A quantitative analysis of kinase inhibitor selectivity
Abstract
Kinase inhibitors are a new class of therapeutics with a propensity to inhibit multiple targets. The biological consequences of multi-kinase activity are poorly defined, and an important step toward understanding the relationship between selectivity, efficacy and safety is the exploration of how inhibitors interact with the human kinome. We present interaction maps for 38 kinase inhibitors across a panel of 317 kinases representing >50% of the predicted human protein kinome. The data constitute the most comprehensive study of kinase inhibitor selectivity to date and reveal a wide diversity of interaction patterns. To enable a global analysis of the results, we introduce the concept of a selectivity score as a general tool to quantify and differentiate the observed interaction patterns. We further investigate the impact of panel size and find that small assay panels do not provide a robust measure of selectivity.
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No correlation was found between frequency of degradation and maximum observed protein abundance fold change for each kinase, with knowledge metrics such as the pubmed score and the number of entries in the PDB.
“We found no correlation between these variables, consistent with evidence that kinase inhibitor pharmacophores display a high degree of polypharmacology”
From Mapping the Degradable Kinome Provides a Resource for Expedited Degrader Development (2020) · cites this paper as Karaman et al., 2008
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- Authors
- Karaman, M. W., Herrgard, S., Treiber, D. K., Gallant, P., Atteridge, C. E., Campbell, B. T., Chan, K. W., Ciceri, P., Davis, M. I., Edeen, P. T., Faraoni, R., Floyd, M., Hunt, J. P., Lockhart, D. J., Milanov, Z. V., Morrison, M. J., Pallares, G., Patel, H. K., Pritchard, S., Wodicka, L. M., & Zarrinkar, P. P.
- Affiliations
- Tufts University (US)
- Corresponding author
- Zarrinkar, Patrick P
- Venue
- Nature Biotechnology
- Published
- 2008-01
- Volume
- 26
- Issue
- 1
- Pages
- 127-132
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2100772783
- Cited by
- 2,466
- References
- 35
- Citations by year
- 2026: 62 · 2025: 85 · 2024: 100 · 2023: 106 · 2022: 94 · 2021: 94 · 2020: 113 · 2019: 114 · 2018: 118 · 2017: 126 · 2016: 145 · 2015: 127 · 2014: 159 · 2013: 198 · 2012: 209
- FWCI
- 288.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- Funders
- Bristol-Myers Squibb
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Kinase Regulation and GTPase Signaling
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- kinase inhibitors, kinase selectivity, human kinome, off-target effects, drug safety
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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