Title |
Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
|
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Published in |
Nature Communications, February 2021
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DOI | 10.1038/s41467-021-21330-0 |
Pubmed ID | |
Authors |
Steve Rodriguez, Clemens Hug, Petar Todorov, Nienke Moret, Sarah A. Boswell, Kyle Evans, George Zhou, Nathan T. Johnson, Bradley T. Hyman, Peter K. Sorger, Mark W. Albers, Artem Sokolov |
Abstract |
Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 20 | 21% |
United Kingdom | 5 | 5% |
Spain | 3 | 3% |
India | 3 | 3% |
Japan | 3 | 3% |
France | 3 | 3% |
Switzerland | 3 | 3% |
United Arab Emirates | 2 | 2% |
Italy | 2 | 2% |
Other | 7 | 7% |
Unknown | 43 | 46% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 68 | 72% |
Scientists | 13 | 14% |
Science communicators (journalists, bloggers, editors) | 7 | 7% |
Practitioners (doctors, other healthcare professionals) | 6 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 301 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 53 | 18% |
Researcher | 48 | 16% |
Student > Master | 18 | 6% |
Student > Bachelor | 18 | 6% |
Professor | 15 | 5% |
Other | 34 | 11% |
Unknown | 115 | 38% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 34 | 11% |
Neuroscience | 23 | 8% |
Pharmacology, Toxicology and Pharmaceutical Science | 22 | 7% |
Computer Science | 19 | 6% |
Medicine and Dentistry | 14 | 5% |
Other | 63 | 21% |
Unknown | 126 | 42% |