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Cell penetration: scope and limitations by the application of cell-penetrating peptides: CPPS, TYPES, UPTAKE, TRAFFICKING, SELECTIVITY, AND CLINICAL STUDIES
Abstract
The penetration of polar or badly soluble compounds through a cell membrane into live cells requires mechanical support or chemical helpers. Cell-penetrating peptides (CPPs) are very promising chemical helpers. Because of their low cytotoxicity and final degradation to amino acids, they are particularly favored in in vivo studies and for clinical applications. Clearly, the future of CPP research is bright; however, the required optimization studies for each drug require considerable individualized attention. Thus, CPPs are not the philosopher's stone. As of today, a large number of such transporter peptides with very different sequences have been identified. These have different uptake mechanisms and can transport different cargos. Intracellular concentrations of cargos can reach a low micromole range and are able to influence intracellular reactions. Internalized ribonucleic acids such as small interfering RNA (siRNA) and mimics of RNA such as peptide nucleic acids, morpholino nucleic acids, and triesters of oligonucleotides can influence transcription and translation. Despite the highly efficient internalization of antibodies, enzymes, and other protein factors, as well as siRNA and RNA mimics, the uptake and stabile insertion of DNA into the genome of the host cells remain substantially challenging. This review describes a wide array of differing CPPs, cargos, cell lines, and tissues. The application of CPPs is compared with electroporation, magnetofection, lipofection, viral vectors, dendrimers, and nanoparticles, including commercially available products. The limitations of CPPs include low cell and tissue selectivity of the first generation and the necessity for formation of fusion proteins, conjugates, or noncovalent complexes to different cargos and of cargo release from intracellular vesicles. Furthermore, the noncovalent complexes require a strong molar excess of CPPs, and extensive experimentation is required to determine the most optimal CPP for any given cargo and cell type. Yet to predict which CPP is optimal for any given target remains a complex question. More recently, there have been promising developments: the enhancement of cell specificity using activatable CPPs, specific transport into cell organelles by insertion of corresponding localization sequences, and the transport of drugs through blood-brain barriers, through the conjunctiva of eyes, skin, and into nerve cells. Proteins, siRNA, and mimics of oligonucleotides can be efficiently transported into cells and have been tested for treatment of certain diseases. The recent state of the art in CPP research is discussed together with the overall scope, limitations, and some recommendations for future research directions.
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Metadata
- Authors
- Reissmann, S.
- Venue
- Journal of Peptide Science
- Publisher
- Wiley
- Published
- 2014-08-11
- Volume
- 20
- Issue
- 10
- Pages
- 760-784
- ISSN
- 1075-2617 (ISSN-L)
- OpenAlex
- W1825085207
- Cited by
- 266
- References
- 243
- FWCI
- 8.76
- Type
- journal article
- Language
- en
- Open access
- closed
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 ›
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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.