The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Drug delivery and drug targeting with parenteral lipid nanoemulsions — A review
Open accessAbstract
Lipid nanosized emulsions or nanoemulsions (NE) are oil in water dispersions with an oil droplet size of about 200nm. This size of oil droplets dispersed in a continuous water phase is a prerequisite for the parenteral, namely intravenous administration. Many parenteral nutrition and drug emulsions on the market confirm the safe use of NE over years. Parenteral emulsions loaded with APIs (active pharmaceutical ingredients) are considered as drug delivery systems (DDS). DDS focuses on the regulation of the in vivo dynamics, such as absorption, distribution, metabolism, and extended bioavailability, thereby improving the effectiveness and the safety of the drugs. Using an emulsion as a DDS, or through the use of surface diversification of the dispersed oil droplets of emulsions, a targeted increase of the API concentration in some parts of the human body can be achieved. This review focuses on NE similar to the marketed once with no or only low amount of additional surfactants beside the emulsifier from a manufacturing point of view (technique, used raw materials).
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Cites
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Metadata
- Authors
- Hörmann, K., & Zimmer, A.
- Venue
- Journal of Controlled Release
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 223
- Pages
- 85-98
- ISSN
- 0168-3659 (ISSN-L)
- OpenAlex
- W2251529759
- Cited by
- 264
- References
- 55
- FWCI
- 7.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Hörmann, K., & Zimmer, A. (2016). Drug delivery and drug targeting with parenteral lipid nanoemulsions — A review. Journal of Controlled Release. https://doi.org/10.1016/j.jconrel.2015.12.016
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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.
