DigiLoCS: A leap forward in predictive organ-on-chip simulations.

Area of Science:

• Pharmacokinetics and Drug Metabolism
• Biomimetic Systems and Digital Twins
• Organ-on-Chip Technology

Background:

• Accurate prediction of human drug clearance from in vitro data is crucial for successful drug development, preventing clinical trial failures due to underdosing or toxicity.
• Existing in vitro models often struggle to bridge the gap between experimental findings and clinical relevance, necessitating improved predictive methodologies.
• Digital twins offer a powerful approach to simulate complex biological systems, but their application to liver-on-chip models for pharmacokinetic prediction requires further development.

Purpose of the Study:

• To develop and validate a digital twin of a liver-on-chip system for simulating human liver clearance.
• To predict human clearance values for a panel of drugs using the developed digital twin.
• To establish a framework for enhancing in vitro to in vivo extrapolation (IVIVE) and bridging the gap between in vitro results and clinical outcomes.

Main Methods:

• Creation of a compartmental physiological model using ordinary differential equations (ODEs) to represent drug concentrations in media, interstitium, and intracellular compartments.
• Integration of quantitative Organ-on-Chip (OoC) and cell-based assay data on drug depletion kinetics.
• Development of the DigiLoCs (Digital Liver-on-Chip) digital twin incorporating hardware and biological information, with ODE-constrained optimization for clearance estimation.

Main Results:

• The digital twin model demonstrated improved prediction of intrinsic liver clearance compared to conventional models.
• The model successfully simulated drug depletion kinetics, establishing a link between chip hardware and intracellular biological processes.
• Application to propranolol as a proof-of-concept validated the model's ability to predict clinical significance and provided insights into passive vs. active drug processes.

Conclusions:

• The developed liver-on-chip digital twin (DigiLoCs) provides a robust platform for accurate prediction of human drug clearance, enhancing IVIVE.
• This approach offers explainability based on physiological parameters and differentiates between metabolic and passive drug disposition processes.
• The study represents a significant advancement in drug development, aiming to reduce time, cost, and patient burden by improving clinical outcome predictions.