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Bioinformatics Approaches for Anti-cancer Drug Discovery
1State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
Abstract:
Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.
Insights
Computational methods using multi-omics data accelerate anti-cancer drug discovery. Bioinformatics approaches integrating genomics and network analysis offer efficient strategies for identifying novel cancer therapies and drug combinations.
Area of Science:
- Bioinformatics
- Computational Biology
- Cancer Therapy
Background:
- Traditional drug discovery methods (in vivo, in vitro) are costly and time-consuming.
- The advent of omics data has revolutionized computational prediction of anti-cancer drugs.
- Biological network theory is increasingly applied to anti-cancer drug discovery.
Purpose of the Study:
- To review and discuss bioinformatics approaches for predicting anti-cancer drugs and drug combinations.
- To highlight the utility of multi-omic data in computational drug discovery.
- To provide an overview of databases and computational methods for cancer therapy development.
Main Methods:
- Integration of high-throughput transcriptome data with drug-response data.
- Application of biological network methodologies (protein-protein interaction, drug-target, disease-gene networks).
- Utilizing multi-omic data including transcriptomics, toxicogenomics, and functional genomics.
Main Results:
- Bioinformatics approaches significantly improve the efficiency of anti-cancer drug discovery.
- Multi-omic data integration enables more accurate prediction of drug efficacy and combinations.
- Network-based methods are effective in identifying potential anti-cancer agents.
Conclusions:
- Computational and bioinformatics strategies are crucial for advancing cancer therapy.
- An overview of current methods and databases can guide the development of novel cancer treatments.
- This review supports the efficient discovery of precision medicines for cancer.
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