Fitting Personalized Mechanistic Mathematical Models of Acute Myeloid Leukaemia to Clinical Patient Data

Dennis Görlich

2021

Abstract

In this position paper, we discussed the potential to fit mechanistic mathematical models of acute myeloid leukaemia to patient data. The overarching aim was to estimate personalized models. We briefly introduced one selected mechanistic ODE model to illustrate the approach. The usually available outcome measures, e.g. in clinical datasets, were aligned with the model’s prediction capabilities. Among the most relevant outcomes (blast load, complete remission, and survival), only blast load turned out to be well suited to be used in the model fitting process. We formulated an optimization problem that, finally, resulted in personalized model parameters. The degree of personalization could be chosen by selecting only a subset of parameters within the optimization problem. To illustrate the fitness landscape for individual patients we performed a grid search and calculated the fitness values for each grid point. The grid search revealed that an optimum exists, but that the fitness landscape can be very noisy. In these cases, gradient-based solvers will perform poorly and other algorithms needs to be chosen. Finally, we belief that personalized model fitting will be a promising approach to integrate mechanistic mathematical models into clinical research.

Paper Citation

in Harvard Style

Görlich D. (2021). Fitting Personalized Mechanistic Mathematical Models of Acute Myeloid Leukaemia to Clinical Patient Data. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-490-9, SciTePress, pages 170-175. DOI: 10.5220/0010345700002865

in Bibtex Style

@conference{bioinformatics21,
author={Dennis Görlich},
title={Fitting Personalized Mechanistic Mathematical Models of Acute Myeloid Leukaemia to Clinical Patient Data},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 3: BIOINFORMATICS},
year={2021},
pages={170-175},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010345700002865},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 3: BIOINFORMATICS
TI - Fitting Personalized Mechanistic Mathematical Models of Acute Myeloid Leukaemia to Clinical Patient Data
SN - 978-989-758-490-9
AU - Görlich D.
PY - 2021
SP - 170
EP - 175
DO - 10.5220/0010345700002865
PB - SciTePress