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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
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Related Experiment Video

Updated: May 28, 2026

A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors
12:40

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

Published on: December 7, 2014

Machine Learning Model Predicts Clinical Adverse Events of Small Molecule Kinase Inhibitors in Cancer Patients Using

Natalie M Jusko1, Albert Cao1, Duxin Sun1

  • 1Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA.

Clinical Pharmacology and Therapeutics
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning predicts adverse events from small molecule kinase inhibitors by analyzing target engagement and tissue selectivity. This framework improves cancer patient safety by forecasting toxicities like rash and neutropenia.

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Last Updated: May 28, 2026

A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors
12:40

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

Published on: December 7, 2014

Area of Science:

  • Pharmacology
  • Computational Biology
  • Oncology

Background:

  • Adverse events (AEs) of small molecule kinase inhibitors (SMKIs) are unpredictable in cancer patients.
  • Preclinical toxicity testing often fails to identify clinical AEs.

Purpose of the Study:

  • Develop a machine learning (ML) framework to predict the occurrence and time to onset of clinical AEs caused by SMKIs.
  • Utilize on-/off-target engagement and tissue/cell selectivity for AE prediction.

Main Methods:

  • Analyzed 1939 AEs from 3,433 patients treated with 16 SMKIs.
  • Linked kinase inhibition (Ki) constants and expression of 442 targets to SMKI exposure (dose-normalized AUC) in plasma and 36 tissues.
  • Constructed random survival forest models and used variable importance (VIMP) to identify kinase targets for tissue-specific AEs.

Main Results:

  • Successfully predicted common AEs (rash, nausea, fatigue, headache) and severe hematological AEs (neutropenia, anemia).
  • VIMP analyses identified novel kinases potentially involved in tissue-specific AEs.
  • External validation showed strong model performance (Pearson correlation coefficients ≥ 0.87).

Conclusions:

  • Integrating exposure, on-/off-target engagement, and tissue selectivity enables robust prediction of SMKI-associated AEs.
  • The ML framework offers a scalable approach for predicting both blood- and organ-related toxicities at patient and population levels.