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Reengineering the pharmaceutical industry by crash-testing molecules
1Department of Pharmaceutical Sciences, University of Maryland, 20 Penn Street, Baltimore, MD 21201, USA. pswaan@rx.umaryland.edu
Drug Discovery Today
|September 27, 2005
Summary
The pharmaceutical industry needs a unified modeling language for predictive computational tools to virtually test drug candidates, reducing late-stage failures and improving the drug pipeline. This approach aims to prevent costly failures in drug discovery and development.
Area of Science:
- Pharmaceutical Sciences
- Computational Chemistry
- Drug Discovery
Background:
- The pharmaceutical industry faces declining drug approvals and increasing late-stage failures.
- Current high-throughput screening methods have not improved the drug development pipeline.
Purpose of the Study:
- To propose a paradigm shift in drug discovery and development.
- To advocate for a shared modeling language and integrated computational tools across the industry.
Main Methods:
- Adopting principles similar to the automotive industry's shared modeling language.
- Implementing virtual 'crash-testing' of drug candidates using predictive computational models.
- Developing a grand unified model by combining relevant computational algorithms.
Main Results:
- Enables simulation of all stages of drug discovery and development.
- Facilitates prioritization of promising drug candidates before synthesis and testing.
- Potential to significantly reduce late-stage failures and avoid costly setbacks.
Conclusions:
- A shared modeling language and integrated computational tools can revolutionize drug discovery.
- Virtual 'crash-testing' can enhance efficiency and success rates in the pharmaceutical industry.
- This approach offers a path to revitalize the drug pipeline and avoid failures.