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Multiscale modeling in drug discovery and development: future opportunities and present challenges
1Pfizer Global Research and Development, La Jolla Laboratories, San Diego, California, USA. vicini@u.washington.edu
Modeling and simulation (M&S) in biology requires specific approaches for success. Applying these principles in drug discovery yields positive results, though challenges persist.
Area of Science:
- Computational Biology
- Biomathematics
- Systems Biology
Background:
- Modeling and simulation (M&S) is a multidisciplinary field employing mathematical, statistical, and computational tools for quantitative predictions.
- The application of M&S in biological systems has a complex history, marked by both significant successes and notable failures.
- Heterogeneity in M&S methodologies may contribute to the observed instances of failure in biological applications.
Purpose of the Study:
- To identify critical factors for successful M&S in biological systems.
- To evaluate the efficacy of these factors in drug discovery and development.
- To highlight remaining challenges in the field.
Main Methods:
- Review of M&S approaches in biological systems.
- Analysis of factors contributing to success (fit-for-purpose, responsiveness, biological relevance, result sharing, staged buy-in).
- Case study application in drug discovery and development.
Main Results:
- Successful M&S outcomes depend on being fit for purpose, responsive, biologically relevant, and supported by proper result sharing and phased buy-in.
- These principles are actively being applied in drug discovery and development, demonstrating positive outcomes.
- Despite advancements, persistent bottlenecks hinder the full potential of M&S in these areas.
Conclusions:
- Adherence to specific M&S principles is crucial for successful application in biological research.
- The integration of M&S in drug discovery shows promise but requires further optimization.
- Addressing remaining bottlenecks is essential for advancing M&S in life sciences.
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