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

Görlich D

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.

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