TY - JOUR
T1 - Nanomedicine Ex Machina
T2 - between model-informed development and artificial intelligence
AU - Nova, Mônica Villa
AU - Lin, Tzu Ping
AU - Shanehsazzadeh, Saeed
AU - Jain, Kinjal
AU - Ng, Samuel Cheng Yong
AU - Wacker, Richard
AU - Chichakly, Karim
AU - Wacker, Matthias G.
PY - 2022/2/18
Y1 - 2022/2/18
N2 - 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.
AB - 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.
U2 - 10.3389/fdgth.2022.799341
DO - 10.3389/fdgth.2022.799341
M3 - Article
SN - 2673-253X
VL - 4
JO - Frontiers in Digital Health
JF - Frontiers in Digital Health
M1 - 799341
ER -
