ArticleToxicological sciences : an official journal of the Society of Toxicology2019
A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro.
Article in Toxicological sciences : an official journal of the Society of Toxicology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 31 citations in OpenAlex.
- Generation of the functionally enhanced proximal tubular cells from human iPSC-derived kidney organoids.Experimental & molecular medicine · 2026Article
- Kidney Organoids in Drug Development: Integrating Technological Advances and Standardization for Effective Implementation.Advanced healthcare materials · 2026Review
- Projection-based molecular feature maps for CNN-driven nephrotoxicity prediction.Archives of toxicology · 2026Article
- Advancing transcriptomics-based mechanistic assessment of nephrotoxicity in vitro using the human RPTEC/TERT1 TXG-MAPr gene co-expression network.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Comparative Analysis of Proximal Tubule Cell Sources for In Vitro Studies of Renal Proximal Tubule Toxicity.Biomedicines · 2025Article
- Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches.Current topics in medicinal chemistry · 2024Review
- Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments.Toxicology and applied pharmacology · 2023Article
- "3D, human renal proximal tubule (RPTEC-TERT1) organoids 'tubuloids' for translatable evaluation of nephrotoxins in high-throughput".PloS one · 2022Article
- Generation and characterization of iPSC-derived renal proximal tubule-like cells with extended stability.Scientific reports · 2021Article
- Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.The AAPS journal · 2021Article
- High-Content Image-Based Single-Cell Phenotypic Analysis for the Testicular Toxicity Prediction Induced by Bisphenol A and Its Analogs Bisphenol S, Bisphenol AF, and Tetrabromobisphenol A in a Three-Dimensional Testicular Cell Co-culture Model.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
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 de-risking and prediction of kidney toxicity in in vitro for new therapeutic candidates and chemical entities.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.