Viral pandemic preparedness: A pluripotent stem cell-based machine-learning platform for simulating SARS-CoV-2 infection to enable drug discovery and repurposing

Autor: Sally Esmail, Wayne R. Danter
Rok vydání: 2020
Předmět:
Pluripotent Stem Cells
0301 basic medicine
Drug
Drug Industry
Genotype
media_common.quotation_subject
drug discovery and repurposing
Genome
Viral
Computational biology
unsupervised learning
SARS‐CoV‐2
Disease Outbreaks
Machine Learning
03 medical and health sciences
0302 clinical medicine
Drug Discovery
Pandemic
Humans
Medicine
Computer Simulation
lcsh:QH573-671
Induced pluripotent stem cell
Lung
Pandemics
Repurposing
DeepNEU
media_common
lcsh:R5-920
Enabling Technologies for Cell‐Based Clinical Translation
lcsh:Cytology
SARS-CoV-2
business.industry
Drug discovery
Drug Repositioning
Cell Biology
General Medicine
antiviral
COVID-19 Drug Treatment
Clinical trial
Drug repositioning
030104 developmental biology
Alveolar Epithelial Cells
Preparedness
pandemic preparedness
lcsh:Medicine (General)
business
030217 neurology & neurosurgery
Developmental Biology
Zdroj: STEM CELLS Translational Medicine
Stem Cells Translational Medicine
Stem Cells Translational Medicine, Vol 10, Iss 2, Pp 239-250 (2021)
ISSN: 2157-6580
2157-6564
Popis: Infection with the SARS‐CoV‐2 virus has rapidly become a global pandemic for which we were not prepared. Several clinical trials using previously approved drugs and drug combinations are urgently underway to improve our current situation. Unfortunately, a vaccine option is optimistically at least a year away. It is imperative that for future viral pandemic preparedness, we have a rapid screening technology for drug discovery and repurposing. The primary purpose of this research project was to evaluate the DeepNEU stem‐cell based platform by creating and validating computer simulations of artificial lung cells infected with SARS‐CoV‐2 to enable the rapid identification of antiviral therapeutic targets and drug repurposing. The data generated from this project indicate that (a) human alveolar type lung cells can be simulated by DeepNEU (v5.0), (b) these simulated cells can then be infected with simulated SARS‐CoV‐2 virus, (c) the unsupervised learning system performed well in all simulations based on available published wet lab data, and (d) the platform identified potentially effective anti‐SARS‐CoV2 combinations of known drugs for urgent clinical study. The data also suggest that DeepNEU can identify potential therapeutic targets for expedited vaccine development. We conclude that based on published data plus current DeepNEU results, continued development of the DeepNEU platform will improve our preparedness for and response to future viral outbreaks. This can be achieved through rapid identification of potential therapeutic options for clinical testing as soon as the viral genome has been confirmed.
DeepNEU is a machine‐learning platform that uses genomic data to simulate induced pluripotent stem cells (aiPSCs) and differentiated cell types like lung cells. In this study, simulated lung cells (aiLUNG) were infected with simulated SARS‐CoV‐2 virus and used for rapid identification of antiviral targets and drug repurposing. Potentially effective anti‐SARS‐CoV‐2 two drug combinations were identified for urgent clinical studies.
Databáze: OpenAIRE
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