AI-Driven Bioadaptive Nanocarrier System for Precision Drug Delivery in Human Physiological Environments

Abstract

Conventional nanocarrier drug delivery systems rely on fixed, pre-programmed release triggers that cannot adapt to the substantial physiological variability encountered across patients, tissues, and disease states. This paper presents a conceptual and simulation-based framework for an AI-driven bioadaptive nanocarrier (AI-BNC) system capable of sensing local physiological cues — including pH, redox potential, enzyme concentration, and temperature — and dynamically adjusting drug release kinetics through an embedded edge-AI decision module coupled to a stimuli-responsive polymer shell. The proposed architecture integrates a reinforcement-learning-based release-policy controller, a convolutional feature encoder for biosensor fusion, and a federated digital-twin training pipeline that allows the system to generalize across simulated patient populations without centralizing sensitive physiological data. We describe the nanocarrier's structural design, the AI perception-decision pipeline, and a physiologically grounded simulation environment used to evaluate performance. Simulated results indicate that the AI-adaptive design achieves substantially higher selectivity between diseased and healthy tissue compartments than passive or single-stimulus-responsive carriers, with an approximate 5.1-fold target-to-off-target release ratio and a 67% reduction in a simulated off-target toxicity index relative to passive controls. We discuss translational challenges, including biocompatibility of embedded electronic components, regulatory pathways for AI-integrated combination products, and manufacturing scalability, and outline a roadmap for progressing from simulation to in vitro and in vivo validation. This work is intended as a design and evaluation framework to guide future experimental and computational research in intelligent, physiologically responsive drug delivery.