Network-Based Drug Repositioning: Approaches, Resources, and Research Directions

Alaimo, Salvatore; Pulvirenti, Alfredo

doi:10.1007/978-1-4939-8955-3_6

Cited by 48 publications

(35 citation statements)

References 65 publications

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“…Thus, which clinical trials to run could potentially be driven by this biomedical insight from ML conducted on existing data. An existing approach for doing this is knowledge graph inference, where a vast network of existing, interrelated data is formed, and ML is used to reason over this network, extracting new insights which would not be possible from looking at individual datasets on their own (Alaimo and Pulvirenti 2018). As more data are accumulated the graph can continually be built out.…”

Section: Drug Repurposing Trialsmentioning

confidence: 99%

Machine learning for clinical trials in the era of COVID-19

Zame

Bica

Shen

et al. 2020

Statistics in Biopharmaceutical Research

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The world is in the midst of a pandemic. We still know little about the disease COVID-19 or about the virus (SARS-CoV-2) that causes it. We do not have a vaccine or a treatment (aside from managing symptoms). We do not know if recovery from COVID-19 produces immunity, and if so for how long, hence we do not know if "herd immunity" will eventually reduce the risk or if a successful vaccine can be developedand this knowledge may be a long time coming. In the meantime, the COVID-19 pandemic is presenting enormous challenges to medical research, and to clinical trials in particular. This article identifies some of those challenges and suggests ways in which machine learning (ML) can help in response to those challenges. We identify three areas of challenge: ongoing clinical trials for non-COVID-19 drugs, clinical trials for repurposing drugs to treat COVID-19, and clinical trials for new drugs to treat COVID-19. Within each of these areas, we identify aspects for which we believe ML can provide invaluable assistance.

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Section: Drug Repurposing Trialsmentioning

confidence: 99%

Machine learning for clinical trials in the era of COVID-19

Zame

Bica

Shen

et al. 2020

Statistics in Biopharmaceutical Research

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“…Reversed expression pattern between drugs and diseases A drug with expression profile opposite to that of a disease are candidate therapeutic agents Considers data across many genes instead of the most significant ones; imputed expression readily available for many tissues and from large GWAS samples, and less susceptible to confounding and reverse causality; understanding of drug mechanism not required; relatively better at uncovering drugs of novel mechanism Expression reversal may not be the only drug mechanism; limitation of cell lines (cannot fully model human conditions); imputation accuracy of some genes may be poor [80] , [81] 5. Network-based methods Integration of multiple sources of data regarding drugs, proteins, genes and diseases relationships to reveal novel drug-disease connections Flexible; ability to integrate multiple sources of data; well established network analysis methods from other fields Integrating data with different nature and potentially different kinds of bias is difficult; complicated parameter optimization; difficulty in determining edge strength; relatively less capable of revealing unexpected repositioning candidates [82] , [83] , [84] , [85] , [86] …”

Section: Approaches To Computational Drug Repositioning Using Gwasmentioning

confidence: 99%

“…This approach is a popular and well-established drug repositioning technique, which offers high flexibility, as it allows for consideration of multiple dimensions of data sources. The types of biological networks that are useful for drug repositioning include, for example, gene regulatory, gene-gene interaction, metabolic, drug-target interaction (DTI) and protein–protein interaction (PPI) [84] networks. It is preferable to integrate multiple biological networks in order to reduce noise and improve biological relevance [83] , [85] .…”

Section: Approaches To Computational Drug Repositioning Using Gwasmentioning

confidence: 99%

Turning genome-wide association study findings into opportunities for drug repositioning

Lau

2020

Computational and Structural Biotechnology Journal

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“…In addition to the previous strategies, there are other repositioning approaches based on several molecular networks. However, they show limited applicability [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

et al. 2020

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Background It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. Methods In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

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Network-Based Drug Repositioning: Approaches, Resources, and Research Directions

Cited by 48 publications

References 65 publications

Machine learning for clinical trials in the era of COVID-19

Machine learning for clinical trials in the era of COVID-19

Turning genome-wide association study findings into opportunities for drug repositioning

RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

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