Evidence map›Paper›PMID 34623558›Full record

ArticleJournal of pharmacokinetics and pharmacodynamics2022

Exposure-response modeling improves selection of radiation and radiosensitizer combinations.

Tim Cardilin, Joachim Almquist, Mats Jirstrand, Astrid Zimmermann, Floriane Lignet, Samer El Bawab, Johan Gabrielsson

Open access · hybridAbstract read
In one paragraph

Article in Journal of pharmacokinetics and pharmacodynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 53% of its field
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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
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 at 3 institutions in 2 countries.

Tim CardilinFraunhofer-Chalmers Centre, Chalmers Science Park, Gothenburg, Sweden. tim.cardilin@fcc.chalmers.se.
Joachim AlmquistFraunhofer-Chalmers Centre, Chalmers Science Park, Gothenburg, Sweden.
Mats JirstrandFraunhofer-Chalmers Centre, Chalmers Science Park, Gothenburg, Sweden.
Astrid ZimmermannTranslation Innovation Platform Oncology, Merck KGaA, Darmstadt, Germany.
Floriane LignetTranslational Medicine, Merck KGaA, Darmstadt, Germany.
Samer El BawabTranslational Medicine, Merck KGaA, Darmstadt, Germany.
Johan GabrielssonMeddoor AB, Gothenburg, Sweden.
Merck (Germany) · DEFraunhofer Chalmers Research Centre for Industrial Mathematics · SEAstraZeneca (Sweden) · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.

Indexed as

NeoplasmsRadiation-Sensitizing AgentsHumansRadiation-Sensitizing AgentsDrug selectionRadiosensitizerTreatment optimizationTumor growth modelTumor Static Exposure

Identifiers

PMID34623558
PMCPMC8940791
OpenAlexW3201889358

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.