A standard database for drug repositioning

CPDP: Contrastive Protein-Drug Pre-Training for Novel Drug Discovery

Novel drug discovery and repositioning remain critical challenges in biomedical research, requiring accurate prediction of drug-target interactions (DTIs). We propose the CPDP framework, which builds upon existing biomedical representation models and integrates contrastive learning with multi-dimensional representations of proteins and drugs to predict DTIs. By aligning the representation space, CPDP enables GNN-based methods to achieve zero-shot learning capabilities, allowing for accurate predictions of unseen drug data. This approach enhances DTI prediction performance, particularly for novel drugs not included in the BioHNs dataset. Experimental results demonstrate CPDP's high accuracy and strong generalization ability in predicting novel biological entities while maintaining effectiveness for traditional drug repositioning tasks.

Influence of statin intervention on peripheral neuropathy in patients treated with anticancer drugs identified from the insurer database

BACKGROUND

Statins, hydroxymethylglutaryl-CoA reductase inhibitors, possess neuroprotective properties. Given the potential neuroprotective properties of statins and their prevalent use in clinical settings, we aimed to investigate their impact on chemotherapy-induced peripheral neuropathy (CIPN) in Japan by assessing both their safety and efficacy in this context.

METHODS

We conducted a retrospective observational study using the Japan Medical Data Centre database, which includes data from 2005 to 2021. We included patients who underwent anticancer therapy and were categorized into non-statin (10,920) and statin (1,537) groups. These groups were matched using a propensity score, resulting in 2,548 non-statin and 1,274 statin users. The primary endpoints were the incidence of CIPN post-first prescription of each anticancer drug and overall survival.

RESULTS

Treatment with statins did not increase the incidence of CIPN (non-statin 27.2% vs. statin 28.4%, P = 0.443). Nevertheless, the incidence of CIPN was significantly high among women (non-statin 28.0% vs. statin 33.2%, P = 0.025). Overall survival was not impacted by statin use (hazard ratio 0.98, 95%CI: 0.83-1.16, P = 0.8846). Among men treated with paclitaxel, we observed an improvement in overall survival (hazard ratio: 0.72; 95% CI: 0.56-0.92; P = 0.0110).

CONCLUSIONS

The use of statins in patients with cancer was not associated with CIPN incidence. However, in men receiving paclitaxel treatment, statins may be linked to improved overall survival. Further studies are necessary to clarify the factors influencing prognosis and CIPN severity.

A knowledge graph approach to drug repurposing for Alzheimer's, Parkinson's and Glioma using drug-disease-gene associations

Drug Repurposing gives us facility to find the new uses of previously developed drugs rather than developing new drugs from start. Particularly during pandemic, drug repurposing caught much attention to provide new applications of the previously approved drugs. In our research, we provide a novel method for drug repurposing based on feature learning process from drug-disease-gene network. In our research, we aimed at finding drug candidates which can be repurposed under neurodegenerative diseases and glioma. We collected association data between drugs, diseases and genes from public resources and primarily examined the data related to Alzheimer's, Parkinson's and Glioma diseases. We created a Knowledge Graph using neo4j by integrating all these datasets and applied scalable feature learning algorithm known as node2vec to create node embeddings. These embeddings were later used to predict the unknown associations between disease and their candidate drugs by finding cosine similarity between disease and drug nodes embedding. We obtained a definitive set of candidate drugs for repurposing. These results were validated from the literature and CodReS online tool to rank the candidate drugs. Additionally, we verified the status of candidate drugs from pharmaceutical knowledge databases to confirm their significance.

Drug functional remapping: a new promise for tumor immunotherapy

The research and development of new anti-cancer drugs face challenges such as high costs, lengthy development cycles, and limited data on side effects. In contrast, the clinical safety and side effects of traditional drugs have been well established through long-term use. The development or repurposing of traditional drugs with potential applications in cancer treatment offers an economical, feasible, and promising strategy for new drug development. This article reviews the novel applications of traditional drugs in tumor immunotherapy, discussing how they can enhance tumor treatment efficacy through functional repositioning, while also reducing development time and costs. Recent advancements in cancer immunotherapy have revolutionized treatment options, but resistance to ICIs remains a significant challenge. Drug repurposing has emerged as a promising strategy to identify novel agents that can enhance the efficacy of immunotherapies by overcoming ICI resistance. A study suggests that drug repositioning has the potential to modulate immune cell activity or alter the tumor microenvironment, thereby circumventing the resistance mechanisms associated with immune checkpoint blockade. This approach provides a rapid and cost-effective pathway for identifying therapeutic candidates that can be quickly transitioned into clinical trials. To improve the effectiveness of tumor immunotherapy, it is crucial to explore systematic methods for identifying repurposed drug candidates. Methods such as high-throughput screening, computational drug repositioning, and bioinformatic analysis have been employed to efficiently identify potential candidates for cancer treatment. Furthermore, leveraging databases related to immunotherapy and drug repurposing can provide valuable resources for drug discovery and facilitate the identification of promising compounds. It focuses on the latest advancements in the use of antidiabetic drugs, antihypertensive agents, weight-loss medications, antifungal agents, and antiviral drugs in tumor immunotherapy, examining their mechanisms of action, clinical application prospects, and associated challenges. In this context, our aim is to explore these strategies and highlight their potential for expanding the therapeutic options available for cancer immunotherapy, providing valuable references for cancer research and treatment.

Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery

Identifying the active natural compounds remains a challenge for drug discovery, and new algorithms need to be developed to predict active ingredients from complex natural products. Here, we proposed Meta-DEP, a Meta-paths-based Drug Efficacy Prediction based on drug-protein-disease heterogeneity network, where Meta-paths contain all the shortest paths between drug targets and disease-related proteins in the network and drug efficacy is measured by a predictive score according to drug disease network proximity. Experiments show that Meta-DEP performs better than traditional network topology analysis on drug-disease interaction prediction task. Further investigations demonstrate that the key targets identified by Meta-DEP for drug efficacy are consistent with clinical pharmacological evidence. To prove that Meta-DEP can be used to discover active natural compounds, we apply it to predict the relationship between the monomeric components of traditional Chinese medicine included in the TCMSP database and diseases. Results indicate that Meta-DEP can accurately predict most of the drug-disease pairs included in the TCMSP database. In addition, biological experiments are directly used to demonstrate that Meta-DEP can mined active compound from traditional Chinese medicine with integrating disease transcriptomic data. Overall, the model developed in this study provides new impetus for driving the natural compound into innovative lead molecule. Code and data are available at https://github.com/t9lex/Meta-DEP .

Drug Repurposing Using Hypergraph Embedding Based on Common Therapeutic Targets of a Drug

Developing a new drug is a long and expensive process that typically takes 10-15 years and costs billions of dollars. This has led to an increasing interest in drug repositioning, which involves finding new therapeutic uses for existing drugs. Computational methods become an increasingly important tool for identifying associations between drugs and new diseases. Graph- and hypergraph-based approaches are a type of computational method that can be used to identify potential associations between drugs and new diseases. Here, we present a drug repurposing method based on hypergraph neural network for predicting drug-disease association in three stages. First, it constructs a heterogeneous graph that contains drug and disease nodes and links between them; in the second stage, it converts the heterogeneous simple graph to a hypergraph with only disease nodes. This is achieved by grouping diseases that use the same drug into a hyperedge. Indeed, all the diseases that are the common therapeutic goal of a drug are placed on a hyperedge. Finally, a graph neural network is used to predict drug-disease association based on the structure of the hypergraph. This model is more efficient than other methods because it uses a hypergraph to model relationships more effectively than graphs. Furthermore, it constructs the hypergraph using only a drug-disease association matrix, eliminating the need for extensive amounts of data. Experimental results show that the hypergraph-based approach effectively captures complex interrelationships between drugs and diseases, leading to improved accuracy of drug-disease association prediction compared to state-of-the-art methods.

KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning

Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. Specifically, a graph regularized approach is applied to integrate multiple drug and disease similarity information, which can effectively eliminate noise data and obtain integrated similarity features of drugs and diseases. Then, topological feature representations of drugs and diseases are learned from constructed biomedical knowledge graphs (KGs) which encompasses known drug-related and disease-related interactions. Next, the similarity features and topological features are fused by utilizing an attention-based feature fusion method. Finally, drug-disease associations are predicted using the graph convolutional network. Experimental results demonstrate that KGRDR achieves better performance when compared with the state-of-the-art drug-disease prediction methods. Moreover, case study results further validate the effectiveness of KGRDR in predicting novel drug-disease interactions.

Improving drug repositioning with negative data labeling using large language models

INTRODUCTION

Drug repositioning offers numerous advantages, such as faster development timelines, reduced costs, and lower failure rates in drug development. Supervised machine learning is commonly used to score drug candidates but is hindered by the lack of reliable negative data-drugs that fail due to inefficacy or toxicity- which is difficult to access, lowering their prediction accuracy and generalization. Positive-Unlabeled (PU) learning has been used to overcome this issue by either randomly sampling unlabeled drugs or identifying probable negatives but still suffers from misclassification or oversimplified decision boundaries.

RESULTS

We proposed a novel strategy using Large Language Models (GPT-4) to analyze all clinical trials on prostate cancer and systematically identify true negatives. This approach showed remarkable improvement in predictive accuracy on independent test sets with a Matthews Correlation Coefficient of 0.76 (± 0.33) compared to 0.55 (± 0.15) and 0.48 (± 0.18) for two commonly used PU learning approaches. Using our labeling strategy, we created a training set of 26 positive and 54 experimentally validated negative drugs. We then applied a machine learning ensemble to this new dataset to assess the repurposing potential of the remaining 11,043 drugs in the DrugBank database. This analysis identified 980 potential candidates for prostate cancer. A detailed review of the top 30 revealed 9 promising drugs targeting various mechanisms such as genomic instability, p53 regulation, or TMPRSS2-ERG fusion.

CONCLUSION

By expanding our negative data labeling approach to all diseases within the ClinicalTrials.gov database, our method could greatly advance supervised drug repositioning, offering a more accurate and data-driven path for discovering new treatments.

Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction - DrugSchizoNet

Drug discovery relies on the precise prognosis of drug-target interactions (DTI). Due to their ability to learn from raw data, deep learning (DL) methods have displayed outstanding performance over traditional approaches. However, challenges such as imbalanced data, noise, poor generalization, high cost, and time-consuming processes hinder progress in this field. To overcome the above challenges, we propose a DL-based model termed DrugSchizoNet for drug interaction (DI) prediction of Schizophrenia. Our model leverages drug-related data from the DrugBank and repoDB databases, employing three key preprocessing techniques. First, data cleaning eliminates duplicate or incomplete entries to ensure data integrity. Next, normalization is performed to enhance security and reduce costs associated with data acquisition. Finally, feature extraction is applied to improve the quality of input data. The three layers of the DrugSchizoNet model are the input, hidden and output layers. In the hidden layer, we employ dropout regularization to mitigate overfitting and improve generalization. The fully connected (FC) layer extracts relevant features, while the LSTM layer captures the sequential nature of DIs. In the output layer, our model provides confidence scores for potential DIs. To optimize the prediction accuracy, we utilize hyperparameter tuning through OB-MOA optimization. Experimental results demonstrate that DrugSchizoNet achieves a superior accuracy of 98.70%. The existing models, including CNN-RNN, DANN, CKA-MKL, DGAN, and CNN, across various evaluation metrics such as accuracy, recall, specificity, precision, F1 score, AUPR, and AUROC are compared with the proposed model. By effectively addressing the challenges of imbalanced data, noise, poor generalization, high cost and time-consuming processes, DrugSchizoNet offers a promising approach for accurate DTI prediction in Schizophrenia. Its superior performance demonstrates the potential of DL in advancing drug discovery and development processes.

Storage and Query of Drug Knowledge Graphs Using Distributed Graph Databases: A Case Study

BACKGROUND

Distributed graph databases are a promising method for storing and conducting complex pathway queries on large-scale drug knowledge graphs to support drug research. However, there is a research gap in evaluating drug knowledge graphs' storage and query performance based on distributed graph databases. This study evaluates the feasibility and performance of distributed graph databases in managing large-scale drug knowledge graphs.

METHODS

First, a drug knowledge graph storage and query system is designed based on the Nebula Graph database. Second, the system's writing and query performance is evaluated. Finally, two drug repurposing benchmarks are used to provide a more extensive and reliable assessment.

RESULTS

The performance of distributed graph databases surpasses that of single-machine databases, including data writing, regular queries, constrained queries, and concurrent queries. Additionally, the advantages of distributed graph databases in writing performance become more pronounced as the data volume increases. The query performance benefits of distributed graph databases also improve with the complexity of query tasks. The drug repurposing evaluation results show that 78.54% of the pathways are consistent with currently approved drug treatments according to repoDB. Additionally, 12 potential pathways for new drug indications are found to have literature support according to DrugRepoBank.

CONCLUSIONS

The proposed system is able to construct, store, and query a large graph of multisource drug knowledge and provides reliable and explainable drug-disease paths for drug repurposing.

Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing

Drug development is known to be a costly and time-consuming process, which is prone to high failure rates. Drug repurposing allows drug discovery by reusing already approved compounds. The outcomes of past clinical trials can be used to predict novel drug-disease associations by leveraging drug- and disease-related similarities. To tackle this classification problem, collaborative filtering with implicit feedback (and potentially additional data on drugs and diseases) has become popular. It can handle large imbalances between negative and positive known associations and known and unknown associations. However, properly evaluating the improvement over the state of the art is challenging, as there is no consensus approach to compare models. We propose a reproducible methodology for comparing collaborative filtering-based drug repurposing. We illustrate this method by comparing 11 models from the literature on eight diverse drug repurposing datasets. Based on this benchmark, we derive guidelines to ensure a fair and comprehensive evaluation of the performance of those models. In particular, an uncontrolled bias on unknown associations might lead to severe data leakage and a misestimation of the model's true performance. Moreover, in drug repurposing, the ability of a model to extrapolate beyond its training distribution is crucial and should also be assessed. Finally, we identified a subcategory of collaborative filtering that seems efficient and robust to distribution shifts. Benchmarks constitute an essential step towards increased reproducibility and more accessible development of competitive drug repurposing methods.

Targeting SLC4A4: A Novel Approach in Colorectal Cancer Drug Repurposing

BACKGROUND

Colorectal cancer (CRC) is a complex and increasingly prevalent malignancy with significant challenges in its treatment and prognosis. This study aims to explore the role of the SLC4A4 transporter as a biomarker in CRC progression and its potential as a therapeutic target, particularly in relation to tumor acidity and immune response.

METHODS

The study utilized computational approaches, including receptor-based virtual screening and high-throughput docking, to identify potential SLC4A4 inhibitors. A model of the human SLC4A4 structure was generated based on CryoEM data (PDB ID 6CAA), and drug candidates from the DrugBank database were evaluated using two computational tools (DrugRep and CB-DOCK2).

RESULTS

The study identified the compound (5R)-N-[(1r)-3-(4-hydroxyphenyl)butanoyl]-2-decanamide (DB07991) as the best ligand, demonstrating favorable binding affinity and stability. Molecular dynamics simulations revealed strong protein-ligand interactions with consistent RMSD (~0.25 nm), RMSF (~0.5 nm), compact Rg (4.0-3.9 nm), and stable SASA profiles, indicating that the SLC4A4 structure remains stable upon ligand binding.

CONCLUSIONS

The findings suggest that DB07991 is a promising drug candidate for further investigation as a therapeutic agent against CRC, particularly for targeting SLC4A4. This study highlights the potential of computational drug repositioning in identifying effective treatments for colorectal cancer.

On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine Learning

To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. Besides, data-sharing practices in many scientific domains have evolved in recent years for reproducibility purposes. In this sense, academic institutions' adoption of these practices has encouraged researchers to publish their data and technical documentation in peer-reviewed publications such as data papers. In this study, we analyze how this broader scientific data documentation meets the needs of the ML community and regulatory bodies for its use in ML technologies. We examine a sample of 4041 data papers of different domains, assessing their coverage and trends in the requested dimensions and comparing them to those from an ML-focused venue (NeurIPS D&B), which publishes papers describing datasets. As a result, we propose a set of recommendation guidelines for data creators and scientific data publishers to increase their data's preparedness for its transparent and fairer use in ML technologies.

Drug-target prediction through self supervised learning with dual task ensemble approach

Drug-Target interaction (DTI) prediction, a transformative approach in pharmaceutical research, seeks novel therapeutic applications for computational method based virtual screening, existing drugs to address untreated diseases and discovery of existing drugs side effects. The proposed model predict DTI through Heterogeneous biological network by combining drug, genes and disease related knowledge. For the purpose of embedding extraction Self-supervised learning (SSL) has been used which, trains models through pretext tasks, eliminating the need for manual annotations. The pretext tasks are related to either structural based information or similarity based information. To mitigate GNN vulnerability to non-robustness, ensemble learning can be incorporated into GNNs, harnessing multiple models to enhance robustness. This paper introduces a Graph neural network based architecture consisting of task based module and ensemble module for link prediction of DTI. The ensemble module of dual task combinations, both in cold start and warm start scenarios achieve very good performance as it provide 0.960 in cold start and 0.970 in warm start mean AUCROC score with less deviation.

CDCM: a correlation-dependent connectivity map approach to rapidly screen drugs during outbreaks of infectious diseases

In the context of the global damage caused by coronavirus disease 2019 (COVID-19) and the emergence of the monkeypox virus (MPXV) outbreak as a public health emergency of international concern, research into methods that can rapidly test potential therapeutics during an outbreak of a new infectious disease is urgently needed. Computational drug discovery is an effective way to solve such problems. The existence of various large open databases has mitigated the time and resource consumption of traditional drug development and improved the speed of drug discovery. However, the diversity of cell lines used in various databases remains limited, and previous drug discovery methods are ineffective for cross-cell prediction. In this study, we propose a correlation-dependent connectivity map (CDCM) to achieve cross-cell predictions of drug similarity. The CDCM mainly identifies drug-drug or disease-drug relationships from the perspective of gene networks by exploring the correlation changes between genes and identifying similarities in the effects of drugs or diseases on gene expression. We validated the CDCM on multiple datasets and found that it performed well for drug identification across cell lines. A comparison with the Connectivity Map revealed that our method was more stable and performed better across different cell lines. In the application of the CDCM to COVID-19 and MPXV data, the predictions of potential therapeutic compounds for COVID-19 were consistent with several previous studies, and most of the predicted drugs were found to be experimentally effective against MPXV. This result confirms the practical value of the CDCM. With the ability to predict across cell lines, the CDCM outperforms the Connectivity Map, and it has wider application prospects and a reduced cost of use.

Schizophrenia Interactome-Derived Repurposable Drugs and Randomized Controlled Trials of Two Candidates

There is a substantial unmet need for effective and patient-acceptable drugs to treat severe mental illnesses such as schizophrenia (SZ). Computational analysis of genomic, transcriptomic, and pharmacologic data generated in the past 2 decades enables repurposing of drugs or compounds with acceptable safety profiles, namely those that are U.S. Food and Drug Administration approved or have reached late stages in clinical trials. We developed a rational approach to achieve this computationally for SZ by studying drugs that target the proteins in its protein interaction network (interactome). This involved contrasting the transcriptomic modulations observed in the disorder and the drug; our analyses resulted in 12 candidate drugs, 9 of which had additional supportive evidence whereby their target networks were enriched for pathways relevant to SZ etiology or for genes that had an association with diseases pathogenically similar to SZ. To translate these computational results to the clinic, these shortlisted drugs must be tested empirically through randomized controlled trials, in which their previous safety approvals obviate the need for time-consuming phase 1 and 2 studies. We selected 2 among the shortlisted candidates based on likely adherence and side-effect profiles. We are testing them through adjunctive randomized controlled trials for patients with SZ or schizoaffective disorder who experienced incomplete resolution of psychotic features with conventional treatment. The integrated computational analysis for identifying and ranking drugs for clinical trials can be iterated as additional data are obtained. Our approach could be expanded to enable disease subtype-specific drug discovery in the future and should also be exploited for other psychiatric disorders.

Research Progresses and Applications of Knowledge Graph Embedding Technique in Chemistry

A knowledge graph (KG) is a technique for modeling entities and their interrelations. Knowledge graph embedding (KGE) translates these entities and relationships into a continuous vector space to facilitate dense and efficient representations. In the domain of chemistry, applying KG and KGE techniques integrates heterogeneous chemical information into a coherent and user-friendly framework, enhances the representation of chemical data features, and is beneficial for downstream tasks, such as chemical property prediction. This paper begins with a comprehensive review of classical and contemporary KGE methodologies, including distance-based models, semantic matching models, and neural network-based approaches. We then catalogue the primary databases employed in chemistry and biochemistry that furnish the KGs with essential chemical data. Subsequently, we explore the latest applications of KG and KGE in chemistry, focusing on risk assessment, property prediction, and drug discovery. Finally, we discuss the current challenges to KG and KGE techniques and provide a perspective on their potential future developments.

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.

Shared etiology of Mendelian and complex disease supports drug discovery

BACKGROUND

Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease.

METHODS

In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations.

RESULTS

Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated.

CONCLUSIONS

Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

Non-Coding RNAs Extended Omnigenic Module of Cancers

The emergence of cancers involves numerous coding and non-coding genes. Understanding the contribution of non-coding RNAs (ncRNAs) to the cancer neighborhood is crucial for interpreting the interaction between molecular markers of cancer. However, there is a lack of systematic studies on the involvement of ncRNAs in the cancer neighborhood. In this paper, we construct an interaction network which encompasses multiple genes. We focus on the fundamental topological indicator, namely connectivity, and evaluate its performance when applied to cancer-affected genes using statistical indices. Our findings reveal that ncRNAs significantly enhance the connectivity of affected genes and mediate the inclusion of more genes in the cancer module. To further explore the role of ncRNAs in the network, we propose a connectivity-based method which leverages the bridging function of ncRNAs across cancer-affected genes and reveals the non-coding RNAs extended omnigenic module (NeOModule). Topologically, this module promotes the formation of cancer patterns involving ncRNAs. Biologically, it is enriched with cancer pathways and treatment targets, providing valuable insights into disease relationships.

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

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

Predicting gene disease associations with knowledge graph embeddings for diseases with curtailed information

Knowledge graph embeddings (KGE) are a powerful technique used in the biomedical domain to represent biological knowledge in a low dimensional space. However, a deep understanding of these methods is still missing, and, in particular, regarding their applications to prioritize genes associated with complex diseases with reduced genetic information. In this contribution, we built a knowledge graph (KG) by integrating heterogeneous biomedical data and generated KGE by implementing state-of-the-art methods, and two novel algorithms: Dlemb and BioKG2vec. Extensive testing of the embeddings with unsupervised clustering and supervised methods showed that KGE can be successfully implemented to predict genes associated with diseases and that our novel approaches outperform most existing algorithms in both scenarios. Our findings underscore the significance of data quality, preprocessing, and integration in achieving accurate predictions. Additionally, we applied KGE to predict genes linked to Intervertebral Disc Degeneration (IDD) and illustrated that functions pertinent to the disease are enriched within the prioritized gene set.

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

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

Protein sequence analysis in the context of drug repurposing

MOTIVATION

Drug repurposing speeds up the development of new treatments, being less costly, risky, and time consuming than de novo drug discovery. There are numerous biological elements that contribute to the development of diseases and, as a result, to the repurposing of drugs.

METHODS

In this article, we analysed the potential role of protein sequences in drug repurposing scenarios. For this purpose, we embedded the protein sequences by performing four state of the art methods and validated their capacity to encapsulate essential biological information through visualization. Then, we compared the differences in sequence distance between protein-drug target pairs of drug repurposing and non - drug repurposing data. Thus, we were able to uncover patterns that define protein sequences in repurposing cases.

RESULTS

We found statistically significant sequence distance differences between protein pairs in the repurposing data and the rest of protein pairs in non-repurposing data. In this manner, we verified the potential of using numerical representations of sequences to generate repurposing hypotheses in the future.

Shared etiology of Mendelian and complex disease supports drug discovery

Background

Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease.

Methods

In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations.

Results

Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated.

Conclusions

Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

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.

Innovative target mining stratagems to navigate drug repurposing endeavours

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

NAFLDkb: A Knowledge Base and Platform for Drug Development against Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with a broad spectrum of histologic manifestations. The rapidly growing prevalence and the complex pathologic mechanisms of NAFLD pose great challenges for treatment development. Despite tremendous efforts devoted to drug development, there are no FDA-approved medicines yet. Here, we present NAFLDkb, a specialized knowledge base and platform for computer-aided drug design against NAFLD. With multiperspective information curated from diverse source materials and public databases, NAFLDkb presents the associations of drug-related entities as individual knowledge graphs. Practical drug discovery tools that facilitate the utilization and expansion of NAFLDkb have also been implemented in the web interface, including chemical structure search, drug-likeness screening, knowledge-based repositioning, and research article annotation. Moreover, case studies of a knowledge graph repositioning model and a generative neural network model are presented herein, where three repositioning drug candidates and 137 novel lead-like compounds were newly established as NAFLD pharmacotherapy options reusing data records and machine learning tools in NAFLDkb, suggesting its clinical reliability and great potential in identifying novel drug-disease associations of NAFLD and generating new insights to accelerate NAFLD drug development. NAFLDkb is freely accessible at https://www.biosino.org/nafldkb and will be updated periodically with the latest findings.

EKGDR: An End-to-End Knowledge Graph-Based Method for Computational Drug Repurposing

The lengthy and expensive process of developing new drugs from scratch, coupled with a high failure rate, has prompted the emergence of drug repurposing/repositioning as a more efficient and cost-effective approach. This approach involves identifying new therapeutic applications for existing approved drugs, leveraging the extensive drug-related data already gathered. However, the diversity and heterogeneity of data, along with the limited availability of known drug-disease interactions, pose significant challenges to computational drug design. To address these challenges, this study introduces EKGDR, an end-to-end knowledge graph-based approach for computational drug repurposing. EKGDR utilizes the power of a drug knowledge graph, a comprehensive repository of drug-related information that encompasses known drug interactions and various categorization information, as well as structural molecular descriptors of drugs. EKGDR employs graph neural networks, a cutting-edge graph representation learning technique, to embed the drug knowledge graph (nodes and relations) in an end-to-end manner. By doing so, EKGDR can effectively learn the underlying causes (intents) behind drug-disease interactions and recursively aggregate and combine relational messages between nodes along different multihop neighborhood paths (relational paths). This process generates representations of disease and drug nodes, enabling EKGDR to predict the interaction probability for each drug-disease pair in an end-to-end manner. The obtained results demonstrate that EKGDR outperforms previous models in all three evaluation metrics: area under the receiver operating characteristic curve (AUROC = 0.9475), area under the precision-recall curve (AUPRC = 0.9490), and recall at the top-200 recommendations (Recall@200 = 0.8315). To further validate EKGDR's effectiveness, we evaluated the top-20 candidate drugs suggested for each of Alzheimer's and Parkinson's diseases.

Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond

BACKGROUND

Biological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank.

RESULTS

We first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics.

CONCLUSION

Overall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.

Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity

In the early stages of drug development, large chemical libraries are typically screened to identify compounds of promising potency against the chosen targets. Often, however, the resulting hit compounds tend to have poor drug metabolism and pharmacokinetics (DMPK), with negative developability features that may be difficult to eliminate. Therefore, starting the drug discovery process with a "null library", compounds that have highly desirable DMPK properties but no potency against the chosen targets, could be advantageous. Here, we explore the opportunities offered by machine learning to realize this strategy in the case of the inhibition of α-synuclein aggregation, a process associated with Parkinson's disease. We apply MolDQN, a generative machine learning method, to build an inhibitory activity against α-synuclein aggregation into an initial inactive compound with good DMPK properties. Our results illustrate how generative modeling can be used to endow initially inert compounds with desirable developability properties.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies.

A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks

The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Networks to construct the micro-to-macro drug centric heterogeneous network (DSMN), and develop a multi-branches HyperGraph learning model, called HGDrug, for Drug multi-task predictions. HGDrug achieves highly accurate and robust predictions on 4 benchmark tasks (drug-drug, drug-target, drug-disease, and drug-side-effect interactions), outperforming 8 state-of-the-art task specific models and 6 general-purpose conventional models. Experiments analysis verifies the effectiveness and rationality of the HGDrug model architecture as well as the multi-branches setup, and demonstrates that HGDrug is able to capture the relations between drugs associated with the same functional groups. In addition, our proposed drug-substructure interaction networks can help improve the performance of existing network models for drug-related prediction tasks.

Uncovering hidden therapeutic indications through drug repurposing with graph neural networks and heterogeneous data

Drug repurposing has gained the attention of many in the recent years. The practice of repurposing existing drugs for new therapeutic uses helps to simplify the drug discovery process, which in turn reduces the costs and risks that are associated with de novo development. Representing biomedical data in the form of a graph is a simple and effective method to depict the underlying structure of the information. Using deep neural networks in combination with this data represents a promising approach to address drug repurposing. This paper presents BEHOR a more comprehensive version of the REDIRECTION model, which was previously presented. Both versions utilize the DISNET biomedical graph as the primary source of information, providing the model with extensive and intricate data to tackle the drug repurposing challenge. This new version's results for the reported metrics in the RepoDB test are 0.9604 for AUROC and 0.9518 for AUPRC. Additionally, a discussion is provided regarding some of the novel predictions to demonstrate the reliability of the model. The authors believe that BEHOR holds promise for generating drug repurposing hypotheses and could greatly benefit the field.

DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing

BACKGROUND

Drug repurposing is an approach that holds promise for identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues with constructing and embedding knowledge graphs.

RESULTS

This study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-disease knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-disease knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using the distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.63%, a BS of 0.119, and an MCC of 69.31%.

CONCLUSION

We demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing. The code and data for DrugRep-HeSiaGraph are publicly available at https://github.com/CBRC-lab/DrugRep-HeSiaGraph .

Multi-Label Classification With Dual Tail-Node Augmentation for Drug Repositioning

Due to the lengthy and costly process of new drug discovery, increasing attention has been paid to drug repositioning, i.e., identifying new drug-disease associations. Current machine learning methods for drug repositioning mainly leverage matrix factorization or graph neural networks, and have achieved impressive performance. However, they often suffer from insufficient training labels of inter-domain associations, while ignore the intra-domain associations. Moreover, they often neglect the importance of tail nodes that have few known associations, which limits their effectiveness in drug repositioning. In this paper, we propose a novel multi-label classification model with dual Tail-Node Augmentation for Drug Repositioning (TNA-DR). We incorporate disease-disease similarity and drug-drug similarity information into k-nearest neighbor ( kNN) augmentation module and contrastive augmentation module, respectively, which effectively complements the weak supervision of drug-disease associations. Furthermore, before employing the two augmentation modules, we filter the nodes by their degrees, so that the two modules are only applied to tail nodes. We conduct 10-fold cross validation experiments on four different real-world datasets, and our model achieves the state-of-the-art performance on all the four datasets. We also demonstrate our model's capability of identifying drug candidates for new diseases and discovering potential new links between existing drugs and diseases.

Unlocking therapeutic potential: integration of drug repurposing and immunotherapy for various disease targeting

Drug repurposing, also known as drug repositioning, entails the application of pre-approved or formerly assessed drugs having potentially functional therapeutic amalgams for curing various disorders or disease conditions distinctive from their original remedial indication. It has surfaced as a substitute for the development of drugs for treating cancer, cardiovascular diseases, neurodegenerative disorders, and various infectious diseases like Covid-19. Although the earlier lines of findings in this area were serendipitous, recent advancements are based on patient centered approaches following systematic, translational, drug targeting practices that explore pathophysiological ailment mechanisms. The presence of definite information and numerous records with respect to beneficial properties, harmfulness, and pharmacologic characteristics of repurposed drugs increase the chances of approval in the clinical trial stages. The last few years have showcased the successful emergence of repurposed drug immunotherapy in treating various diseases. In this light, the present review emphasises on incorporation of drug repositioning with Immunotherapy targeted for several disorders.

Clinical development of new drugs for adults and children with cancer, 2010-2020

BACKGROUND

Many new molecular entities enter clinical development to evaluate potential therapeutic benefits for oncology patients. We characterized adult and pediatric development of the set of new molecular entities that started clinical testing in 2010-2015 worldwide.

METHODS

We extracted data from AdisInsight, an extensive database of global pharmaceutical development, and the FDA.gov website. We followed the cohort of new molecular entities initiating first-in-human phase I clinical trials in 2010-2015 to the end of 2020. For each new molecular entity, we determined whether it was granted US Food and Drug Administration (FDA) approval, studied in a trial open to pediatric enrollment, or stalled during development. We characterized the cumulative incidence of these endpoints using statistical methods for censored data.

RESULTS

The 572 new molecular entities starting first-in-human studies in 2010-2015 were studied in 6142 trials by the end of 2020. Most new molecular entities were small molecules (n = 316, 55.2%), antibodies (n = 148, 25.9%), or antibody-drug conjugates (n = 44, 7.7%). After a mean follow-up of 8.0 years, 173 new molecular entities did not advance beyond first-in-human trials, and 39 were approved by the FDA. New molecular entities had a 10.4% estimated probability (95% confidence interval = 6.6% to 14.1%) of being approved by the FDA within 10 years of first-in-human trials. After a median of 4.6 years since start of first-in-human trials, 67 (11.7%) new molecular entities were tested in trials open to pediatric patients, and 5 (0.9%) were approved for pediatric indications.

CONCLUSIONS

More efficient clinical development strategies are needed to evaluate new cancer therapies, especially for children, and incorporate approaches to ensure knowledge gain from investigational products that stall in development.

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

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

Non-canonical pathways in the pathophysiology and therapeutics of bipolar disorder

Bipolar disorder (BD) is characterized by extreme mood swings ranging from manic/hypomanic to depressive episodes. The severity, duration, and frequency of these episodes can vary widely between individuals, significantly impacting quality of life. Individuals with BD spend almost half their lives experiencing mood symptoms, especially depression, as well as associated clinical dimensions such as anhedonia, fatigue, suicidality, anxiety, and neurovegetative symptoms. Persistent mood symptoms have been associated with premature mortality, accelerated aging, and elevated prevalence of treatment-resistant depression. Recent efforts have expanded our understanding of the neurobiology of BD and the downstream targets that may help track clinical outcomes and drug development. However, as a polygenic disorder, the neurobiology of BD is complex and involves biological changes in several organelles and downstream targets (pre-, post-, and extra-synaptic), including mitochondrial dysfunction, oxidative stress, altered monoaminergic and glutamatergic systems, lower neurotrophic factor levels, and changes in immune-inflammatory systems. The field has thus moved toward identifying more precise neurobiological targets that, in turn, may help develop personalized approaches and more reliable biomarkers for treatment prediction. Diverse pharmacological and non-pharmacological approaches targeting neurobiological pathways other than neurotransmission have also been tested in mood disorders. This article reviews different neurobiological targets and pathophysiological findings in non-canonical pathways in BD that may offer opportunities to support drug development and identify new, clinically relevant biological mechanisms. These include: neuroinflammation; mitochondrial function; calcium channels; oxidative stress; the glycogen synthase kinase-3 (GSK3) pathway; protein kinase C (PKC); brain-derived neurotrophic factor (BDNF); histone deacetylase (HDAC); and the purinergic signaling pathway.

HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting

Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system. The index system often organizes biomedical terms in a hierarchy to provide the aligned entities with fine-grained granularity. To address the challenge of scarce supervision in the biomedical knowledge fusion (BKF) task, researchers have proposed various unsupervised methods. However, these methods heavily rely on ad-hoc lexical and structural matching algorithms, which fail to capture the rich semantics conveyed by biomedical entities and terms. Recently, neural embedding models have proved effective in semantic-rich tasks, but they rely on sufficient labeled data to be adequately trained. To bridge the gap between the scarce-labeled BKF and neural embedding models, we propose HiPrompt, a supervision-efficient knowledge fusion framework that elicits the few-shot reasoning ability of large language models through hierarchy-oriented prompts. Empirical results on the collected KG-Hi-BKF benchmark datasets demonstrate the effectiveness of HiPrompt.

Multitask joint strategies of self-supervised representation learning on biomedical networks for drug discovery

Self-supervised representation learning (SSL) on biomedical networks provides new opportunities for drug discovery; however, effectively combining multiple SSL models is still challenging and has been rarely explored. We therefore propose multitask joint strategies of SSL on biomedical networks for drug discovery, named MSSL2drug. We design six basic SSL tasks that are inspired by the knowledge of various modalities, inlcuding structures, semantics and attributes in heterogeneous biomedical networks. Importantly, fifteen combinations of multiple tasks are evaluated using a graph-attention-based multitask adversarial learning framework in two drug discovery scenarios. The results suggest two important findings: (1) combinations of multimodal tasks achieve better performance than other multitask joint models; (2) the local–global combination models yield higher performance than random two-task combinations when there are the same number of modalities. We thus conjecture that the multimodal and local–global combination strategies can be treated as the guideline of multitask SSL for drug discovery.

DrugRep-KG: Toward Learning a Unified Latent Space for Drug Repurposing Using Knowledge Graphs

Drug repurposing or repositioning (DR) refers to finding new therapeutic applications for existing drugs. Current computational DR methods face data representation and negative data sampling challenges. Although retrospective studies attempt to operate various representations, it is a crucial step for an accurate prediction to aggregate these features and bring the associations between drugs and diseases into a unified latent space. In addition, the number of unknown associations between drugs and diseases, which is considered negative data, is much higher than the number of known associations, or positive data, leading to an imbalanced dataset. In this regard, we propose the DrugRep-KG method, which applies a knowledge graph embedding approach for representing drugs and diseases, to address these challenges. Despite the typical DR methods that consider all unknown drug-disease associations as negative data, we select a subset of unknown associations, provided the disease occurs because of an adverse reaction to a drug. DrugRep-KG has been evaluated based on different settings and achieves an AUC-ROC (area under the receiver operating characteristic curve) of 90.83% and an AUC-PR (area under the precision-recall curve) of 90.10%, which are higher than in previous works. Besides, we checked the performance of our framework in finding potential drugs for coronavirus infection and skin-related diseases: contact dermatitis and atopic eczema. DrugRep-KG predicted beclomethasone for contact dermatitis, and fluorometholone, clocortolone, fluocinonide, and beclomethasone for atopic eczema, all of which have previously been proven to be effective in other studies. Fluorometholone for contact dermatitis is a novel suggestion by DrugRep-KG that should be validated experimentally. DrugRep-KG also predicted the associations between COVID-19 and potential treatments suggested by DrugBank, in addition to new drug candidates provided with experimental evidence. The data and code underlying this article are available at https://github.com/CBRC-lab/DrugRep-KG.

Discovery of Mycobacterium tuberculosis CYP121 New Inhibitor via Structure-based Drug Repurposing

Tuberculosis (TB) remains a serious threat to human health with the advent of multi-drug resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). The urge to find novel drugs to deal with the appearance of drug-resistant TB and its variants is highly needed. This study aims to find new CYP121 inhibitors by screening 8,773 compounds from the drug repositioning database RepoDB. The selection of CYP121 potential inhibitors was based on two criteria: the new inhibitor should bind to CYP121 with higher affinity than its original ligand and interact with catalytically important residues for the function of CYP121. The ligands were docked onto CYP121 using AutoDock Vina, and the molecular dynamics simulation of the selected ligand was conducted using YASARA Structure. We found that antrafenine, an anti-inflammatory and analgesic agent with high CYP inhibitory promiscuity, was bound to CYP121 with a binding affinity of -12.6 kcal/mol and interacted with important residues at the CYP121 binding site. Molecular dynamics analysis of CYP121 bound to the original ligand and antrafenine showed that both ligands affected the dynamics of residues located distantly from the active site. Antrafenine caused more structural changes to CYP121 than the original ligand, as indicated by a significantly higher number of affected residues and rigid body movements caused by the binding of antrafenine to CYP121.

New frontier radioiodinated probe based on in silico resveratrol repositioning for microtubules dynamic targeting

PURPOSE

As the 'de novo' drug discovery faces a highly attrition rates, drug repositioning procures a heighten concern in identifying novel uses for existing medications. This study aimed to fabricate radioiodinated resveratrol as a potent microtubules interfering agent for cancer theragnosis.

METHODS

Resveratrol was radiolabeled with radioactive iodine where the radioiodination efficiency was enlightened and the computational approaches were employed to investigate the affinity and specificity with tubulins. Furthermore, the in-vivo distribution and pharmacokinetic studies in normal and tumor induced mice were investigated.

RESULTS

The maximum radioiodination yield (94.6 ± 1.66) was achieved at optimum preparation parameters stated as 100 μg/mL of oxidizing agent, 100 μg/ml of resveratrol, reaction time of 30 min and reaction pH 5. The in silico studies showed that di-iodinated resveratrol (compound 6) exhibited the best binding score (-34.46) and interaction with the β-tubulin binding site. The in vivo distribution in tumor models revealed a significant accumulation (4.02% ID/g) in tumor lesion at 60 min p.i. The rate of drug elimination demonstrated a mono-exponential decline of radioactivity versus time in the blood.

CONCLUSION

Radioiodinated resveratrol revealed good microtubules targeting which render it as a novel theranostic probe for cancer management.

Drug screening and biomarker gene investigation in cancer therapy through the human transcriptional regulatory network

A complex and vast biological network regulates all biological functions in the human body in a sophisticated manner, and abnormalities in this network can lead to disease and even cancer. The construction of a high-quality human molecular interaction network is possible with the development of experimental techniques that facilitate the interpretation of the mechanisms of drug treatment for cancer. We collected 11 molecular interaction databases based on experimental sources and constructed a human protein-protein interaction (PPI) network and a human transcriptional regulatory network (HTRN). A random walk-based graph embedding method was used to calculate the diffusion profiles of drugs and cancers, and a pipeline was constructed by using five similarity comparison metrics combined with a rank aggregation algorithm, which can be implemented for drug screening and biomarker gene prediction. Taking NSCLC as an example, curcumin was identified as a potentially promising anticancer drug from 5450 natural small molecules, and combined with differentially expressed genes, survival analysis, and topological ranking, we obtained BIRC5 (survivin), which is both a biomarker for NSCLC and a key target for curcumin. Finally, the binding mode of curcumin and survivin was explored using molecular docking. This work has a guiding significance for antitumor drug screening and the identification of tumor markers.

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.