Literature DB >> 31026540

Identification and description of the uncertainty, variability, bias and influence in quantitative structure-activity relationships (QSARs) for toxicity prediction.

Mark T D Cronin1, Andrea-Nicole Richarz2, Terry W Schultz3.   

Abstract

Improving regulatory confidence in, and acceptance of, a prediction of toxicity from a quantitative structure-activity relationship (QSAR) requires assessment of its uncertainty and determination of whether the uncertainty is acceptable. Thus, it is crucial to identify potential uncertainties fundamental to QSAR predictions. Based on expert review, sources of uncertainties, variabilities and biases, as well as areas of influence in QSARs for toxicity prediction were established. These were grouped into three thematic areas: uncertainties, variabilities, potential biases and influences associated with 1) the creation of the QSAR, 2) the description of the QSAR, and 3) the application of the QSAR, also showing barriers for their use. Each thematic area was divided into a total of 13 main areas of concern with 49 assessment criteria covering all aspects of QSAR development, documentation and use. Two case studies were undertaken on different types of QSARs that demonstrated the applicability of the assessment criteria to identify potential weaknesses in the use of a QSAR for a specific purpose such that they may be addressed and mitigation strategies can be proposed, as well as enabling an informed decision on the adequacy of the model in the considered context.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Assessment criteria; Barriers; Bias; Influence; QSAR; Toxicity prediction; Uncertainty; Variability

Mesh:

Year:  2019        PMID: 31026540     DOI: 10.1016/j.yrtph.2019.04.007

Source DB:  PubMed          Journal:  Regul Toxicol Pharmacol        ISSN: 0273-2300            Impact factor:   3.271


  4 in total

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Authors:  Jan L Brozek; Carlos Canelo-Aybar; Elie A Akl; James M Bowen; John Bucher; Weihsueh A Chiu; Mark Cronin; Benjamin Djulbegovic; Maicon Falavigna; Gordon H Guyatt; Ami A Gordon; Michele Hilton Boon; Raymond C W Hutubessy; Manuela A Joore; Vittal Katikireddi; Judy LaKind; Miranda Langendam; Veena Manja; Kristen Magnuson; Alexander G Mathioudakis; Joerg Meerpohl; Dominik Mertz; Roman Mezencev; Rebecca Morgan; Gian Paolo Morgano; Reem Mustafa; Martin O'Flaherty; Grace Patlewicz; John J Riva; Margarita Posso; Andrew Rooney; Paul M Schlosser; Lisa Schwartz; Ian Shemilt; Jean-Eric Tarride; Kristina A Thayer; Katya Tsaioun; Luke Vale; John Wambaugh; Jessica Wignall; Ashley Williams; Feng Xie; Yuan Zhang; Holger J Schünemann
Journal:  J Clin Epidemiol       Date:  2020-09-24       Impact factor: 6.437

Review 2.  Practices and Trends of Machine Learning Application in Nanotoxicology.

Authors:  Irini Furxhi; Finbarr Murphy; Martin Mullins; Athanasios Arvanitis; Craig A Poland
Journal:  Nanomaterials (Basel)       Date:  2020-01-08       Impact factor: 5.076

Review 3.  Quo vadis blood protein adductomics?

Authors:  Gabriele Sabbioni; Billy W Day
Journal:  Arch Toxicol       Date:  2021-11-13       Impact factor: 5.153

4.  Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks.

Authors:  Joseph D Romano; Yun Hao; Jason H Moore
Journal:  Pac Symp Biocomput       Date:  2022
  4 in total

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