One of the key challenges in current cancer research is the development of computational strategies to support clinicians in the identification of successful personalized treatments. Control theory might be an effective approach to this end, as proven by the long-established application to therapy design and testing. In this respect, we here introduce the Control Theory for Therapy Design (CT4TD) framework, which employs optimal control theory on patient-specific pharmacokinetics (PK) and pharmacodynamics (PD) models, to deliver optimized therapeutic strategies. The definition of personalized PK/PD models allows to explicitly consider the physiological heterogeneity of individuals and to adapt the therapy accordingly, as opposed to standard clinical practices. CT4TD can be used in two distinct scenarios. At the time of the diagnosis, CT4TD allows to set optimized personalized administration strategies, aimed at reaching selected target drug concentrations, while minimizing the costs in terms of toxicity and adverse effects. Moreover, if longitudinal data on patients under treatment are available, our approach allows to adjust the ongoing therapy, by relying on simplified models of cancer population dynamics, with the goal of minimizing or controlling the tumor burden. CT4TD is highly scalable, as it employs the efficient dCRAB/RedCRAB optimization algorithm, and the results are robust, as proven by extensive tests on synthetic data. Furthermore, the theoretical framework is general, and it might be applied to any therapy for which a PK/PD model can be estimated, and for any kind of administration and cost. As a proof of principle, we present the application of CT4TD to Imatinib administration in Chronic Myeloid leukemia, in which we adopt a simplified model of cancer population dynamics. In particular, we show that the optimized therapeutic strategies are diversified among patients, and display improvements with respect to the current standard regime.
Angaroni, F., Graudenzi, A., Rossignolo, M., Maspero, D., Calarco, T., Piazza, R., et al. (2020). An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 8 [10.3389/fbioe.2020.00523].
|Citazione:||Angaroni, F., Graudenzi, A., Rossignolo, M., Maspero, D., Calarco, T., Piazza, R., et al. (2020). An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 8 [10.3389/fbioe.2020.00523].|
|Tipo:||Articolo in rivista - Articolo scientifico|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||Si|
|Titolo:||An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments|
|Autori:||Angaroni, F; Graudenzi, A; Rossignolo, M; Maspero, D; Calarco, T; Piazza, R; Montangero, S; Antoniotti, M|
ANGARONI, FABRIZIO (Primo)
GRAUDENZI, ALEX (Corresponding)
ANTONIOTTI, MARCO (Ultimo)
|Data di pubblicazione:||2020|
|Rivista:||FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.3389/fbioe.2020.00523|
|Appare nelle tipologie:||01 - Articolo su rivista|