Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27740/25:10257469

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

<a href="/cs/project/EH22_008%2F0004587" target="_blank" >EH22_008/0004587: Technologie za hranicí nanosvěta</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

MOLECULAR PHARMACEUTICS

ISSN

1543-8384

e-ISSN

1543-8392

Svazek periodika

22

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

32

Strana od-do

"1110–1141"

Kód UT WoS článku

001409050600001

EID výsledku v databázi Scopus

2-s2.0-85217002683