Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15640%2F25%3A73629837" target="_blank" >RIV/61989592:15640/25:73629837 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/25:10257469
Result on the web
<a href="https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.4c00744" target="_blank" >https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.4c00744</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1021/acs.molpharmaceut.4c00744" target="_blank" >10.1021/acs.molpharmaceut.4c00744</a>
Alternative languages
Result language
angličtina
Original language name
Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10403 - Physical chemistry
Result continuities
Project
<a href="/en/project/EH22_008%2F0004587" target="_blank" >EH22_008/0004587: Technology Beyond Nanoscale</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
MOLECULAR PHARMACEUTICS
ISSN
1543-8384
e-ISSN
1543-8392
Volume of the periodical
22
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
32
Pages from-to
"1110–1141"
UT code for WoS article
001409050600001
EID of the result in the Scopus database
2-s2.0-85217002683