Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble in water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design of LNCs, a detailed understanding of the composition-structure-function relationships is missing. This review presents currently available computational methods for LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, with the focus on molecular dynamics simulations in all-atom and coarse-grained resolution. Their strengths and weaknesses are discussed, highlighting the aspects necessary for obtaining reliable results in the simulations. Furthermore, a machine learning, i.e., data-based learning, approach to the design of lipid-mediated API delivery is introduced. The data produced by the experimental and theoretical approaches provide valuable insights. Processing these data can help optimize the design of LNCs for better performance. In the final section of this Review, state-of-the-art of computer simulations of LNCs are reviewed, specifically addressing the compatibility of experimental and computational insights.

• Klíčová slova
• ionizable lipid, lipid nanocarrier, lipid nanoparticle, liposome, molecular simulation, vesicle,
• MeSH
• léčivé přípravky chemie   aplikace a dávkování   MeSH
• lékové transportní systémy * metody   MeSH
• lidé MeSH
• lipidy * chemie   MeSH
• nanočástice chemie   MeSH
• nosiče léků * chemie   MeSH
• počítačová simulace MeSH
• simulace molekulární dynamiky MeSH
• strojové učení MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Názvy látek
• léčivé přípravky MeSH
• lipidy * MeSH
• nosiče léků * MeSH