Journal
BIOMATERIALS
Volume 266, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.biomaterials.2020.120469
Keywords
Immune; Immunotherapy; Nanomaterial design; Database; Machine learning
Funding
- National Key Research and Development Project [2019YFC1804603]
- National Natural Science Foundation of China [21722703, 31770550]
- Ministry of Education of China [T2017002]
- Natural Science Foundation of Tianjin City [19JCJQJC62500, 18JCYBJC23600]
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Exploring the interactions between the immune system and nanomaterials is crucial for designing safe and effective nanomaterials. A lack of databases on these interactions hinders the discovery of new nanomaterials for immunotherapy. Biomimetic nanocoating to enhance immune system clearance of nanomaterials is a promising strategy.
Exploring the interactions between the immune system and nanomaterials (NMs) is critical for designing effective and safe NMs, but large knowledge gaps remain to be filled prior to clinical applications (e.g., immunotherapy). The lack of databases on interactions between the immune system and NMs affects the dis-covery of new NMs for immunotherapy. Complement activation and inhibition by NMs have been widely studied, but the general rules remain unclear. Biomimetic nanocoating to promote the clearance of NMs by the immune system is an alternative strategy for the immune response mediation of the biological corona. Immune response predictions based on NM properties can facilitate the design of NMs for immunotherapy, and artificial intelligences deserve much attention in the field. This review addresses the knowledge gaps regarding immune response and immunotherapy in relation to NMs, effective immunotherapy and material design without adverse immune responses.
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