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Cited by 284

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

Determination of half-maximal inhibitory concentration using biosensor-based protein interaction analysis

Open access
Aykul, Senem, Martinez-Hackert, Erik·2016·Analytical Biochemistry

Abstract

The half-maximal inhibitory concentration (IC50) is the most widely used and informative measure of a drug’s efficacy. It indicates how much drug is needed to inhibit a biological process by half, thus providing a measure of potency of an antagonist drug in pharmacological research. Most approaches to determine IC50 of a pharmacological compound are based on assays that utilize whole cell systems. While they generally provide outstanding potency information, results can depend on the experimental cell line used and may not differentiate a compound’s ability to inhibit specific interactions. Here we show using the secreted Transforming Growth Factor-β (TGF-β) family ligand BMP-4 and its receptors as example that surface plasmon resonance can be used to accurately determine IC50 values of individual ligand-receptor pairings. The molecular resolution achievable with this approach can help distinguish inhibitors that specifically target individual complexes, or that can inhibit multiple functional interactions at the same time.

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Metadata

Authors
Aykul, S., & Martinez-Hackert, E.
Venue
Analytical Biochemistry
Publisher
Elsevier BV
Published
2016-09
Volume
508
Pages
97-103
ISSN
0003-2697 (ISSN-L)
OpenAlex
W2471711949
Cited by
284
References
27
FWCI
2.86
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Determination of half-maximal inhibitory concentration using biosensor-based protein interaction analysis. https://app.cochat.ai/papers/10.1016/j.ab.2016.06.025
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Aykul, S., & Martinez-Hackert, E. (2016). Determination of half-maximal inhibitory concentration using biosensor-based protein interaction analysis. Analytical Biochemistry. https://doi.org/10.1016/j.ab.2016.06.025

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.