Pathway-Based Drug Repositioning for Cancers: Computational Prediction and Experimental Validation
Michio Iwata1, Lisa Hirose2, Hiroshi Kohara2,3
1Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering , Kyushu Institute of Technology , 680-4 Kawazu , Iizuka , Fukuoka 820-8502 , Japan.
Abstract:
Developing drugs with anticancer activity and low toxic side-effects at low costs is a challenging issue for cancer chemotherapy. In this work, we propose to use molecular pathways as the therapeutic targets and develop a novel computational approach for drug repositioning for cancer treatment. We analyzed chemically induced gene expression data of 1112 drugs on 66 human cell lines and searched for drugs that inactivate pathways involved in the growth of cancer cells (cell cycle) and activate pathways that contribute to the death of cancer cells (e.g., apoptosis and p53 signaling). Finally, we performed a large-scale prediction of potential anticancer effects for all the drugs and experimentally validated the prediction results via three in vitro cellular assays that evaluate cell viability, cytotoxicity, and apoptosis induction. Using this strategy, we successfully identified several potential anticancer drugs. The proposed pathway-based method has great potential to improve drug repositioning research for cancer treatment.
Insights
This study introduces a novel computational method for drug repositioning to find anticancer drugs. The pathway-based approach successfully identified potential cancer treatments with improved efficacy and reduced toxicity.
Area of Science:
- Computational biology
- Pharmacology
- Oncology
Background:
- Developing cost-effective anticancer drugs with minimal toxicity remains a significant challenge in cancer chemotherapy.
- Targeting molecular pathways offers a promising strategy for novel cancer therapeutics.
Purpose of the Study:
- To develop a novel computational approach for drug repositioning using molecular pathways as therapeutic targets for cancer treatment.
- To identify potential anticancer drugs by analyzing gene expression data and predicting pathway modulation.
Main Methods:
- Analyzed gene expression data from 1112 drugs across 66 human cell lines.
- Identified drugs that inactivate cancer-promoting pathways (cell cycle) and activate cancer-cell-death pathways (apoptosis, p53 signaling).
- Performed large-scale prediction of anticancer effects and validated findings using in vitro assays (cell viability, cytotoxicity, apoptosis induction).
Main Results:
- Successfully identified several potential anticancer drugs through the computational and experimental validation process.
- Demonstrated the effectiveness of the pathway-based drug repositioning strategy.
Conclusions:
- The proposed pathway-based computational method shows great potential for improving drug repositioning research in cancer treatment.
- This approach can aid in discovering novel anticancer drugs with potentially lower toxicity and cost.
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