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Updated: May 20, 2025
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
Drug repurposing for non-small cell lung cancer by predicting drug response using pathway-level graph convolutional
I T Anjusha1, K A Abdul Nazeer1, N Saleena1
1Department of Computer Science and Engineering, National Institute of Technology Calicut, Kozhikode, India.
Abstract:
Drug repurposing is the process of identifying new clinical indications for an existing drug. Some of the recent studies utilized drug response prediction models to identify drugs that can be repurposed. By representing cell-line features as a pathway-pathway interaction network, we can better understand the connections between cellular processes and drug response mechanisms. Existing deep learning models for drug response prediction do not integrate known biological pathway-pathway interactions into the model. This paper presents a drug response prediction model that applies a graph convolution operation on a pathway-pathway interaction network to represent features of cancer cell-lines effectively. The model is used to identify potential drug repurposing candidates for Non-Small Cell Lung Cancer (NSCLC). Experiment results show that the inclusion of graph convolutional model applied on a pathway-pathway interaction network makes the proposed model more effective in predicting drug response than the state-of-the-art methods. Specifically, the model has shown better performance in terms of Root Mean Squared Error, Coefficient of Determination, and Pearson's Correlation Coefficient when applied to the GDSC1000 dataset. Also, most of the drugs that the model predicted as top candidates for NSCLC treatment are either undergoing clinical studies or have some evidence in the PubMed literature database.
Insights
This study introduces a novel drug repurposing method for Non-Small Cell Lung Cancer (NSCLC) by integrating pathway interactions into deep learning models. The approach enhances drug response prediction accuracy, identifying promising repurposing candidates.
Area of Science:
- Computational biology
- Pharmacology
- Oncology
Background:
- Drug repurposing identifies new uses for existing medications.
- Predicting drug response is crucial for identifying viable candidates.
- Current deep learning models lack integration of biological pathway interactions.
Purpose of the Study:
- To develop an advanced drug response prediction model.
- To integrate biological pathway-pathway interactions into deep learning for enhanced prediction.
- To identify potential drug repurposing candidates for Non-Small Cell Lung Cancer (NSCLC).
Main Methods:
- Developed a novel deep learning model incorporating graph convolution operations.
- Utilized a pathway-pathway interaction network to represent cancer cell-line features.
- Applied the model to predict drug response on the GDSC1000 dataset.
Main Results:
- The proposed model demonstrated superior performance in drug response prediction compared to state-of-the-art methods.
- Achieved improved metrics including Root Mean Squared Error, Coefficient of Determination, and Pearson's Correlation Coefficient.
- Identified several potential drug repurposing candidates for NSCLC, with validation in clinical studies and literature.
Conclusions:
- Integrating pathway-pathway interactions significantly improves drug response prediction accuracy.
- The developed model is effective for identifying novel drug repurposing opportunities for NSCLC.
- This approach holds promise for accelerating drug discovery and development.
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