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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
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Systems biology in drug discovery and development
1BioSeek, A Division of DiscoveRx, 310 Utah Avenue, Suite 100, South San Francisco, CA 94080, USA.
Drug Discovery Today
|October 15, 2013
Summary
Integrating diverse biological data is key to developing new medicines. Advanced network models and experimental systems can predict drug effects, enabling personalized medicine and improving drug development success.
Area of Science:
- Biomedical research
- Systems biology
- Pharmacogenomics
Background:
- Human biology complexity hinders new drug development.
- Systems biology generates vast high-throughput omics data.
- Integrating diverse data is crucial for disease prediction.
Purpose of the Study:
- To highlight the need for integrated network-based models in human disease biology.
- To emphasize the importance of improved experimental systems for predicting drug effects.
- To advance personalized medicine and drug discovery.
Main Methods:
- Integration of multi-omics data.
- Development of network-based models.
- Utilizing advanced experimental systems.
Main Results:
- Shift towards data integration in systems biology.
- Potential for improved prediction of disease outcomes.
- Enhanced ability to predict drug effects in patients.
Conclusions:
- Integrated models and relevant experimental systems are vital for personalized medicine.
- Improved prediction can increase new drug success rates.
- Facilitates identification of novel uses for existing drugs.
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