Systematic, network-based characterization of therapeutic target inhibitors.
PLoS Comput Biol
; 13(10): e1005599, 2017 Oct.
Article
em En
| MEDLINE
| ID: mdl-29023443
ABSTRACT
A large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by conventional biochemical assays. We introduce OncoLead, a novel network based approach for the systematic prioritization of candidate inhibitors for arbitrary targets of therapeutic interest. In vitro and in vivo validation confirmed that OncoLead analysis can recapitulate known inhibitors as well as prioritize novel, context-specific inhibitors of difficult targets, such as MYC and STAT3. We used OncoLead to generate the first unbiased drug/regulator interaction map, representing compounds modulating the activity of cancer-relevant transcription factors, with potential in precision medicine.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Biologia Computacional
/
Descoberta de Drogas
/
Proteínas de Neoplasias
/
Neoplasias
/
Antineoplásicos
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
PLoS Comput Biol
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Estados Unidos