An unsupervised computational pipeline identifies potential repurposable drugs to treat Huntington's disease and multiple sclerosis

Menestrina, Luca; Recanatini, Maurizio

doi:10.1016/j.ailsci.2022.100042

Cited by 1 publication

(1 citation statement)

References 64 publications

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“…Following the approach proposed and validated in the two just mentioned articles, the concept of drug–disease proximity has been widely applied to prioritize potentially repurposable drugs in several therapeutic areas, like, for example, cancers (Cheng et al, 2019), COVID‐19 (Gysi et al, 2021; Patten et al, 2022; Zhou, Hou, Shen, Huang, et al, 2020; Zhou, Hou, Shen, Mehra, et al, 2020), and neurological diseases (Fang et al, 2021; Menestrina & Recanatini, 2022; Peng et al, 2020), as it allows to quantitatively rank drugs on the basis of the distance of their known targets from the disease module on the human protein–protein interactome.…”

Section: Predictive Network Modelsmentioning

confidence: 99%

Network modeling helps to tackle the complexity of drug–disease systems

Recanatini

Menestrina

2023

WIREs Mechanisms of Disease

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From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug–disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of “disease module”, as well as the introduction of more articulated models, like multi‐scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug–disease systems highlighting some open issues. This article is categorized under: Neurological Diseases > Computational Models Infectious Diseases > Computational Models Cardiovascular Diseases > Computational Models

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