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Updated: Feb 11, 2026
Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures
Published on: December 26, 2016
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.
Abstract:
Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identification of effective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying effective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as effective candidates for drug development.
Insights
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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