Engineering and Technology
| Open Access |
DOI: Physics-Informed Digital Twin for Enhanced In Vitro-to-In Vivo Extrapolation in Liver Microphysiological Systems
Samarth Patel , Independent Researcher, USAAbstract
Current microphysiological systems lack predic- tive fidelity for human pharmacokinetics due to oversimplified mathematical representations that conflate biological processes. We introduce a physics-informed digital twin framework that deconvolves active metabolic clearance from passive drug trans- port phenomena in liver-on-chip platforms. Our computational architecture employs a three-compartment ordinary differential equation model mapping media, interstitial, and intracellular domains, integrating hardware-specific microfluidic constraints with compound physicochemical properties. The framework was validated across 32 compounds spanning multiple hepatic microphysiological systems, demonstrating superior predictive accuracy with a mean clearance ratio of 1.04 ± 0.31 versus 0.56 ± 0.44 for conventional single-compartment models. By disentangling permeability, partitioning, and metabolic pathways, the digital twin enables mechanistic interpretation of observed kinetics while maintaining compatibility with standard experi- mental protocols. The open-source R implementation facilitates seamless integration with physiologically-based pharmacokinetic modeling for clinical translation. This paper establishes a compu- tational foundation for precision drug development using organ- on-chip technologies.
Keywords
Digital twin, organ-on-chip, microphysiological systems, pharmacokinetics, in vitro to in vivo extrapolation, physiologically-based modeling, clearance prediction
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