Description

Osteosarcomas are bone tumours, primarily affecting children and adolescents, displaying a high risk of metastasis. Moreover, the 5-year survival rate is less than 25%, and high recurrence rates are observed. The clinical treatment combining surgery and chemotherapy faces two major problems: high doses of anticancer agents lead to severe side effects, which may be delayed, and the resistance of tumour cells to treatments is increasing.

Through specific computational studies, i.e. by using molecular dynamics and quantum calculations in the framework of DFT, we propose to investigate two alternatives to these problems: (i) a new route of treatment administration with vectorization using biomimetic synthetic apatites for local delivery in the bone of active molecules already used for osteosarcomas treatments, and (ii) a way to overcome resistance to anticancer agents by chemically modifying drug molecules and increasing their effectiveness. Overall, this will reduce the patient's drug doses. Thanks to computational simulations, biomimetic apatites for vectorization of anticancer agents or their model molecules will be studied in relation to delivery properties. In parallel, the mechanism of action (MoA) of these molecules will be studied at the molecular level to propose and validate modified anticancer agents. In a last step, classical molecular dynamics calculations will produce a new model of biomimetic apatites that can be used for the adsorption of large molecules. These computational studies will also provide answers regarding the possibility of co-adsorbing anticancer agents and other drug molecules (analgesics, painkillers, antibiotics...). 

Developing computational models for the in-silico investigation of drug treatments is essential for accelerating and optimizing the development of new therapeutic strategies. In the specific context of osteosarcoma, we propose to investigate both optimal drug-delivery strategies and the design of chemically modified molecules. These computational studies will enable the identification and prioritization of the most promising candidates, thereby reducing the number of compounds for subsequent in vitro and in vivo tests.

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