Cancer chemotherapy and pharmacology · 2026
Development of a simplified population pharmacokinetic-pharmacodynamic framework for exposure-informed risk stratification of neutropenia during amrubicin treatment.
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Makino Yoshinori, Ogawa Takanori, Makihara-Ando Reiko, Sakiyama Naomi, Yamazaki Masahito, Naito Chizuru, Takayama Hiroki, Ueda Genji, Kohyama Shunsuke, Todo Maki, Kuwata Yasuhiro, Hirasaki Masataka, Hamaguchi Tetsuya
Аннотация
BACKGROUND: Amrubicin is an important chemotherapeutic agent for small cell lung cancer; however, severe neutropenia is a major dose-limiting toxicity. Exposure to its active metabolite, amrubicinol, has been associated with neutropenia severity, suggesting potential utility for exposure-informed risk stratification. Given the lack of a clearly defined therapeutic window for cytotoxic agents, alternative strategies to mitigate toxicity are needed.
METHODS: A previously published population pharmacokinetic-pharmacodynamic (PK-PD) model of amrubicin was simplified to enhance its practical applicability by removing the enterohepatic circulation component and associated genetic covariate. Model performance was evaluated using prediction-corrected visual predictive checks and nonparametric bootstrapping. A prototype interactive application was developed using R Shiny and RsNLME to enable Bayesian estimation and visualization of individual PK and neutrophil profiles based on sparse first-cycle data.
RESULTS: The simplified model adequately described the plasma concentration-time profiles of amrubicin and amrubicinol and the time course of neutrophil counts. Internal validation demonstrated consistency between observed and simulated data. The application enabled visualization of predicted neutrophil trajectories and associated uncertainty, facilitating exposure-informed assessment of neutropenia risk with visualization of predictive uncertainty.
CONCLUSION: We developed a simplified PK-PD framework and implemented it in a prototype application to support risk stratification of neutropenia during amrubicin treatment. The framework does not provide dose recommendations but may support identification of patients who could benefit from prophylactic supportive care, such as pegylated G-CSF. Prospective studies are ongoing to evaluate its clinical utility.
METHODS: A previously published population pharmacokinetic-pharmacodynamic (PK-PD) model of amrubicin was simplified to enhance its practical applicability by removing the enterohepatic circulation component and associated genetic covariate. Model performance was evaluated using prediction-corrected visual predictive checks and nonparametric bootstrapping. A prototype interactive application was developed using R Shiny and RsNLME to enable Bayesian estimation and visualization of individual PK and neutrophil profiles based on sparse first-cycle data.
RESULTS: The simplified model adequately described the plasma concentration-time profiles of amrubicin and amrubicinol and the time course of neutrophil counts. Internal validation demonstrated consistency between observed and simulated data. The application enabled visualization of predicted neutrophil trajectories and associated uncertainty, facilitating exposure-informed assessment of neutropenia risk with visualization of predictive uncertainty.
CONCLUSION: We developed a simplified PK-PD framework and implemented it in a prototype application to support risk stratification of neutropenia during amrubicin treatment. The framework does not provide dose recommendations but may support identification of patients who could benefit from prophylactic supportive care, such as pegylated G-CSF. Prospective studies are ongoing to evaluate its clinical utility.