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Background <p>Imatinib therapeutic drug monitoring (TDM) contributes at optimizing exposure, yet its implementation is limited by logistical constraints, including strict sampling time requirements. Model-Informed Precision Dosing (MIPD) offers a promising approach to dosage individualization by leveraging population pharmacokinetic (popPK) models, thereby mitigating the constraints of sample collection time. Integrating MIPD and Point-of-Care (POC) analytical methods in ambulatory settings could further improve TDM feasibility and accessibility. This study serves as a proof of concept for MIPD integration in the workflow of imatinib TDM. To identify optimal sampling time for predicting imatinib steady-state trough concentrations (C<sub>min, SS</sub>) using a popPK model for an MIPD application.</p> Methods <p>A popPK model developed from data of 146 patients (244 concentrations) was used to simulate individual concentration-time profiles of 1000 patients under standard dosing (400&#xa0;mg once or twice daily) from treatment initiation to steady-state (reached after 11 days of treatment). Empirical Bayes estimates were generated from single or paired sampling time points and used to predict C<sub>min, SS</sub>. Predictive performance was evaluated using mean prediction error, coefficient of determination, root mean square prediction error (RMSPE), and successful prediction rates based on recommended therapeutic ranges.</p> Results <p>Sampling 5–24&#xa0;h and 1–12&#xa0;h post-dose for once and twice daily administrations, respectively, yielded an RMSPE &lt; 41% and a successful prediction rate around 70%.</p> Conclusion <p>The proposed time windows provide flexible sampling strategies, confirming the benefits of MIPD for the convenience of imatinib TDM, thus improving the clinical feasibility of POC TDM.</p>

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Optimizing imatinib sampling strategies through a population approach: a crucial step to model-informed precision dosing validation for point-of-care therapeutic drug monitoring

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Yuan J. Pétermann,
  • Junrui Chen,
  • Lavinia Alberi,
  • Simon Alexandra,
  • François Veuve,
  • Eva Choong,
  • Yann Thoma,
  • Roberto Rigamonti,
  • Bruno Da Rocha Carvalho,
  • Sandro Carrara,
  • François R. Girardin,
  • Thierry Buclin,
  • Chantal Csajka,
  • Monia Guidi

摘要

Background

Imatinib therapeutic drug monitoring (TDM) contributes at optimizing exposure, yet its implementation is limited by logistical constraints, including strict sampling time requirements. Model-Informed Precision Dosing (MIPD) offers a promising approach to dosage individualization by leveraging population pharmacokinetic (popPK) models, thereby mitigating the constraints of sample collection time. Integrating MIPD and Point-of-Care (POC) analytical methods in ambulatory settings could further improve TDM feasibility and accessibility. This study serves as a proof of concept for MIPD integration in the workflow of imatinib TDM. To identify optimal sampling time for predicting imatinib steady-state trough concentrations (Cmin, SS) using a popPK model for an MIPD application.

Methods

A popPK model developed from data of 146 patients (244 concentrations) was used to simulate individual concentration-time profiles of 1000 patients under standard dosing (400 mg once or twice daily) from treatment initiation to steady-state (reached after 11 days of treatment). Empirical Bayes estimates were generated from single or paired sampling time points and used to predict Cmin, SS. Predictive performance was evaluated using mean prediction error, coefficient of determination, root mean square prediction error (RMSPE), and successful prediction rates based on recommended therapeutic ranges.

Results

Sampling 5–24 h and 1–12 h post-dose for once and twice daily administrations, respectively, yielded an RMSPE < 41% and a successful prediction rate around 70%.

Conclusion

The proposed time windows provide flexible sampling strategies, confirming the benefits of MIPD for the convenience of imatinib TDM, thus improving the clinical feasibility of POC TDM.