Evidence map›Paper›PMID 37149596›Full record

ArticleBMC cancer2023

Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology.

Marcus Baaz, Tim Cardilin, Floriane Lignet, Astrid Zimmermann, Samer El Bawab, Johan Gabrielsson, Mats Jirstrand

Abstract read
In one paragraph

Article in BMC cancer, 2023. 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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0citing papers 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

7 authors.

Marcus BaazFraunhofer-Chalmers Research Centre for Industrial Mathematics, Gothenburg, Sweden. Marcus.Baaz@fcc.chalmers.se.ORCID http://orcid.org/0000-0002-5098-017X
Tim CardilinFraunhofer-Chalmers Research Centre for Industrial Mathematics, Gothenburg, Sweden.
Floriane LignetTranslational Medicine, Quantitative Pharmacology, Merck Healthcare KGaA, Darmstadt, Germany.
Astrid ZimmermannTranslation Innovation Platform Oncology, Merck Healthcare KGaA, Darmstadt, Germany.
Samer El BawabTranslational Medicine, Quantitative Pharmacology, Merck Healthcare KGaA, Darmstadt, Germany.
Johan GabrielssonMeddoor AB, Gothenburg, Sweden.
Mats JirstrandFraunhofer-Chalmers Research Centre for Industrial Mathematics, Gothenburg, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data.

methodsWe used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves.

resultsThe model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 [Formula: see text] of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication.

conclusionsA simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.

Indexed as

Antineoplastic AgentsNeoplasmsRadiation-Sensitizing AgentsAnimalsCombined Modality TherapyHumansMiceAntineoplastic AgentsRadiation-Sensitizing AgentsCombination therapyInter-study variabilityNon-linear mixed effectsRadiation therapyTumor static exposure

Identifiers

PMID37149596
PMCPMC10164338

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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.