The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Cell-Permeable Cyclic Peptides from Synthetic Libraries Inspired by Natural Products
Abstract
Drug design efforts are turning to a new generation of therapeutic targets, such as protein-protein interactions (PPIs), that had previously been considered "undruggable" by typical small molecules. There is an emerging view that accessing these targets will require molecules that are larger and more complex than typical small molecule drugs. Here, we present a methodology for the discovery of geometrically diverse, membrane permeable cyclic peptide scaffolds based on the synthesis and permeability screening of a combinatorial library, followed by deconvolution of membrane-permeable scaffolds to identify cyclic peptides with good to excellent passive cell permeabilities. We use a combination of experimental and computational approaches to investigate structure-permeability relationships in one of these scaffolds, and uncover structural and conformational factors that govern passive membrane diffusion in a related set of cyclic peptide diastereomers. Further, we investigate the dependency of permeability on side-chain identity of one of these scaffolds through single-point diversifications to show the adaptability of these scaffolds toward development of permeability-biased libraries suitable for bioactivity screens. Overall, our results demonstrate that many novel, cell permeable scaffolds exist beyond those found in extant natural products, and that such scaffolds can be rapidly identified using a combination of synthesis and deconvolution which can, in principle, be applied to any type of macrocyclic template.
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
How many drug targets are there?
Overington, J. P., Al-Lazikani, B., & Hopkins, A. L. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd2199 · Cited by 3,505
Kansy, M., Senner, F., & Gubernator, K. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm970530e · Cited by 1,304
Direct inhibition of the NOTCH transcription factor complex
Moellering, R. E., Cornejo, M., Davis, T. N., Bianco, C. D., Aster, J. C., Blacklow, S. C., Kung, A. L., Gilliland, D. G., Verdine, G. L., & Bradner, J. E. (2009) · Nature · doi:10.1038/nature08543 · Cited by 752
PAMPA—critical factors for better predictions of absorption
Avdeef, A., Bendels, S., Di, L. I., Faller, B., Kansy, M., Sugano, K., & Yamauchi, Y. (2007) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.21068 · Cited by 240
Cited by
The evolution of commercial drug delivery technologies
Vargason, A. M., Anselmo, A. C., & Mitragotri, S. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00698-w · Cited by 1,361
Intracellular Delivery by Membrane Disruption: Mechanisms, Strategies, and Concepts
Stewart, M. P., Langer, R., & Jensen, K. F. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.7b00678 · Cited by 747
A Comprehensive Review on Current Advances in Peptide Drug Development and Design
Lee, A. C.-L., Harris, J. L., Khanna, K. K., & Hong, J.-H. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20102383 · Cited by 686
New Modalities for Challenging Targets in Drug Discovery
Valeur, E., Guéret, S. M., Adihou, H., Gopalakrishnan, R., Lemurell, M., Waldmann, H., Grossmann, T. N., & Plowright, A. T. (2017) · Angewandte Chemie International Edition · doi:10.1002/anie.201611914 · Cited by 382
Cyclic peptide drugs approved in the last two decades (2001–2021)
Zhang, H., & Chen, S. (2022) · RSC Chemical Biology · doi:10.1039/d1cb00154j · Cited by 319
Quantifying the chameleonic properties of macrocycles and other high-molecular-weight drugs
Whitty, A., Zhong, M., Viarengo, L., Beglov, D., Hall, D. R., & Vajda, S. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.02.005 · Cited by 285
Bockus, A. T., Schwochert, J. A., Pye, C. R., Townsend, C. E., Sok, V., Bednarek, M. A., & Lokey, R. S. (2015) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b00919 · Cited by 135
Self-citationDrug‐Like Properties in Macrocycles above MW 1000: Backbone Rigidity versus Side‐Chain Lipophilicity
Furukawa, A., Schwochert, J., Pye, C. R., Asano, D., Edmondson, Q. D., Turmon, A. C., Klein, V. G., Ono, S., Okada, O., & Lokey, R. S. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.202004550 · Cited by 79
Self-citation
Metadata
- Authors
- Hewitt, W. M., Leung, S. S. F., Pye, C. R., Ponkey, A. R., Bednarek, M., Jacobson, M. P., & Lokey, R. S.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-01-08
- Volume
- 137
- Issue
- 2
- Pages
- 715-721
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W2332082155
- Cited by
- 210
- References
- 4
- FWCI
- 8.15
- 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 ›
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.