One of the key challenges in current cancer research is the development of reliable methods for the definition of personalized therapeutic strategies, based on increasingly available experimental data on single patients. To this end, methods from control theory can be effectively employed on patient-specific pharmacokinetic and pharmacodynamic models to generate robust data-driven experimental hypotheses. Here we introduce the Control Theory for Therapy Design (CT4TD) theoretical framework for the generation of optimized personalized therapeutic strategies in cancer patients, based on optimal control theory and population dynamics modeling. The CT4TD framework can help clinicians in designing patient-specific therapeutic regimens, with the specific goal of optimizing the efficacy of the cure while reducing the costs, especially in terms of toxicity and adverse effects. CT4TD can be used at the time of the diagnosis in order to set optimized personalized therapies to reach selected target drug concentrations. Furthermore, if longitudinal data on patients under treatment are available, our approach introduces the possibility of adjusting the therapy with the explicit goal of minimizing the tumor burden measured in each case. As a case study, we present the application of CT4TD to Imatinib administration in Chronic Myeloid Leukemia, in which we show that the optimized therapeutic strategies are extremely diversified among patients, and display improvements with respect to the currently employed regimes. Interestingly, we prove that much of the variance in therapeutic response observed among patients is due to the individual differences in pharmacokinetics, rather than in pharmacodynamics.

Angaroni, F., Graudenzi, A., Rossignolo, M., Maspero, D., Calarco, T., Piazza, R., et al. (2019). Personalized therapy design for liquid tumors via optimal control theory [Altro] [10.1101/662858].

Personalized therapy design for liquid tumors via optimal control theory

ANGARONI, FABRIZIO
Primo
;
Graudenzi, Alex
Secondo
;
Maspero, Davide;Piazza, Rocco;Antoniotti, Marco
Ultimo
2019

Abstract

One of the key challenges in current cancer research is the development of reliable methods for the definition of personalized therapeutic strategies, based on increasingly available experimental data on single patients. To this end, methods from control theory can be effectively employed on patient-specific pharmacokinetic and pharmacodynamic models to generate robust data-driven experimental hypotheses. Here we introduce the Control Theory for Therapy Design (CT4TD) theoretical framework for the generation of optimized personalized therapeutic strategies in cancer patients, based on optimal control theory and population dynamics modeling. The CT4TD framework can help clinicians in designing patient-specific therapeutic regimens, with the specific goal of optimizing the efficacy of the cure while reducing the costs, especially in terms of toxicity and adverse effects. CT4TD can be used at the time of the diagnosis in order to set optimized personalized therapies to reach selected target drug concentrations. Furthermore, if longitudinal data on patients under treatment are available, our approach introduces the possibility of adjusting the therapy with the explicit goal of minimizing the tumor burden measured in each case. As a case study, we present the application of CT4TD to Imatinib administration in Chronic Myeloid Leukemia, in which we show that the optimized therapeutic strategies are extremely diversified among patients, and display improvements with respect to the currently employed regimes. Interestingly, we prove that much of the variance in therapeutic response observed among patients is due to the individual differences in pharmacokinetics, rather than in pharmacodynamics.
Altro
Si
Scientifica
Personalized therapy design; Control theory; RedCRAB; Chronic myleoid leukemia
English
preprint
Angaroni, F., Graudenzi, A., Rossignolo, M., Maspero, D., Calarco, T., Piazza, R., et al. (2019). Personalized therapy design for liquid tumors via optimal control theory [Altro] [10.1101/662858].
Angaroni, F; Graudenzi, A; Rossignolo, M; Maspero, D; Calarco, T; Piazza, R; Montangero, S; Antoniotti, M
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/232290
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