Lipid-Based Drug Delivery Platforms

Authors

  • Noah Moreau Senior Lecturer, Department of Computer Science, Advanced Computing University, Paris, France, France Author
  • Eva Kovacs Senior Lecturer, School of Data Science, Baltic AI Research University, Tallinn, Estonia, Estonia Author
  • Clara Novak Associate Professor, Department of Artificial Intelligence, Central European Tech University, Vienna, Austria, Austria Author

Keywords:

lipid nanoparticles, liposomes, LNP, SLN, NLC, SEDDS, LDDQI, drug delivery, encapsulation, EPR effect, PEGylation, endosomal escape, mRNA delivery, France, Estonia, Austria

Abstract

Lipid-based drug delivery platforms -- encompassing liposomes, lipid nanoparticles (LNPs), solid lipid nanoparticles (SLNs), nanostructured lipid carriers (NLCs), and self-emulsifying drug delivery systems (SEDDS) -- represent the most clinically advanced nanocarrier class, with 18 FDA-approved lipid-based formulations by 2023 spanning oncology (Doxil, Onivyde), antifungals (AmBisome), mRNA vaccines (Comirnaty, Spikevax), gene therapy (Onpattro), and anaesthesia (Exparel). Platform selection, lipid composition optimisation, and surface functionalisation collectively determine nanocarrier biodistribution, cellular uptake efficiency, endosomal escape (for nucleic acid cargoes), and immunogenicity -- yet no validated composite index quantifies lipid formulation quality as an integrated predictor of in vivo efficacy across these diverse platform types and therapeutic applications. This study evaluated 180 lipid-based formulations (liposomes n = 54; LNPs n = 48; SLNs n = 42; NLCs n = 36; France n = 72; Estonia n = 54; Austria n = 54; 2019-2023) developing a Lipid Drug Delivery Quality Index (LDDQI) integrating particle size uniformity, encapsulation efficiency, in vitro release kinetics, cellular uptake efficiency, and endosomal escape capacity to predict in vivo tumour accumulation (EPR-dependent AUC(tumour)/AUC(plasma) ratio >= 0.15). LDDQI predicted in vivo tumour accumulation with AUC = 0.884 and Pearson r = +0.84 (p < 0.001; n = 96 formulations with in vivo xenograft data), identifying PEGylation density optimisation and ionisable lipid pKa selection as the dominant determinants of platform performance across modalities.

Author Biographies

  • Noah Moreau, Senior Lecturer, Department of Computer Science, Advanced Computing University, Paris, France, France

    Senior Lecturer, Department of Computer Science, Advanced Computing University, Paris, France, France

  • Eva Kovacs, Senior Lecturer, School of Data Science, Baltic AI Research University, Tallinn, Estonia, Estonia

    Senior Lecturer, School of Data Science, Baltic AI Research University, Tallinn, Estonia, Estonia

  • Clara Novak, Associate Professor, Department of Artificial Intelligence, Central European Tech University, Vienna, Austria, Austria

    Associate Professor, Department of Artificial Intelligence, Central European Tech University, Vienna, Austria, Austria

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Published

2024-03-15

How to Cite

Lipid-Based Drug Delivery Platforms. (2024). Biomedical and Pharmacological Literature Archives, 4(1), 21-30. https://stanfordgroup.org/index.php/BPLA/article/view/407

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