1. Biochemistry and Chemical Biology
  2. Computational and Systems Biology

Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting

  1. Ian R Outhwaite
  2. Sukrit Singh
  3. Benedict-Tilman Berger
  4. Stefan Knapp
  5. John D Chodera
  6. Markus A Seeliger  Is a corresponding author
  1. Stony Brook University, United States
  2. Memorial Sloan Kettering Cancer Center, United States
  3. Goethe University Frankfurt, Germany
https://doi.org/10.7554/eLife.86189
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  1. Ian R Outhwaite
  2. Sukrit Singh
  3. Benedict-Tilman Berger
  4. Stefan Knapp
  5. John D Chodera
  6. Markus A Seeliger
(2023)
Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting
eLife 12:e86189.
https://doi.org/10.7554/eLife.86189
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Abstract

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicates the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound-multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.

Data availability

Instructions to run MMS, code, datasets, and MMS results are available at: https://github.com/iouthwaite/inhibitor_combinations

Article and author information

Author details

  1. Ian R Outhwaite

    Department of Pharmacological Sciences, Stony Brook University, Stony Brook, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2037-3261

  2. Sukrit Singh

    Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1914-4955

  3. Benedict-Tilman Berger

    Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Frankfurt, Germany
    Competing interests
    Benedict-Tilman Berger, is the CEO and a shareholder of CELLinib GmbH, Frankfurt, Germany..

  4. Stefan Knapp

    Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Frankfurt, Germany
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5995-6494

  5. John D Chodera

    Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    John D Chodera, is a current member of the Scientific Advisory Boards of OpenEye Scientific Software, Interline Therapeutics, and Redesign Science. The Chodera laboratory receives or has received funding from the National Institute of Health, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, Vir Biotechnology, XtalPi, Interline Therapeutics, and the Molecular Sciences Software Institute, the Starr Cancer Consortium, the Open Force Field Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, and the Sloan Kettering Institute. A complete funding history for the Chodera lab can be found at http://choderalab.org/funding..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0542-119X

  6. Markus A Seeliger

    Department of Pharmacological Sciences, Stony Brook University, Stony Brook, United States
    For correspondence
    markus.seeliger@stonybrook.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0990-1756

Funding

National Institutes of Health (R35GM119437)

  • Markus A Seeliger

National Institutes of Health (T32GM136572)

  • Ian R Outhwaite

National Institutes of Health (R01GM121505)

  • John D Chodera

Damon Runyon Cancer Research Foundation (DRQ-14-22)

  • Sukrit Singh

National Institutes of Health (T32GM008444)

  • Ian R Outhwaite

Structural Genomics Consortium

  • Stefan Knapp

German Translational Cancer Network

  • Stefan Knapp

Deutsche Forschungsgemeinschaft (1399)

  • Benedict-Tilman Berger
  • Stefan Knapp

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Yongliang Yang, Dalian University of Technology, China

Version history

  1. Received: January 14, 2023
  2. Accepted: December 3, 2023
  3. Accepted Manuscript published: December 4, 2023 (version 1)

Copyright

© 2023, Outhwaite et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Ian R Outhwaite
  2. Sukrit Singh
  3. Benedict-Tilman Berger
  4. Stefan Knapp
  5. John D Chodera
  6. Markus A Seeliger
(2023)
Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting
eLife 12:e86189.
https://doi.org/10.7554/eLife.86189

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