ArticleScientific reports2015
Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Projection-based molecular feature maps for CNN-driven nephrotoxicity prediction.Archives of toxicology · 2026Article
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Advances in Cytotoxicity Testing: From In Vitro Assays to In Silico Models.International journal of molecular sciences · 2025Review
- Impact of nephrotoxins and oxidants on survival and transport function of hiPSC-derived renal proximal tubular cells.Archives of toxicology · 2025Article
- Human and rat renal proximal tubule in vitro models for ADME applications.Archives of toxicology · 2025Review
- Proximal tubule-on-chip as a model for predicting cation transport and drug transporter dynamics.Scientific reports · 2025Article
- Next-Gen Therapeutics: Pioneering Drug Discovery with iPSCs, Genomics, AI, and Clinical Trials in a Dish.Annual review of pharmacology and toxicology · 2025Review
- Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules.Pharmaceuticals (Basel, Switzerland) · 2024Article
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
- Sensitivity of Human Induced Pluripotent Stem Cells and Thereof Differentiated Kidney Proximal Tubular Cells towards Selected Nephrotoxins.International journal of molecular sciences · 2023Article
- Generation of proximal tubule-enhanced kidney organoids from human pluripotent stem cells.Nature protocols · 2023Review
- Cells sorted off hiPSC-derived kidney organoids coupled with immortalized cells reliably model the proximal tubule.Communications biology · 2023Article
- The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review.Biosensors · 2023Review
- Machine Learning in Tissue Engineering.Tissue engineering. Part A · 2023Review
- Moving Towards Induced Pluripotent Stem Cell-based Therapies with Artificial Intelligence and Machine Learning.Stem cell reviews and reports · 2022Review
- Article
- Generation and characterization of iPSC-derived renal proximal tubule-like cells with extended stability.Scientific reports · 2021Article
- hiPSCs for predictive modelling of neurodegenerative diseases: dreaming the possible.Nature reviews. Neurology · 2021Review
- Human Pluripotent Stem-Cell-Derived Models as a Missing Link in Drug Discovery and Development.Pharmaceuticals (Basel, Switzerland) · 2021Review
- 3D Cell Printing of Tissue/Organ-Mimicking Constructs for Therapeutic and Drug Testing Applications.International journal of molecular sciences · 2020Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The renal proximal tubule is a main target for drug-induced toxicity. The prediction of proximal tubular toxicity during drug development remains difficult. Any in vitro methods based on induced pluripotent stem cell-derived renal cells had not been developed, so far. Here, we developed a rapid 1-step protocol for the differentiation of human induced pluripotent stem cells (hiPSC) into proximal tubular-like cells. These proximal tubular-like cells had a purity of >90% after 8 days of differentiation and could be directly applied for compound screening. The nephrotoxicity prediction performance of the cells was determined by evaluating their responses to 30 compounds. The results were automatically determined using a machine learning algorithm called random forest. In this way, proximal tubular toxicity in humans could be predicted with 99.8% training accuracy and 87.0% test accuracy. Further, we studied the underlying mechanisms of injury and drug-induced cellular pathways in these hiPSC-derived renal cells, and the results were in agreement with human and animal data. Our methods will enable the development of personalized or disease-specific hiPSC-based renal in vitro models for compound screening and nephrotoxicity prediction.
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