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The hepatotoxic potential of protein kinase inhibitors predicted with Random Forest and Artificial Neural Networks

Verena Schöning1, Stephan Krähenbühl1, Jürgen Drewe1

  • 1Department of Clinical Pharmacology, University Hospital Basel, CH 4031 Basel, Switzerland.

Toxicology Letters
|October 14, 2018
PubMed

Insights

Protein kinase inhibitors (PKIs) can cause liver damage. This study analyzed 165 PKIs, finding a generally high probability of hepatotoxicity, though it varies by target kinase.

Area of Science:

  • Biochemistry
  • Pharmacology
  • Computational Biology

Background:

  • Protein kinases (PKs) are crucial for cellular functions.
  • Dysregulation of PKs is linked to various diseases.
  • Protein kinase inhibitors (PKIs) are used for treatment but can cause serious adverse events, including hepatotoxicity.

Purpose of the Study:

  • To investigate the structure-activity relationship of PKIs concerning hepatotoxicity.
  • To analyze the probability of human hepatotoxicity for a set of 165 PKIs.

Main Methods:

  • Compiled a dataset of 165 PKIs.
  • Utilized machine learning algorithms, specifically Random Forest and Artificial Neural Networks, to predict hepatotoxicity.
  • Analyzed the structure-activity relationship in relation to hepatotoxicity.

Main Results:

  • The estimated probability of hepatotoxicity was generally high across individual PKIs.
  • A discernible difference in hepatotoxic potential was observed based on the target kinase of the PKI.
  • The conserved site of action among protein kinases contributes to the similarity of PKIs.

Conclusions:

  • Hepatotoxicity is a significant and persistent concern associated with PKIs.
  • Understanding the target kinase is crucial for predicting and potentially mitigating hepatotoxic risks.
  • Further research into PKI-induced hepatotoxicity is warranted.
Keywords:
Artificial Neural NetworksDrug induced liver injury (DILI)Protein kinase inhibitorsQSARRandom Forest

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