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Updated: Jan 5, 2026

A Simple Method to Identify Kinases That Regulate Embryonic Stem Cell Pluripotency by High-throughput Inhibitor Screening
Published on: May 12, 2017
Novel Screening Method Identifies PI3Kα, mTOR, and IGF1R as Key Kinases Regulating Cardiomyocyte Survival
Manar Elmadani1, Suleiman Khan2, Olli Tenhunen3
1Research Unit of Biomedicine Department of Pharmacology and Toxicology University of Oulu Finland.
Abstract:
Background Small molecule kinase inhibitors (KIs) are a class of agents currently used for treatment of various cancers. Unfortunately, treatment of cancer patients with some of the KIs is associated with cardiotoxicity, and there is an unmet need for methods to predict their cardiotoxicity. Here, we utilized a novel computational method to identify protein kinases crucial for cardiomyocyte viability. Methods and Results One hundred forty KIs were screened for their toxicity in cultured neonatal cardiomyocytes. The kinase targets of KIs were determined based on integrated data from binding assays. The key kinases mediating the toxicity of KIs to cardiomyocytes were identified by using a novel machine learning method for target deconvolution that combines the information from the toxicity screen and from the kinase profiling assays. The top kinases identified by the model were phosphoinositide 3-kinase catalytic subunit alpha, mammalian target of rapamycin, and insulin-like growth factor 1 receptor. Knockdown of the individual kinases in cardiomyocytes confirmed their role in regulating cardiomyocyte viability. Conclusions Combining the data from analysis of KI toxicity on cardiomyocytes and KI target profiling provides a novel method to predict cardiomyocyte toxicity of KIs.
Insights
Predicting cancer drug cardiotoxicity is crucial. This study identifies key protein kinases affecting heart cell survival, offering a new computational method to forecast potential harm from kinase inhibitors.
Area of Science:
- Biochemistry
- Pharmacology
- Computational Biology
Background:
- Small molecule kinase inhibitors (KIs) are vital cancer therapeutics.
- Some KIs cause cardiotoxicity, necessitating predictive methods.
- Identifying kinases impacting cardiomyocyte viability is a key challenge.
Purpose of the Study:
- To develop a novel computational method for identifying protein kinases critical to cardiomyocyte survival.
- To predict the cardiotoxicity of kinase inhibitors.
Main Methods:
- Screening 140 KIs for toxicity in cultured neonatal cardiomyocytes.
- Determining KI kinase targets using integrated binding assay data.
- Applying a machine learning model for target deconvolution, combining toxicity and kinase profiling data.
Main Results:
- The computational model identified key kinases mediating KI cardiotoxicity.
- Top identified kinases include phosphoinositide 3-kinase catalytic subunit alpha, mammalian target of rapamycin, and insulin-like growth factor 1 receptor.
- Experimental knockdown of these kinases confirmed their role in cardiomyocyte viability.
Conclusions:
- A novel computational approach integrating KI toxicity and target profiling can predict cardiotoxicity.
- This method aids in identifying potentially cardiotoxic kinase inhibitors early in development.
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