Artificial intelligence in nanotechnology for treatment of diseases

纳米技术 医学 材料科学
作者
Soroush Heydari,Niloofar Masoumi,Erfan Esmaeeli,Seyed Mohammad Ayyoubzadeh,Fatemeh Ghorbani‐Bidkorbeh,Mahnaz Ahmadi
出处
期刊:Journal of Drug Targeting [Taylor & Francis]
卷期号:32 (10): 1247-1266 被引量:52
标识
DOI:10.1080/1061186x.2024.2393417
摘要

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.
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