Artificial Intelligence in Novel Drug Delivery Systems

Abstract

Artificial Intelligence (AI) is rapidly transforming the field of Novel Drug Delivery Systems (NDDS) by enabling precise, adaptive, and patient-centered therapeutic approaches. Conventional drug delivery strategies often face limitations such as poor bioavailability, non-specific targeting, dose variability, and prolonged development timelines. The integration of AI technologies, including machine learning, deep learning, neural networks, and predictive analytics, has significantly improved the design, optimization, and evaluation of advanced drug delivery platforms. AI-driven models facilitate the prediction of physicochemical properties, drug–polymer interactions, release kinetics, nanoparticle behavior, and pharmacokinetic–pharmacodynamic relationships with high accuracy. Furthermore, AI supports the development of smart delivery systems capable of site-specific targeting, controlled release, and personalized medicine. In nanotechnology-based carriers such as liposomes, dendrimers, polymeric nanoparticles, micelles, and hydrogels, AI assists in formulation optimization and toxicity prediction, thereby reducing experimental burden and development costs. The application of AI in image analysis, biosensor integration, and real-time therapeutic monitoring has also accelerated innovations in stimuli-responsive and intelligent drug delivery systems. Despite its promising advantages, challenges including data quality, algorithm transparency, regulatory acceptance, cybersecurity, and ethical concerns remain significant barriers to widespread implementation. Nevertheless, continuous advancements in computational sciences, big data analytics, and interdisciplinary research are expected to strengthen the role of AI in future pharmaceutical development. This review highlights the current applications, advantages, limitations, and future perspectives of AI in NDDS, emphasizing its potential to revolutionize precision therapeutics and next-generation healthcare systems.