Ofelia Pagani
Design and Experimental Validation of a Machine Learning Model to Predict the Interaction of Poly(β-amino esters) Nanoparticles with Melanoma Cells.
Rel. Valentina Alice Cauda, Nuria Oliva Jorge, Cristina Fornaguera, Micheal Bruyns Haylett. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2026
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Abstract
Melanoma is a highly aggressive skin cancer, characterised by high cellular heterogeneity and phenotypic plasticity, which promote the onset of therapeutic resistance and limit the effectiveness of conventional treatments. In this context, nucleic acid-based therapies represent a promising approach due to their targeted modulation thanks to their specificity. However, their clinical application is conditioned by the availability of delivery systems capable of transporting genetic material effectively and safely to target cells. Among non-viral strategies, poly(β-aminoesters) (pBAEs) polymers have gained relevance as promising vectors due to their biodegradability and low toxicity. In particular, pBAEs modified with terminal oligopeptides (OM-pBAE) offer improved cellular internalisation and excellent biocompatibility.
Nevertheless, the rational optimisation of these nanoparticles remains complex due to non-linear structure–function relationships and strong dependence on the cellular context
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