Identification of potential pan-coronavirus therapies using a computational drug repurposing platform

Hwang, Woochang; Han, Namshik

doi:10.1016/j.ymeth.2021.11.002

Methods

2022

DOI: 10.1016/j.ymeth.2021.11.002

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Identification of potential pan-coronavirus therapies using a computational drug repurposing platform

Woochang Hwang

Namshik Han

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2024

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Cited by 3 publications

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“…In the third category, drug repurposing studies have been highlighted in the works of Hwang et al [6] , Lazniewski et al [7] , and Saha et al [8] . Hwang et al [6] developed a computational drug repurposing platform to detect unique therapies for patients infected with coronaviruses. It utilizes virus-host interactions and differential proteins expressed 24 hours after infection to be known from wet lab data.…”

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confidence: 99%

Computational methods and strategies for combating COVID-19

Basu

Plewczyński²

2022

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mentioning

confidence: 99%

Computational methods and strategies for combating COVID-19

Basu

Plewczyński²

2022

Methods

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Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities

Cousins,

Nayar,

Altman

2024

Annual Review of Biomedical Data Science

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Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

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Shared genetics between breast cancer and predisposing diseases identifies novel breast cancer treatment candidates

Lalagkas,

Melamed

2024

Hum Genomics

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Background Current effective breast cancer treatment options have severe side effects, highlighting a need for new therapies. Drug repurposing can accelerate improvements to care, as FDA-approved drugs have known safety and pharmacological profiles. Some drugs for other conditions, such as metformin, an antidiabetic, have been tested in clinical trials for repurposing for breast cancer. Here, we exploit the genetics of breast cancer and linked predisposing diseases to propose novel drug repurposing opportunities. We hypothesize that if a predisposing disease contributes to breast cancer pathology, identifying the pleiotropic genes related to the risk of cancer could prioritize drugs, among all drugs treating a predisposing disease. We aim to develop a method to not only prioritize drugs for repurposing, but also to highlight shared etiology explaining repurposing. Methods We compile breast cancer’s predisposing diseases from literature. For each predisposing disease, we use GWAS summary statistics data to identify genes in loci showing genetic correlation with breast cancer. Then, we use a network approach to link these shared genes to canonical pathways. Similarly, for all drugs treating the predisposing disease, we link their targets to pathways. In this manner, we are able to prioritize a list of drugs based on each predisposing disease, with each drug linked to a set of implicating pathways. Finally, we evaluate our recommendations against drugs currently under investigation for breast cancer. Results We identify 84 loci harboring mutations with positively correlated effects between breast cancer and its predisposing diseases; these contain 194 identified shared genes. Out of the 112 drugs indicated for the predisposing diseases, 74 drugs can be linked to shared genes via pathways (candidate drugs for repurposing). Fifteen out of these candidate drugs are already in advanced clinical trial phases or approved for breast cancer (OR = 9.28, p = 7.99e-03, one-sided Fisher’s exact test), highlighting the ability of our approach to identify likely successful candidate drugs for repurposing. Conclusions Our novel approach accelerates drug repurposing for breast cancer by leveraging shared genetics with its known predisposing diseases. The result provides 59 novel candidate drugs alongside biological insights supporting each recommendation.

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Identification of potential pan-coronavirus therapies using a computational drug repurposing platform

Cited by 3 publications

References 42 publications

Computational methods and strategies for combating COVID-19

Computational methods and strategies for combating COVID-19

Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities

Shared genetics between breast cancer and predisposing diseases identifies novel breast cancer treatment candidates

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