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Data-driven modeling of pharmacological systems using endpoint information fusion
Chang-Sei Kim1, Nima Fazeli1, Jin-Oh Hahn1
1Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA.
Computers in Biology and Medicine
|April 12, 2015
Summary
This study shows that pharmacological systems are identifiable using endpoint responses, especially with effect compartments. Persistently exciting dose profiles are crucial for accurate, low-variance data-driven models.
Area of Science:
- Pharmacology
- Systems Biology
- Mathematical Modeling
Background:
- Pharmacological systems modeling is complex, often relying on conventional steady-state dose-response, pharmacokinetic-pharmacodynamic (PKPD), and indirect response models.
- Deriving data-driven models from endpoint responses alone presents challenges in system identifiability.
Purpose of the Study:
- To investigate the feasibility of deriving data-driven models for a class of pharmacological systems through information fusion of endpoint responses.
- To analyze the identifiability of these systems, determining if models can be derived solely from observed responses.
Main Methods:
- Formalized and analyzed the relationship between multiple endpoint responses in various pharmacological system models.
- Conducted identifiability analysis to assess model parameter estimation from response data.
Main Results:
- Demonstrated that the investigated class of pharmacological systems is fully identifiable when all responses involve effect compartments.
- Identified that persistently exciting dose profiles are necessary for accurate data-driven model derivation with low variance.
Conclusions:
- The study confirms the identifiability of a broad class of pharmacological systems using endpoint response data.
- Highlights the importance of specific input (dose) profiles for robust and reliable data-driven model development in pharmacology.
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