ArticleToxicological sciences : an official journal of the Society of Toxicology2012
Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data.
Article in Toxicological sciences : an official journal of the Society of Toxicology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 77 citations in OpenAlex.
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- The evolving role of investigative toxicology in the pharmaceutical industry.Nature reviews. Drug discovery · 2023Review
- Machine Learning Models for Predicting Liver Toxicity.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Progress in data interoperability to support computational toxicology and chemical safety evaluation.Toxicology and applied pharmacology · 2019Review
- Multi-dimensional in vitro bioactivity profiling for grouping of glycol ethers.Regulatory toxicology and pharmacology : RTP · 2019Article
- Re: 'Application of the key characteristics of carcinogens in cancer hazard evaluation': response to Goodman, Lynch and Rhomberg.Carcinogenesis · 2018Article
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- The Need for, and the Role of the Toxicological Chemist in the Design of Safer Chemicals.Toxicological sciences : an official journal of the Society of Toxicology · 2018Article
- Nanomedicine: Principles, Properties, and Regulatory Issues.Frontiers in chemistry · 2018Review
- Human corneal cell culture models for drug toxicity studies.Drug delivery and translational research · 2016Review
- The Next Generation of Risk Assessment Multi-Year Study-Highlights of Findings, Applications to Risk Assessment, and Future Directions.Environmental health perspectives · 2016Review
- Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- A chemical-biological similarity-based grouping of complex substances as a prototype approach for evaluating chemical alternatives.Green chemistry : an international journal and green chemistry resource : GC · 2016Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection in drug development or in a search for less hazardous and more sustainable alternatives for chemicals in commerce. The development of traditional QSAR models is enabled by numerical descriptors representing the inherent chemical properties that can be easily defined for any number of molecules; however, traditional QSAR models often have limited predictive power due to the lack of data and complexity of in vivo endpoints. Although it has been indeed difficult to obtain experimentally derived toxicity data on a large number of chemicals in the past, the results of quantitative in vitro screening of thousands of environmental chemicals in hundreds of experimental systems are now available and continue to accumulate. In addition, publicly accessible toxicogenomics data collected on hundreds of chemicals provide another dimension of molecular information that is potentially useful for predictive toxicity modeling. These new characteristics of molecular bioactivity arising from short-term biological assays, i.e., in vitro screening and/or in vivo toxicogenomics data can now be exploited in combination with chemical structural information to generate hybrid QSAR-like quantitative models to predict human toxicity and carcinogenicity. Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded applicability domain for wider chemical space coverage.
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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.