Artificial intelligence-assisted design and optimization of stimuli-responsive nanocarriers for smart drug delivery

Stimuli-responsive nanostructures are a revolutionary breakthrough in the controlled delivery of drugs, allowing for their precise spatiotemporal control. These intelligent materials are designed to respond to internal stimuli (such as pH, redox gradients, enzymatic activity) or external cues (such as temperature, light, magnetic fields), thus offering greater flexibility and functionality in biomedical applications. Recent achievements have been aimed at the development of multi-stimuli-responsive systems, which utilize a combination of several stimuli to achieve sophisticated control, greater stability, and greater therapeutic accuracy in complex biological media. At the same time, the incorporation of artificial intelligence (AI) into the design and optimization of these nanostructures has brought about real, data-driven breakthroughs. Thus, supervised machine learning algorithms have been employed to predict the drug-loading efficiency and gene-delivery ability of lipid and polymeric nanoparticles based solely on their compositional characteristics, thus facilitating the rational selection of optimal formulations without the need for extensive experimental screening. Moreover, AI-based modeling tools have been shown to possess the capability to predict complete drug release profiles in response to varying pH or redox environments, thus enabling the pre-optimization of release kinetics tailored to specific pathological microenvironments. With the integration of patient-specific biological information such as genomic signatures and biomarker profiles, AI-assisted approaches also allow for the personalization of carrier composition and sensitivity to stimuli. This review offers a thorough examination of the latest developments in stimuli-responsive nanostructures and their integration with AI. This complementary combination is revolutionizing the way carriers are designed, shifting from trial-and-error methods to predictive and personalized drug delivery systems, thus propelling the development of next-generation precision nanomedicine.