{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:52:44Z","timestamp":1781877164270,"version":"3.54.5"},"posted":{"date-parts":[[2026,6,17]]},"group-title":"Bioengineering","reference-count":74,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.biorxiv.org\/about\/FAQ#license"}],"funder":[{"name":"National Cancer Institute","award":["R01 CA293906-01A1"],"award-info":[{"award-number":["R01 CA293906-01A1"]}]},{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"crossref","award":["R21 AI176764-01"],"award-info":[{"award-number":["R21 AI176764-01"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,6,17]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>\n                  Efficient, cell type-selective delivery of genetic payloads remains a central challenge in the development of gene and cell therapies. Lipid nanoparticles (LNPs) offer a versatile delivery platform, but their optimization is hindered by reliance on brute-force screening methods that are laborious, resource-intensive, and focus on single targets. Here, we present FALCON (Framework for Active Learning-driven Compositional Optimization of Nanoparticles), a closed-loop pipeline that leverages iterative screening, surrogate modeling, and multi-objective optimization to accelerate LNP compositional design. In B cell-targeted validation experiments, FALCON-optimized LNPs achieved a 1.8-fold increase in splenic B cell transfection\n                  <jats:italic>in vivo<\/jats:italic>\n                  compared with reference compositions. When optimized for selectivity, FALCON LNPs displayed an 84-fold improvement in selective transfection of splenic B cells over off-target liver populations and enabled spleen-tropic behavior across factorial panels of varying ionizable and helper lipid chemistries. In vaccine studies, these LNPs induced higher IgG2c antibody titers and a more Th1-biased immune profile. FALCON was also deployed to optimize LNPs for myeloid cell-selective delivery, achieving enhanced\n                  <jats:italic>in vivo<\/jats:italic>\n                  selectivity following systemic administration both across and within spleen and liver compartments. Our results establish FALCON as a useful tool for data-driven design of LNP compositions for precision gene delivery.\n                <\/jats:p>","DOI":"10.64898\/2026.06.13.731774","type":"posted-content","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T23:20:14Z","timestamp":1781738414000},"source":"Crossref","is-referenced-by-count":0,"title":["FALCON: Closed-Loop Multi-Objective Optimization of Lipid Nanoparticles for Cell-Selective mRNA Delivery"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7772-2528","authenticated-orcid":false,"given":"Wu Han","family":"Toh","sequence":"first","affiliation":[{"name":"Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA"},{"name":"Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA"},{"name":"Department of Biology, Johns Hopkins University, Baltimore, MD, USA"},{"name":"Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, 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