{"doi":"10.1002/advs.202002020","title":"A Versatile and Robust Platform for the Scalable Manufacture of Biomimetic Nanovaccines","abstract":"Biomimetic strategies are useful for designing potent vaccines. Decorating a nanoparticulate adjuvant with cell membrane fragments as the antigen-presenting source exemplifies, such as a promising strategy. For translation, a standardizable, consistent, and scalable approach for coating nanoadjuvant with the cell membrane is important. Here a turbulent mixing and self-assembly method called flash nanocomplexation (FNC) for producing cell membrane-coated nanovaccines in a scalable manner is demonstrated. The broad applicability of this FNC technique compared with bulk-sonication by using ten different core materials and multiple cell membrane types is shown. FNC-produced biomimetic nanoparticles have promising colloidal stability and narrow particle polydispersity, indicating an equal or more homogeneous coating compared to the bulk-sonication method. The potency of a nanovaccine comprised of B16-F10 cancer cell membrane decorating mesoporous silica nanoparticles loaded with the adjuvant CpG is then demonstrated. The FNC-fabricated nanovaccines when combined with anti-CTLA-4 show potency in lymph node targeting, DC antigen presentation, and T cell immune activation, leading to prophylactic and therapeutic efficacy in a melanoma mouse model. This study advances the design of a biomimetic nanovaccine enabled by a robust and versatile nanomanufacturing technique.","journal":"Advanced Science","year":2021,"id":151171,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":85,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9497,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":319586,"name":"Chao Yang","orcid":"0000-0003-3623-4311","position":1,"is_corresponding":false},{"id":642508,"name":"Fan Zhang","orcid":"0000-0002-6823-2700","position":2,"is_corresponding":false},{"id":235102,"name":"Mingqiang Li","orcid":"0000-0002-5178-4138","position":3,"is_corresponding":false},{"id":642509,"name":"Zhaoxu Tu","orcid":"0000-0001-7020-5325","position":4,"is_corresponding":false},{"id":642510,"name":"Lizhong Mu","orcid":"0000-0002-7094-3309","position":5,"is_corresponding":false},{"id":249847,"name":"Jianati Dawulieti","orcid":"0000-0002-3171-8942","position":6,"is_corresponding":false},{"id":249850,"name":"Yeh‐Hsing Lao","orcid":"0000-0002-7990-199X","position":7,"is_corresponding":false},{"id":642511,"name":"Zixuan Xiao","orcid":"0009-0005-2107-2067","position":8,"is_corresponding":false},{"id":252751,"name":"Huize Yan","orcid":null,"position":9,"is_corresponding":false},{"id":269799,"name":"Wen Sun","orcid":"0000-0003-4316-5350","position":10,"is_corresponding":false},{"id":78559,"name":"Dan Shao","orcid":"0000-0002-5243-042X","position":11,"is_corresponding":false},{"id":235103,"name":"Kam W. Leong","orcid":"0000-0002-8133-4955","position":12,"is_corresponding":false},{"id":249851,"name":"Hanze Hu","orcid":"0000-0003-2109-1311","position":0,"is_corresponding":true}],"reference_count":52,"raw_metadata":null,"created_at":"2026-07-18T23:43:11.293086Z","pmid":"34386315","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}