Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFN$\alpha$ therapy against Myeloproliferative Neoplasms
| Autor: | Hermange, Gurvan, Vainchenker, William, Plo, Isabelle, Cournède, Paul-Henry |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Mathematical Medicine and Biology: A Journal of the IMA, Volume 41, Issue 2, June 2024, Pages 110-134 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1093/imammb/dqae006 |
| Popis: | Myeloproliferative Neoplasms (MPN) are blood cancers that appear after acquiring a driver mutation in a hematopoietic stem cell. These hematological malignancies result in the overproduction of mature blood cells and, if not treated, induce a risk of cardiovascular events and thrombosis. Pegylated IFN$\alpha$ is commonly used to treat MPN, but no clear guidelines exist concerning the dose prescribed to patients. We applied a model selection procedure and ran a hierarchical Bayesian inference method to decipher how dose variations impact the response to the therapy. We inferred that IFN$\alpha$ acts on mutated stem cells by inducing their differentiation into progenitor cells; the higher the dose, the higher the effect. We found that the treatment can induce long-term remission when a sufficient (patient-dependent) dose is reached. We determined this minimal dose for individuals in a cohort of patients and estimated the most suitable starting dose to give to a new patient to increase the chances of being cured. Comment: 23 pages,11 figures, and 2 tables for the article; the appendix starts on page 25, with 67 additional pages |
| Databáze: | arXiv |
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