Promising strategies for overcoming cancer drug resistance: from nanomedicine to artificial intelligence
Chiara Martinelli, Marco Biglietti · Zenodo (CERN European Organization for Nuclear Research) · 2022
Cancer is one of the most diffused and deadly diseases worldwide. Unfortunately, due to the very heterogeneous nature of tumors, it has been very challenging finding efficient treatments. Standard clinical procedures present many adverse side effects and may often cause drug resistance with consequent therapy failure, onset of metastases and relapse. Combination therapy has demonstrated limited success due to the difficulties in matching different molecules pharmacokinetic properties and in tuning the best dosage in order to achieve the desired effects. Recently, innovations in the nanotechnology field have allowed to design ad hoc nanocarriers able to selectively deliver drugs to target cells and release them upon specific triggers. Artificial intelligence approaches have been also developed and advances in the computational modeling field have greatly impacted human healthcare. The possibility to exploit algorithms for predicting drug responsiveness based on data retrieved from databases is greatly improving clinical strategies and supporting therapeutic decisions. In this review, we report recent advances in the nanomedical and artificial intelligence fields and describe novel strategies adopted for counteracting cancer drug resistance. Limits and promises of these approaches are discussed, together with some examples of preclinical and clinical applications.