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Chemotherapy-induced cachexia and model-informed dosing to preserve lean mass in cancer treatment.

Authors :
Farhang-Sardroodi, Suzan
La Croix, Michael A.
Wilkie, Kathleen P.
Source :
PLoS Computational Biology. 3/21/2022, Vol. 18 Issue 3, p1-25. 25p. 3 Charts, 15 Graphs.
Publication Year :
2022

Abstract

Although chemotherapy is a standard treatment for cancer, it comes with significant side effects. In particular, certain agents can induce severe muscle loss, known as cachexia, worsening patient quality of life and treatment outcomes. 5-fluorouracil, an anti-cancer agent used to treat several cancers, has been shown to cause muscle loss. Experimental data indicates a non-linear dose-dependence for muscle loss in mice treated with daily or week-day schedules. We present a mathematical model of chemotherapy-induced muscle wasting that captures this non-linear dose-dependence. Area-under-the-curve metrics are proposed to quantify the treatment's effects on lean mass and tumour control. Model simulations are used to explore alternate dosing schedules, aging effects, and morphine use in chemotherapy treatment with the aim of better protecting lean mass while actively targeting the tumour, ultimately leading to improved personalization of treatment planning and improved patient quality of life. Author summary: In this paper we present a novel mathematical model for muscle loss due to cancer chemotherapy treatment. Loss of muscle mass relates to increased drug toxicity and side-effects, and to decreased patient quality of life and survival rates. With our model, we examine the therapeutic efficacy of various dosing schedules with the aim of controlling a growing tumour while also preserving lean mass. Preservation of body composition, in addition to consideration of inflammation and immune interactions, the gut microbiome, and other systemic health measures, may lead to improved patient-specific treatment plans that improve patient quality of life. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1553734X
Volume :
18
Issue :
3
Database :
Academic Search Index
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
155868495
Full Text :
https://doi.org/10.1371/journal.pcbi.1009505