Prediction of adverse drug reactions due to genetic predisposition using deep neural networks.
Mol Inform
; 43(6): e202400021, 2024 Jun.
Article
en En
| MEDLINE
| ID: mdl-38850150
ABSTRACT
Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the market, some drugs can cause strong ADRs that can vary depending on an individual polymorphism. The development of Genome-wide association studies (GWAS) allowed the discovery of genetic variants of interest that may cause these effects. In this study, the objective was to investigate a deep learning approach to predict genetic variations potentially related to ADRs. We used single nucleotide polymorphisms (SNPs) information from dbSNP to create a network based on ADR-drug-target-mutations and extracted matrixes of interaction to build deep Neural Networks (DNN) models. Considering only information about mutations known to impact drug efficacy and drug safety from PharmGKB and drug adverse reactions based on the MedDRA System Organ Classes (SOCs), these DNN models reached a balanced accuracy of 0.61 in average. Including molecular fingerprints representing structural features of the drugs did not improve the performance of the models. To our knowledge, this is the first model that exploits DNN to predict ADR-drug-target-mutations. Although some improvements are suggested, these models can be of interest to analyze multiple compounds over all of the genes and polymorphisms information accessible and thus pave the way in precision medicine.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
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Predisposición Genética a la Enfermedad
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Polimorfismo de Nucleótido Simple
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Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos
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Aprendizaje Profundo
Límite:
Humans
Idioma:
En
Revista:
Mol Inform
Año:
2024
Tipo del documento:
Article
País de afiliación:
Francia