Drug Repurposing for Newly Emerged Diseases via Network‐based Inference on a Gene‐disease‐drug Network

Qin, Li; Wang, Jiye; Wu, Zengrui; Li, Weihua; Liu, Guixia; Tang, Yun

doi:10.1002/minf.202200001

Cited by 4 publications

(6 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…111 In 2022, we proposed a new method, 109 named NEDNBI (newly emerged disease via network-based inference), and applied it in the gene−disease−drug heterogeneous network. 109 Then, 8 of 20 predicted drug candidates had been tested in the clinical trials, and two drugs, methylprednisolone and dexamethasone, were unapproved treatments for COVID-19.…”

Section: Prediction and Elucidation Of Mechanisms Of Therapeutic Effe...mentioning

confidence: 99%

“…Three of them, Ataluren, Carfilzomib, and Aripiprazole, were significantly enriched in the immune response pathways, suggesting their potentials as promising inhibitors for COVID-19 . Moreover, several network-based methods, such as NBI and Path-based methods, were also applied in the drug repositioning for various diseases, including cancer, COVID-19, type 2 diabetes, and Alzheimer’s disease . In 2022, we proposed a new method, named NEDNBI (newly emerged disease via network-based inference), and applied it in the gene–disease–drug heterogeneous network .…”

Section: Applications In Drug Discoverymentioning

confidence: 99%

“…107 Moreover, several network-based methods, such as NBI and Path-based methods, were also applied in the drug repositioning for various diseases, including cancer, 108 COVID-19, 109 type 2 diabetes, 110 and Alzheimer's disease. 111 In 2022, we proposed a new method, 109 named NEDNBI (newly emerged disease via network-based inference), and applied it in the gene−disease−drug heterogeneous network. 109 Then, 8 of 20 predicted drug candidates had been tested in the clinical trials, and two drugs, methylprednisolone and dexamethasone, were unapproved treatments for COVID-19.…”

Section: Prediction and Elucidation Of Mechanisms Of Therapeutic Effe...mentioning

confidence: 99%

See 2 more Smart Citations

Network-Based Methods and Their Applications in Drug Discovery

Yu,

Wu,

Wang

et al. 2023

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

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 networkbased 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.

show abstract

Section: Prediction and Elucidation Of Mechanisms Of Therapeutic Effe...mentioning

confidence: 99%

Section: Applications In Drug Discoverymentioning

confidence: 99%

Section: Prediction and Elucidation Of Mechanisms Of Therapeutic Effe...mentioning

confidence: 99%

See 1 more Smart Citation

Network-Based Methods and Their Applications in Drug Discovery

Yu,

Wu,

Wang

et al. 2023

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Building on the combined similarity measures, a new bi-random walk algorithm called MBiRW was developed to infer potential associations between drugs and diseases. Qin et al (2022) proposed a network-based inference model for new emerging diseases, which used genes as a bridge in a tripartite drug–gene–disease network to infer latent drug–disease associations. Additionally, to account for the structures of networks and the biological aspects related to drugs and indications, Zhao et al (2022) presented a novel graph representation model based on heterogeneous networks, namely, HINGRL.…”

Section: Introductionmentioning

confidence: 99%

ITRPCA: a new model for computational drug repositioning based on improved tensor robust principal component analysis

Yang,

Duan

et al. 2023

Front. Genet.

View full text Add to dashboard Cite

Background: Drug repositioning is considered a promising drug development strategy with the goal of discovering new uses for existing drugs. Compared with the experimental screening for drug discovery, computational drug repositioning offers lower cost and higher efficiency and, hence, has become a hot issue in bioinformatics. However, there are sparse samples, multi-source information, and even some noises, which makes it difficult to accurately identify potential drug-associated indications.Methods: In this article, we propose a new scheme with improved tensor robust principal component analysis (ITRPCA) in multi-source data to predict promising drug–disease associations. First, we use a weighted k-nearest neighbor (WKNN) approach to increase the overall density of the drug–disease association matrix that will assist in prediction. Second, a drug tensor with five frontal slices and a disease tensor with two frontal slices are constructed using multi-similarity matrices and an updated association matrix. The two target tensors naturally integrate multiple sources of data from the drug-side aspect and the disease-side aspect, respectively. Third, ITRPCA is employed to isolate the low-rank tensor and noise information in the tensor. In this step, an additional range constraint is incorporated to ensure that all the predicted entry values of a low-rank tensor are within the specific interval. Finally, we focus on identifying promising drug indications by analyzing drug–disease association pairs derived from the low-rank drug and low-rank disease tensors.Results: We evaluate the effectiveness of the ITRPCA method by comparing it with five prominent existing drug repositioning methods. This evaluation is carried out using 10-fold cross-validation and independent testing experiments. Our numerical results show that ITRPCA not only yields higher prediction accuracy but also exhibits remarkable computational efficiency. Furthermore, case studies demonstrate the practical effectiveness of our method.

show abstract

“…2016 computer simulations identified saracatinib; 2017 clinical trials began. This study evaluates the impact of pharmacological repurposing on AD staging using a networkbased approach 86. In 2014−2019, saracatinib was studied for mild Alzheimer's; after 52 weeks, researchers tested glucose metabolism, cognition, and brain growth.…”

mentioning

confidence: 99%

Drug Repurposing: A Leading Strategy for New Threats and Targets

Mishra,

Vasanthan,

Malliappan

2024

ACS Pharmacol. Transl. Sci.

View full text Add to dashboard Cite

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.

show abstract

Drug Repurposing for Newly Emerged Diseases via Network‐based Inference on a Gene‐disease‐drug Network

Cited by 4 publications

References 56 publications

Network-Based Methods and Their Applications in Drug Discovery

Network-Based Methods and Their Applications in Drug Discovery

ITRPCA: a new model for computational drug repositioning based on improved tensor robust principal component analysis

Drug Repurposing: A Leading Strategy for New Threats and Targets

Contact Info

Product

Resources

About