Related Experiment Video

Updated: Sep 3, 2025

A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors
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A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors

Published on: December 7, 2014

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Decoding kinase-adverse event associations for small molecule kinase inhibitors

Xiajing Gong1, Meng Hu1, Jinzhong Liu1

  • 1Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA.

Nature Communications
|July 27, 2022
PubMed

Insights

This study uses machine learning to link small molecule kinase inhibitors (SMKIs) to adverse events (AEs). The findings aid in predicting patient safety risks and developing safer cancer therapies.

Area of Science:

  • Oncology
  • Pharmacology
  • Computational Biology

Background:

  • Small molecule kinase inhibitors (SMKIs) are crucial in cancer treatment, often approved rapidly.
  • Understanding the link between kinase targets and adverse events (AEs) is vital for patient safety.

Purpose of the Study:

  • To develop a machine learning model to predict adverse events (AEs) associated with small molecule kinase inhibitors (SMKIs).
  • To create a publicly accessible tool for identifying kinase-target adverse event pairs.
  • To enhance precision medicine and drug development by assessing SMKI safety profiles.

Main Methods:

  • Constructed a multi-domain dataset from 4638 patients across 16 FDA-approved SMKI registrational trials.
  • Employed a machine learning model to analyze relationships between 442 kinases and 2145 AEs.
  • Validated the model internally and externally using independent SMKI datasets.

Main Results:

  • Systematically evaluated potential associations between 442 kinases and 2145 AEs.
  • Developed an interactive web application, "Identification of Kinase-Specific Signal" (https://gongj.shinyapps.io/ml4ki).
  • The model demonstrated utility in identifying and verifying kinase-adverse event pairs.

Conclusions:

  • The developed model serves as a platform for discovering novel kinase-adverse event (KI-AE) pairs.
  • It functions as a precision medicine tool to forecast clinical safety signals and mitigate patient risks.
  • The tool supports modern drug development by enabling safety-focused screening and comparison of SMKI therapies.

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