Network-Based Inference Methods for Drug Repositioning

Chen, Hailin; Zhang, Heng; Zhang, Zuping; Cao, Yiqin; Wenliang, Tang

doi:10.1155/2015/130620

Cited by 67 publications

(43 citation statements)

References 36 publications

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“…In this paper, we employed the random walk with restart on drug-protein bipartite network to predict drugs with lifespan-extending effects on C.elegans . In fact, label propagation on bipartite networks has been widely used in various fields, such as drug repositioning [53], personal recommendation [54] and political polarity classification [55]. …”

Section: Discussionmentioning

confidence: 99%

Screening lifespan-extending drugs in Caenorhabditis elegans via label propagation on drug-protein networks

et al. 2016

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BackgroundOne of the most challenging tasks in the exploration of anti-aging is to discover drugs that can promote longevity and delay the incidence of age-associated diseases of human. Up to date, a number of drugs, including some antioxidants, metabolites and synthetic compounds, have been found to effectively delay the aging of nematodes and insects.ResultsWe proposed a label propagation algorithm on drug-protein network to infer drugs that can extend the lifespan of C. elegans. We collected a set of drugs of which functions on lifespan extension of C. elegans have been reliably determined, and then built a large-scale drug-protein network by collecting a set of high-confidence drugprotein interactions. A label propagation algorithm was run on the drug-protein bipartite network to predict new drugs with lifespan-extending effect on C. elegans. We calibrated the performance of the proposed method by conducting performance comparison with two classical models, kNN and SVM. We also showed that the screened drugs significantly mediate in the aging-related pathways, and have higher chemical similarities to the effective drugs than ineffective drugs in promoting longevity of C. elegans. Moreover, we carried out wet-lab experiments to verify a screened drugs, 2- Bromo-4’-nitroacetophenone, and found that it can effectively extend the lifespan of C. elegans. These results showed that our method is effective in screening lifespanextending drugs in C. elegans.ConclusionsIn this paper, we proposed a semi-supervised algorithm to predict drugs with lifespan-extending effects on C. elegans. In silico empirical evaluations and in vivo experiments in C. elegans have demonstrated that our method can effectively narrow down the scope of candidate drugs needed to be verified by wet lab experiments.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0362-4) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Screening lifespan-extending drugs in Caenorhabditis elegans via label propagation on drug-protein networks

et al. 2016

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“…The large data about drug-disease, gene expression (microarrays) or protein-protein interactions or gene-protein interactions or signaling pathway mapping, signature matching, genome-wide association studies (GWAS) can be used for providing drug-disease gene association networking by the systematic integration and coordination of computation and bioinformatics, modeling (such as docking for structural modeling), experimentation, statistics, machine learning etc. [46][47][48][49]. The various data resources are available that can be used for bio-informatics based exploration followed by experimental validation for any drug repurposing possibilities in any disease.…”

Section: Strategies For Drug Repositioningmentioning

confidence: 99%

Drug repurposing for breast cancer therapy: Old weapon for new battle

Aggarwal

Verma

Aggarwal

et al. 2021

Seminars in Cancer Biology

101

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Despite tremendous resources being invested in prevention and treatment, breast cancer remains a leading cause of cancer deaths in women globally. The available treatment modalities are very costly and produces severe side effects. Drug repurposing that relate to new uses for old drugs has emerged as a novel approach for drug development. Repositioning of old, clinically approved, off patent non-cancer drugs with known targets, into newer indication is like using old weapons for new battle. The advances in genomics, proteomics and information computational biology has facilitated the process of drug repurposing. Repositioning approach not only fastens the process of drug development but also offers more effective, cheaper, safer drugs with lesser/known side effects. During the last decade, drugs such as alkylating agents, anthracyclins, antimetabolite, CDK4/6 inhibitor, aromatase inhibitor, mTOR inhibitor and mitotic inhibitors has been repositioned for breast cancer treatment. The repositioned drugs have been successfully used for the treatment of most aggressive triple negative breast cancer. The literature review suggest that serendipity plays a major role in the drug development. This article describes the comprehensive overview of the current scenario of drug repurposing for the breast cancer treatment. The strategies as well as several examples of repurposed drugs are provided. The challenges associated with drug repurposing are discussed. Current breast cancer therapyUnlike a decade ago, clinicians today have multiple choices for breast cancer treatment depending on the size, stage, grade, metastatic behavior, aggressiveness and intrinsic molecular subtyping of tumor, age, menopausal status, overall health, comorbidities, and preferences of the patient [9-13]. Chemotherapy, hormone therapy, immunotherapy, radiotherapy, and surgery are the common modalities for breast cancer [10,14]. Primary choice of treatment usually includes surgery with the aim of complete resection of the major tumor mass on the first hand. Breast conserving (lumpectomy) and breast reconstruction surgeries, mastectomy or lymph node dissections are performed initially in the breast cancer patients [15]. Surgery may be preceded with the systemic neoadjuvant therapies in order to shrink the tumor for effective surgery and to maximize breast conservation. For example, in HER2+ cases, Trastuzumab (Herceptin) and Pertuzumab (Perjeta)

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“…However, the research and development of anticancer drugs are time-consuming and costly tasks. In recent years, many researchers and pharmaceutical enterprises turned their attentions to finding new medical indications for approved drugs [4], referred to as drug positioning or drug repurposing, because it provides a relatively low-cost and high-efficiency approach for drug discovery [5]. Nevertheless, most successfully repositioned drugs up to date have been the consequence of incidental observations of unexpected efficacy and side effects of the drugs in development or on the market [6].…”

Section: Introductionmentioning

confidence: 99%

“…Martinez et al have proposed a network-based prioritization method, DrugNet, which integrated the information of diseases, drugs and targets to perform drug-disease and disease-drug prioritization simultaneously [15]. Chen et al formulated the drug-disease association prediction problem as recommending preferable diseases for drugs so that two existing recommendation methods, ProbS and HeatS, were used to infer drug-disease associations [4]. Yu et al used protein complexes as an intermediate bridge to construct a tripartite network consisting of drugs, protein complexes, and disease, on which the likelihood probabilities of drug-disease associations were inferred [16].…”

Section: Introductionmentioning

confidence: 99%

Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks

et al. 2016

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BackgroundSince traditional drug research and development is often time-consuming and high-risk, there is an increasing interest in establishing new medical indications for approved drugs, referred to as drug repositioning, which provides a relatively low-cost and high-efficiency approach for drug discovery. With the explosive growth of large-scale biochemical and phenotypic data, drug repositioning holds great potential for precision medicine in the post-genomic era. It is urgent to develop rational and systematic approaches to predict new indications for approved drugs on a large scale.ResultsIn this paper, we propose the two-pass random walks with restart on a heterogenous network, TP-NRWRH for short, to predict new indications for approved drugs. Rather than random walk on bipartite network, we integrated the drug-drug similarity network, disease-disease similarity network and known drug-disease association network into one heterogenous network, on which the two-pass random walks with restart is implemented. We have conducted performance evaluation on two datasets of drug-disease associations, and the results show that our method has higher performance than six existing methods. A case study on the Alzheimer’s disease showed that nine of top 10 predicted drugs have been approved or investigational for neurodegenerative diseases. The experimental results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.ConclusionsWe proposed a two-pass random walk with restart on the drug-disease heterogeneous network, referred to as TP-NRWRH, to predict new indications for approved drugs. Performance evaluation on two independent datasets showed that TP-NRWRH achieved higher performance than six existing methods on 10-fold cross validations. The case study on the Alzheimer’s disease showed that nine of top 10 predicted drugs have been approved or are investigational for neurodegenerative diseases. The results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.

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Network-Based Inference Methods for Drug Repositioning

Cited by 67 publications

References 36 publications

Screening lifespan-extending drugs in Caenorhabditis elegans via label propagation on drug-protein networks

Screening lifespan-extending drugs in Caenorhabditis elegans via label propagation on drug-protein networks

Drug repurposing for breast cancer therapy: Old weapon for new battle

Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks

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