AI -Driven Nanomedicine for Drug Delivery: Interplay of Nanocarrier Design, Biologicalbarrier and Therapeutic Performance

By enabling individualised diagnostics and treatments, artificial intelligence (AI) is revolutionising the management of chronic illnesses. In this context, nanotechnology-based drug delivery systems support patient-specific interventions through precise and data-driven design.21 Because of their small size and adjustable surface characteristics, nanocarriers provide better efficacy, stability, and decreased toxicity when compared to traditional drug formulations. For the delivery of drugs, numerous organic and inorganic nanocarriers have been created. Important characterisation techniques, translational difficulties, and the developing role of artificial intelligence in nanocarrier design and optimisation are highlighted in this review.22 The rational design of programmable and stimuli-responsive drug delivery systems is made possible by the convergence of artificial intelligence and nano architectonics. This strategy moves nanomedicine from empirical formulation to intelligent, clinically transferable precision therapeutics by combining data-driven target identification, nanocarrier optimisation, and predictive modelling.23 By enabling predictive, data-driven nanocarrier design, artificial intelligence and big data analytics are revolutionising drug delivery based on nanomaterials. AI enhances reproducibility, speeds up optimisation, and facilitates clinical translation by combining physicochemical, pharmacokinetic, and biological datasets. AI-assisted nanomedicine is now positioned as a major force behind precision and patient- centered treatments thanks to this convergence.24 Improved cancer treatment is possible with targeted drug delivery using nanocarriers, but this approach is constrained by intricate tumour biology and nano-bio interactions. Artificial intelligence speeds up the creation of efficient, tumor-specific cancer treatments by enabling data-driven nanocarrier design and predictive optimisation.25 The convergence of artificial intelligence and nanomedicine is transforming targeted drug delivery and personalised therapeutics by enabling predictive modelling, optimised pharmacokinetics, and reduced experimental burden. Despite challenges in data standardisation, transparency, and regulation, interdisciplinary collaboration is essential to fully realise the clinical potential of AI-enabled nanomedicine.26 Drug delivery by nanoparticles promises targeted and controlled therapeutics, and is however hampered by difficult design issues. Artificial intelligence (AI) provides data-driven approaches to optimize NP properties, predict their biological interactions and speed up formulation development. This review showcases recent AI-based strategies and challenges along with prospect in smart nanomedicine.27