PMID- 35283223
OWN - NLM
STAT- MEDLINE
DCOM- 20220426
LR  - 20220519
IS  - 1872-8294 (Electronic)
IS  - 0169-409X (Linking)
VI  - 184
DP  - 2022 May
TI  - Artificial intelligence to bring nanomedicine to life.
PG  - 114194
LID - S0169-409X(22)00084-9 [pii]
LID - 10.1016/j.addr.2022.114194 [doi]
AB  - The technology of drug delivery systems (DDSs) has demonstrated an outstanding 
      performance and effectiveness in production of pharmaceuticals, as it is proved 
      by many FDA-approved nanomedicines that have an enhanced selectivity, manageable 
      drug release kinetics and synergistic therapeutic actions. Nonetheless, to date, 
      the rational design and high-throughput development of nanomaterial-based DDSs 
      for specific purposes is far from a routine practice and is still in its infancy, 
      mainly due to the limitations in scientists' capabilities to effectively acquire, 
      analyze, manage, and comprehend complex and ever-growing sets of experimental 
      data, which is vital to develop DDSs with a set of desired functionalities. At 
      the same time, this task is feasible for the data-driven approaches, high 
      throughput experimentation techniques, process automatization, artificial 
      intelligence (AI) technology, and machine learning (ML) approaches, which is 
      referred to as The Fourth Paradigm of scientific research. Therefore, an 
      integration of these approaches with nanomedicine and nanotechnology can 
      potentially accelerate the rational design and high-throughput development of 
      highly efficient nanoformulated drugs and smart materials with pre-defined 
      functionalities. In this Review, we survey the important results and milestones 
      achieved to date in the application of data science, high throughput, as well as 
      automatization approaches, combined with AI and ML to design and optimize DDSs 
      and related nanomaterials. This manuscript mission is not only to reflect the 
      state-of-art in data-driven nanomedicine, but also show how recent findings in 
      the related fields can transform the nanomedicine's image. We discuss how all 
      these results can be used to boost nanomedicine translation to the clinic, as 
      well as highlight the future directions for the development, data-driven, high 
      throughput experimentation-, and AI-assisted design, as well as the production of 
      nanoformulated drugs and smart materials with pre-defined properties and 
      behavior. This Review will be of high interest to the chemists involved in 
      materials science, nanotechnology, and DDSs development for biomedical 
      applications, although the general nature of the presented approaches enables 
      knowledge translation to many other fields of science.
CI  - Copyright (c) 2022 Elsevier B.V. All rights reserved.
FAU - Serov, Nikita
AU  - Serov N
AD  - International Institute "Solution Chemistry of Advanced Materials and 
      Technologies", ITMO University, Saint-Petersburg 191002, Russian Federation.
FAU - Vinogradov, Vladimir
AU  - Vinogradov V
AD  - International Institute "Solution Chemistry of Advanced Materials and 
      Technologies", ITMO University, Saint-Petersburg 191002, Russian Federation. 
      Electronic address: vinogradov@scamt-itmo.ru.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Review
DEP - 20220310
PL  - Netherlands
TA  - Adv Drug Deliv Rev
JT  - Advanced drug delivery reviews
JID - 8710523
RN  - 0 (Smart Materials)
SB  - IM
MH  - Artificial Intelligence
MH  - Humans
MH  - Machine Learning
MH  - *Nanomedicine
MH  - Nanotechnology
MH  - *Smart Materials
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Data science
OT  - Fourth paradigm
OT  - Machine learning
OT  - Materials science
OT  - Nanomedicine
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2022/03/15 06:00
MHDA- 2022/04/27 06:00
CRDT- 2022/03/14 05:42
PHST- 2021/12/16 00:00 [received]
PHST- 2022/03/04 00:00 [revised]
PHST- 2022/03/07 00:00 [accepted]
PHST- 2022/03/15 06:00 [pubmed]
PHST- 2022/04/27 06:00 [medline]
PHST- 2022/03/14 05:42 [entrez]
AID - S0169-409X(22)00084-9 [pii]
AID - 10.1016/j.addr.2022.114194 [doi]
PST - ppublish
SO  - Adv Drug Deliv Rev. 2022 May;184:114194. doi: 10.1016/j.addr.2022.114194. Epub 
      2022 Mar 10.