Predicting Drug Blood-Brain Barrier Penetration with Adverse Event Report Embeddings.
AMIA Annu Symp Proc
; 2022: 1163-1172, 2022.
Article
em En
| MEDLINE
| ID: mdl-37128462
ABSTRACT
Adverse event reports (AER) are widely used for post-market drug safety surveillance and drug repurposing, with the assumption that drugs with similar side-effects may have similar therapeutic effects also. In this study, we used distributed representations of drugs derived from the Food and Drug Administration (FDA) AER system using aer2vec, a method of representing AER, with drug embeddings emerging from a neural network trained to predict the probability of adverse drug effects given observed drugs. We combined these representations with molecular features to predict permeability of the blood-brain barrier to drugs, a prerequisite to their application to treat conditions of the central nervous system. Across multiple machine learning classifiers, the addition of distributed representations improved performance over prior methods using drug-drug similarity estimates derived from discrete representations of AER system data. Embedding-based approaches outperformed those using discrete statistics, with improvements in absolute AUC of 5% and 9%, corresponding to improvements of 9% and 13% over performance with molecular features only. Performance was retained when reducing embedding dimensions from 500 to 6, indicating that they are neither attributable to overfitting, nor to a difference in the number of trainable parameters. These results indicate that aer2vec distributed representations carry information that is valuable for drug repurposing.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Barreira Hematoencefálica
/
Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
AMIA Annu Symp Proc
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2022
Tipo de documento:
Article