ArticleScientific reports2018
Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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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.
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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.
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Who cites it
25 citing papers in PubMed, 45 citations in OpenAlex.
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- Impact of Chemical Quality on High-Throughput in Vitro Assays: A Tox21 Study.Chemical research in toxicology · 2026Article
- Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems.Computational toxicology (Amsterdam, Netherlands) · 2026Article
- Review
- Application of cytochrome P450 enzyme assays to predict p53 inducers and AChE inhibitors that require metabolic activation.Toxicology and applied pharmacology · 2025Article
- Bayesian Optimization over Multiple Experimental Fidelities Accelerates Automated Discovery of Drug Molecules.ACS central science · 2025Article
- Analytical Quality Evaluation of the Tox21 Compound Library.Chemical research in toxicology · 2025Review
- Prediction of chemical-induced acute toxicity using in vitro assay data and chemical structure.Toxicology and applied pharmacology · 2024Article
- Per-and polyfluoroalkyl substances and disrupted sleep: mediating roles of proteins.Environmental advances · 2024Article
- A comparison of the TempO-Seq and Affymetrix microarray platform using RTqPCR validation.BMC genomics · 2024Article
- In vitro profiling of pesticides within the Tox21 10K compound library for bioactivity and potential toxicity.Toxicology and applied pharmacology · 2023Article
- Machine learning approaches to predict drug efficacy and toxicity in oncology.Cell reports methods · 2023Review
- Prediction of drug-induced liver injury and cardiotoxicity using chemical structure and in vitro assay data.Toxicology and applied pharmacology · 2022Article
- Automating Predictive Toxicology Using ComptoxAI.Chemical research in toxicology · 2022Article
- Converging global crises are forcing the rapid adoption of disruptive changes in drug discovery.Drug discovery today · 2021Article
- Quantitative characterization of recombinase-based digitizer circuits enables predictable amplification of biological signals.Communications biology · 2021Article
- The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology.Chemical research in toxicology · 2021Review
- Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity.Chemical research in toxicology · 2021Article
- Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library.Molecules (Basel, Switzerland) · 2020Article
- Predictive Models for Human Organ Toxicity Based onChemical research in toxicology · 2020Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
In vitro assay data have recently emerged as a potential alternative to traditional animal toxicity studies to aid in the prediction of adverse effects of chemicals on humans. Here we evaluate the data generated from a battery of quantitative high-throughput screening (qHTS) assays applied to a large and diverse collection of chemicals, including approved drugs, for their capacity in predicting human toxicity. Models were built with animal in vivo toxicity data, in vitro human cell-based assay data, as well as in combination with chemical structure and/or drug-target information to predict adverse effects observed for drugs in humans. Interestingly, we found that the models built with the human cell-based assay data performed close to those of the models based on animal in vivo toxicity data. Furthermore, expanding the biological space coverage of assays by including additional drug-target annotations was shown to significantly improve model performance. We identified a small set of targets, which, when added to the current suite of in vitro human cell-based assay data, result in models that greatly outperform those built with the existing animal toxicity data. Assays can be developed for this set of targets to screen compounds for construction of robust models for human toxicity prediction.
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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.