Machine learning identifies candidates for drug repurposing in Alzheimer's disease.
Nat Commun
; 12(1): 1033, 2021 02 15.
Article
em En
| MEDLINE
| ID: mdl-33589615
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Drogas em Investigação
/
Nootrópicos
/
Fármacos Neuroprotetores
/
Medicamentos sob Prescrição
/
Doença de Alzheimer
/
Aprendizado de Máquina
/
Proteínas do Tecido Nervoso
Tipo de estudo:
Clinical_trials
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Nat Commun
Ano de publicação:
2021
Tipo de documento:
Article