AI-driven optimisation of drug delivery systems for therapeutic enhanced efficacy

Abstract

By guaranteeing that medicinal materials reach the intended spot in the body with maximum efficacy and few side effects, drug delivery systems (DDS) are essential to modern medicine. However, time-consuming and expensive trial-and-error methods are frequently used in traditional drug delivery systems. Predictive modelling, formulation optimisation, and tailored therapy have all been made possible by the development of artificial intelligence (AI), especially machine learning and deep learning, which have drastically changed pharmaceutical research. This study examines how AI-assisted optimisation affects therapeutic results in drug delivery systems. Advanced drug carrier design, pharmacokinetic and pharmacodynamic behaviour prediction, and real-time drug release monitoring are all made possible by AI technologies. Additionally, specialized delivery methods like nanoparticle-based carriers, which increase drug bioavailability and lower toxicity, are made possible by AI-driven models. AI has a lot of promise, but there are still issues with data quality, regulations, and model interpretability. Future medication delivery is anticipated to be revolutionized by the integration of AI with digital health platforms, nanotechnology, and precision medicine. This study highlights important technologies, applications, difficulties, and opportunities while examining the function of AI-assisted optimization in drug delivery systems and assessing its potential to enhance therapeutic outcomes.