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Has the time come for predictive computer modeling in CNS drug discovery and development?
H Geerts1, A Spiros, P Roberts
11] Department of Biomedical Engineering, In Silico Biosciences, Berwyn, Pennsylvania, USA [2] Department of Biomedical Engineering, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
CPT: Pharmacometrics & Systems Pharmacology
|July 10, 2013
Summary
Quantitative systems pharmacology (QSP) can enhance central nervous system (CNS) research and development. Integrating computational neuroscience with clinical data may improve the success rate of CNS R&D projects.
Area of Science:
- Pharmacometrics and Systems Pharmacology
- Computational Neuroscience
- Drug Development
Background:
- Central nervous system (CNS) research and development (R&D) projects have historically faced low success rates.
- Existing approaches may not fully integrate complex biological systems and pharmacological data.
Purpose of the Study:
- To evaluate quantitative systems pharmacology (QSP) as a new paradigm for CNS R&D.
- To explore how computational neuroscience modeling can improve CNS project success rates.
Main Methods:
- Integrating computational neuroscience models with drug target engagement, human pathology, and imaging data.
- Utilizing clinical studies in human subjects for calibration and validation.
- Developing a humanized computer-based integration of physiological and pharmacological knowledge.
Main Results:
- The proposed QSP approach offers a potential improvement over traditional methods.
- Enhanced understanding of neuronal circuit interactions is key to de-risking CNS projects.
Conclusions:
- Quantitative systems pharmacology (QSP) represents a promising new paradigm for CNS R&D.
- A humanized, integrated computational approach can substantially de-risk CNS projects by improving the understanding of complex biological systems.
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