Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma.

ABSTRACT: Targeted therapies for inhibiting the growth of cancer cells or inducing apoptosis are urgently needed for effective rhabdomyosarcoma (RMS) treatment. However, identifying cancer-targeting compounds with few side effects, among the many potential compounds, is expensive and time-consuming. A computational approach to reduce the number of potential candidate drugs can facilitate the discovery of attractive lead compounds. To address this and obtain reliable predictions of novel cell-line-specific drugs, we apply prediction models that have the potential to improve drug discovery approaches for RMS treatment. The results of two prediction models were ensemble and validated via in vitro experiments. The computational models were trained using data extracted from the Genomics of Drug Sensitivi

SUBMITTER: Baek B 

PROVIDER: S-EPMC10817174 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature