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Mechanistic systems modeling to guide drug discovery and development.
Brian J Schmidt1, Jason A Papin, Cynthia J Musante
1Department of Bioengineering, University of California at San Diego, La Jolla, CA 92093-0412, USA.
Drug Discovery Today
|September 25, 2012
Summary
Mechanistic systems models can improve drug development by better informing therapeutic target selection. These models help reduce risks and optimize decisions for advancing compounds through clinical research.
Area of Science:
- Pharmacology and Drug Development
- Computational Biology
- Systems Biology
Background:
- Drug development involves significant investment before human trial data confirm efficacy.
- Basic science advances, including omics data and physiological studies, increase understanding of disease.
- Uncertainties in predicting therapeutic target success are a major challenge.
Purpose of the Study:
- To evaluate the utility of mechanistic systems models in drug development.
- To enhance decision-making processes for selecting therapeutic targets.
- To reduce risks associated with advancing drug candidates.
Main Methods:
- Utilizing computational knowledge from omics data sets.
- Analyzing studies on cellular and systemic responses to stimuli.
- Developing and applying mechanistic systems models.
Main Results:
- Mechanistic systems models provide a framework for integrating diverse biological data.
- These models can simulate biological processes to predict target behavior.
- Informed target selection can mitigate risks in preclinical and clinical research.
Conclusions:
- Mechanistic systems models offer a valuable tool for improving drug development efficiency.
- Integrating systems biology approaches can lead to more successful therapeutic interventions.
- Enhanced predictive modeling is crucial for optimizing pharmaceutical R&D investments.
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