Intelligent deep learning model for targeted cancer drug delivery

Nanotechnology and information communication technology (ICT) are being combined to develop innovative drug delivery systems for targeted sites, such as tumor cells. The particulate targeted drug delivery (PTDD) system involves drugs containing nanoparticles embedded in nanoscale devices (referred to as bio-nanomachines) that can cross vascular barriers, resulting in an increased concentration of the drug in the targeted cell or tumor. An artificial intelligence bio-cyber interface (AIBCI) operates in both forward and reverse directions, enabling the transfer or control of a desired drug dose without affecting healthy cells, facilitated by the Internet of Biological Nano Things (IoBNT). This paper proposes a multi-compartmental model with an AI bio-cyber interface based on molecular communication technology. The proposed model is formulated as a set of multi-differential equations designed to identify molecular communication-based bio-nanomachines, enabling the quantification of drug concentration at the targeted cell. Unlike conventional compartmental models, the present model is designed to connect both the exterior and interior of the human body. The results suggest that the model has the potential to improve the capacity of target cells to respond to therapeutic drugs while reducing adverse effects on healthy cells. The intra-body nanonetwork proposed in the present study proved superior performance in magnetifying the drug concentrations in diseased cells.