Evidence map›Paper›PMID 42415128›Full record

ArticleBMC pharmacology & toxicology2026

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

Yuan J Pétermann, Junrui Chen, Lavinia Alberi, Simon Alexandra, François Veuve, Eva Choong, Yann Thoma, Roberto Rigamonti, Bruno Da Rocha Carvalho, Sandro Carrara and 4 more

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In one paragraph

Article in BMC pharmacology & toxicology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Yuan J PétermannCentre for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital, University of Lausanne, Rue du Bugnon 17, Lausanne, CH-1011, Switzerland.
Junrui ChenBio/CMOS Interfaces Lab, École Polytechnique Fédérale de Lausanne, Rue de la Maladiere 71b, Neuchâtel, 2000, Switzerland.
Lavinia AlberiBio/CMOS Interfaces Lab, École Polytechnique Fédérale de Lausanne, Rue de la Maladiere 71b, Neuchâtel, 2000, Switzerland.
Simon AlexandraDepartment of Clinical Pharmacology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
François VeuveDepartment of Clinical Pharmacology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Eva ChoongDepartment of Clinical Pharmacology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Yann ThomaSchool of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland, Yverdon-les-Bains, 1401, Switzerland.
Roberto RigamontiSchool of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland, Yverdon-les-Bains, 1401, Switzerland.
Bruno Da Rocha CarvalhoSchool of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland, Yverdon-les-Bains, 1401, Switzerland.
Sandro CarraraBio/CMOS Interfaces Lab, École Polytechnique Fédérale de Lausanne, Rue de la Maladiere 71b, Neuchâtel, 2000, Switzerland.
François R GirardinDepartment of Clinical Pharmacology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Thierry BuclinDepartment of Clinical Pharmacology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Chantal CsajkaCentre for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital, University of Lausanne, Rue du Bugnon 17, Lausanne, CH-1011, Switzerland.
Monia GuidiCentre for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital, University of Lausanne, Rue du Bugnon 17, Lausanne, CH-1011, Switzerland. monia.guidi@chuv.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImatinib 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

methodsA 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 C

resultsSampling 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%.

conclusionThe 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.

Indexed as

Antineoplastic AgentsDrug MonitoringImatinib MesylateModels, BiologicalPoint-of-Care SystemsProtein Kinase InhibitorsBayes TheoremFemaleHumansMaleAntineoplastic AgentsImatinib MesylateProtein Kinase InhibitorsImatinibModel-informed precision dosingOptimal sampling strategyPoint-of-care testingTherapeutic drug monitoring

Identifiers

PMID42415128

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.