Predicting Drug Blood-Brain Barrier Penetration with Adverse Event Report Embeddings.
AMIA Annu Symp Proc
; 2022: 1163-1172, 2022.
Article
in 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Blood-Brain Barrier
/
Drug-Related Side Effects and Adverse Reactions
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
AMIA Annu Symp Proc
Journal subject:
INFORMATICA MEDICA
Year:
2022
Document type:
Article