A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro

Susanne Ramm, Petar Todorov, Vidya Chandrasekaran, Anders Dohlman, Maria B Monteiro, Mira Pavkovic, Jeremy Muhlich, Harish Shankaran, William W Chen, Jerome T Mettetal, Vishal S Vaidya

Research output: Contribution to JournalArticleAcademicpeer-review


The failure to predict kidney toxicity of new chemical entities early in the development process before they reach humans remains a critical issue. Here, we used primary human kidney cells and applied a systems biology approach that combines multidimensional datasets and machine learning to identify biomarkers that not only predict nephrotoxic compounds but also provide hints toward their mechanism of toxicity. Gene expression and high-content imaging-derived phenotypical data from 46 diverse kidney toxicants were analyzed using Random Forest machine learning. Imaging features capturing changes in cell morphology and nucleus texture along with mRNA levels of HMOX1 and SQSTM1 were identified as the most powerful predictors of toxicity. These biomarkers were validated by their ability to accurately predict kidney toxicity of four out of six candidate therapeutics that exhibited toxicity only in late stage preclinical/clinical studies. Network analysis of similarities in toxic phenotypes was performed based on live-cell high-content image analysis at seven time points. Using compounds with known mechanism as reference, we could infer potential mechanisms of toxicity of candidate therapeutics. In summary, we report an approach to generate a multidimensional biomarker panel for mechanistic de-risking and prediction of kidney toxicity in in vitro for new therapeutic candidates and chemical entities.

Original languageEnglish
Pages (from-to)54-69
Number of pages16
JournalToxicological sciences : an official journal of the Society of Toxicology
Issue number1
Publication statusPublished - 1 May 2019
Externally publishedYes

Bibliographical note

© The Author(s) 2019. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.


  • In vitro
  • Kidney toxicity
  • Mechanism
  • Prediction
  • Systems toxicology


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