Frontiers in pharmacology · 2026
Handling missed dose of riluzole by population pharmacokinetic model informed remedial regimen.
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Liu Changlong, Tang Qing, Yu Lingyan, Yu Zhenwei
Аннотация
BACKGROUND: Patients with amyotrophic lateral sclerosis (ALS) frequently exhibit nonadherence characterized by delayed or missed doses while taking riluzole. This study aimed to provide a population pharmacokinetic (PPK) model informed individualized remedial regimens for patients who experience delayed or missed doses of riluzole.
METHODS: A PPK model for riluzole was developed via a nonlinear mixed-effect modeling approach based on 3,024 concentration-time points from 72 subjects. The final model was validated by multiple methods. Six remedial regimens for missed doses were simulated for concentration-time profiles under different scenarios, and the optimal remedial regimen was selected based on the least time of concentration beyond the normal range.
RESULTS: The PPK model of riluzole was best described with a two-compartment model with body weight as a covariate of peripheral distribution volume. The typical values of CL/F, Vc/F, Vp/F and Q were 67 L/h, 242 L, 376 L and 33.1 L/h, respectively. The model was showed acceptable performance by goodness-of-fit, visual predictive check, bootstrap analysis and normalized distribution prediction errors. Model-based remedial regimens for different missed scenarios were proposed.
CONCLUSION: A riluzole PPK model was developed and internally evaluated using multiple diagnostic approaches. Model-based remedial dosing strategies for different missed-dose scenarios were identified based on predefined PK criteria and Monte Carlo simulations. These exploratory strategies provide quantitative insights into riluzole exposure during nonadherent dosing conditions.
METHODS: A PPK model for riluzole was developed via a nonlinear mixed-effect modeling approach based on 3,024 concentration-time points from 72 subjects. The final model was validated by multiple methods. Six remedial regimens for missed doses were simulated for concentration-time profiles under different scenarios, and the optimal remedial regimen was selected based on the least time of concentration beyond the normal range.
RESULTS: The PPK model of riluzole was best described with a two-compartment model with body weight as a covariate of peripheral distribution volume. The typical values of CL/F, Vc/F, Vp/F and Q were 67 L/h, 242 L, 376 L and 33.1 L/h, respectively. The model was showed acceptable performance by goodness-of-fit, visual predictive check, bootstrap analysis and normalized distribution prediction errors. Model-based remedial regimens for different missed scenarios were proposed.
CONCLUSION: A riluzole PPK model was developed and internally evaluated using multiple diagnostic approaches. Model-based remedial dosing strategies for different missed-dose scenarios were identified based on predefined PK criteria and Monte Carlo simulations. These exploratory strategies provide quantitative insights into riluzole exposure during nonadherent dosing conditions.