Recent advances in drug repositioning for the discovery of new anticancer drugs

A whole-animal phenotypic drug screen identifies suppressors of atherogenic lipoproteins

Lipoproteins are essential for lipid transport in all bilaterians. A single Apolipoprotein B (ApoB) molecule is the inseparable structural scaffold of each ApoB-containing lipoprotein (B-lps), which are responsible for transporting lipids to peripheral tissues. The cellular mechanisms that regulate ApoB and B-lp production, secretion, transport, and degradation remain to be fully defined. In humans, elevated levels of vascular B-lps play a causative role in cardiovascular disease. Previously, we have detailed that human B-lp biology is remarkably conserved in the zebrafish using an in vivo chemiluminescent reporter of ApoB (LipoGlo) that does not disrupt ApoB function. Thus, the LipoGlo model is an ideal system for identifying novel mechanisms of ApoB modulation and, due to the ability of zebrafish to generate many progeny, is particularly amenable to large-scale phenotypic drug screening. Here, we report a screen of roughly 3000 compounds that identified 49 unique ApoB-lowering hits. Nineteen hits passed orthogonal screening criteria. A licorice root component, enoxolone, significantly lowered B-lps only in animals that express a functional allele of the nuclear hormone receptor Hepatocyte Nuclear Factor 4⍺ (HNF4⍺). Consistent with this result, inhibitors of HNF4⍺ also reduce B-lp levels. These data demonstrate that mechanism(s) of action can be rapidly determined from a whole animal zebrafish phenotypic screen. Given the well documented role of HNF4⍺ in human B-lp biology, these data validate the LipoGlo screening platform for identifying small molecule modulators of B-lps that play a critical role in a leading cause of worldwide mortality.

Drug repurposing using artificial intelligence, molecular docking, and hybrid approaches: A comprehensive review in general diseases vs Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD), the most prevalent form of dementia, remains enigmatic in its origins despite the widely accepted "amyloid hypothesis," which implicates amyloid-beta peptide aggregates in its pathogenesis and progression. Despite advancements in technology and healthcare, the incidence of AD continues to rise. The traditional drug development process remains time-consuming, often taking years to bring an AD treatment to market. Drug repurposing has emerged as a promising strategy for developing cost-effective and efficient therapeutic options by identifying new uses for existing approved drugs, thus accelerating drug development.

OBJECTIVES

This study aimed to examine two key drug repurposing methodologies in general diseases and specifically in AD, which are artificial intelligent (AI) approach and molecular docking approach. In addition, the hybrid approach that integrates AI with molecular docking techniques will be explored too.

METHODOLOGY

This study systematically compiled a comprehensive collection of relevant academic articles, scientific papers, and research studies which were published up until November 2024 (as of the writing of this review paper). The final selection of papers was filtered to include studies related to Alzheimer's disease and general diseases, and then categorized into three groups: AI articles, molecular docking articles, and hybrid articles.

RESULTS

As a result, 331 papers were identified that employed AI for drug repurposing in general diseases, and 58 papers focused specifically in AD. For molecular docking in drug repurposing, 588 papers addressed general diseases, while 46 papers were dedicated to AD. The hybrid approach combining AI and molecular docking in drug repurposing has 52 papers for general diseases and 9 for AD. A comparative review was done across the methods, results, strengths, and limitations in those studies. Challenges of drug repurposing in AD are explored and future prospects are proposed.

DISCUSSION AND CONCLUSION

Drug repurposing emerges as a compelling and effective strategy within AD research. Both AI and molecular docking methods exhibit significant potential in this domain. AI algorithms yield more precise predictions, thus facilitating the exploration of new therapeutic avenues for existing drugs. Similarly, molecular docking techniques revolutionize drug-target interaction modelling, employing refined algorithms to screen extensive drug databases against specific target proteins. This review offers valuable insights for guiding the utilization of AI, molecular docking, or their hybrid in AD drug repurposing endeavors. The hope is to speed up the timeline of drug discovery which could improve the therapeutic approach to AD.

Drug repurposing for sustainable antimicrobial use: exploring pharmacists' awareness, attitudes, facilitators and barriers

This study utilized a validated questionnaire that aimed to assess pharmacists' awareness and attitude towards drug repurposing for antimicrobial use. Despite the reasonable awareness, pharmacists reported unfavourable attitudes. Pharmacists with a B.Pharm. or Pharm.D. degree reported less awareness than pharmacists with a master's or PhD degree. In contrast, pharmacists who dispensed fewer than 10 prescriptions or 10-29 prescriptions had significantly higher awareness than those who dispensed 30 prescriptions daily or more. Pharmacists who had 1-5 years of experience and those who dispensed less than 10 prescriptions daily had significantly lower attitude scores than their counterpats. The most reported facilitator was the reduced risk of antimicrobial resistance, while the most reported barrier was patient safety. Pharmacists' perception of drug repurposing for antimicrobial needs to improve via implementing educational sessions that highlight the impact of drug repurposing on supporting the fight against antimicrobial resistance and promoting a more sustainable and resilient healthcare system.

Negative regulation of thyroid adenoma-associated protein (THADA) in the cardiac glycoside-induced anti-cancer effect

Cardiac glycosides, known as inhibitors of Na+,K+-ATPase, have anti-cancer effects such as suppression of cancer cell proliferation and induction of cancer cell death. Here, we examined the signaling pathway elicited by cardiac glycosides in the human hepatocellular carcinoma HepG2 cells and human epidermoid carcinoma KB cells. Three kinds of cardiac glycosides (ouabain, oleandrin, and digoxin) inhibited the cancer cell proliferation and decreased the expression level of thyroid adenoma-associated protein (THADA). Interestingly, the knockdown of THADA inhibited cancer cell proliferation, and the proliferation was significantly rescued by re-expression of THADA in the THADA-knockdown cells. In addition, the THADA-knockdown markedly decreased the expression level of L-type amino acid transporter LAT1. Cardiac glycosides also reduced the LAT1 expression. The LAT1 inhibitor, JPH203, significantly weakened the cancer cell proliferation. These results suggest that the binding of cardiac glycosides to Na+,K+-ATPase negatively regulates the THADA-LAT1 pathway, exerting the anti-proliferative effect in cancer cells.

Deep multiple instance learning on heterogeneous graph for drug-disease association prediction

Drug repositioning offers promising prospects for accelerating drug discovery by identifying potential drug-disease associations (DDAs) for existing drugs and diseases. Previous methods have generated meta-path-augmented node or graph embeddings for DDA prediction in drug-disease heterogeneous networks. However, these approaches rarely develop end-to-end frameworks for path instance-level representation learning as well as the further feature selection and aggregation. By leveraging the abundant topological information in path instances, more fine-grained and interpretable predictions can be achieved. To this end, we introduce deep multiple instance learning into drug repositioning by proposing a novel method called MilGNet. MilGNet employs a heterogeneous graph neural network (HGNN)-based encoder to learn drug and disease node embeddings. Treating each drug-disease pair as a bag, we designed a special quadruplet meta-path form and implemented a pseudo meta-path generator in MilGNet to obtain multiple meta-path instances based on network topology. Additionally, a bidirectional instance encoder enhances the representation of meta-path instances. Finally, MilGNet utilizes a multi-scale interpretable predictor to aggregate bag embeddings with an attention mechanism, providing predictions at both the bag and instance levels for accurate and explainable predictions. Comprehensive experiments on five benchmarks demonstrate that MilGNet significantly outperforms ten advanced methods. Notably, three case studies on one drug (Methotrexate) and two diseases (Renal Failure and Mismatch Repair Cancer Syndrome) highlight MilGNet's potential for discovering new indications, therapies, and generating rational meta-path instances to investigate possible treatment mechanisms. The source code is available at https://github.com/gu-yaowen/MilGNet.

Repurposing fluvoxamine as an inhibitor for NUDT5 in breast cancer cell: an in silico and in vitro study

Drug repurposing is necessary to accelerate drug discovery and meet the drug needs. This study investigated the possibility of using fluvoxamine to inhibit the cellular metabolizing enzyme NUDT5 in breast cancer. Computational and experimental techniques were used to evaluate the structural flexibility, binding stability, and chemical reactivity of the drugs. These findings indicated that fluvoxamine effectively suppressed the activity of NUDT5, as evidenced by a binding score of - 8.514 kcal/mol. Furthermore, the binding positions of fluvoxamine and NUDT5 were optimized. Fluvoxamine attachment to the active sites of Trp28, Trp46, Glu47, Arg51, Arg84, and Leu98 in NUDT5 has been shown to alter the metabolism of ADPr. These alterations play a role in ATP production in the breast cancer cells. In addition, an MTT assay conducted on the MCF-7 cell line using fluvoxamine revealed an IC50 value of 53.86 ± 0.05 µM. Fluvoxamine-induced apoptosis was confirmed as evidenced by AO/EtBr and DAPI staining.

Graphical abstract

Effect of fluvoxamine on breast cancer cells.

Application of Original Prostate Cancer Progression Model Interacting with Fibroblasts in Preclinical Research

Prostate cancer (PCa) is a heterogeneous disease that exhibits androgen sensitivity and responsiveness to androgen deprivation therapy (ADT). However, ADT induces only temporary remission, and the majority of PCa cases eventually progress to castration-resistant PCa (CRPC). During the development and progression of CRPC, androgen sensitivity and androgen receptor (AR) dependency in PCa cells are often deceased or lost due to ADT or spontaneously arising AR variants even before starting ADT. To prevent CRPC, a clinical PCa model derived from an AR-positive cancer cell line with weak or no androgen sensitivity is required. The human prostate LNCaP cell line is a good model for PCa because of its androgen sensitivity and AR dependency in terms of cell growth and gene expression. Notably, LNCaP cells are heterogeneous cells comprising different clones with natural variations in androgen sensitivity and AR dependency resulting from spontaneously occurring changes. In our group, to obtain androgen-insensitive or weakly sensitive clones spontaneously derived from parental LNCaP cells, we performed a limiting dilution of parental LNCaP cells and obtained several sublines with varying levels of androgen sensitivity and AR dependency. In addition, we established an androgen-insensitive subline from parental LNCaP cells by continuous passage under hormone-depleted conditions. This article provides a unique perspective on our original PCa progression model interacting with fibroblasts and its application in preclinical research.

Pharmacoproteomics reveals energy metabolism pathways as therapeutic targets of ivermectin in ovarian cancer toward 3P medical approaches

Objective

Ovarian cancer is the malignant tumor with the highest mortality rate in the female reproductive system, enormous socio-economic burden, and limited effective drug therapy. There is an urgent need to find novel effective drugs for ovarian cancer therapy. Our previous in vitro studies demonstrate that ivermectin effectively inhibits ovarian cancer cells and affects energy metabolism pathways. This study aims to clarify in vivo mechanisms and therapeutic targets of ivermectin in the treatment of ovarian cancer to establish predictive biomarkers, guide personalized treatments, and improve preventive strategies in the framework of 3P medicine.

Methods

A TOV-21G tumor-bearing mouse model was constructed based on histopathological data and biochemical parameters. TMT-based proteomic analysis was performed on tumor tissues from the different treatment groups. All significantly differentially abundant proteins were characterized by hierarchical clustering, Gene Ontology (GO) enrichment analyses, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. In addition, the data were integrated and analyzed with the proteomic data of clinical ovarian cancer tissues from our previous study and the proteomic data of ivermectin intervention in ovarian cancer cells to identify key regulators of ivermectin.

Results

Ivermectin (10 mg/kg) had a significant anti-ovarian cancer effect in mice, with a tumor inhibitory rate of 61.5%. Molecular changes in tumor tissue of ivermectin-treated mice were established, and protein-protein interaction (PPI) analysis showed that the main differential pathway networks included the TCA cycle, propanoate metabolism, 2-0xocarboxyacid metabolism, and other pathways. Integrating our previous clinical ovarian cancer tissue and cell experimental data, this study found that ivermectin significantly interfered with the energy metabolic pathways of ovarian cancer, including glycolysis, TCA cycle, oxidative phosphorylation, and other related pathways.

Conclusions

This study evaluated the anti-ovarian cancer effect in vitro and in vivo, and its specific regulatory effect on energy metabolism. The expressions of drug target molecules in the energy metabolism pathway of ovarian cancer will be used to guide the diagnosis and prevention of ovarian cancer. The significant efficacy of ivermectin will be applied to the treatment of ovarian cancer and personalized medication. This has guiding significance for the clinical diagnosis, treatment, personalized medication, and prognosis evaluation of ovarian cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00385-1.

Genomic strategies for drug repurposing

Functional genomics, a multidisciplinary subject, investigates the functions of genes and their products in biological systems to better understand diseases and find new drugs. Drug repurposing is an economically efficient approach that entails discovering novel therapeutic applications for already-available medications. Genomics enables the identification of illness and therapeutic molecular characteristics and interactions, which in turn facilitates the process of drug repurposing. Techniques like gene expression profiling and Mendelian randomization are helpful in identifying possible medication candidates. Progress in computer science allows for the investigation and modeling of gene expression networks that involve large amounts of data. The amalgamation of data concerning DNA, RNA, and protein functions bears similarity to pharmacogenomics, a crucial aspect in crafting cancer therapeutics. Functional genomics in drug discovery, particularly for cancer, is still not thoroughly investigated, despite the existence of a significant amount of literature on the subject. Next-generation sequencing and proteomics present highly intriguing opportunities. Publicly available databases and mining techniques facilitate the development of cancer treatments based on functional genomics. Broadening the exploration and utilization of functional genomics holds significant potential for advancing drug discovery and repurposing, particularly within the realm of oncology.

The Research Progress of Metformin Regulation of Metabolic Reprogramming in Malignant Tumors

BACKGROUND

Metabolism reprogramming is a crucial hallmark of malignant tumors. Tumor cells demonstrate enhanced metabolic efficiency, converting nutrient inputs into glucose, amino acids, and lipids essential for their malignant proliferation and progression. Metformin, a commonly prescribed medication for type 2 diabetes mellitus, has garnered attention for its potential anticancer effects beyond its established hypoglycemic benefits.

METHODS

This review adopts a comprehensive approach to delineate the mechanisms underlying metabolite abnormalities within the primary metabolic processes of malignant tumors.

RESULTS

This review examines the abnormal activation of G protein-coupled receptors (GPCRs) in these metabolic pathways, encompassing aerobic glycolysis with increased lactate production in glucose metabolism, heightened lipid synthesis and cholesterol accumulation in lipid metabolism, and glutamine activation alongside abnormal protein post-translational modifications in amino acid and protein metabolism. Furthermore, the intricate metabolic pathways and molecular mechanisms through which metformin exerts its anticancer effects are synthesized and analyzed, particularly its impacts on AMP-activated protein kinase activation and the mTOR pathway. The analysis reveals a multifaceted understanding of how metformin can modulate tumor metabolism, targeting key nodes in metabolic reprogramming essential for tumor growth and progression. The review compiles evidence that supports metformin's potential as an adjuvant therapy for malignant tumors, highlighting its capacity to interfere with critical metabolic pathways.

CONCLUSION

In conclusion, this review offers a comprehensive overview of the plausible mechanisms mediating metformin's influence on tumor metabolism, fostering a deeper comprehension of its anticancer mechanisms. By expanding the clinical horizons of metformin and providing insight into metabolism-targeted tumor therapies, this review lays the groundwork for future research endeavors aimed at refining and advancing metabolic intervention strategies for cancer treatment.

First report on exploration of structural features of natural compounds (NPACT database) for anti-breast cancer activity (MCF-7): QSAR-based virtual screening, molecular docking, ADMET, MD simulation, and DFT studies

Due to the high toxicity, poor efficacy and resistance associated with current anti-breast cancer drugs, there's growing interest in natural products (NPs) for their potential anti-cancer properties. Computational modelling of NPs to identify key structural features can aid in developing novel natural inhibitors. In this study, we developed statistically significant QSAR models based on NPs from the NPACT database, which have shown potential anticancer activity against the MCF-7 cancer cell lines. All the developed QSAR models were statistically robust, meeting both internal (R 2  = 0.666-0.669, R 2 adj  = 0.657-0.660, Q 2 Loo  = 0.636-0.638) and external (Q 2 F n  = 0.686-0.714, CCC ext = 0.830-0.847) validation criteria. Consequently, they were utilized to virtually screen a series of NPs from the COCONUT database in the search for novel natural inhibitors. Molecular docking studies were conducted on the identified compounds against the human HER2 protein (PDB ID: 3PP0), which is a crucial target in breast cancer. Molecular docking analysis demonstrated that compounds 4608 and 2710 achieved the highest docking scores, with CDOCKER interaction energies of -72.67 kcal/mol and - 72.63 kcal/mol respectively. Compounds 4608 and 2710 were identified as the most promising candidates upon performing triplicate 100 ns MD simulation study using the CHARMM36 force field. DFT studies was performed to evaluate their stability and reactivity as potential drug molecules. This research contributes to the development of new natural inhibitors for breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00266-5.

Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation

Computational drug repositioning, through predicting drug-disease associations (DDA), offers significant potential for discovering new drug indications. Current methods incorporate graph neural networks (GNN) on drug-disease heterogeneous networks to predict DDAs, achieving notable performances compared to traditional machine learning and matrix factorization approaches. However, these methods depend heavily on network topology, hampered by incomplete and noisy network data, and overlook the wealth of biomedical knowledge available. Correspondingly, large language models (LLMs) excel in graph search and relational reasoning, which can possibly enhance the integration of comprehensive biomedical knowledge into drug and disease profiles. In this study, we first investigate the contribution of LLM-inferred knowledge representation in drug repositioning and DDA prediction. A zero-shot prompting template was designed for LLM to extract high-quality knowledge descriptions for drug and disease entities, followed by embedding generation from language models to transform the discrete text to continual numerical representation. Then, we proposed LLM-DDA with three different model architectures (LLM-DDANode Feat, LLM-DDADual GNN, LLM-DDAGNN-AE) to investigate the best fusion mode for LLM-based embeddings. Extensive experiments on four DDA benchmarks show that, LLM-DDAGNN-AE achieved the optimal performance compared to 11 baselines with the overall relative improvement in AUPR of 23.22%, F1-Score of 17.20%, and precision of 25.35%. Meanwhile, selected case studies of involving Prednisone and Allergic Rhinitis highlighted the model's capability to identify reliable DDAs and knowledge descriptions, supported by existing literature. This study showcases the utility of LLMs in drug repositioning with its generality and applicability in other biomedical relation prediction tasks.

Quality by design (QbD) based approach for development of itraconazole-loaded transferosomes for skin cancer: in vitro, ex vivo and cell line studies

OBJECTIVE

Itraconazole (ITZ), a widely used systemic antifungal drug, has been ingeniously repurposed for its antitumor effects. In the present work, we have prepared and optimized the ITZ-loaded transferosomes by Quality by Design (QbD) approach and repurposed them for skin cancer.

METHODS

The transferosomal formulation was optimized by employing a QbD approach with the design of experiment. A combination of screening and optimization design was used for formulation optimization. The optimized formulation was characterized by particle size, PDI, zeta potential, FTIR, XRD, and surface morphology using TEM. In vitro and ex vivo studies were performed using Franz diffusion cells. An in vitro cell line study was performed on the human melanoma A375 cell line.

RESULTS

The optimized formulation has a particle size of 192.37 ± 13.19 nm, PDI of 0.41 ± 0.03, zeta potential -47.80 ± 3.66, and an entrapment efficiency of 64.11 ± 3.75%. In vitro release studies showed that ITZ encapsulated transferosomes offer higher and sustained release than pure drugs. Ex vivo drug penetration and retention studies show that the penetration and retention of transferosomes are more visible in the skin than in the drug. The cell viability study confirms that ITZ encapsulated transferosomes have almost 2-fold more potency against the A375 cell line than pure drug.

CONCLUSION

ITZ encapsulated transferosomes were successfully prepared and optimized using a combination of screening and optimization designs. Based on ex vivo and cell line studies, we conclude that ITZ-loaded transferosomes could aid melanoma management alongside standard therapies.

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

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

Discovery of Novel Azaphenothiazine Derivatives to Suppress Endometrial Cancer by Targeting GRP75 to Impair Its Interaction with IP3R and Mitochondrial Ca2+ Homeostasis

Endometrial cancer (EC) is the most common cancer of the female reproductive tract, and there is an urgent need to develop new candidate drugs with good efficacy and safety to improve the survival rate and life quality of EC patients. Herein, a series of new azaphenothiazine derivatives were designed and synthesized and their anti-EC activities were evaluated. Among them, compound 33 showed excellent antiproliferative activities against both progesterone-sensitive ISK cells and progesterone-resistant KLE cells. Moreover, 33 could significantly inhibit colony formation and migration of EC cells and induce cell apoptosis. Remarkably, 33 significantly suppressed KLE xenograft tumor growth without influencing body weights or key organs. In addition, 33 exhibited good pharmacokinetic properties and low extrapyramidal side effects. Mechanism research indicated that 33 reduced Ca2+ levels in mitochondria by targeting GRP75 and disrupting its interaction with IP3R. Overall, 33 showed promising potential as an anti-EC candidate agent.

Cimetidine repurposed as a potential immunomodulatory agent against colorectal carcinoma: A systematic review

OBJECTIVE

To determine the survival benefit and immunomodulatory effects of cimetidine pre-, peri- or post-operatively in patients with colorectal cancer (CRC).

METHODS

A systematic review was conducted using PubMed and Cochrane Library to retrieve randomized control trials (RCTs) that investigated the effects of cimetidine on survival and immunomodulation via improvement in tumor infiltrating lymphocytes (TILs) and peripheral blood lymphocytes. The review was carried out in accordance with the extended Preferred Reporting Items for Systematic Reviews and Meta-analyses.

RESULTS

Four studies with the total of 267 patients were included in this systematic review. Treatment duration varied from 5 days to 1 year. Two studies reported a significant TIL response in the resected specimens after administering cimetidine, while one RCT showed an escalation of CD3, CD4 and CD57 lymphocytes in peripheral blood compared to the baseline following cimetidine treatment (p < 0.01). Of the three trials that examined the effects of cimetidine on survival, only two studies revealed significant survival benefit while the remaining study only showed a trend towards survival benefit.

CONCLUSION

Repurposing of existing drugs like cimetidine has a potential to offer a survival benefit by acting as an immunomodulatory agent in patients undergoing curative resection for CRC. However, the heterogeneity seen in current studies and the evolvement of adjunctive therapies for CRC warrant large-scale, well-designed prospective RCTs to establish the efficacy of cimetidine in CRC.

Lumacaftor as a potential repurposed drug in targeting breast cancer stem cells: insights from in silico study

CONTEXT

Breast cancer stem cells (BCSCs) are a small subset of cells within breast tumors with characteristics similar to normal stem cells. Despite advancements in chemotherapy and targeted therapy for breast cancer, the prognosis for breast cancer patients has remained poor due to drug resistance, reoccurrence, and metastasis. Growing evidence suggests that deregulation of the self-renewal pathways, like the Wnt signaling pathway mediated by β-catenin, plays a crucial role in the survival of breast cancer stem cells. Targeting the Wnt signaling pathway in breast cancer stem cells offers a promising avenue for developing effective therapeutic strategies targeting these cells, potentially leading to improved patient outcomes and reduced tumor recurrence.

METHODS

For this purpose, we have screened a 1615 FDA-approved drug library against our target protein, β-catenin, which is involved in the Wnt signaling pathway using molecular docking analysis, molecular dynamics (MD) simulations, and molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations.

RESULTS

Molecular docking studies showed that the Lumacaftor- β-catenin complex had the lowest docking score of - 8.7 kcal/mol towards β-catenin protein than the reference inhibitor. Molecular dynamic simulations and MM/PBSA calculations were also performed for the Lumacaftor-β-catenin complex to establish the stability of the interactions involved. Considering its promising attributes and encouraging results, Lumacaftor holds significant potential as a novel therapeutic option to target BCSCs. This study opens avenues for further investigation and may pave the way for developing therapeutic potential in breast cancer treatment. Further confirmation is warranted through in vitro and clinical studies to validate the findings of this study.

Computational Modeling to Identify Drugs Targeting Metastatic Castration-Resistant Prostate Cancer Characterized by Heightened Glycolysis

Metastatic castration-resistant prostate cancer (mCRPC) remains a deadly disease due to a lack of efficacious treatments. The reprogramming of cancer metabolism toward elevated glycolysis is a hallmark of mCRPC. Our goal is to identify therapeutics specifically associated with high glycolysis. Here, we established a computational framework to identify new pharmacological agents for mCRPC with heightened glycolysis activity under a tumor microenvironment, followed by in vitro validation. First, using our established computational tool, OncoPredict, we imputed the likelihood of drug responses to approximately 1900 agents in each mCRPC tumor from two large clinical patient cohorts. We selected drugs with predicted sensitivity highly correlated with glycolysis scores. In total, 77 drugs predicted to be more sensitive in high glycolysis mCRPC tumors were identified. These drugs represent diverse mechanisms of action. Three of the candidates, ivermectin, CNF2024, and P276-00, were selected for subsequent vitro validation based on the highest measured drug responses associated with glycolysis/OXPHOS in pan-cancer cell lines. By decreasing the input glucose level in culture media to mimic the mCRPC tumor microenvironments, we induced a high-glycolysis condition in PC3 cells and validated the projected higher sensitivity of all three drugs under this condition (p < 0.0001 for all drugs). For biomarker discovery, ivermectin and P276-00 were predicted to be more sensitive to mCRPC tumors with low androgen receptor activities and high glycolysis activities (AR(low)Gly(high)). In addition, we integrated a protein-protein interaction network and topological methods to identify biomarkers for these drug candidates. EEF1B2 and CCNA2 were identified as key biomarkers for ivermectin and CNF2024, respectively, through multiple independent biomarker nomination pipelines. In conclusion, this study offers new efficacious therapeutics beyond traditional androgen-deprivation therapies by precisely targeting mCRPC with high glycolysis.

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.

System biology approaches for drug repurposing

Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.

Regulatory considerations and intellectual property rights of repurposed drugs

Drug repurposing has emerged as a promising approach in the drug discovery and development process as it offers safe and effective therapeutic options in a time effective manner. Though the issues related to pre-clinical and clinical aspects of drug development process are greatly addressed during drug repurposing yet regulatory perspectives gain even more However, like traditional drug development the repurposed drugs face multiple challenges. Such challenges range from the patenting rights, novelty of repurposing, data and market exclusivity to affordability and equitable access to the patient population. In order to optimize the market access of repurposed drugs, regulatory organizations throughout the world have developed accelerated approval procedures. The regulatory bodies have recognized the importance of repurposing approaches and repurposed drugs. Regulatory bodies can encourage the development of repurposed drugs by providing incentives to pharmaceutical companies and more accessible and affordable repurposed agents for the general population. This chapter summarizes the regulatory and ethical considerations pertaining to the repurposed drugs and highlights a few cases of intellectual property rights for repurposed drugs that have helped improve patient's access to safe, efficacious and cost-effective therapeutic options.

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.

Protective effect of olopatadine hydrochloride against LPS-induced acute lung injury: via targeting NF-κB signaling pathway

BACKGROUND

Morbidity and mortality rates associated with acute lung injury/acute respiratory distress syndrome (ALI/ARDS) are high (30-40%). Nuclear factor-kappa B (NF-κB) is a transcription factor, associated with transcription of numerous cytokines leading to cytokine storm, and thereby, plays a major role in ALI/ARDS and in advanced COVID-19 syndrome.

METHODS

Considering the role of NF-κB in ALI, cost-effective in silico approaches were utilized in the study to identify potential NF-κB inhibitor based on the docking and pharmacokinetic results. The identified compound was then pharmacologically validated in lipopolysaccharide (LPS) rodent model of acute lung injury. LPS induces ALI by altering alveolar membrane permeability, recruiting activated neutrophils and macrophages to the lungs, and compromising the alveolar membrane integrity and ultimately impairs the gaseous exchange. Furthermore, LPS exposure is associated with exaggerated production of various proinflammatory cytokines in lungs.

RESULTS

Based on in silico studies Olopatadine Hydrochloride (Olo), an FDA-approved drug was found as a potential NF-κB inhibitor which has been reported for the first time, and considered further for the pharmacological validation. Intraperitoneal LPS administration resulted in ALI/ARDS by fulfilling 3 out of the 4 criteria described by ATS committee (2011) published workshop report. However, treatment with Olo attenuated LPS-induced elevation of proinflammatory markers (IL-6 and NF-κB), oxidative stress, neutrophil infiltration, edema, and damage in lungs. Histopathological studies also revealed that Olo treatment significantly ameliorated LPS-induced lung injury, thus conferring improvement in survival. Especially, the effects produced by Olo medium dose (1 mg/kg) were comparable to dexamethasone standard.

CONCLUSION

In nutshell, inhibition of NF-κB pathway by Olo resulted in protection and reduced mortality in LPS- induced ALI and thus has potential to be used clinically to arrest disease progression in ALI/ARDS, since the drug is already in the market. However, the findings warrant further extensive studies, and also future studies can be planned to elucidate its role in COVID-19-associated ARDS or cytokine storm.

ATP-Binding Cassette Subfamily G Member 2 in Acute Myeloid Leukemia: A New Molecular Target?

Despite the progress in the knowledge of disease pathogenesis and the identification of many molecular markers as potential targets of new therapies, the cure of acute myeloid leukemia remains challenging. Disease recurrence after an initial response and the development of resistance to old and new therapies account for the poor survival rate and still make allogeneic stem cell transplantation the only curative option. Multidrug resistance (MDR) is a multifactorial phenomenon resulting from host-related characteristics and leukemia factors. Among these, the overexpression of membrane drug transporter proteins belonging to the ABC (ATP-Binding Cassette)-protein superfamily, which diverts drugs from their cellular targets, plays an important role. Moreover, a better understanding of leukemia biology has highlighted that, at least in cancer, ABC protein's role goes beyond simple drug transport and affects many other cell functions. In this paper, we summarized the current knowledge of ABCG2 (formerly Breast Cancer Resistance Protein, BCRP) in acute myeloid leukemia and discuss the potential ways to overcome its efflux function and to revert its ability to confer stemness to leukemia cells, favoring the persistence of leukemia progenitors in the bone marrow niche and justifying relapse also after therapy intensification with allogeneic stem cell transplantation.

Drug Repositioning Using Computer-aided Drug Design (CADD)

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

New 2-alkoxycyanopyridine derivatives as inhibitors of EGFR, HER2, and DHFR: Synthesis, anticancer evaluation, and molecular modeling studies

New series of substituted 2-alkoxycyanopyridine derivatives were synthesized and evaluated for their in vitro and in vivo anticancer activities. Comparing the evaluated activities against cancer cell lines to the broad-spectrum anticancer doxorubicin, and the kinase inhibitor sorafenib, compounds 3a, 4b, 4c, 7a, and 8d demonstrated superior anticancer efficacy with elevated safety profiles and selectivity indices, particularly against MCF7 breast cancer. For exploration of their mechanism of action, assays for inhibition of EGFR, HER2 kinase, and DHFR were performed. The promising synthesized compounds exhibited potent dual kinase EGFR/HER2 inhibitory activity with IC50values of 0.248/0.156 μM for 4b and 0.138/0.092 μM for 4c. Additionally, with IC50 values of 0.138 and 0.193 M, respectively, 4b and 4c had the greatest DHFR inhibitory activity that was comparable to methotrexate. In the MCF7 breast cancer cell line, they caused arrest at the S phase of the cell cycle and exhibited apoptosis induction activity. With restored caspase-3 immunoexpression, the anti-breast cancer assay performed in vivo of 4b and 4c demonstrated a substantial decrease in tumor volume. Results from molecular modeling were in agreement with biological assays proving the importance of the 3-caynopyridine, two substituted phenyl rings attached to central pyridine ring, and propoxy side chain moieties for binding with the receptors. As 4c works by inhibiting both EGFR/HER2 kinase, DHFR enzymes, in addition to cellular apoptosis, it could be viewed as a model of compounds possessing a multi-targeting anticancer activity. Collectively, compounds 4b and 4c might represent prototypes for further development as anticancer molecules.

Structural and molecular characterization of lopinavir and ivermectin as breast cancer resistance protein (BCRP/ABCG2) inhibitors

A current clinical challenge in cancer is multidrug resistance (MDR) mediated by ABC transporters. Breast cancer resistance protein (BCRP) or ABCG2 transporter is one of the most important ABC transporters implicated in MDR and the use of inhibitors is a promising approach to overcome the resistance in cancer. This study aimed to characterize the molecular mechanism of ABCG2 inhibitors identified by a repurposing drug strategy using antiviral, anti-inflammatory and antiparasitic agents. Lopinavir and ivermectin can be considered as pan-inhibitors of ABC transporters, since both compounds inhibited ABCG2, P-glycoprotein and MRP1. They inhibited ABCG2 activity showing IC50 values of 25.5 and 23.4 µM, respectively. These drugs were highly cytotoxic and not transported by ABCG2. Additionally, these drugs increased the 5D3 antibody binding and did not affect the mRNA and protein expression levels. Cell-based analysis of the type of inhibition suggested a non-competitive inhibition, which was further corroborated by in silico approaches of molecular docking and molecular dynamics simulations. These results showed an overlap of the lopinavir and ivermectin binding sites on ABCG2, mainly interacting with E446 residue. However, the substrate mitoxantrone occupies a different site, binding to the F436 region, closer to the L554/L555 plug. In conclusion, these results revealed the mechanistic basis of lopinavir and ivermectin interaction with ABCG2. See also the Graphical abstract(Fig. 1).

Repurposing Drugs: An Empowering Approach to Drug Discovery and Development

Drug discovery and development is a time-consuming and costly procedure that necessitates a substantial effort. Drug repurposing has been suggested as a method for developing medicines that takes less time than developing brand new medications and will be less expensive. Also known as drug repositioning or re-profiling, this strategy has been in use from the time of serendipitous drug discoveries to the modern computer aided drug designing and use of computational chemistry. In the light of the COVID-19 pandemic too, drug repurposing emerged as a ray of hope in the dearth of available medicines. Data availability by electronic recording, libraries, and improvements in computational techniques offer a vital substrate for systemic evaluation of repurposing candidates. In the not-too-distant future, it could be possible to create a global research archive for us to access, thus accelerating the process of drug development and repurposing. This review aims to present the evolution, benefits and drawbacks including current approaches, key players and the legal and regulatory hurdles in the field of drug repurposing. The vast quantities of available data secured in multiple drug databases, assisting in drug repurposing is also discussed.

Exploiting the molecular subtypes and genetic landscape in pancreatic cancer: the quest to find effective drugs

Pancreatic Ductal Adenocarcinoma (PDAC) is a very lethal disease that typically presents at an advanced stage and is non-compliant with most treatments. Recent technologies have helped delineate associated molecular subtypes and genetic variations yielding important insights into the pathophysiology of this disease and having implications for the identification of new therapeutic targets. Drug repurposing has been evaluated as a new paradigm in oncology to accelerate the application of approved or failed target-specific molecules for the treatment of cancer patients. This review focuses on the impact of molecular subtypes on key genomic alterations in PDAC, and the progress made thus far. Importantly, these alterations are discussed in light of the potential role of drug repurposing in PDAC.

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.

Predicting Drug-Gene-Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning

Drug repositioning offers the significant advantage of greatly reducing the cost and time of drug discovery by identifying new therapeutic indications for existing drugs. In particular, computational approaches using networks in drug repositioning have attracted attention for inferring potential associations between drugs and diseases efficiently based on the network connectivity. In this article, we proposed a network-based drug repositioning method to construct a drug-gene-disease tensor by integrating drug-disease, drug-gene, and disease-gene associations and predict drug-gene-disease triple associations through tensor decomposition. The proposed method, which ensembles generalized tensor decomposition (GTD) and multi-layer perceptron (MLP), models drug-gene-disease associations through GTD and learns the features of drugs, genes, and diseases through MLP, providing more flexibility and non-linearity than conventional tensor decomposition. We experimented with drug-gene-disease association prediction using two distinct networks created by chemical structures and ATC codes as drug features. Moreover, we leveraged drug, gene, and disease latent vectors obtained from the predicted triple associations to predict drug-disease, drug-gene, and disease-gene pairwise associations. Our experimental results revealed that the proposed ensemble method was superior for triple association prediction. The ensemble model achieved an AUC of 0.96 in predicting triple associations for new drugs, resulting in an approximately 7% improvement over the performance of existing models. It also showed competitive accuracy for pairwise association prediction compared with previous methods. This study demonstrated that incorporating genetic information leads to notable advancements in drug repositioning.

Drug-Repurposing Screening Identifies a Gallic Acid Binding Site on SARS-CoV-2 Non-structural Protein 7

SARS-CoV-2 is the agent responsible for acute respiratory disease COVID-19 and the global pandemic initiated in early 2020. While the record-breaking development of vaccines has assisted the control of COVID-19, there is still a pressing global demand for antiviral drugs to halt the destructive impact of this disease. Repurposing clinically approved drugs provides an opportunity to expediate SARS-CoV-2 treatments into the clinic. In an effort to facilitate drug repurposing, an FDA-approved drug library containing 2400 compounds was screened against the SARS-CoV-2 non-structural protein 7 (nsp7) using a native mass spectrometry-based assay. Nsp7 is one of the components of the SARS-CoV-2 replication/transcription complex essential for optimal viral replication, perhaps serving to off-load RNA from nsp8. From this library, gallic acid was identified as a compound that bound tightly to nsp7, with an estimated K d of 15 μM. NMR chemical shift perturbation experiments were used to map the ligand-binding surface of gallic acid on nsp7, indicating that the compound bound to a surface pocket centered on one of the protein's four α-helices (α2). The identification of the gallic acid-binding site on nsp7 may allow development of a SARS-CoV-2 therapeutic via artificial-intelligence-based virtual docking and other strategies.

Computational screening of FDA-approved drugs to identify potential aromatase receptor inhibitors for polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is the most common cause of infertility without ovulation. Aromatase inhibitors were first proposed as new ovulation-inducing drugs in anovulatory women with an inadequate response to clomiphene. Letrozole is an aromatase inhibitor used as an ovulation inducer in infertile women due to PCOS. However, there is no definitive treatment for women with PCOS and the treatments are mostly symptomatic. In this study, we intend to introduce alternative drugs to letrozole using the library of FDA-approved drugs and evaluate the interaction of these drugs with the aromatase receptor. For this aim, molecular docking was performed to identify interactions of FDA-approved drugs with essential residues in the active site of the aromatase receptor. 1614 FDA-approved drugs were docked with aromatase receptor using AutoDock Vina. Molecular dynamics (MD) simulation study was also performed for 100 ns to verify the stability of the drug-receptor complexes. MMPBSA analysis evaluate the binding energy of selected complexes. Finally, acetaminophen, alendronate, ascorbic acid, aspirin, glutamine, hydralazine, mesalazine and pseudoephedrine drugs showed the best results in interaction with aromatase receptor based on computational studies. These drugs can be introduced as an alternative to letrozole for treating PCOS.Communicated by Ramaswamy H. Sarma.

Role of histone methylation in skin cancers: Histone methylation-modifying enzymes as a new class of targets for skin cancer treatment

Histone methylation, one of the most prominent epigenetic modifications, plays a vital role in gene transcription, and aberrant histone methylation levels cause tumorigenesis. Histone methylation is a reversible enzyme-dependent reaction, and histone methyltransferases and demethylases are involved in this reaction. This review addresses the biological and clinical relevance of these histone methylation-modifying enzymes for skin cancer. In particular, the roles of histone lysine methyltransferases, histone arginine methyltransferase, lysine-specific demethylases, and JmjC demethylases in skin cancer are discussed in detail. In addition, we summarize the efficacy of several epigenetic inhibitors targeting histone methylation-modifying enzymes in cutaneous cancers, such as basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma. In conclusion, we propose histone methylation-modifying enzymes as novel targets for next-generation pharmaceuticals in the treatment of skin cancers and further provide a rationale for the development of epigenetic drugs (epidrugs) that target specific histone methylases/demethylases in cutaneous tumors.

Drug Repurposing: An Effective Tool in Modern Drug Discovery

Drug repurposing is using an existing drug for a new treatment that was not indicated before. It has received immense attention during the COVID-19 pandemic emergency. Drug repurposing has become the need of time to fasten the drug discovery process and find quicker solutions to the over-exerted healthcare scenario and drug needs. Drug repurposing involves identifying the drug, evaluating its efficiency using preclinical models, and proceeding to phase II clinical trials. Identification of the drug candidate can be made through computational and experimental approaches. This approach usually utilizes public databases for drugs. Data from primary and translational research, clinical trials, anecdotal reports regarding off-label uses, and other published human data information available are included. Using artificial intelligence algorithms and other bioinformatics tools, investigators systematically try to identify the interaction between drugs and protein targets. It can be combined with genetic data, clinical analysis, structure (molecular docking), pathways, signatures, targets, phenotypes, binding assays, and artificial intelligence to get an optimum outcome in repurposing. This article describes the strategies involved in drug repurposing and enlists a series of repurposed drugs and their indications.

Exploration of potential inhibitors for SARS-CoV-2 Mpro considering its mutants via structure-based drug design, molecular docking, MD simulations, MM/PBSA, and DFT calculations

The main protease (Mpro) of SARS-COV-2 plays a vital role in the viral life cycle and pathogenicity. Due to its specific attributes, this 3-chymotrypsin like protease can be a reliable target for the drug design to combat COVID-19. Since the advent of COVID-19, Mpro has undergone many mutations. Here, the impact of 10 mutations based on their frequency and five more based on their proximity to the active site was investigated. For comparison purposes, the docking process was also performed against the Mpros of SARS-COV and MERS-COV. Four inhibitors with the highest docking score (11b, α-ketoamide 13b, Nelfinavir, and PF-07321332) were selected for the structure-based ligand design via fragment replacement, and around 2000 new compounds were thus obtained. After the screening of these new compounds, the pharmacokinetic properties of the best ones were predicted. In the last step, comparative molecular dynamics (MD) simulations, molecular mechanics Poisson-Boltzmann surface area calculations (MM/PBSA), and density functional theory calculations were performed. Among the 2000 newly designed compounds, three of them (NE1, NE2, and NE3), which were obtained by modifications of Nelfinavir, showed the highest affinity against all the Mpro targets. Together, NE1 compound is the best candidate for follow-up Mpro inhibition and drug development studies.

FOXM1-Mediated Regulation of Reactive Oxygen Species and Radioresistance in Oral Squamous Cell Carcinoma Cells

Radioresistance is a major obstacle to the successful treatment of oral squamous cell carcinoma (OSCC). To help overcome this issue, we have developed clinically relevant radioresistant (CRR) cell lines generated by irradiating parental cells over time, which are useful for OSCC research. In the present study, we conducted gene expression analysis using CRR cells and their parental lines to investigate the regulation of radioresistance in OSCC cells. Based on gene expression changes over time in CRR cells and parental lines subjected to irradiation, forkhead box M1 (FOXM1) was selected for further analysis in terms of its expression in OSCC cell lines, including CRR cell lines and clinical specimens. We suppressed or upregulated the expression of FOXM1 in OSCC cell lines, including CRR cell lines, and examined radiosensitivity, DNA damage, and cell viability under various conditions. The molecular network regulating radiotolerance was also investigated, especially the redox pathway, and the radiosensitizing effect of FOXM1 inhibitors was examined as a potential therapeutic application. We found that FOXM1 was not expressed in normal human keratinocytes but was expressed in several OSCC cell lines. The expression of FOXM1 was upregulated in CRR cells compared with that detected in the parental cell lines. In a xenograft model and clinical specimens, FOXM1 expression was upregulated in cells that survived irradiation. FOXM1-specific small interfering RNA (siRNA) treatment increased radiosensitivity, whereas FOXM1 overexpression decreased radiosensitivity, and DNA damage was altered significantly under both conditions, as well as the levels of redox-related molecules and reactive oxygen species production. Treatment with the FOXM1 inhibitor thiostrepton had a radiosensitizing effect and overcame radiotolerance in CRR cells. According to these results, the FOXM1-mediated regulation of reactive oxygen species could be a novel therapeutic target for the treatment of radioresistant OSCC; thus, treatment strategies targeting this axis might overcome radioresistance in this disease.

Drug Repurposing in Non-Small Cell Lung Carcinoma: Old Solutions for New Problems

Lung cancer is the second most common cancer and the leading cause of cancer-related deaths in 2022. The majority (80%) of lung cancer cases belong to the non-small cell lung carcinoma (NSCLC) subtype. Despite the increased screening efforts, the median five-year survival of metastatic NSCLC remains low at approximately 3%. Common treatment approaches for NSCLC include surgery, multimodal chemotherapy, and concurrent radio and chemotherapy. NSCLC exhibits high rates of resistance to treatment, driven by its heterogeneity and the plasticity of cancer stem cells (CSCs). Drug repurposing offers a faster and cheaper way to develop new antineoplastic purposes for existing drugs, to help overcome therapy resistance. The decrease in time and funds needed stems from the availability of the pharmacokinetic and pharmacodynamic profiles of the Food and Drug Administration (FDA)-approved drugs to be repurposed. This review provides a synopsis of the drug-repurposing approaches and mechanisms of action of potential candidate drugs used in treating NSCLC, including but not limited to antihypertensives, anti-hyperlipidemics, anti-inflammatory drugs, anti-diabetics, and anti-microbials.

Drug repositioning based on heterogeneous networks and variational graph autoencoders

Predicting new therapeutic effects (drug repositioning) of existing drugs plays an important role in drug development. However, traditional wet experimental prediction methods are usually time-consuming and costly. The emergence of more and more artificial intelligence-based drug repositioning methods in the past 2 years has facilitated drug development. In this study we propose a drug repositioning method, VGAEDR, based on a heterogeneous network of multiple drug attributes and a variational graph autoencoder. First, a drug-disease heterogeneous network is established based on three drug attributes, disease semantic information, and known drug-disease associations. Second, low-dimensional feature representations for heterogeneous networks are learned through a variational graph autoencoder module and a multi-layer convolutional module. Finally, the feature representation is fed to a fully connected layer and a Softmax layer to predict new drug-disease associations. Comparative experiments with other baseline methods on three datasets demonstrate the excellent performance of VGAEDR. In the case study, we predicted the top 10 possible anti-COVID-19 drugs on the existing drug and disease data, and six of them were verified by other literatures.

Optimization of the Solvent and In Vivo Administration Route of Auranofin in a Syngeneic Non-Small Cell Lung Cancer and Glioblastoma Mouse Model

The antineoplastic activity of the thioredoxin reductase 1 (TrxR) inhibitor, auranofin (AF), has already been investigated in various cancer mouse models as a single drug, or in combination with other molecules. However, there are inconsistencies in the literature on the solvent, dose and administration route of AF treatment in vivo. Therefore, we investigated the solvent and administration route of AF in a syngeneic SB28 glioblastoma (GBM) C57BL/6J and a 344SQ non-small cell lung cancer 129S2/SvPasCrl (129) mouse model. Compared to daily intraperitoneal injections and subcutaneous delivery of AF via osmotic minipumps, oral gavage for 14 days was the most suitable administration route for high doses of AF (10-15 mg/kg) in both mouse models, showing no measurable weight loss or signs of toxicity. A solvent comprising 50% DMSO, 40% PEG300 and 10% ethanol improved the solubility of AF for oral administration in mice. In addition, we confirmed that AF was a potent TrxR inhibitor in SB28 GBM tumors at high doses. Taken together, our results and results in the literature indicate the therapeutic value of AF in several in vivo cancer models, and provide relevant information about AF's optimal administration route and solvent in two syngeneic cancer mouse models.

A weighted non-negative matrix factorization approach to predict potential associations between drug and disease

BACKGROUND

Associations of drugs with diseases provide important information for expediting drug development. Due to the number of known drug-disease associations is still insufficient, and considering that inferring associations between them through traditional in vitro experiments is time-consuming and costly. Therefore, more accurate and reliable computational methods urgent need to be developed to predict potential associations of drugs with diseases.

METHODS

In this study, we present the model called weighted graph regularized collaborative non-negative matrix factorization for drug-disease association prediction (WNMFDDA). More specifically, we first calculated the drug similarity and disease similarity based on the chemical structures of drugs and medical description information of diseases, respectively. Then, to extend the model to work for new drugs and diseases, weighted [Formula: see text] nearest neighbor was used as a preprocessing step to reconstruct the interaction score profiles of drugs with diseases. Finally, a graph regularized non-negative matrix factorization model was used to identify potential associations between drug and disease.

RESULTS

During the cross-validation process, WNMFDDA achieved the AUC values of 0.939 and 0.952 on Fdataset and Cdataset under ten-fold cross validation, respectively, which outperforms other competing prediction methods. Moreover, case studies for several drugs and diseases were carried out to further verify the predictive performance of WNMFDDA. As a result, 13(Doxorubicin), 13(Amiodarone), 12(Obesity) and 12(Asthma) of the top 15 corresponding candidate diseases or drugs were confirmed by existing databases.

CONCLUSIONS

The experimental results adequately demonstrated that WNMFDDA is a very effective method for drug-disease association prediction. We believe that WNMFDDA is helpful for relevant biomedical researchers in follow-up studies.

Identification of the miRNA-mRNA regulatory network associated with radiosensitivity in esophageal cancer based on integrative analysis of the TCGA and GEO data

BACKGROUND

The current study set out to identify the miRNA-mRNA regulatory networks that influence the radiosensitivity in esophageal cancer based on the The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases.

METHODS

Firstly, esophageal cancer-related miRNA-seq and mRNA-seq data were retrieved from the TCGA database, and the mRNA dataset of esophageal cancer radiotherapy was downloaded from the GEO database to analyze the differential expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) in radiosensitive and radioresistant samples, followed by the construction of the miRNA-mRNA regulatory network and Gene Ontology and KEGG enrichment analysis. Additionally, a prognostic risk model was constructed, and its accuracy was evaluated by means of receiver operating characteristic analysis.

RESULTS

A total of 125 DEmiRNAs and 42 DEmRNAs were closely related to the radiosensitivity in patients with esophageal cancer. Based on 47 miRNA-mRNA interactions, including 21 miRNAs and 21 mRNAs, the miRNA-mRNA regulatory network was constructed. The prognostic risk model based on 2 miRNAs (miR-132-3p and miR-576-5p) and 4 mRNAs (CAND1, ZDHHC23, AHR, and MTMR4) could accurately predict the prognosis of esophageal cancer patients. Finally, it was verified that miR-132-3p/CAND1/ZDHHC23 and miR-576-5p/AHR could affect the radiosensitivity in esophageal cancer.

CONCLUSION

Our study demonstrated that miR-132-3p/CAND1/ZDHHC23 and miR-576-5p/AHR were critical molecular pathways related to the radiosensitivity of esophageal cancer.

Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning

Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug–disease associations. This review reveals existing network-based approaches for predicting drug–disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug–drug similarities based on chemical structures and ATC codes, ontology-based disease–disease similarities, and drug–disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug–drug similarity measurement are crucial for improving disease-side prediction.

Drug repurposing-an emerging strategy in cancer therapeutics

Cancer is a complex disease affecting millions of people around the world. Despite advances in surgical and radiation therapy, chemotherapy continues to be an important therapeutic option for the treatment of cancer. The current treatment is expensive and has several side effects. Also, over time, cancer cells develop resistance to chemotherapy, due to which there is a demand for new drugs. Drug repurposing is a novel approach that focuses on finding new applications for the old clinically approved drugs. Current advances in the high-dimensional multiomics landscape, especially proteomics, genomics, and computational omics-data analysis, have facilitated drug repurposing. The drug repurposing approach provides cheaper, effective, and safe drugs with fewer side effects and fastens the process of drug development. The review further delineates each repurposed drug's original indication and mechanism of action in cancer. Along with this, the article also provides insight upon artificial intelligence and its application in drug repurposing. Clinical trials are vital for determining medication safety and effectiveness, and hence the clinical studies for each repurposed medicine in cancer, including their stages, status, and National Clinical Trial (NCT) identification, are reported in this review article. Various emerging evidences imply that repurposing drugs is critical for the faster and more affordable discovery of anti-cancerous drugs, and the advent of artificial intelligence-based computational tools can accelerate the translational cancer-targeting pipeline.

Repositioning Drugs for Rare Diseases Based on Biological Features and Computational Approaches

Rare diseases are a group of uncommon diseases in the world population. To date, about 7000 rare diseases have been documented. However, most of them do not have a known treatment. As a result of the relatively low demand for their treatments caused by their scarce prevalence, the pharmaceutical industry has not sufficiently encouraged the research to develop drugs to treat them. This work aims to analyse potential drug-repositioning strategies for this kind of disease. Drug repositioning seeks to find new uses for existing drugs. In this context, it seeks to discover if rare diseases could be treated with medicines previously indicated to heal other diseases. Our approaches tackle the problem by employing computational methods that calculate similarities between rare and non-rare diseases, considering biological features such as genes, proteins, and symptoms. Drug candidates for repositioning will be checked against clinical trials found in the scientific literature. In this study, 13 different rare diseases have been selected for which potential drugs could be repositioned. By verifying these drugs in the scientific literature, successful cases were found for 75% of the rare diseases studied. The genetic associations and phenotypical features of the rare diseases were examined. In addition, the verified drugs were classified according to the anatomical therapeutic chemical (ATC) code to highlight the types with a higher predisposition to be repositioned. These promising results open the door for further research in this field of study.

An FDA-Approved Antifungal, Ketoconazole, and Its Novel Derivative Suppress tGLI1-Mediated Breast Cancer Brain Metastasis by Inhibiting the DNA-Binding Activity of Brain Metastasis-Promoting Transcription Factor tGLI1

The goal of this study is to identify pharmacological inhibitors that target a recently identified novel mediator of breast cancer brain metastasis (BCBM), truncated glioma-associated oncogene homolog 1 (tGLI1). Inhibitors of tGLI1 are not yet available. To identify compounds that selectively kill tGLI1-expressing breast cancer, we screened 1527 compounds using two sets of isogenic breast cancer and brain-tropic breast cancer cell lines engineered to stably express the control, GLI1, or tGLI1 vector, and identified the FDA-approved antifungal ketoconazole (KCZ) to selectively target tGLI1-positive breast cancer cells and breast cancer stem cells, but not tGLI1-negative breast cancer and normal cells. KCZ's effects are dependent on tGLI1. Two experimental mouse metastasis studies have demonstrated that systemic KCZ administration prevented the preferential brain metastasis of tGLI1-positive breast cancer and suppressed the progression of established tGLI1-positive BCBM without liver toxicities. We further developed six KCZ derivatives, two of which (KCZ-5 and KCZ-7) retained tGLI1-selectivity in vitro. KCZ-7 exhibited higher blood-brain barrier penetration than KCZ/KCZ-5 and more effectively reduced the BCBM frequency. In contrast, itraconazole, another FDA-approved antifungal, failed to suppress BCBM. The mechanistic studies suggest that KCZ and KCZ-7 inhibit tGLI1's ability to bind to DNA, activate its target stemness genes Nanog and OCT4, and promote tumor proliferation and angiogenesis. Our study establishes the rationale for using KCZ and KCZ-7 for treating and preventing BCBM and identifies their mechanism of action.

Nitroxoline suppresses metastasis in bladder cancer via EGR1/circNDRG1/miR-520h/smad7/EMT signaling pathway

Bladder cancer is one of the most common and deadly cancer worldwide. Current chemotherapy has shown limited efficacy in improving outcomes for patients. Nitroxoline, an old and widely used oral antibiotic, which was known to treat for urinary tract infection for decades. Recent studies suggested that nitroxoline suppressed the tumor progression and metastasis, especially in bladder cancer. However, the underlying mechanism for anti-tumor activity of nitroxoline remains unclear.

Methods: CircRNA microarray was used to explore the nitroxoline-mediated circRNA expression profile of bladder cancer lines. Transwell and wound-healing assay were applied to evaluate the capacity of metastasis. ChIP assay was chosen to prove the binding of promotor and transcription factor. RNA-pulldown assay was performed to explore the sponge of circRNA and microRNA.

Results: We first identified the circNDRG1 (has_circ_0085656) as a novel candidate circRNA. Transwell and wound-healing assay demonstrated that circNDRG1 inhibited the metastasis of bladder cancer. ChIP assay showed that circNDRG1 was regulated by the transcription factor EGR1 by binding the promotor of host gene NDRG1. RNA-pulldown assay proved that circNDRG1 sponged miR-520h leading to the overexpression of smad7, which was a negative regulatory protein of EMT.

Conclusions: Our research revealed that nitroxoline may suppress metastasis in bladder cancer via EGR1/circNDRG1/miR-520h/smad7/EMT signaling pathway.

Drug repurposing: An emerging strategy in alleviating skin cancer

Skin cancer is one of the most common forms of cancer. Several million people are estimated to have affected with this condition worldwide. Skin cancer generally includes melanoma and non-melanoma with the former being the most dangerous. Chemotherapy has been one of the key therapeutic strategies employed in the treatment of skin cancer, especially in advanced stages of the disease. It could be also used as an adjuvant with other treatment modalities depending on the type of skin cancer. However, there are several shortfalls associated with the use of chemotherapy such as non-selectivity, tumour resistance, life-threatening toxicities, and the exorbitant cost of medicines. Furthermore, new drug discovery is a lengthy and costly process with minimal likelihood of success. Thus, drug repurposing (DR) has emerged as a new avenue where the drug approved formerly for the treatment of one disease can be used for the treatment of another disease like cancer. This approach is greatly beneficial over the de novo approach in terms of time and cost. Moreover, there is minimal risk of failure of repurposed therapeutics in clinical trials. There are a considerable number of studies that have reported on drugs repurposed for the treatment of skin cancer. Thus, the present manuscript offers a comprehensive overview of drugs that have been investigated as repurposing candidates for the efficient treatment of skin cancers mainly melanoma and its oncogenic subtypes, and non-melanoma. The prospects of repurposing phytochemicals against skin cancer are also discussed. Furthermore, repurposed drug delivery via topical route and repurposed drugs in clinical trials are briefed. Based on the findings from the reported studies discussed in this manuscript, drug repurposing emerges to be a promising approach and thus is expected to offer efficient treatment at a reasonable cost in devitalizing skin cancer.

Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction

Identifying new therapeutic indications for existing drugs is a major challenge in drug repositioning. Most computational drug repositioning methods focus on known targets. Analyzing multiple aspects of various protein associations provides an opportunity to discover underlying drug-associated proteins that can be used to improve the performance of the drug repositioning approaches. In this study, machine learning models were developed based on the similarities of diversified biological features, including protein interaction, topological network, sequence alignment, and biological function to predict protein pairs associating with the same drugs. The crucial set of features was identified, and the high performances of protein pair predictions were achieved with an area under the curve (AUC) value of more than 93%. Based on drug chemical structures, the drug similarity levels of the promising protein pairs were used to quantify the inferred drug-associated proteins. Furthermore, these proteins were employed to establish an augmented drug-protein matrix to enhance the efficiency of three existing drug repositioning techniques: a similarity constrained matrix factorization for the drug-disease associations (SCMFDD), an ensemble meta-paths and singular value decomposition (EMP-SVD) model, and a topology similarity and singular value decomposition (TS-SVD) technique. The results showed that the augmented matrix helped to improve the performance up to 4% more in comparison to the original matrix for SCMFDD and EMP-SVD, and about 1% more for TS-SVD. In summary, inferring new protein pairs related to the same drugs increase the opportunity to reveal missing drug-associated proteins that are important for drug development via the drug repositioning technique.