Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Frontiers in Digital Health

Saeed Shanehsazzadeh¹  Mônica Villa Nova²  Tzu Ping Lin³  Samuel Cheng Yong Ng³  Kinjal Jain³  Matthias G. Wacker³  Richard Wacker⁴  Karim Chichakly⁵ 

[1] Biological Resources Imaging Laboratory, Mark Wainwright Analytical Centre, UNSW Sydney, Sydney, NSW, Australia;Department of Pharmacy, State University of Maringá, Maringá, Brazil;Wacker Research Lab, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore;YellowMap AG, Karlsruhe, Germany;isee systems, Lebanon, NH, United States;

关键词: nanomedicine;    liposomes;    nanoparticles;    artificial intelligence - AI;    design of experiment - DoE;    machine learning - ML;   

DOI  :  10.3389/fdgth.2022.799341

来源: DOAJ

【 摘 要 】

Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.

【 授权许可】

Unknown