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Automated proper lumping for simplification of linear physiologically based pharmacokinetic systems
Shan Pan1,2, Stephen B Duffull3
1School of Pharmacy, University of Otago, Dunedin, New Zealand. shan.pan@kcl.ac.uk.
Physiologically based pharmacokinetic (PBPK) models can be simplified using lumping techniques. Simulated annealing (SA) proved most efficient for creating simpler PBPK models for drug development.
Area of Science:
- Pharmacokinetics and Drug Development
- Systems Biology and Computational Modeling
Background:
- Physiologically based pharmacokinetic (PBPK) models are crucial in early drug development but their complexity hinders population pharmacokinetic (PK) analyses.
- Simplifying complex PBPK models is necessary for their application in data-driven PK studies.
Purpose of the Study:
- To explore and automate methods for simplifying linear PBPK models using a proper lumping technique.
- To evaluate the efficiency and robustness of different simplification methods, including simulated annealing (SA).
Main Methods:
- Developed four methods to simplify a 17-state linear fentanyl PBPK model: full enumeration, non-adaptive random search (NARS), scree plot plus NARS, and simulated annealing (SA).
- A criterion was set for the lumped model's area under the concentration-time curve (AUC) to differ by a maximum of 0.002% from the original model.
Main Results:
- Full enumeration identified a 4-state lumped model. NARS also found a lumped model but required extensive sampling.
- The scree plot method accelerated NARS. Simulated annealing (SA) consistently delivered a 4-state lumped model.
- SA demonstrated robustness and efficiency in simplifying the fentanyl PBPK model.
Conclusions:
- Simulated annealing is a robust and efficient method for simplifying linear PBPK models.
- Simplified PBPK models retain fundamental mechanisms and are suitable for data-driven PK analyses.
- The SA approach may be broadly applicable to other large-scale linear systems.
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