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Decoding kinase-adverse event associations for small molecule kinase inhibitors

Author

Listed:
  • Xiajing Gong

    (Food and Drug Administration)

  • Meng Hu

    (Food and Drug Administration)

  • Jinzhong Liu

    (Food and Drug Administration)

  • Geoffrey Kim

    (BeiGene)

  • James Xu

    (Potomac Oncology and Hematology)

  • Amy McKee

    (Parexel)

  • Todd Palmby

    (BeiGene)

  • R. Angelo Claro

    (Food and Drug Administration)

  • Liang Zhao

    (Food and Drug Administration)

Abstract

Small molecule kinase inhibitors (SMKIs) are being approved at a fast pace under expedited programs for anticancer treatment. In this study, we construct a multi-domain dataset from a total of 4638 patients in the registrational trials of 16 FDA-approved SMKIs and employ a machine-learning model to examine the relationships between kinase targets and adverse events (AEs). Internal and external (datasets from two independent SMKIs) validations have been conducted to verify the usefulness of the established model. We systematically evaluate the potential associations between 442 kinases with 2145 AEs and made publicly accessible an interactive web application “Identification of Kinase-Specific Signal” ( https://gongj.shinyapps.io/ml4ki ). The developed model (1) provides a platform for experimentalists to identify and verify undiscovered KI-AE pairs, (2) serves as a precision-medicine tool to mitigate individual patient safety risks by forecasting clinical safety signals and (3) can function as a modern drug development tool to screen and compare SMKI target therapies from the safety perspective.

Suggested Citation

  • Xiajing Gong & Meng Hu & Jinzhong Liu & Geoffrey Kim & James Xu & Amy McKee & Todd Palmby & R. Angelo Claro & Liang Zhao, 2022. "Decoding kinase-adverse event associations for small molecule kinase inhibitors," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32033-5
    DOI: 10.1038/s41467-022-32033-5
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    References listed on IDEAS

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    1. Jacqulyne P. Robichaux & Xiuning Le & R. S. K. Vijayan & J. Kevin Hicks & Simon Heeke & Yasir Y. Elamin & Heather Y. Lin & Hibiki Udagawa & Ferdinandos Skoulidis & Hai Tran & Susan Varghese & Junqin H, 2021. "Structure-based classification predicts drug response in EGFR-mutant NSCLC," Nature, Nature, vol. 597(7878), pages 732-737, September.
    2. Ishwaran, Hemant & Kogalur, Udaya B. & Gorodeski, Eiran Z. & Minn, Andy J. & Lauer, Michael S., 2010. "High-Dimensional Variable Selection for Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 205-217.
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