Characterizing Variation in Acute Myelogenous Leukemia and Therapeutic Interactions using Mathematical Modeling

Patients with acute myelogenous leukemia (AML) receive several rounds of chemotherapy and potentially hematopoietic stem cell transplants (HSCTs) to cure their disease. However, many of these patients often develop adverse outcomes such as graft rejection. In order to prevent adverse outcomes, we need to determine how to individualize optimal chemotherapy and immunotherapy regimens to suppress host immunity and appropriately engraft donor cells. Mathematical models can be used to personalize medicine by providing insight into the mechanisms of HSCTs, an individual\u27s health, and an optimal treatment plan. We developed an ordinary differential equations (ODE) semi-mechanistic model of multi-lineage hematopoiesis to explicate individual differences in AML. 22,796 simulated parameter sets met the dynamic constraints of the model we found from literature and data. We found that the mitosis rate and self-renewal probability of progenitor cells are inversely related for proper homeostasis. This led to identifying a potential secondary drug target mechanism for reducing cancer, as the mitosis rate is the current pharmaceutical target. We validated the model using data from four treatment modalities. The inclusion of activated T-cells allowed us to model donor chimerism post-HSCT. We determined that the discrimination between progenitor lymphocytes derived from the host versus those derived from the donor allow us to predict graft rejection. Increased immunosuppression in simulations that lead to graft rejection allows for graft rescue in 44% of these simulations. Thus, mathematical modeling of the management and cure of AML has led to further characterization of the development of individual AML and personalized optimal treatment regimens