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Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures
Published on: December 26, 2016
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A side-effect free method for identifying cancer drug targets
Md Izhar Ashraf1,2, Seng-Kai Ong3, Shama Mujawar3
1The Institute of Mathematical Sciences, Chennai, 600113, India.
Scientific Reports
|April 29, 2018
Summary
Identifying effective cancer drug targets is challenging due to gene side effects. This study introduces a network analysis method, KFC, to pinpoint promising drug targets and avoid costly drug withdrawals.
Area of Science:
- Systems Biology
- Computational Biology
- Pharmacology
Background:
- Identifying effective drug targets with minimal side effects is a significant challenge in drug development.
- Pleiotropic gene effects can lead to drug withdrawal, necessitating improved target identification strategies.
- Protein interactome analysis, particularly centrality measures, has been valuable for identifying potential drug targets.
Purpose of the Study:
- To develop an integrated network analysis method for identifying effective cancer drug targets.
- To elucidate reasons behind drug withdrawals by analyzing complex biological networks.
- To propose a novel approach for enhancing drug development by avoiding potential pitfalls.
Main Methods:
- Extracted cancer-related proteins from pathway databases with experimental datasets.
- Constructed a protein-protein interaction network (interactome) for cancer.
- Applied an integrated method combining k-core, functional connectivity, and centrality (KFC) for network analysis.
Main Results:
- Identified plausible reasons for past drug withdrawals by analyzing network properties.
- Uncovered specific proteins (e.g., ESR1, HDAC2, F2) associated with drug withdrawal risks.
- Shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1, SOCS3) as promising drug development candidates based on KFC criteria.
Conclusions:
- The KFC method offers a robust approach for identifying effective drug targets in complex diseases like cancer.
- This integrative network analysis can help pharmaceutical industries avoid developing drugs with a high risk of withdrawal.
- The identified candidate proteins represent promising avenues for future cancer drug development.
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