Drug Repositioning to Accelerate Drug Development Using Social Media Data: Computational Study on Parkinson Disease

Zhao, Mengnan; Yang, Christopher C.

doi:10.2196/jmir.9646

Cited by 14 publications

(11 citation statements)

References 49 publications

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“…The use of AI is prevalent in drug discovery, and many pharmaceutical corporations have established in-house partnerships or initiatives with AI companies. Some organizations are currently using AI approaches to find novel uses for late-stage drug candidates or repurpose existing drugs [ 34 , 35 ]. However, data of sufficient quality are needed to train systems for COVID-19 AI implementation.…”

Section: Resultsmentioning

confidence: 99%

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Adly¹,

Adly²,

Adly³

2020

J Med Internet Res

122

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Background Artificial intelligence (AI) and the Internet of Intelligent Things (IIoT) are promising technologies to prevent the concerningly rapid spread of coronavirus disease (COVID-19) and to maximize safety during the pandemic. With the exponential increase in the number of COVID-19 patients, it is highly possible that physicians and health care workers will not be able to treat all cases. Thus, computer scientists can contribute to the fight against COVID-19 by introducing more intelligent solutions to achieve rapid control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease. Objective The objectives of this review were to analyze the current literature, discuss the applicability of reported ideas for using AI to prevent and control COVID-19, and build a comprehensive view of how current systems may be useful in particular areas. This may be of great help to many health care administrators, computer scientists, and policy makers worldwide. Methods We conducted an electronic search of articles in the MEDLINE, Google Scholar, Embase, and Web of Knowledge databases to formulate a comprehensive review that summarizes different categories of the most recently reported AI-based approaches to prevent and control the spread of COVID-19. Results Our search identified the 10 most recent AI approaches that were suggested to provide the best solutions for maximizing safety and preventing the spread of COVID-19. These approaches included detection of suspected cases, large-scale screening, monitoring, interactions with experimental therapies, pneumonia screening, use of the IIoT for data and information gathering and integration, resource allocation, predictions, modeling and simulation, and robotics for medical quarantine. Conclusions We found few or almost no studies regarding the use of AI to examine COVID-19 interactions with experimental therapies, the use of AI for resource allocation to COVID-19 patients, or the use of AI and the IIoT for COVID-19 data and information gathering/integration. Moreover, the adoption of other approaches, including use of AI for COVID-19 prediction, use of AI for COVID-19 modeling and simulation, and use of AI robotics for medical quarantine, should be further emphasized by researchers because these important approaches lack sufficient numbers of studies. Therefore, we recommend that computer scientists focus on these approaches, which are still not being adequately addressed.

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Section: Resultsmentioning

confidence: 99%

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Adly¹,

Adly²,

Adly³

2020

J Med Internet Res

122

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“…Furthermore, of all the PD connections, 300 have a discovered drug therapy, and some of these drug therapies have previously been shown to be applicable for PD. For example Zhao et al 46 showed the drug repurposing potential between PD and Asthma, Alzheimer disease, Depression, Epilepsy, Multiple scleroris, Fatigue and Immunological disorders. Of these, Multiple sclerosis, immunological disorders and eplipesy were all part of the pairings discovered by our method too, giving further justifications for the domain-based pairings resulting in drug repurposing.…”

Section: Discussionmentioning

confidence: 99%

DomainScope: A disease network based on protein domain connections

Voskanian-kordi¹,

Funai²,

Kann

2019

Preprint

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Protein domains are highly conserved functional units of proteins. Because they carry functionally significant information, the majority of the coding disease variants are located on domains. Additionally, domains are specific units of the proteins that can be targeted for drug delivery purposes. Here, using information about variants sites associated with diseases, a disease network was built, based on their sharing the same domain and domain variation site. The result was 49,990 disease pairs linked by domain variant site and 533,687 disease pairs that share the same mutated domain. These pairs were compared to disease pairs made using previous methods such as gene identity and gene variant site identity, which revealed that over 8,000 of these pairs were not only missing from the gene pairings but also not found commonly together in literature. The disease network was analyzed from their disease subject categories, which when compared to the gene-based disease network revealed that the domain method results in higher number of connections across disease categories versus within a disease category. Further, a study into the drug repurposing possibilities of the disease network created using domain revealed that 16,902 of the disease pairs had a drug reported for one disease but not the other, highlighting the drug repurposing potential of this new methodology.

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“…Another recently reported application of social media was a move for drug repositioning to accelerate drug development in Parkinson disease where, without social media, such development would likely be delayed. 19…”

Section: Education and Researchmentioning

confidence: 99%

Use of Social Media in Health Care—Opportunities, Challenges, and Ethical Considerations

et al. 2021

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The American Academy of Neurology (AAN) Code of Professional Conduct (CPC) 1 formalizes the standards of professional behavior for AAN members. The CPC provides the ethical principles and professional standards that neurologists and other neuroscience professionals should or must observe in their clinical and scientific activities. While ethical principles do not change with time, developments in technology and social structure may lead to a change in how these principles are applied. One such technological development is social media-a class of communication tools that has rapidly grown in number and popularity in recent years. The AAN developed this position statement to review in-depth how social media use has transformed clinical practice, with a particular focus on neurologic practice, and, by exploring the relevant principles in the CPC, 1 to provide an ethical framework for neurology professionals and trainees to consider when engaging in social media. This statement complements the CPC; it is not a replacement for the AAN Web site's Code of Conduct, Privacy Policy, or Terms of Service.

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Drug Repositioning to Accelerate Drug Development Using Social Media Data: Computational Study on Parkinson Disease

Cited by 14 publications

References 49 publications

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

DomainScope: A disease network based on protein domain connections

Use of Social Media in Health Care—Opportunities, Challenges, and Ethical Considerations

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