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Updated: Jul 4, 2025
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma
Bin Baek1, Eunmi Jang2, Sejin Park1
1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea.
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 Sensitivity in Cancer database and tested on two RMS cell lines to select potential RMS drug candidates. Among 235 candidate drugs, 22 were selected following the result of the computational approach, and three candidate drugs were identified (NSC207895, vorinostat, and belinostat) that showed selective effectiveness in RMS cell lines in vitro via the induction of apoptosis. Our in vitro experiments have demonstrated that our proposed methods can effectively identify and repurpose drugs for treating RMS.
Insights
Computational models identified three drugs (NSC207895, vorinostat, belinostat) effective against rhabdomyosarcoma (RMS) by inducing apoptosis. This approach accelerates the discovery of targeted therapies for RMS cancer treatment.
Area of Science:
- Oncology
- Computational Biology
- Pharmacology
Background:
- Effective rhabdomyosarcoma (RMS) treatment requires targeted therapies to inhibit cancer cell growth or induce apoptosis.
- Identifying safe and effective drug candidates is costly and time-consuming.
- Computational approaches can streamline the drug discovery process for RMS.
Purpose of the Study:
- To develop and validate computational models for predicting novel, cell-line-specific drugs for RMS treatment.
- To identify potential drug candidates for RMS using a computational approach combined with in vitro validation.
Main Methods:
- Utilized two prediction models trained on data from the Genomics of Drug Sensitivity in Cancer database.
- Ensemble of prediction models and validation through in vitro experiments on RMS cell lines.
- Screened 235 candidate drugs, selecting 22 based on computational predictions.
Main Results:
- Identified three candidate drugs: NSC207895, vorinostat, and belinostat.
- These drugs demonstrated selective effectiveness in RMS cell lines through apoptosis induction in vitro.
- The computational approach successfully narrowed down potential drug candidates.
Conclusions:
- The proposed computational methods effectively identify and repurpose drugs for RMS treatment.
- Validated in vitro experiments confirm the efficacy of identified drugs in inducing apoptosis in RMS cells.
- This strategy holds promise for accelerating the development of targeted RMS therapies.
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