Model-based optimisation and individualisation of treatment strategies in haematology
The central objective of this project is to demonstrate that recently developed dynamic mathematical models of normal and leukaemic haematopoiesis can be used to practically impact clinical decision- making. The project is based on mathematical models of the three major haematopoietic cell lineages (i.e. erythropoiesis, granulocytopoiesis, thrompopoiesis) and their regulation by growth factors as well as on models of haematopoietic stem cells. These models have successfully been applied to describe healthy and disturbed haematopoiesis including clonal cell expansion and, based hereon, onset and treatment of clonal diseases, such as chronic myeloid leukaemia (CML). The models have been developed and consolidated in cooperation with basic research partners and clinical trial groups over the last 10 years and can now be considered mature enough to be applied to clinical decision-making. This process clearly includes both, continuous further development and adaptation of the models to new biological insights. In particular, the models will explicitly account for patient heterogeneity, which allows us to make clinically relevant predictions regarding optimised treatment in dependence on individual factors.
The proposed interdisciplinary consortium brings together mathematical modellers and bioinformaticians with clinical haematologists and biologists. We build on a number of already long-standing and very successful collaborations of several of the participants. Consortium members have already demonstrated that model-based design of clinical trials on lymphoma and leukaemia treatments can result in improved therapy outcomes. Several partners are responsible for internationally leading clinical trial groups, a fact that allows our consortium to obtain data from these trials and to initiate novel clinical trials, investigating model-based predictions.
Subprojects in HaematoOPT:
Keywords: Haematopoiesis, growth factors, chemotherapy, leukeamia, mathematical model, computer simulation, clinical descison making