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Cited by 361

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

The Challenge of Drugging Undruggable Targets in Cancer: Lessons Learned from Targeting BCL-2 Family Members

Verdine, Gregory L., Walensky, Loren D.·2007·Clinical Cancer Research

Abstract

The genomic and proteomic revolutions have provided us with an ever-increasing number of mechanistic insights into cancer pathogenesis. Mutated genes and pathologic protein products have emerged as the basis for modern anticancer drug development. With the increasing realization of the importance of disrupting oncogenic protein-protein interaction, new challenges have emerged for classical small molecule and protein-based drug modalities, i.e., the critical need to target flat and extended protein surfaces. Here, we highlight two distinct technologies that are being used to bridge the pharmacologic gap between small molecules and protein therapeutics. With the BCL-2 family of survival proteins as their substrate for intracellular targeting, we conclude that peptide stapling and fragment-based drug discovery show promise to traverse the critical surface features of proteins that drive human cancer.

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Crossref · 10.1158/1078-0432.ccr-07-2184

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Metadata

Authors
Verdine, G. L., & Walensky, L. D.
Venue
Clinical Cancer Research
Published
2007-12-15
Volume
13
Issue
24
Pages
7264-7270
ISSN
  • 1078-0432 (ISSN-L)
  • 1557-3265
OpenAlex
W2123196322
Cited by
361
References
5
FWCI
4.31
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The Challenge of Drugging Undruggable Targets in Cancer: Lessons Learned from Targeting BCL-2 Family Members. https://app.cochat.ai/papers/10.1158/1078-0432.ccr-07-2184
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Verdine, G. L., & Walensky, L. D. (2007). The Challenge of Drugging Undruggable Targets in Cancer: Lessons Learned from Targeting BCL-2 Family Members. Clinical Cancer Research. https://doi.org/10.1158/1078-0432.ccr-07-2184

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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.