Yuri Kheifetz

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Aims: Thrombocytopoenia is a common major side-effect of cytotoxic cancer therapies. A clinically relevant problem is to predict an individual’s thrombotoxicity in the next planned chemotherapy cycle in order to decide on treatment adaptation. To support this task, two dynamical mathematical models of thrombopoiesis under chemotherapy were proposed, a simple semi-mechanistic model and a comprehensive mechanistic model. In this study, we compare the performance of these models. Methods: We consider close-meshed individual time series data of 135 non-Hodgkin’s lymphoma patients treated with six cycles of CHOP/CHOEP chemotherapies. Individual parameter estimates were derived on the basis of these data considering a varying number of cycles per patient. Parsimony assumptions were applied to optimize parameter identifiability. Models are compared by determining deviations of predicted and observed degrees of thrombocytopoenia in the next cycles. Results: The mechanistic model results in superior fits of individual time series data. Moreover, prediction accuracy of future cycle toxicities by the mechanistic model is higher even if it used data of two cycles, while the semi-mechanistic model used data of five cycles for the corresponding calibrations. Conclusions: We successfully established a quantitative and clinically relevant method for comparing prediction performance of biomathematical models of thrombopoiesis under chemotherapy. We showed that the more comprehensive mechanistic model outperforms the semi-mechanistic model. We aim at implementing the mechanistic model into clinical practice to assess its utility in real life clinical decision making