AI-Driven Personalized Nanomedicine for Cancer Therapy

Abstract

Two significant obstacles in the path of cancer therapeutics are that drugs do not always penetrate enough of a tumour, and that even identical drugs can have very different effects on patients. Nanomedicine solves the first issue by re-engineering the delivery of drugs, while artificial intelligence (AI) provides a solution to the second, in that AI can learn from patient-specific data instead of relying on population averages. This paper proposes a four-layered, structured conceptual framework that integrates the fields of AI and personalized nanomedicine in cancer therapeutics, including multi-omics patient data acquisition, machine-learning-assisted nanoparticle design, predictive modelling of biodistribution and therapeutic response, and closed-loop adaptive dosing. Based on trends observed in the nanomedicine and computational oncology community, we provide illustrative quantitative comparisons of simulated tumor-targeting efficiency, model learning curves, toxicity outcomes, and platform suitability, and demonstrate how methods like random forest, deep neural networks, and graph neural networks can in principle shorten the design-to-clinical translation pipeline. We compare and contrast the three main types of nanoparticle carriers (liposomal, polymeric, and inorganic) from the viewpoint of their ability to be tuned using AI; and we candidly consider the technical, regulatory, manufacturing, and ethical obstacles that stand in the way of their clinical translation. We end with a federated multi-institutional data sharing research agenda, explainable AI, standardised characterisation protocols, and adaptive regulatory pathways as prerequisites, in our view, to a scalable, equitable approach to AI-personalised cancer nanomedicine.