RESUMEN
Phenotypic assays have a proven track record for generating leads that become first-in-class therapies. Whole cell assays that inform on a phenotype or mechanism also possess great potential in drug repositioning studies by illuminating new activities for the existing pharmacopeia. The National Center for Advancing Translational Sciences (NCATS) pharmaceutical collection (NPC) is the largest reported collection of approved small molecule therapeutics that is available for screening in a high-throughput setting. Via a wide-ranging collaborative effort, this library was analyzed in the Open Innovation Drug Discovery (OIDD) phenotypic assay modules publicly offered by Lilly. The results of these tests are publically available online at www.ncats.nih.gov/expertise/preclinical/pd2 and via the PubChem Database (https://pubchem.ncbi.nlm.nih.gov/) (AID 1117321). Phenotypic outcomes for numerous drugs were confirmed, including sulfonylureas as insulin secretagogues and the anti-angiogenesis actions of multikinase inhibitors sorafenib, axitinib and pazopanib. Several novel outcomes were also noted including the Wnt potentiating activities of rotenone and the antifolate class of drugs, and the anti-angiogenic activity of cetaben.
Asunto(s)
Reposicionamiento de Medicamentos , Línea Celular Tumoral , Aprobación de Drogas , Evaluación Preclínica de Medicamentos , Ensayos Analíticos de Alto Rendimiento , Humanos , Concentración 50 Inhibidora , Fenotipo , Bibliotecas de Moléculas Pequeñas/farmacologíaRESUMEN
The NCI Developmental Therapeutics Program Human Tumor cell line data set is a publicly available database that contains cellular assay screening data for over 40 000 compounds tested in 60 human tumor cell lines. The database also contains microarray assay gene expression data for the cell lines, and so it provides an excellent information resource particularly for testing data mining methods that bridge chemical, biological, and genomic information. In this paper we describe a formal knowledge discovery approach to characterizing and data mining this set and report the results of some of our initial experiments in mining the set from a chemoinformatics perspective.