Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease.
PLoS Comput Biol
; 17(2): e1008631, 2021 02.
Article
em En
| MEDLINE
| ID: mdl-33544718
ABSTRACT
For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doenças Inflamatórias Intestinais
/
Medicina de Precisão
/
Reposicionamento de Medicamentos
/
Imunossupressores
Tipo de estudo:
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
/
Humans
Idioma:
En
Revista:
PLoS Comput Biol
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos