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 optimised 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, met{} allows to set optimised personalised 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 minimising or controlling the tumour 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 Leukaemia, in which we adopt a simplified model of cancer population dynamics. In particular, we show that the optimised therapeutic strategies are diversified among patients, and display improvements with respect to the current standard regime.
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