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Linear-In-Flux-Expressions Methodology: Toward a Robust Mathematical Framework for Quantitative Systems Pharmacology
Sean T McQuade1, Ruth E Abrams2, Jeffrey S Barrett2
1Center for Computational and Integrative Biology, Rutgers University-Camden, Camden, NJ, USA.
Gene Regulation and Systems Biology
|March 28, 2018
Summary
Quantitative Systems Pharmacology (QSP) modeling advances drug discovery by creating virtual patient populations. The new Linear-In-Flux-Expressions (LIFE) method systematically simulates patient variability for better drug development.
Area of Science:
- Pharmacology
- Computational Biology
- Systems Biology
Background:
- Quantitative Systems Pharmacology (QSP) models are crucial for understanding drug mechanisms and effects.
- Developing virtual populations is key for QSP simulations, requiring parameter identification and sampling strategies.
Purpose of the Study:
- To introduce a novel, systematic method for creating virtual populations in QSP modeling.
- To improve the simulation of patient pharmacokinetic and pharmacodynamic variability.
Main Methods:
- Developed the Linear-In-Flux-Expressions (LIFE) method.
- LIFE represents structural relationships between model parameters to propagate variability.
- Applied the method to a cholesterol metabolism model.
Main Results:
- The LIFE method provides a robust approach to virtual population generation.
- Demonstrated the method's effectiveness using a cholesterol metabolism model.
- Enhanced capture of variability in clinical data.
Conclusions:
- The LIFE methodology offers a significant advancement for QSP simulators.
- Improved virtual populations lead to better understanding of drug and disease variability.
- Facilitates linking the right drug to the right patient through enhanced modeling.
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