Challenges and opportunities of drug repositioning

Repurposing auranofin and meclofenamic acid as energy-metabolism inhibitors and anti-cancer drugs

OBJECTIVE

Cytotoxicity of the antirheumatic drug auranofin (Aur) and the non-steroidal anti-inflammatory drug meclofenamic acid (MA) on several cancer cell lines and isolated mitochondria was examined to assess whether these drugs behave as oxidative phosphorylation inhibitors.

METHODS

The effect of Aur or MA for 24 h was assayed on metastatic cancer and non-cancer cell proliferation, energy metabolism, mitophagy and metastasis; as well as on oxygen consumption rates of cancer and non-cancer mitochondria.

RESULTS

Aur doses in the low micromolar range were required to decrease proliferation of metastatic HeLa and MDA-MB-231 cells, whereas one or two orders of magnitude higher levels were required to affect proliferation of non-cancer cells. MA doses required to affect cancer cell growth were one order of magnitude higher than those of Aur. At the same doses, Aur impaired oxidative phosphorylation in isolated mitochondria and intact cells through mitophagy induction, as well as glycolysis. Consequently, cell migration and invasiveness were severely affected. The combination of Aur with very low cisplatin concentrations promoted that the effects on cellular functions were potentiated.

CONCLUSION

Aur surges as a highly promising anticancer drug, suggesting that efforts to establish this drug in the clinical treatment protocols are warranted and worthy to undertake.

Drug repurposing against antibiotic resistant bacterial pathogens

The growing prevalence of MDR and XDR bacterial pathogens is posing a critical threat to global health. Traditional antibiotic development paths have encountered significant challenges and are drying up thus necessitating innovative approaches. Drug repurposing, which involves identifying new therapeutic applications for existing drugs, offers a promising alternative to combat resistant pathogens. By leveraging pre-existing safety and efficacy data, drug repurposing accelerates the development of new antimicrobial therapy regimes. This review explores the potential of repurposing existing FDA approved drugs against the ESKAPE and other clinically relevant bacterial pathogens and delves into the identification of suitable drug candidates, their mechanisms of action, and the potential for combination therapies. It also describes clinical trials and patent protection of repurposed drugs, offering perspectives on this evolving realm of therapeutic interventions against drug resistance.

The Role of AI in Drug Discovery

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

SGLT2 Inhibitors Empagliflozin and Canagliflozin Ameliorate Allergic Asthma Responses in Mice

Inhibitors of sodium/glucose cotransporter 2 (SGLT2), such as empagliflozin and canagliflozin, have been widely used to block glucose reabsorption in the proximal tubules of kidneys in patients with diabetes. A meta-analysis suggested that SGLT2 inhibitors are associated with a decreased risk of asthma development. Therefore, we investigated whether SGLT2 inhibitors could suppress allergic asthma. Empagliflozin and canagliflozin suppressed the in vitro degranulation reaction induced by antigens in a concentration-dependent manner in RBL-2H3 mast cells. Empagliflozin and canagliflozin were administered to BALB/c mice sensitized to ovalbumin (OVA). The administration of empagliflozin or canagliflozin significantly suppressed OVA-induced airway hyper-responsiveness and increased the number of immune cells and pro-inflammatory cytokine mRNA expression levels in bronchoalveolar lavage fluid. The administration of empagliflozin and canagliflozin also suppressed OVA-induced histopathological changes in the lungs. Empagliflozin and canagliflozin also suppressed serum IgE levels. These results suggested that empagliflozin and canagliflozin may be applicable for the treatment of allergic asthma by suppressing immune responses.

Lomitapide repurposing for treatment of malignancies: A promising direction

The development of novel drugs from basic science to clinical practice requires several years, much effort, and cost. Drug repurposing can promote the utilization of clinical drugs in cancer therapy. Recent studies have shown the potential effects of lomitapide on treating malignancies, which is currently used for the treatment of familial hypercholesterolemia. We systematically review possible functions and mechanisms of lomitapide as an anti-tumor compound, regarding the aspects of apoptosis, autophagy, and metabolism of tumor cells, to support repurposing lomitapide for the clinical treatment of tumors.

Strategies of Pharmacological Repositioning for the Treatment of Medically Relevant Mycoses

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

MiRAGE: mining relationships for advanced generative evaluation in drug repositioning

MOTIVATION

Drug repositioning, the identification of new therapeutic uses for existing drugs, is crucial for accelerating drug discovery and reducing development costs. Some methods rely on heterogeneous networks, which may not fully capture the complex relationships between drugs and diseases. However, integrating diverse biological data sources offers promise for discovering new drug-disease associations (DDAs). Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. However, the challenge lies in effectively integrating different biological data sources to identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms.

RESULTS

In response to this challenge, we present MiRAGE, a novel computational method for drug repositioning. MiRAGE leverages a three-step framework, comprising negative sampling using hard negative mining, classification employing random forest models, and feature selection based on feature importance. We evaluate MiRAGE on multiple benchmark datasets, demonstrating its superiority over state-of-the-art algorithms across various metrics. Notably, MiRAGE consistently outperforms other methods in uncovering novel DDAs. Case studies focusing on Parkinson's disease and schizophrenia showcase MiRAGE's ability to identify top candidate drugs supported by previous studies. Overall, our study underscores MiRAGE's efficacy and versatility as a computational tool for drug repositioning, offering valuable insights for therapeutic discoveries and addressing unmet medical needs.

Approaches of pre-clinical and clinical trials of repurposed drug

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

Fusing graph transformer with multi-aggregate GCN for enhanced drug-disease associations prediction

BACKGROUND

Identification of potential drug-disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data.

RESULTS

In this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug-disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug-drug, drug-disease, and disease-disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation.

CONCLUSIONS

Rigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug-disease association prediction, which is beneficial for drug repositioning and drug safety research.

Prediction of drug-disease associations based on reinforcement symmetric metric learning and graph convolution network

Accurately identifying novel indications for drugs is crucial in drug research and discovery. Traditional drug discovery is costly and time-consuming. Computational drug repositioning can provide an effective strategy for discovering potential drug-disease associations. However, the known experimentally verified drug-disease associations is relatively sparse, which may affect the prediction performance of the computational drug repositioning methods. Moreover, while the existing drug-disease prediction method based on metric learning algorithm has achieved better performance, it simply learns features of drugs and diseases only from the drug-centered perspective, and cannot comprehensively model the latent features of drugs and diseases. In this study, we propose a novel drug repositioning method named RSML-GCN, which applies graph convolutional network and reinforcement symmetric metric learning to predict potential drug-disease associations. RSML-GCN first constructs a drug-disease heterogeneous network by integrating the association and feature information of drugs and diseases. Then, the graph convolutional network (GCN) is applied to complement the drug-disease association information. Finally, reinforcement symmetric metric learning with adaptive margin is designed to learn the latent vector representation of drugs and diseases. Based on the learned latent vector representation, the novel drug-disease associations can be identified by the metric function. Comprehensive experiments on benchmark datasets demonstrated the superior prediction performance of RSML-GCN for drug repositioning.

Drug Repurposing in the Chemotherapy of Infectious Diseases

Repurposing is a universal mechanism for innovation, from the evolution of feathers to the invention of Velcro tape. Repurposing is particularly attractive for drug development, given that it costs more than a billion dollars and takes longer than ten years to make a new drug from scratch. The COVID-19 pandemic has triggered a large number of drug repurposing activities. At the same time, it has highlighted potential pitfalls, in particular when concessions are made to the target product profile. Here, we discuss the pros and cons of drug repurposing for infectious diseases and analyze different ways of repurposing. We distinguish between opportunistic and rational approaches, i.e., just saving time and money by screening compounds that are already approved versus repurposing based on a particular target that is common to different pathogens. The latter can be further distinguished into divergent and convergent: points of attack that are divergent share common ancestry (e.g., prokaryotic targets in the apicoplast of malaria parasites), whereas those that are convergent arise from a shared lifestyle (e.g., the susceptibility of bacteria, parasites, and tumor cells to antifolates due to their high rate of DNA synthesis). We illustrate how such different scenarios can be capitalized on by using examples of drugs that have been repurposed to, from, or within the field of anti-infective chemotherapy.

Utilizing the drug repurposing strategy on current drugs: new leads for peptic ulcers via biochemical and biomolecular dynamics studies

The hyperactivity of urease enzymes plays a crucial role in the development of hepatic coma, hepatic encephalopathy, urolithiasis, gastric and peptic ulcers. Additionally, these enzymes adversely impact the soil's nitrogen efficiency for crop production. In the current study 100 known drugs were tested against Jack Bean urease and Proteus mirabilis urease and identified three inhibitors i.e. terbutaline (compound 1), Ketoprofen (compound 2) and norepinephrine bitartrate (compound 3). As a result, these compounds showed excellent inhibition against Jack Bean urease i.e. (IC50 = 2.1-11.3 µM), and Proteus mirabilis urease (4.8-11.9 µM). Moreover, in silico studies demonstrate maximum interactions of compounds in the enzyme's active site. Furthermore, intermolecular interactions between compounds and enzyme atoms were examined using STD-NMR spectrophotometry. In parallel, molecular dynamics simulation was carried out to study compounds dynamic behavior within the urease binding region. Urease remained stable during most of the simulation time and ligands were bound in the protein active pocket as observed from the Root mean square deviation (RMSD) and ligand RMSD analyses. Furthermore, these compounds display interactions with the crucial residues, including His492 and Asp633, in 100 ns simulations. In the binding energy analysis, norepinephrine bitartrate exhibited the highest binding energy (-76.32 kcal/mol) followed by Ketoprofen (-65.56 kcal/mol) and terbutaline (-62.15 kcal/mol), as compared to acetohydroxamic acid (-52.86 kcal/mol). The current findings highlight the potential of drug repurposing as an effective approach for identifying novel anti-urease compounds.Communicated by Ramaswamy H. Sarma.

Ivermectin induces nonprotective autophagy by downregulating PAK1 and apoptosis in lung adenocarcinoma cells

INTRODUCTION

LUAD (Lung adenocarcinoma), the most common subtype of lung carcinoma and one of the highest incidences and mortality cancers in the world remains still a substantial treatment challenge. Ivermectin, an avermectin derivative, has been traditionally used as an antiparasitic agent in human and veterinary medicine practice during the last few decades. Though ivermectin has been shown to be effective against a variety of cancers, however, there is few available data reporting the antitumor effects of ivermectin in LUAD.

METHODS

The effect of ivermectin on cell viability and proliferative ability of LUAD cells was evaluated using CCK-8 and colony formation assay. Apoptosis rate and autophagy flux were detected using flow cytometry based on PI/Annexin V staining and confocal laser scanning microscope based on LC3-GFP/RFP puncta, respectively. Western blotting experiment was conducted to verify the results of changes in apoptosis and autophagy. LUAD-TCGA and GEO databases were used to analyse the expression and predictive value of PAK1 in LUAD patients. Xenograft model and immumohistochemical staining were used for verification of the inhibitor effect of ivermectin in vivo.

RESULTS

Ivermectin treatment strikingly impeded the colony formation, and the viability of the cell, along with cell proliferation, and caused the apoptosis and enhanced autophagy flux in LUAD cells. In addition, ivermectin-induced nonprotective autophagy was confirmed by treating LUAD cells with 3-MA, an autophagy inhibitor. Mechanistically, we found that ivermectin inhibited PAK1 protein expression in LUAD cells and we confirmed that overexpression of PAK1 substantially inhibited ivermectin-induced autophagy in LUAD cells. Based on TCGA and GEO databases, PAK1 was highly expressed in LUAD tissues as compared with normal tissues. Furthermore, LUAD patients with high PAK1 level have poor overall survival. Finally, in vivo experiments revealed that ivermectin efficiently suppressed the cellular growth of LUAD among nude mice.

CONCLUSION

This study not only revealed the mechanism of ivermectin inhibited the growth of LUAD but also supported an important theoretical basis for the development of ivermectin during the therapy for LUAD.

Trends and Applications in Computationally Driven Drug Repurposing

Drug repurposing is a widely used approach originally developed to aid in the identification of new uses of already existing drugs outside the scope of the original medical indication [...].

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.

Cage-based sensors for circular dichroism analysis

Quantitative chiral sensing relying on circular dichroism (CD) is very important for determining the enantiomeric excess or concentration of small molecules without strong chromophores, because they form chiral complexes with sensors, yielding strong CD signals. Three-dimensional cages are promising platforms for chiral CD due to their stereochemical flexibility and their variety of cavity and external binding sites that can be used as chiral CD sensors. In this minireview, we discuss recent advances, future challenges, and opportunities in the quantitative sensing of small molecules in host-guest and peripheral complexes with cage sensors by chiral CD. We aim to provide inspiration for the rational design of cage sensors for quantitative chiral sensing of small molecules based on CD.

AMMVF-DTI: A Novel Model Predicting Drug-Target Interactions Based on Attention Mechanism and Multi-View Fusion

Accurate identification of potential drug-target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents significant challenges. In this study, we propose a novel end-to-end deep learning model called AMMVF-DTI (attention mechanism and multi-view fusion), which leverages a multi-head self-attention mechanism to explore varying degrees of interaction between drugs and target proteins. More importantly, AMMVF-DTI extracts interactive features between drugs and proteins from both node-level and graph-level embeddings, enabling a more effective modeling of DTIs. This advantage is generally lacking in existing DTI prediction models. Consequently, when compared to many of the start-of-the-art methods, AMMVF-DTI demonstrated excellent performance on the human, C. elegans, and DrugBank baseline datasets, which can be attributed to its ability to incorporate interactive information and mine features from both local and global structures. The results from additional ablation experiments also confirmed the importance of each module in our AMMVF-DTI model. Finally, a case study is presented utilizing our model for COVID-19-related DTI prediction. We believe the AMMVF-DTI model can not only achieve reasonable accuracy in DTI prediction, but also provide insights into the understanding of potential interactions between drugs and targets.

DAHNGC: A Graph Convolution Model for Drug-Disease Association Prediction by Using Heterogeneous Network

In the field of drug development and repositioning, the prediction of drug-disease associations is a critical task. A recently proposed method for predicting drug-disease associations based on graph convolution relies heavily on the features of adjacent nodes within the homogeneous network for characterizing information. However, this method lacks node attribute information from heterogeneous networks, which could hardly provide valuable insights for predicting drug-disease associations. In this study, a novel drug-disease association prediction model called DAHNGC is proposed, which is based on a graph convolutional neural network. This model includes two feature extraction methods that are specifically designed to extract the attribute characteristics of drugs and diseases from both homogeneous and heterogeneous networks. First, the DropEdge technique is added to the graph convolutional neural network to alleviate the oversmoothing problem and obtain the characteristics of the same nodes of drugs or diseases in the homogeneous network. Then, an automatic feature extraction method in the heterogeneous network is designed to obtain the features of drugs or diseases at different nodes. Finally, the obtained features are put into the fully connected network for nonlinear transformation, and the potential drug-disease pairs are obtained by bilinear decoding. Experimental results demonstrate that the DAHNGC model exhibits good predictive performance for drug-disease associations.

Systematic Screening of Associations between Medication Use and Risk of Neurodegenerative Diseases Using a Mendelian Randomization Approach

BACKGROUND

Systematically assessing the causal associations between medications and neurodegenerative diseases is significant in identifying disease etiology and novel therapies. Here, we investigated the putative causal associations between 23 existing medication categories and major neurodegenerative diseases (NDs), including Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS).

METHODS

A two-sample mendelian randomization (MR) approach was conducted. Estimates were calculated using the inverse-variance weighted (IVW) method as the main model. A sensitivity analysis and a pleiotropy analysis were performed to identify potential violations.

RESULTS

Genetically predisposition to antihypertensives (OR = 0.809, 95% CI = 0.668-0.981, p = 0.031), thyroid preparations (OR = 0.948, 95% CI = 0.909-0.988, p = 0.011), and immunosuppressants (OR = 0.879, 95% CI = 0.789-0.979, p = 0.018) was associated with a decreased risk of AD. Genetic proxies for thyroid preparations (OR = 0.934, 95% CI = 0.884-0.988, p = 0.017), immunosuppressants (OR = 0.825, 95% CI = 0.699-0.973, p = 0.022), and glucocorticoids (OR = 0.862, 95% CI = 0.756-0.983, p = 0.027) were causally associated with a decreased risk of PD. Genetically determined antithrombotic agents (OR = 1.234, 95% CI = 1.042-1.461, p = 0.015), HMG CoA reductase inhibitors (OR = 1.085, 95% CI = 1.025-1.148, p = 0.005), and salicylic acid and derivatives (OR = 1.294, 95% CI = 1.078-1.553, p = 0.006) were associated with an increased risk of ALS.

CONCLUSIONS

We presented a systematic view concerning the causal associations between medications and NDs, which will promote the etiology discovery, drug repositioning and patient management for NDs.

Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network

Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative 'multi-modality attributes' learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

Drug Repurposing During The COVID-19 Pandemic: Lessons For Expediting Drug Development And Access

The COVID-19 pandemic created a large, sudden unmet public health need for rapid access to safe and effective treatments. Against this backdrop, policy makers and researchers have looked to drug repurposing-using a drug previously approved for one indication to target a new indication-as a means to accelerate the identification and development of COVID-19 treatments. Using detailed data on US clinical trials initiated during the pandemic, we examined the trajectory and sources of drug repurposing initiatives for COVID-19. We found a rapid increase in repurposing efforts at the start of the pandemic, followed by a transition to greater de novo drug development. The drugs tested for repurposing treat a wide range of indications but were typically initially approved for other infectious diseases. Finally, we documented substantial variation by trial sponsor (academic, industry, or government) and generic status: Industry sponsorship for repurposing occurred much less frequently for drugs with generic competitors already on the market. Our findings inform drug repurposing policy for both future emerging diseases and drug development in general.

Identification of Promising Drug Candidates against Prostate Cancer through Computationally-Driven Drug Repurposing

Prostate cancer (PC) is one of the most common types of cancer in males. Although early stages of PC are generally associated with favorable outcomes, advanced phases of the disease present a significantly poorer prognosis. Moreover, currently available therapeutic options for the treatment of PC are still limited, being mainly focused on androgen deprivation therapies and being characterized by low efficacy in patients. As a consequence, there is a pressing need to identify alternative and more effective therapeutics. In this study, we performed large-scale 2D and 3D similarity analyses between compounds reported in the DrugBank database and ChEMBL molecules with reported anti-proliferative activity on various PC cell lines. The analyses included also the identification of biological targets of ligands with potent activity on PC cells, as well as investigations on the activity annotations and clinical data associated with the more relevant compounds emerging from the ligand-based similarity results. The results led to the prioritization of a set of drugs and/or clinically tested candidates potentially useful in drug repurposing against PC.

A trial of topiramate for patients with hereditary spinocerebellar ataxia

In an open pilot trial, six patients with various hereditary forms of spinocerebellar ataxia (SCA) were assigned to topiramate (50 mg/day) for 24 weeks. Four patients completed the protocol without adverse events. Of these four patients, topiramate was effective for three patients. Some patients with SCA could respond to treatment with topiramate.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis

BACKGROUND

Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy.

METHODS

We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning.

RESULTS

To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF.

CONCLUSIONS

All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

Repurposing of Drugs for Cardiometabolic Disorders: An Out and Out Cumulation

Cardiometabolic disorders (CMD) is a constellation of metabolic predisposing factors for atherosclerosis such as insulin resistance (IR) or diabetes mellitus (DM), systemic hypertension, central obesity, and dyslipidemia. Cardiometabolic diseases (CMDs) continue to be the leading cause of mortality in both developed and developing nations, accounting for over 32% of all fatalities globally each year. Furthermore, dyslipidemia, angina, arrhythmia, heart failure, myocardial infarction (MI), and diabetes mellitus are the major causes of death, accounting for an estimated 19 million deaths in 2012. CVDs will kill more than 23 million individuals each year by 2030. Nonetheless, new drug development (NDD) in CMDs has been increasingly difficult in recent decades due to increased costs and a lower success rate. Drug repositioning in CMDs looks promising in this scenario for launching current medicines for new therapeutic indications. Repositioning is an ancient method that dates back to the 1960s and is mostly based on coincidental findings during medication trials. One significant advantage of repositioning is that the drug's safety profile is well known, lowering the odds of failure owing to undesirable toxic effects. Furthermore, repositioning takes less time and money than NDD. Given these facts, pharmaceutical corporations are becoming more interested in medication repositioning. In this follow-up, we discussed the notion of repositioning and provided some examples of repositioned medications in cardiometabolic disorders.

Drug repositioning prediction for psoriasis using the adverse event reporting database

Introduction

Inverse signals produced from disproportional analyses using spontaneous drug adverse event reports can be used for drug repositioning purposes. The purpose of this study is to predict drug candidates using a computational method that integrates reported drug adverse event data, disease-specific gene expression profiles, and drug-induced gene expression profiles.

Methods

Drug and adverse events from 2015 through 2020 were downloaded from the United States Food and Drug Administration Adverse Event Reporting System (FAERS). The reporting odds ratio (ROR), information component (IC) and empirical Bayes geometric mean (EBGM) were used to calculate the inverse signals. Psoriasis was selected as the target disease. Disease specific gene expression profiles were obtained by the meta-analysis of the Gene Expression Omnibus (GEO). The reverse gene expression scores were calculated using the Library of Integrated Network-based Cellular Signatures (LINCS) and their correlations with the inverse signals were obtained.

Results

Reversal genes and the candidate compounds were identified. Additionally, these correlations were validated using the relationship between the reverse gene expression scores and the half-maximal inhibitory concentration (IC50) values from the Chemical European Molecular Biology Laboratory (ChEMBL).

Conclusion

Inverse signals produced from a disproportional analysis can be used for drug repositioning and to predict drug candidates against psoriasis.

Drug repurposing strategy part 1: from approved drugs to agri-bactericides leads

Phytopathogenic bacteria are a major cause of crop mortality and yield reduction, especially in field cultivation. The lack of effective chemistry agri-bactericides is responsible for challenging field prevention and treatment, prompting the development of long-lasting solutions to prevent, reduce, or manage some of the most devastating plant diseases facing modern agriculture today and in the future. Therefore, there is an urgent need to find lead drugs preventing and treating phytopathogenic bacterial infection. Drug repurposing, a strategy used to identify novel uses for existing approved drugs outside of their original indication, takes less time and investment than Traditional R&D Strategies in the process of drug development. Based on this method, we conduct a screen of 700 chemically diverse and potentially safe drugs against Xanthomonas oryzae PV. oryzae ACCC 11602 (Xoo), Xanthomonas axonopodis PV. citri (Xac), and Pectobacterium atrosepticum ACCC 19901 (Pa). Furthermore, the structure-activity relationship and structural similarity analysis of active drugs classify potent agri-bactericides into 8 lead series: salicylanilides, cationic nitrogen-containing drugs, azole antifungals, N-containing group, hydroxyquinolines, piperazine, kinase inhibitor and miscellaneous groups. MIC values were evaluated as antibacterial activities in this study. Identifying highly active lead compounds from the screening of approved drugs and comparison with the currently applied plant pathogenic bactericide to validate the bactericidal activity of the best candidates and assess if selected molecules or scaffolds lead to develop new antibacterial agents in the future. In conclusion, this study provides a possibility for the development of potent and highly selective agri-bactericides leads.

Knowledge Mapping of Drug Repositioning's Theme and Development

Background

In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends.

Methodology

The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field.

Results

The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning.

Conclusion

The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.

Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization

Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However, traditional methods to develop new drugs are costly and time-consuming, which makes drug repositioning a promising exploration direction for this purpose. In this study, we collected known antiviral drugs to form five virus-drug association datasets, and then explored drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization (VDA-GKSBMF). By the 5-fold cross-validation, we found that VDA-GKSBMF has an area under curve (AUC) value of 0.8851, 0.8594, 0.8807, 0.8824, and 0.8804, respectively, on the five datasets, which are higher than those of other state-of-art algorithms in four datasets. Based on known virus-drug association data, we used VDA-GKSBMF to prioritize the top-k candidate antiviral drugs that are most likely to be effective against SARS-CoV-2. We confirmed that the top-10 drugs can be molecularly docked with virus spikes protein/human ACE2 by AutoDock on five datasets. Among them, four antiviral drugs ribavirin, remdesivir, oseltamivir, and zidovudine have been under clinical trials or supported in recent literatures. The results suggest that VDA-GKSBMF is an effective algorithm for identifying potential antiviral drugs against SARS-CoV-2.

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches

Background

The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches.

Methods

We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control.

Results

From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside.

Conclusions

Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-022-03686-y.

Regulome-based characterization of drug activity across the human diseasome

Drugs are expected to recover the cell system away from the impaired state to normalcy through disease treatment. However, the understanding of gene regulatory machinery underlying drug activity or disease pathogenesis is far from complete. Here, we perform large-scale regulome analysis for various diseases in terms of gene regulatory machinery. Transcriptome signatures were converted into regulome signatures of transcription factors by integrating publicly available ChIP-seq data. Regulome-based correlations between diseases and their approved drugs were much clearer than the transcriptome-based correlations. For example, an inverse correlation was observed for cancers, whereas a positive correlation was observed for immune system diseases. After demonstrating the usefulness of the regulome-based drug discovery method in terms of accuracy and applicability, we predicted new drugs for nonsmall cell lung cancer and validated the anticancer activity in vitro. The proposed method is useful for understanding disease–disease relationships and drug discovery.

Identification of Drug-Disease Associations Using a Random Walk with Restart Method and Supervised Learning

Drug-disease correlations play an important role in revealing the mechanism of disease, finding new indications of available drugs, or drug repositioning. A variety of computational approaches were proposed to find drug-disease correlations and achieve good performances. However, these methods used a variety of network information, but integrated networks were rarely used. In addition, the role of known drug-disease association data has not been fully played. In this work, we designed a combination algorithm of random walk and supervised learning to find the drug-disease correlations. We used an integrated network to update the model and selected a gene set as the start of random walk based on the known drug-disease correlations data. The experimental results show that the proposed method can effectively find the correlation between drugs and diseases, and the prediction accuracy is 82.7%. We found that there are 8 pairs of drug-disease relationships that have not yet been reported, and 5 of them have pharmacodynamic effects on Parkinson's disease. We also found that a key linkage between Parkinson's disease and phenylhexol, a drug for the treatment of Parkinson's disease α-synuclein and tau protein, provides a useful exploration for the effectiveness of the treatment of Parkinson's disease.

Small Molecules as Toll-like Receptor 4 Modulators Drug and In-House Computational Repurposing

The innate immunity toll-like receptor 4 (TLR4) system is a receptor of paramount importance as a therapeutic target. Virtual screening following a “computer-aided drug repurposing” approach was applied to the discovery of novel TLR4 modulators with a non-lipopolysaccharide-like structure. We screened almost 29,000 approved drugs and drug-like molecules from commercial, public, and in-house academia chemical libraries and, after biological assays, identified several compounds with TLR4 antagonist activity. Our computational protocol showed to be a robust approach for the identification of hits with drug-like scaffolds as possible inhibitors of the TLR4 innate immune pathways. Our collaborative work broadens the chemical diversity for inspiration of new classes of TLR4 modulators.

How to balance valuable innovation with affordable access to medicines in Belgium?

Background: Countries are struggling to provide affordable access to medicines while supporting the market entry of innovative, expensive products. This Perspective aims to discuss challenges and avenues for balancing health care system objectives of access, affordability and innovation related to medicines in Belgium (and in other countries). Methods: This Perspective focuses on the R&D, regulatory approval and market access phases, with particular attention to oncology medicines, precision medicines, orphan medicines, advanced therapies, repurposed medicines, generics and biosimilars. The authors conducted a narrative review of the peer-reviewed literature, of the grey literature (such as policy documents and reports of consultancy agencies), and of their own research. Results: Health care stakeholders need to consider various initiatives for balancing innovation with access to medicines, which relate to clinical and non-clinical outcomes (e.g. supporting the conduct of pragmatic clinical trials, treatment optimisation and patient preference studies, optimising the use of real-world evidence in market access decision making), value assessment (e.g. increasing the transparency of the reimbursement system and criteria, tailoring the design of managed entry agreements to specific types of uncertainty), affordability (e.g. harnessing the role of generics and biosimilars in encouraging price competition, maximising opportunities for personalising and repurposing medicines) and access mechanisms (e.g. promoting collaboration and early dialogue between stakeholders including patients). Conclusion: Although there is no silver bullet that can balance valuable innovation with affordable access to medicines, (Belgian) policy and decision makers should continue to explore initiatives that exploit the potential of both the on-patent and off-patent pharmaceutical markets.

Current medicinal chemistry strategies in the discovery of novel HIV-1 ribonuclease H inhibitors

During HIV-1 genome replication, the viral reverse transcriptase-associated ribonuclease H (RT-associated RNase H) activity hydrolyzes the RNA strand of RNA/DNA heteroduplex intermediates. As of today, HIV-1 RNase H inhibitors (RHIs) remain at an investigational level, although none of them reached clinical trials. Therefore, RNase H remains as an attractive target for drug design and development. In this paper, we review the current status of medicinal chemistry strategies aimed at the discovery of novel RHIs, while discussing problems encountered in their characterization and further development, thereby providing an update on recent progress in the field.

Combinatorial treatment with statins and niclosamide prevents CRC dissemination by unhinging the MACC1-β-catenin-S100A4 axis of metastasis

Colorectal cancer (CRC) is the second-most common malignant disease worldwide, and metastasis is the main culprit of CRC-related death. Metachronous metastases remain to be an unpredictable, unpreventable, and fatal complication, and tracing the molecular chain of events that lead to metastasis would provide mechanistically linked biomarkers for the maintenance of remission in CRC patients after curative treatment. We hypothesized, that Metastasis-associated in colorectal cancer-1 (MACC1) induces a secretory phenotype to enforce metastasis in a paracrine manner, and found, that the cell-free culture medium of MACC1-expressing CRC cells induces migration. Stable isotope labeling by amino acids in cell culture mass spectrometry (SILAC-MS) of the medium revealed, that S100A4 is significantly enriched in the MACC1-specific secretome. Remarkably, both biomarkers correlate in expression data of independent cohorts as well as within CRC tumor sections. Furthermore, combined elevated transcript levels of the metastasis genes MACC1 and S100A4 in primary tumors and in blood plasma robustly identifies CRC patients at high risk for poor metastasis-free (MFS) and overall survival (OS). Mechanistically, MACC1 strengthens the interaction of β-catenin with TCF4, thus inducing S100A4 synthesis transcriptionally, resulting in elevated secretion to enforce cell motility and metastasis. In cell motility assays, S100A4 was indispensable for MACC1-induced migration, as shown via knock-out and pharmacological inhibition of S100A4. The direct transcriptional and functional relationship of MACC1 and S100A4 was probed by combined targeting with repositioned drugs. In fact, the MACC1-β-catenin-S100A4 axis by statins (MACC1) and niclosamide (S100A4) synergized in inhibiting cancer cell motility in vitro and metastasis in vivo. The MACC1-β-catenin-S100A4 signaling axis is causal for CRC metastasis. Selectively repositioned drugs synergize in restricting MACC1/S100A4-driven metastasis with cross-entity potential.

Need for a Standardized Translational Drug Development Platform: Lessons Learned from the Repurposing of Drugs for COVID-19

In the absence of drugs to treat or prevent COVID-19, drug repurposing can be a valuable strategy. Despite a substantial number of clinical trials, drug repurposing did not deliver on its promise. While success was observed with some repurposed drugs (e.g., remdesivir, dexamethasone, tocilizumab, baricitinib), others failed to show clinical efficacy. One reason is the lack of clear translational processes based on adequate preclinical profiling before clinical evaluation. Combined with limitations of existing in vitro and in vivo models, there is a need for a systematic approach to urgent antiviral drug development in the context of a global pandemic. We implemented a methodology to test repurposed and experimental drugs to generate robust preclinical evidence for further clinical development. This translational drug development platform comprises in vitro, ex vivo, and in vivo models of SARS-CoV-2, along with pharmacokinetic modeling and simulation approaches to evaluate exposure levels in plasma and target organs. Here, we provide examples of identified repurposed antiviral drugs tested within our multidisciplinary collaboration to highlight lessons learned in urgent antiviral drug development during the COVID-19 pandemic. Our data confirm the importance of assessing in vitro and in vivo potency in multiple assays to boost the translatability of pre-clinical data. The value of pharmacokinetic modeling and simulations for compound prioritization is also discussed. We advocate the need for a standardized translational drug development platform for mild-to-moderate COVID-19 to generate preclinical evidence in support of clinical trials. We propose clear prerequisites for progression of drug candidates for repurposing into clinical trials. Further research is needed to gain a deeper understanding of the scope and limitations of the presented translational drug development platform.

Drug repurposing: a systematic review on root causes, barriers and facilitators

BACKGROUND

Repurposing is a drug development strategy receiving heightened attention after the Food and Drug Administration granted emergency use authorization of several repurposed drugs to treat Covid-19. There remain knowledge gaps on the root causes, facilitators and barriers for repurposing.

METHOD

This systematic review used controlled vocabulary and free text terms to search ABI/Informa, Academic Search Premier, Business Source Complete, Cochrane Library, EconLit, Google Scholar, Ovid Embase, Ovid Medline, Pubmed, Scopus, and Web of Science Core Collection databases for the characteristics, reasons and example of companies deprioritizing development of promising drugs and barriers, facilitators and examples of successful re-purposing.

RESULTS

We identified 11,814 articles, screened 5,976 for relevance, found 437 eligible for full text review, 115 of which were included in full analysis. Most articles (66%, 76/115) discussed why promising drugs are abandoned, with lack of efficacy or superiority to other therapies (n = 59), strategic business reasons (n = 35), safety problems (n = 28), research design decisions (n = 12), the complex nature of a studied disease or drug (n = 7) and regulatory bodies requiring more information (n = 2) among top reasons. Key barriers to repurposing include inadequate resources (n = 42), trial data access and transparency around abandoned compounds (n = 20) and expertise (n = 11). Additional barriers include uncertainty about the value of repurposing (n = 13), liability risks (n = 5) and intellectual property (IP) challenges (n = 26). Facilitators include the ability to form multi-partner collaborations (n = 38), access to compound databases and database screening tools (n = 32), regulatory modifications (n = 5) and tax incentives (n = 2).

CONCLUSION

Promising drugs are commonly shelved due to insufficient efficacy or superiority to alternate therapies, poor market prospects, and industry consolidation. Inadequate resources and data access and challenges negotiating IP are key barriers to repurposing reaching its full potential as a core approach in drug development. Multi-partner collaborations and the availability and use of compound databases and tax incentives are key facilitators for repurposing. More research is needed on the current value of repurposing in drug development and how to better facilitate resources to support it, where valuable, especially financial, staffing for out-licensing shelved products, and legal expertise to negotiate IP agreements in multi-partner collaborations.

TRIAL REGISTRATION

The protocol was registered on Open Science Framework ( https://osf.io/f634k/ ) as it was not eligible for registration on PROSPERO as the review did not focus on a health-related outcome.

Locally Applied Repositioned Hormones for Oral Bone and Periodontal Tissue Engineering: A Narrative Review

Bone and periodontium are tissues that have a unique capacity to repair from harm. However, replacing or regrowing missing tissues is not always effective, and it becomes more difficult as the defect grows larger. Because of aging and the increased prevalence of debilitating disorders such as diabetes, there is a considerable increase in demand for orthopedic and periodontal surgical operations, and successful techniques for tissue regeneration are still required. Even with significant limitations, such as quantity and the need for a donor area, autogenous bone grafts remain the best solution. Topical administration methods integrate osteoconductive biomaterial and osteoinductive chemicals as hormones as alternative options. This is a promising method for removing the need for autogenous bone transplantation. Furthermore, despite enormous investigation, there is currently no single approach that can reproduce all the physiologic activities of autogenous bone transplants. The localized bioengineering technique uses biomaterials to administer different hormones to capitalize on the host’s regeneration capacity and capability, as well as resemble intrinsic therapy. The current study adds to the comprehension of the principle of hormone redirection and its local administration in both bone and periodontal tissue engineering.

Structure-based pharmacophore modeling, virtual screening approaches to identifying the potent hepatitis C viral protease and polymerase novel inhibitors

Hepatitis C is an infectious disease that leads to acute and chronic liver illnesses. Currently, there are no effective vaccines against this deadly virus. Direct acting antiviral (DAA) drugs are given in the combination with ribavirin and pegylated interferon which lead to adverse effects. Through in silico analysis, the structure-based docking study was performed against NS3/4A protease and NS5B polymerase proteins of HCV. In the current study, multiple e-pharmacophore-based virtual screening methods such as HTVS, SP, and XP were carried out to screen natural compounds and enamine databases. Our result outcomes revealed that CID AE-848/13196185 and CID AE-848/36959205 compounds show good binding interactions with protease protein. In addition, CID 15081408 and CID 173568 show better binding interactions with the polymerase protein. Further to validate the docking results, we performed molecular dynamics simulation for the top hit compounds bound with protease and polymerase proteins to illustrate conformational differences in the stability compared with the active site of the cocrystal inhibitor. Thus, the current study emphasizes these compounds could be an effective drug to treat HCV.

Improved drug-target interaction prediction with intermolecular graph transformer

The identification of active binding drugs for target proteins (referred to as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieve better performance than molecular docking, existing models often neglect topological or spatial of intermolecular information, hindering prediction performance. We recognize this problem and propose a novel approach called the Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art (SoTA) approaches by 9.1% and 20.5% over the second best option for binding activity and binding pose prediction, respectively, and exhibits superior generalization ability to unseen receptor proteins than SoTA approaches. Furthermore, IGT exhibits promising drug screening ability against severe acute respiratory syndrome coronavirus 2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. Source code and datasets are available at https://github.com/microsoft/IGT-Intermolecular-Graph-Transformer.

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

Drug repositioning: Progress and challenges in drug discovery for various diseases

Compared with traditional de novo drug discovery, drug repurposing has become an attractive drug discovery strategy due to its low-cost and high efficiency. Through a comprehensive analysis of the candidates that have been identified with drug repositioning potentials, it is found that although some drugs do not show obvious advantages in the original indications, they may exert more obvious effects in other diseases. In addition, some drugs have a synergistic effect to exert better clinical efficacy if used in combination. Particularly, it has been confirmed that drug repositioning has benefits and values on the current public health emergency such as the COVID-19 pandemic, which proved the great potential of drug repositioning. In this review, we systematically reviewed a series of representative drugs that have been repositioned for different diseases and illustrated successful cases in each disease. Especially, the mechanism of action for the representative drugs in new indications were explicitly explored for each disease, we hope this review can provide important insights for follow-up research.

Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing

Chemical-induced gene expression profiles provide critical information of chemicals in a biological system, thus offering new opportunities for drug discovery. Despite their success, large-scale analysis leveraging gene expressions is limited by time and cost. Although several methods for predicting gene expressions were proposed, they only focused on imputation and classification settings, which have limited applications to real-world scenarios of drug discovery. Therefore, a chemical-induced gene expression ranking (CIGER) framework is proposed to target a more realistic but more challenging setting in which overall rankings in gene expression profiles induced by de novo chemicals are predicted. The experimental results show that CIGER significantly outperforms existing methods in both ranking and classification metrics. Furthermore, a drug screening pipeline based on CIGER is proposed to identify potential treatments of drug-resistant pancreatic cancer. Our predictions have been validated by experiments, thereby showing the effectiveness of CIGER for phenotypic compound screening of precision medicine.

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A new deep-learning method (CIGER) for chemical-induced gene expression ranking

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CIGER can predict gene expression for de novo chemicals from chemical structures

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We discovered drugs for the treatment of drug-resistant pancreatic cancer

In recent years, a phenotype-based drug discovery approach using chemical-induced gene expressions has shown to be effective in drug discovery and precision medicine. However, it is not feasible to experimentally determine chemical-induced gene expressions for all available chemicals of interest, thereby hindering the application of gene expression-based compound screening on a large scale. Thus, it is crucial to design a computational approach that can generate gene expression information for any chemicals. We proposed a new, deep-learning framework named chemical-induced gene expression ranking (CIGER) to predict a landmark gene expression profile (i.e., gene ranking) induced by de novo chemicals based on their chemical structures. Leveraging CIGER, we predicted and experimentally validated that several existing drugs can increase the therapeutic response on drug-resistant pancreatic cancer. Our results demonstrated the effectiveness of CIGER for precision drug discovery in practice.

Integration of Neighbor Topologies Based on Meta-Paths and Node Attributes for Predicting Drug-Related Diseases

Identifying new disease indications for existing drugs can help facilitate drug development and reduce development cost. The previous drug–disease association prediction methods focused on data about drugs and diseases from multiple sources. However, they did not deeply integrate the neighbor topological information of drug and disease nodes from various meta-path perspectives. We propose a prediction method called NAPred to encode and integrate meta-path-level neighbor topologies, multiple kinds of drug attributes, and drug-related and disease-related similarities and associations. The multiple kinds of similarities between drugs reflect the degrees of similarity between two drugs from different perspectives. Therefore, we constructed three drug–disease heterogeneous networks according to these drug similarities, respectively. A learning framework based on fully connected neural networks and a convolutional neural network with an attention mechanism is proposed to learn information of the neighbor nodes of a pair of drug and disease nodes. The multiple neighbor sets composed of different kinds of nodes were formed respectively based on meta-paths with different semantics and different scales. We established the attention mechanisms at the neighbor-scale level and at the neighbor topology level to learn enhanced neighbor feature representations and enhanced neighbor topological representations. A convolutional-autoencoder-based module is proposed to encode the attributes of the drug–disease pair in three heterogeneous networks. Extensive experimental results indicated that NAPred outperformed several state-of-the-art methods for drug–disease association prediction, and the improved recall rates demonstrated that NAPred was able to retrieve more actual drug–disease associations from the top-ranked candidates. Case studies on five drugs further demonstrated the ability of NAPred to identify potential drug-related disease candidates.

Text Mining-Based Drug Discovery for Connective Tissue Disease–Associated Pulmonary Arterial Hypertension

Background: The current medical treatments for connective tissue disease–associated pulmonary arterial hypertension (CTD-PAH) do not show favorable efficiency for all patients, and identification of novel drugs is desired.

Methods: Text mining was performed to obtain CTD- and PAH-related gene sets, and the intersection of the two gene sets was analyzed for functional enrichment through DAVID. The protein–protein interaction network of the overlapping genes and the significant gene modules were determined using STRING. The enriched candidate genes were further analyzed by Drug Gene Interaction database to identify drugs with potential therapeutic effects on CTD-PAH.

Results: Based on text mining analysis, 179 genes related to CTD and PAH were identified. Through enrichment analysis of the genes, 20 genes representing six pathways were obtained. To further narrow the scope of potential existing drugs, we selected targeted drugs with a Query Score ≥5 and Interaction Score ≥1. Finally, 13 drugs targeting the six genes were selected as candidate drugs, which were divided into four drug–gene interaction types, and 12 of them had initial drug indications approved by the FDA. The potential gene targets of the drugs on this list are IL-6 (one drug) and IL-1β (two drugs), MMP9 (one drug), VEGFA (three drugs), TGFB1 (one drug), and EGFR (five drugs). These drugs might be used to treat CTD-PAH.

Conclusion: We identified 13 drugs targeting six genes that may have potential therapeutic effects on CTD-PAH.

Dissecting the Mechanism of Action of Spiperone-A Candidate for Drug Repurposing for Colorectal Cancer

Simple Summary

Despite advances in primary and adjuvant treatments, approximately 50% of colorectal cancer (CRC) patients still die from recurrence and metastatic disease. Thus, alternative and more effective therapeutic approaches are expected to be developed. Drug repurposing is increasing interest in cancer therapy, as it represents a cheaper and faster alternative strategy to de novo drug synthesis. Psychiatric medications are promising as a new generation of antitumor drugs. Here, we demonstrate that spiperone—a licensed drug for the treatment of schizophrenia—induces apoptosis in CRC cells. Our data reveal that spiperone’s cytotoxicity in CRC cells is mediated by phospholipase C activation, intracellular calcium homeostasis dysregulation, and irreversible endoplasmic reticulum stress induction, resulting in lipid metabolism alteration and Golgi apparatus damage. By identifying new targetable pathways in CRC cells, our findings represent a promising starting point for the design of novel therapeutic strategies for CRC.

Abstract

Approximately 50% of colorectal cancer (CRC) patients still die from recurrence and metastatic disease, highlighting the need for novel therapeutic strategies. Drug repurposing is attracting increasing attention because, compared to traditional de novo drug discovery processes, it may reduce drug development periods and costs. Epidemiological and preclinical evidence support the antitumor activity of antipsychotic drugs. Herein, we dissect the mechanism of action of the typical antipsychotic spiperone in CRC. Spiperone can reduce the clonogenic potential of stem-like CRC cells (CRC-SCs) and induce cell cycle arrest and apoptosis, in both differentiated and CRC-SCs, at clinically relevant concentrations whose toxicity is negligible for non-neoplastic cells. Analysis of intracellular Ca²⁺ kinetics upon spiperone treatment revealed a massive phospholipase C (PLC)-dependent endoplasmic reticulum (ER) Ca²⁺ release, resulting in ER Ca²⁺ homeostasis disruption. RNA sequencing revealed unfolded protein response (UPR) activation, ER stress, and induction of apoptosis, along with IRE1-dependent decay of mRNA (RIDD) activation. Lipidomic analysis showed a significant alteration of lipid profile and, in particular, of sphingolipids. Damage to the Golgi apparatus was also observed. Our data suggest that spiperone can represent an effective drug in the treatment of CRC, and that ER stress induction, along with lipid metabolism alteration, represents effective druggable pathways in CRC.