Machine learning and network medicine approaches for drug repositioning for COVID-19

Santos, Suzana de Siqueira; Torres, Mateo; Galeano, Diego; Sánchez, María del Mar; Cernuzzi, Luca; Paccanaro, Alberto

doi:10.1016/j.patter.2021.100396

Cited by 27 publications

(18 citation statements)

References 73 publications

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“…Recent artificial intelligence-based drug discovery and drug repositioning techniques can dramatically shorten the preclinical research period and cost while increasing the possibility of treating various neurological diseases that were not previously available [ 88 , 89 ]. Several online tools have been developed and are available for free to detect repurposing drugs according to gene expression profiles [ 23 , 90 , 91 ]. Among them, the Drug Repurposing Hub, which was used for the detection of potential drugs for SCI at each time point in this study, has the largest number of compounds in neurology and psychiatry [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

The Time Sequence of Gene Expression Changes after Spinal Cord Injury

Mun

Han

Hyun

2022

Cells

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Gene expression changes following spinal cord injury (SCI) are time-dependent, and an accurate understanding of these changes can be crucial in determining time-based treatment options in a clinical setting. We performed RNA sequencing of the contused spinal cord of rats at five different time points from the very acute to chronic stages (1 hour, 1 day, 1 week, 1 month, and 3 months) following SCI. We identified differentially expressed genes (DEGs) and Gene Ontology (GO) terms at each time point, and 14,257 genes were commonly expressed at all time points. The biological process of the inflammatory response was increased at 1 hour and 1 day, and the cellular component of the integral component of the synaptic membrane was increased at 1 day. DEGs associated with cell activation and the innate immune response were highly enriched at 1 week and 1 month, respectively. A total of 2841 DEGs were differentially expressed at any of the five time points, and 18 genes (17 upregulated and 1 downregulated) showed common expression differences at all time points. We found that interleukin signaling, neutrophil degranulation, eukaryotic translation, collagen degradation, LGI–ADAM interactions, GABA receptor, and L1CAM-ankyrin interactions were prominent after SCI depending on the time post injury. We also performed gene–drug network analysis and found several potential antagonists and agonists which can be used to treat SCI. We expect to discover effective treatments in the clinical field through further studies revealing the efficacy and safety of potential drugs.

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Section: Discussionmentioning

confidence: 99%

The Time Sequence of Gene Expression Changes after Spinal Cord Injury

Mun

Han

Hyun

2022

Cells

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“…A combination of different techniques can provide constructive insight, as shown by De Siqueira Santos and coworkers [ 91 ], who exploited machine learning and network medicine as complementary approaches for drug repositioning for COVID-19. For the first purpose, the group developed a matrix decomposition algorithm to rank broad-spectrum antivirals and predict effective drug–virus relationships.…”

Section: Artificial Intelligence and Network Medicine For A Therapy T...mentioning

confidence: 99%

Looking at COVID-19 from a Systems Biology Perspective

2022

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The sudden outbreak and worldwide spread of the SARS-CoV-2 pandemic pushed the scientific community to find fast solutions to cope with the health emergency. COVID-19 complexity, in terms of clinical outcomes, severity, and response to therapy suggested the use of multifactorial strategies, characteristic of the network medicine, to approach the study of the pathobiology. Proteomics and interactomics especially allow to generate datasets that, reduced and represented in the forms of networks, can be analyzed with the tools of systems biology to unveil specific pathways central to virus–human host interaction. Moreover, artificial intelligence tools can be implemented for the identification of druggable targets and drug repurposing. In this review article, we provide an overview of the results obtained so far, from a systems biology perspective, in the understanding of COVID-19 pathobiology and virus–host interactions, and in the development of disease classifiers and tools for drug repurposing.

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“…Employing these methods within data science should increase confidence and accuracy of predictions, allowing AI to be incorporated into healthcare. Santos et al., 2022, “Machine learning and network medicine approaches for drug repositioning for COVID-19.” 4 I wished to highlight this paper owing to its relevance of drug repurposing in the age of a global pandemic. It seems critical to be able to develop tools that are capable for screening current antivirals, in the context of virus-specific proteins, and identify those that may be effective, subsequently, greatly reducing drug development time.…”

Section: Main Textmentioning

confidence: 99%

Navigating an early career in genomics and data science, written from the perspective of a current PhD student

Tooze

2022

Patterns

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Machine learning and network medicine approaches for drug repositioning for COVID-19

Cited by 27 publications

References 73 publications

The Time Sequence of Gene Expression Changes after Spinal Cord Injury

The Time Sequence of Gene Expression Changes after Spinal Cord Injury

Looking at COVID-19 from a Systems Biology Perspective

Navigating an early career in genomics and data science, written from the perspective of a current PhD student

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