Advances in artificial intelligence for drug delivery and development: A comprehensive review

被引:16
|
作者
Gholap A.D. [1 ]
Uddin M.J. [2 ]
Faiyazuddin M. [3 ,4 ]
Omri A. [5 ]
Gowri S. [6 ]
Khalid M. [7 ,8 ,9 ]
机构
[1] Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Maharashtra, Palghar
[2] Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur
[3] School of Pharmacy, Al–Karim University, Bihar, Katihar
[4] Centre for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu
[5] Department of Chemistry and Biochemistry, The Novel Drug and Vaccine Delivery Systems Facility, Laurentian University, Sudbury, P3E 2C6, ON
[6] PG & Research, Department of Physics, Cauvery College for Women, Tamil Nadu, Tiruchirapalli
[7] James Watt School of Engineering, University of Glasgow, Glasgow
[8] Sunway Centre for Electrochemical Energy and Sustainable Technology (SCEEST), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Selangor Darul Ehsan, Bandar Sunway
[9] University Centre for Research and Development, Chandigarh University, Punjab, Mohali
关键词
Artificial intelligence; Drug design; Drug discovery; Nanorobots; Personalized medicine; Sensor-based devices;
D O I
10.1016/j.compbiomed.2024.108702
中图分类号
学科分类号
摘要
Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas. © 2024
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