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

Author(s):Show less -

Ramm, Susanne ^{[1, 2]} ; Todorov, Petar ^{[1, 3]} ; Chandrasekaran, Vidya ^[1] ; Dohlman, Anders ^[1] ; Monteiro, Maria B. ^[1] ; Pavkovic, Mira ^{[1, 2]} ; Muhlich, Jeremy ^[1] ; Shankaran, Harish ^[3] ; Chen, William W. ^[1] ; Mettetal, Jerome T. ^[3] ; Vaidya, Vishal S. ^{[1, 2, 4]}

Affiliation

^[1] Harvard Med Sch, Harvard Program Therapeut Sci, Lab Syst Pharmacol, Boston, MA 02115 - USA

^[2] Brigham & Womens Hosp, Dept Med, Div Renal, Boston, MA 02115 - USA

^[3] AstraZeneca, IMED Biotech Unit, Drug Safety & Metab, Safety & ADME Modeling, Waltham, MA 02451 - USA

^[4] Harvard TH Chan Sch Publ Hlth, Dept Environm Hlth, Boston, MA 02115 - USA

• Document type: Journal article
• Source: TOXICOLOGICAL SCIENCES; v. 169, n. 1, p. 54-69, MAY 2019.
• Web of Science Citations: 1

Abstract

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 derisking and prediction of kidney toxicity in in vitro for new therapeutic candidates and chemical entities. (AU)

• FAPESP's process: 16/04935-2 - Validation of candidate mRNAs and miRNAs as biomarkers of diabetic nephropathy in a North-American cohort of type 1 and type 2 diabetes patients
• Grantee: Maria Beatriz Camargo Monteiro Caillaud
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor