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DOI 10.1007/s00432-017-2540-1

Медицинская библиотека · Journal of cancer research and clinical oncology · 2017

Model-based optimization of G-CSF treatment during cytotoxic chemotherapy.

Schirm Sibylle, Engel Christoph, Loibl Sibylle, Loeffler Markus, Scholz Markus

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Аннотация

PURPOSE: Although G-CSF is widely used to prevent or ameliorate leukopenia during cytotoxic chemotherapies, its optimal use is still under debate and depends on many therapy parameters such as dosing and timing of cytotoxic drugs and G-CSF, G-CSF pharmaceuticals used and individual risk factors of patients.

METHODS

We integrate available biological knowledge and clinical data regarding cell kinetics of bone marrow granulopoiesis, the cytotoxic effects of chemotherapy and pharmacokinetics and pharmacodynamics of G-CSF applications (filgrastim or pegfilgrastim) into a comprehensive model. The model explains leukocyte time courses of more than 70 therapy scenarios comprising 10 different cytotoxic drugs. It is applied to develop optimized G-CSF schedules for a variety of clinical scenarios.

RESULTS

Clinical trial results showed validity of model predictions regarding alternative G-CSF schedules. We propose modifications of G-CSF treatment for the chemotherapies 'BEACOPP escalated' (Hodgkin's disease), 'ETC' (breast cancer), and risk-adapted schedules for 'CHOP-14' (aggressive non-Hodgkin's lymphoma in elderly patients).

CONCLUSIONS

We conclude that we established a model of human granulopoiesis under chemotherapy which allows predictions of yet untested G-CSF schedules, comparisons between them, and optimization of filgrastim and pegfilgrastim treatment. As a general rule of thumb, G-CSF treatment should not be started too early and patients could profit from filgrastim treatment continued until the end of the chemotherapy cycle.

Для цитирования: Schirm Sibylle, Engel Christoph, Loibl Sibylle, Loeffler Markus, Scholz Markus. Model-based optimization of G-CSF treatment during cytotoxic chemotherapy.. Journal of cancer research and clinical oncology. 2017. DOI: 10.1007/s00432-017-2540-1.

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