The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Biophysical Interactions with Model Lipid Membranes: Applications in Drug Discovery and Drug Delivery
Open accessAbstract
The transport of drugs or drug delivery systems across the cell membrane is a complex biological process, often difficult to understand because of its dynamic nature. In this regard, model lipid membranes, which mimic many aspects of cell-membrane lipids, have been very useful in helping investigators to discern the roles of lipids in cellular interactions. One can use drug-lipid interactions to predict pharmacokinetic properties of drugs, such as their transport, biodistribution, accumulation, and hence efficacy. These interactions can also be used to study the mechanisms of transport, based on the structure and hydrophilicity/hydrophobicity of drug molecules. In recent years, model lipid membranes have also been explored to understand their mechanisms of interactions with peptides, polymers, and nanocarriers. These interaction studies can be used to design and develop efficient drug delivery systems. Changes in the lipid composition of cells and tissue in certain disease conditions may alter biophysical interactions, which could be explored to develop target-specific drugs and drug delivery systems. In this review, we discuss different model membranes, drug-lipid interactions and their significance, studies of model membrane interactions with nanocarriers, and how biophysical interaction studies with lipid model membranes could play an important role in drug discovery and drug delivery.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Pluronic® block copolymers as novel polymer therapeutics for drug and gene delivery
Kabanov, A. V., Batrakova, E. V., & Alakhov, V. Y. (2002) · Journal of Controlled Release · doi:10.1016/s0168-3659(02)00009-3 · Cited by 1,474
Membranes: A meeting point for lipids, proteins and therapies
Escribá, P. V., González‐Ros, J. M., Goñi, F. M., Kinnunen, P. K. J., Vigh, L., Sánchez‐Magraner, L., Fernández, A. M., Busquets, X., Horváth, I., & Barceló‐Coblijn, G. (2008) · Journal of Cellular and Molecular Medicine · doi:10.1111/j.1582-4934.2008.00281.x · Cited by 454
Cited by
A Review of the Structure, Preparation, and Application of NLCs, PNPs, and PLNs
Li, Q., Cai, T., Huang, Y., Xia, X., Cole, S., & Cai, Y. (2017) · Nanomaterials · doi:10.3390/nano7060122 · Cited by 282
Metadata
- Authors
- Peetla, C., Stine, A., & Labhasetwar, V.
- Venue
- Molecular Pharmaceutics
- Publisher
- American Chemical Society (ACS)
- Published
- 2009-05-26
- Volume
- 6
- Issue
- 5
- Pages
- 1264-1276
- ISSN
- 1543-8384 (ISSN-L)
- 1543-8392
- OpenAlex
- W2091402748
- Cited by
- 559
- References
- 2
- FWCI
- 5.76
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.