LAPSE:2022.0114v1
Published Article
LAPSE:2022.0114v1
Predicting the Potency of Anti-Alzheimer’s Drug Combinations Using Machine Learning
Thomas J. Anastasio
October 31, 2022
Clinical trials of single drugs intended to slow the progression of Alzheimer’s Disease (AD) have been notoriously unsuccessful. Combinations of repurposed drugs could provide effective treatments for AD. The challenge is to identify potentially effective combinations. To meet this challenge, machine learning (ML) was used to extract the knowledge from two leading AD databases, and then “the machine” predicted which combinations of the drugs in common between the two databases would be the most effective as treatments for AD. Specifically, three-layered artificial neural networks (ANNs) with compound, gated units in their internal layer were trained using ML to predict the cognitive scores of participants, separately in either database, given other data fields including age, demographic variables, comorbidities, and drugs taken. The predictions from the separately trained ANNs were statistically highly significantly correlated. The best drug combinations, jointly determined from both sets of predictions, were high in nonsteroidal anti-inflammatory drugs; anticoagulant, lipid-lowering, and antihypertensive drugs; and female hormones. The results suggest that the neurodegenerative processes that underlie AD and other dementias could be effectively treated using a combination of repurposed drugs. Predicted drug combinations could be evaluated in clinical trials.
Keywords
Alzheimer’s disease, Artificial Intelligence, artificial neural network, drug combination, drug repurposing, Machine Learning, multifactorial disorder, neurodegeneration, polypharmacy
Subject
Suggested Citation
Anastasio TJ. Predicting the Potency of Anti-Alzheimer’s Drug Combinations Using Machine Learning. (2022). LAPSE:2022.0114v1
Author Affiliations
Anastasio TJ: Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Journal Name
Processes
Volume
9
Issue
2
First Page
264
Year
2021
Publication Date
2021-01-29
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr9020264, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2022.0114v1
This Record
External Link

doi:10.3390/pr9020264
Publisher Version
Download
Files
[Download 1v1.pdf] (3.3 MB)
Oct 31, 2022
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
205
Version History
[v1] (Original Submission)
Oct 31, 2022
 
Verified by curator on
Oct 31, 2022
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2022.0114v1
 
Original Submitter
Mina Naeini
Links to Related Works
Directly Related to This Work
Publisher Version