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<p>Lipid nanoparticles (LNPs) are essential carriers for genetic medicines, yet optimizing their design remains challenging due to numerous parameters. Computational methods—including molecular dynamics (MD), computational fluid dynamics (CFD), and machine learning (ML)—offer molecular insights and predictive power. This perspective highlights recent advances, ongoing challenges, and the need for multiscale modeling frameworks and standardized experimental datasets to systematically explore LNP design space and improve the efficacy of next-generation formulations.</p>

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Challenges and opportunities in computational studies for lipid nanoparticle development

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月1日

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  • Younghoon Oh,
  • Sean K. Bedingfield,
  • Severin T. Schneebeli,
  • Jianing Li,
  • Arezoo M. Ardekani,
  • Kyle J. Colston,
  • Scott P. Brown

摘要

Lipid nanoparticles (LNPs) are essential carriers for genetic medicines, yet optimizing their design remains challenging due to numerous parameters. Computational methods—including molecular dynamics (MD), computational fluid dynamics (CFD), and machine learning (ML)—offer molecular insights and predictive power. This perspective highlights recent advances, ongoing challenges, and the need for multiscale modeling frameworks and standardized experimental datasets to systematically explore LNP design space and improve the efficacy of next-generation formulations.