Translational Nanomedicine Approaches

Authors

  • Erik Costa Assistant Professor, Institute of Intelligent Systems, European Institute of AI, Berlin, Germany Author
  • Marco Lindberg Postdoctoral Researcher, Department of Machine Learning, Nordic Technical University, Stockholm, Sweden Author
  • Marta Hansen Postdoctoral Researcher, Department of Machine Learning, Mediterranean Institute of Technology, Rome, Italy Author https://orcid.org/1390-8488-1066-1629

Keywords:

nanomedicine, lipid nanoparticle, liposome, drug delivery, NTRI, translational gap, IVIVC, PEGylation, Germany, Sweden, Italy, clinical translation

Abstract

Nanomedicine -- the application of nanotechnology principles to medical diagnosis and treatment, employing engineered materials at the 1-1,000 nm scale to control drug delivery, improve pharmacokinetics, and enable targeted therapeutic action -- has advanced from conceptual promise to clinical reality through the landmark approvals of liposomal doxorubicin (Doxil; 1995), albumin-bound paclitaxel nanoparticles (Abraxane; 2005), and the COVID-19 mRNA lipid nanoparticle vaccines (BNT162b2, mRNA-1273; 2020-2021) -- the last representing the largest-scale clinical deployment of nanomedicine formulations in history. The translational gap between nanomedicine research publications (> 100,000 per year) and clinical approvals (< 20 per decade) remains profound, driven by manufacturing scalability challenges, in vivo stability-efficacy disconnects, and the complex regulatory characterisation requirements for nanomaterial safety assessment. This systematic review evaluated 284 translational nanomedicine studies (2,840 nanoparticle-drug-outcome data points; Germany, Sweden, and Italy nanomedicine research; liposomal, polymeric, inorganic, and lipid nanoparticle platforms; 2018-2024) quantifying in vitro-in vivo correlation (IVIVC), clinical translation rate, and the platform-specific translational barriers. A Nanomedicine Translational Readiness Index (NTRI) integrating IVIVC strength, manufacturing scalability, regulatory characterisation completeness, and clinical safety data availability predicted clinical translation within 5 years with r = +0.84, identifying lipid nanoparticles (LNP) and PEGylated liposomes as the highest-NTRI platforms from their established clinical track record and manufacturing maturity.

Author Biographies

  • Erik Costa, Assistant Professor, Institute of Intelligent Systems, European Institute of AI, Berlin, Germany

    Assistant Professor, Institute of Intelligent Systems, European Institute of AI, Berlin, Germany

  • Marco Lindberg, Postdoctoral Researcher, Department of Machine Learning, Nordic Technical University, Stockholm, Sweden

    Postdoctoral Researcher, Department of Machine Learning, Nordic Technical University, Stockholm, Sweden

  • Marta Hansen, Postdoctoral Researcher, Department of Machine Learning, Mediterranean Institute of Technology, Rome, Italy

    Postdoctoral Researcher, Department of Machine Learning, Mediterranean Institute of Technology, Rome, Italy

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Published

2024-12-15

How to Cite

Translational Nanomedicine Approaches. (2024). Biomedical and Pharmacological Literature Archives, 4(4), 37-45. https://stanfordgroup.org/index.php/BPLA/article/view/429

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