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PreprintCC BYChemRxiv

Computer Learning Can Streamline Development of Lipid Nanoparticle Vaccine Delivery Constructs

  • Eric H. Rosenn1,2
  • Max Engel3,4
  • Violet Munarova3,4
  • Maya Lipshitz3,4
  • Nata Edell3,4
  • Dania Halperin5,6
  • Eryn Fox7,8
8 affiliations
  1. 1Tandon School of Engineering
  2. 2New York University
  3. 3College of Osteopathic Medicine
  4. 4Touro University
  5. 5School of Medicine
  6. 6Tel Aviv University
  7. 7Department of Immunology
  8. 8Montefiore Medical Center
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Abstract

Lipid nanoparticle (LNP) delivery constructs provide a precise and targeted therapeutic platform for nucleic-acid and subunit vaccines. By shielding fragile antigens and genetic cargo from premature degradation, these constructs help vaccines overcome the physiological barriers that separate an injection site from its intracellular target, increasing specificity, potency, and the precision of controlled release. This capability has broadened the range of immunomodulatory targets in the fight against infectious disease and has opened the door to vaccines that treat tumors and cancer. Increasingly, artificial intelligence (AI) and machine learning (ML) are being applied to predict the LNP formulations that maximize mRNA-vaccine efficacy, and the same computational strategies are now being extended across diverse vaccine modalities. By approximating in vivo pharmacokinetics and pharmacodynamics, in silico methods promote rational, data-driven design that is reshaping the drug-development pipeline. In this Review we summarize current literature on the optimization of LNP delivery constructs for vaccine therapeutics using ML, survey the rapid clinical translation of LNP-mRNA technology since 2021, and discuss the limitations and predictive capabilities of computer-simulated experimental models.

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Cite

BibTeXRIS
BibTeX
@misc{rosenn2026computer,
  title = {{Computer Learning Can Streamline Development of Lipid Nanoparticle Vaccine Delivery Constructs}},
  author = {Eric H. Rosenn and Max Engel and Violet Munarova and Maya Lipshitz and Nata Edell and Dania Halperin and Eryn Fox},
  howpublished = {ChemRxiv},
  year = {2026},
  month = sep,
  doi = {10.26434/chemrxiv.15009316/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009316/v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Computer Learning Can Streamline Development of Lipid Nanoparticle Vaccine Delivery Constructs
AU  - Eric H. Rosenn
AU  - Max Engel
AU  - Violet Munarova
AU  - Maya Lipshitz
AU  - Nata Edell
AU  - Dania Halperin
AU  - Eryn Fox
PY  - 2026
DA  - 2026/09/22
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15009316/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009316/v1
AB  - Lipid nanoparticle (LNP) delivery constructs provide a precise and targeted therapeutic platform for nucleic-acid and subunit vaccines. By shielding fragile antigens and genetic cargo from premature degradation, these constructs help vaccines overcome the physiological barriers that separate an injection site from its intracellular target, increasing specificity, potency, and the precision of controlled release. This capability has broadened the range of immunomodulatory targets in the fight against infectious disease and has opened the door to vaccines that treat tumors and cancer. Increasingly, artificial intelligence (AI) and machine learning (ML) are being applied to predict the LNP formulations that maximize mRNA-vaccine efficacy, and the same computational strategies are now being extended across diverse vaccine modalities. By approximating in vivo pharmacokinetics and pharmacodynamics, in silico methods promote rational, data-driven design that is reshaping the drug-development pipeline. In this Review we summarize current literature on the optimization of LNP delivery constructs for vaccine therapeutics using ML, survey the rapid clinical translation of LNP-mRNA technology since 2021, and discuss the limitations and predictive capabilities of computer-simulated experimental models.
ER  -