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Macrocyclic Drugs and Clinical Candidates: What Can Medicinal Chemists Learn from Their Properties?

Giordanetto, Fabrizio, Kihlberg, Jan·2014·Journal of Medicinal Chemistry

Abstract

Macrocycles are ideal in efforts to tackle "difficult" targets, but our understanding of what makes them cell permeable and orally bioavailable is limited. Analysis of approximately 100 macrocyclic drugs and clinical candidates revealed that macrocycles are predominantly used for infectious disease and in oncology and that most belong to the macrolide or cyclic peptide class. A significant number (N = 34) of these macrocycles are administered orally, revealing that oral bioavailability can be obtained at molecular weights up to and above 1 kDa and polar surface areas ranging toward 250 Å(2). Moreover, insight from a group of "de novo designed" oral macrocycles in clinical studies and understanding of how cyclosporin A and model cyclic hexapeptides cross cell membranes may unlock wider opportunities in drug discovery. However, the number of oral macrocycles is still low and it remains to be seen if they are outliers or if macrocycles will open up novel oral druggable space.

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Metadata

Authors
Giordanetto, F., & Kihlberg, J.
Venue
Journal of Medicinal Chemistry
Published
2013-09-17
Volume
57
Issue
2
Pages
278-295
ISSN
  • 0022-2623 (ISSN-L)
  • 1520-4804
OpenAlex
W1968711897
Cited by
563
References
3
FWCI
19.10
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Macrocyclic Drugs and Clinical Candidates: What Can Medicinal Chemists Learn from Their Properties? https://app.cochat.ai/papers/10.1021/jm400887j
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Giordanetto, F., & Kihlberg, J. (2014). Macrocyclic Drugs and Clinical Candidates: What Can Medicinal Chemists Learn from Their Properties? Journal of Medicinal Chemistry. https://doi.org/10.1021/jm400887j

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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.