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An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
Published on: December 3, 2020
Virtual Twin Approach Using Physiologically Based Pharmacokinetic Modeling to Support Precision Dosing of Valproic
Yoo Jin Jang1, Dong-Gyu Heo2, Eunjin Hong2
1Department of Psychiatry, Smsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Seoul, South Korea.
Clinical Pharmacology and Therapeutics
|June 8, 2026
Summary
Physiologically based pharmacokinetic (PBPK) models create virtual twins to predict valproic acid (VPA) drug exposure in older adults, enabling personalized dosing before steady state is reached.
Area of Science:
- Pharmacokinetics and Pharmacodynamics
- Geriatric Pharmacology
- Computational Modeling
Background:
- Geriatric patients require personalized drug dosing due to physiological variability and limited pharmacokinetic data.
- Valproic acid (VPA) is used for bipolar disorder in older adults, but current dosing relies on therapeutic drug monitoring (TDM) after steady state.
- Accurate prediction of drug exposure is crucial for optimizing treatment in elderly populations.
Purpose of the Study:
- To evaluate a physiologically based pharmacokinetic (PBPK)-guided virtual twin (VT) framework for predicting individual VPA exposure in geriatric patients.
- To assess the feasibility of prospective dose optimization using PBPK-guided VTs in real-world clinical settings.
- To compare PBPK-VT predictions with measured therapeutic drug monitoring (TDM) concentrations.
Main Methods:
- Developed an extended-release VPA PBPK model using the Simcyp Simulator.
- Collected clinical data from 74 elderly patients receiving VPA.
- Validated the PBPK model against published PK data and independent TDM data.
- Generated patient-specific VTs by incorporating demographic and physiological covariates.
- Simulated individual PK profiles and compared them with measured TDM concentrations.
Main Results:
- The PBPK model adequately reproduced observed VPA PK, with predicted parameters within 0.73- to 1.37-fold of observed values.
- PBPK-guided VTs achieved an absolute average fold error of 1.13 (90% CI: 1.10-1.16).
- Approximately 90% of VT predictions fell within the 0.8- to 1.25-fold range of observed concentrations.
Conclusions:
- PBPK-guided virtual twins can accurately predict individual VPA exposure in geriatric patients using routinely available clinical data.
- This framework has the potential to enable prospective dose optimization, complementing traditional TDM.
- The study supports the advancement of precision dosing strategies in geriatric pharmacotherapy.
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