Although to a variable extent, these drugs also reduce CAF motility and hinder their ability to remodel the extracellular matrix, for example by reducing MMP-2 release. Furthermore, conditioned medium and co-culture experiments showed that antiarrhythmics can, at least in part, reverse the protumor effects exerted by CAFs on PCa cell growth and plasticity, both in androgen-sensitive and castration-resistant cell lines. Consistently, the transcriptome of antiarrhythmic-treated CAFs resembles that of tumor-suppressive NPFs. In vivo experiments confirmed that the conditioned medium or the direct coinjection of antiarrhythmic-treated CAFs reduced the tumor growth rate of PCa xenografts. CONCLUSIONS: Collectively, such data suggest a new therapeutic strategy for PCa based on the repositioning of antiarrhythmic drugs with the aim of normalizing CAF phenotype and creating a less permissive tumor microenvironment.

Atorvastatin attenuates intestinal mucositis induced by 5-fluorouracil in mice by modulating the epithelial barrier and inflammatory response.

Chemotherapy-induced intestinal mucositis is a major side effect of cancer treatment. Statins are 3-hydroxy-3-methyl glutaryl coenzyme reductase inhibitors used to treat hypercholesterolemia and atherosclerotic diseases. Recent studies have demonstrated that atorvastatin (ATV) has antioxidant, anti-inflammatory, and resulting from the regulation of different molecular pathways. In the present study, we investigated the effects of ATV on intestinal homeostasis in 5-fluorouracil (5-FU)-induced mucositis. Our results showed that ATV protected the intestinal mucosa from epithelial damage caused by 5-FU mainly due to inflammatory infiltrate and intestinal permeability reduction, downregulation of inflammatory markers, such as Tlr4, MyD88, NF-κB, Tnf-a, Il1ß, and Il6 dose-dependent. ATV also improved epithelial barrier function by upregulating the mRNA transcript levels of mucin 2 (MUC2), and ZO-1 and occludin tight junction proteins. The results suggest that the ATV anti-inflammatory and protective effects on 5-FU-induced mice mucositis involve the inhibition of the TLR4/MYD88/NPRL3/NF-κB, iNos, and caspase 3.

Identification of Molecular Mechanisms of Ameloblastoma and Drug Repositioning by Integration of Bioinformatics Analysis and Molecular Docking Simulation.

Background: Ameloblastoma (AM) is a benign tumor locally originated from odontogenic epithelium that is commonly found in the jaw. This tumor makes aggressive invasions and has a high recurrence rate. This study aimed to investigate the differentially expressed genes (DEGs), biological function alterations, disease targets, and existing drugs for AM using bioinformatics analysis. Methods: The data set of AM was retrieved from the GEO database (GSE132474) and identified the DEGs using bioinformatics analysis. The biological alteration analysis was applied to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Protein-protein interaction (PPI) network analysis and hub gene identification were screened through NetworkAnalyst. The transcription factor-protein network was constructed via OmicsNet. We also identified candidate compounds from L1000CDS2 database. The target of AM and candidate compounds were verified using docking simulation. Results: Totally, 611 DEGs were identified. The biological function enrichment analysis revealed glycosaminoglycan and GABA (γ-aminobutyric acid) signaling were most significantly up-regulated and down-regulated in AM, respectively. Subsequently, hub genes and transcription factors were screened via the network and showed FOS protein was found in both networks. Furthermore, we evaluated FOS protein to be a therapeutic target in AMs. Candidate compounds were screened and verified using docking simulation. Tanespimycin showed the greatest affinity binding value to bind FOS protein. Conclusions: This study presented the underlying molecular mechanisms of disease pathogenesis, biological alteration, and important pathways of AMs and provided a candidate compound, Tanespimycin, targeting FOS protein for the treatment of AMs.

Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information.

BACKGROUND: Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately. OBJECTIVE: We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance. METHODS: Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning. RESULTS: Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods. CONCLUSION: The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.

Application of artificial intelligence and machine learning in drug repurposing.

The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.