ArticleBMC systems biology2014
Modelling chemotherapy effects on granulopoiesis.
Article in BMC systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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.
Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Individual hematotoxicity prediction of further chemotherapy cycles by dynamic mathematical models in patients with gastrointestinal tumors.Journal of cancer research and clinical oncology · 2023Article
- A whole-body circulatory neutrophil model with application to predicting clinical neutropenia from in vitro studies.CPT: pharmacometrics & systems pharmacology · 2021Article
- Predicting Chemotherapy-Induced Neutropenia and Granulocyte Colony-Stimulating Factor Response Using Model-Based In Vitro to Clinical Translation.The AAPS journal · 2020Article
- A physiological model of granulopoiesis to predict clinical drug induced neutropenia from in vitro bone marrow studies: with application to a cell cycle inhibitor.Journal of pharmacokinetics and pharmacodynamics · 2020Article
- Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology.BMC medical informatics and decision making · 2020Article
- Oral Neutrophils: Underestimated Players in Oral Cancer.Frontiers in immunology · 2020Review
- Modeling individual time courses of thrombopoiesis during multi-cyclic chemotherapy.PLoS computational biology · 2019Article
- Pharmacokinetic-pharmacodynamic modelling of neutrophil response to G-CSF in healthy subjects and patients with chemotherapy-induced neutropenia.British journal of clinical pharmacology · 2018Article
- Model-based optimization of G-CSF treatment during cytotoxic chemotherapy.Journal of cancer research and clinical oncology · 2018Article
- Transit and lifespan in neutrophil production: implications for drug intervention.Journal of pharmacokinetics and pharmacodynamics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough the growth-factor G-CSF is widely used to prevent granulotoxic side effects of cytotoxic chemotherapies, its optimal use is still unknown since treatment outcome depends on many parameters such as dosing and timing of chemotherapies, pharmaceutical derivative of G-CSF used and individual risk factors. We showed in the past that a pharmacokinetic and -dynamic model of G-CSF and human granulopoiesis can be used to predict the performance of yet untested G-CSF schedules. However, only a single chemotherapy was considered so far.
resultsModel assumptions proved to be feasible in explaining granulotoxicity of 10 different chemotherapeutic drugs or drug-combinations applied in 33 different schedules with and without G-CSF. Risk groups of granulotoxicity were traced back to differences in toxicity parameters.
conclusionWe established a comprehensive model of combined G-CSF and chemotherapy action in humans which allows us to predict and compare the outcome of alternative G-CSF schedules. We aim to apply the model in different clinical contexts to optimize and individualize G-CSF treatment.
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Registered trials
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.