Annabelle Ballesta, hôpital Paul Brousse

Multi-scale Systems Pharmacology to Personalize Anticancer Drug Combination and Schedule

vendredi 22 janvier 2021, 11h00 - 12h00

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The quest for personalized cancer management has fostered the development of new technologies enabling the longitudinal assessment of patient- and tumor-specific features at the cellular, tissue and whole organism scales. To ensure the translation of these multi-type datasets into individualized therapies and subsequent patient benefit, systems medicine approaches are required. Hence, my team designs systems pharmacology methodologies for the personalization of anticancer drug combinations and timing. The developped mathematical models represent ,through ordinary differential equations, the intracellular networks of proteins involved in drug pharmacokinetics-pharmacodynamics (PK-PD), DNA damage response, cell proliferation and cell death, which constitute a reliable physiological basis for the prediction of drug cytotoxicity.  I will presnt first a « proof of concept » of therapeutic optimization building on the identification of molecular and dynamical differences between the tumor and healthy organs which are targets of treatment toxicities. Next, because this complex molecular physiology and its temporal organization are unlikely to be completely assessed directly in individual cancer patients, I will present multi-scale methodologies integrating in vitro, pre-clinical and clinical investigations towards the design of patient-specific models and multi-drug therapies, in the context of brain tumors. Finally, physiological rhythms over the 24h span are further included as a major domain of host-tumor differences since normal tissues usually display a robust circadian organization that may be disrupted in malignant tumors. I will present a multi-scale pipeline to personalize cancer chronotherapy connecting ODE-based PK-PD models with partial differential equations to represent drug transport in the infusion pump and to machine leraning to initate patient stratification.