In Silico Approaches to Predict Drug-Transporter Interaction Profiles: Data Mining, Model Generation, and Link to Cholestasis.
Methods Mol Biol
; 1981: 383-396, 2019.
Article
en En
| MEDLINE
| ID: mdl-31016669
ABSTRACT
Transport proteins play a crucial role in drug distribution, disposition, and clearance by mediating cellular drug influx and efflux. Inhibition of these transporters may lead to drug-drug interactions or even drug-induced liver injury, such as cholestasis, which comprises a major challenge in drug development process. Thus, computer-based (in silico) models that can predict the pharmacological and toxicological profiles of these small molecules with respect to liver transporters may help in the early prioritization of compounds and hence may lower the high attrition rates. In this chapter, we provide a protocol for in silico prediction of cholestasis by generating validated predictive models. In addition to the two-dimensional molecular descriptors, we include transporter inhibition predictions as descriptors and evaluate the influence of the same on the performance of the cholestasis models.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Colestasis
/
Enfermedad Hepática Inducida por Sustancias y Drogas
/
Minería de Datos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Methods Mol Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2019
Tipo del documento:
Article
País de afiliación:
Austria