1
|
Wedlake AJ, Folia M, Piechota S, Allen TEH, Goodman JM, Gutsell S, Russell PJ. Structural Alerts and Random Forest Models in a Consensus Approach for Receptor Binding Molecular Initiating Events. Chem Res Toxicol 2020; 33:388-401. [PMID: 31850746 DOI: 10.1021/acs.chemrestox.9b00325] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
A molecular initiating event (MIE) is the gateway to an adverse outcome pathway (AOP), a sequence of events ending in an adverse effect. In silico predictions of MIEs are a vital tool in a modern, mechanism-focused approach to chemical risk assessment. For 90 biological targets representing important human MIEs, structural alert-based models have been constructed with an automated procedure that uses Bayesian statistics to iteratively select substructures. These models give impressive average performance statistics (an average of 92% correct predictions across targets), significantly improving on previous models. Random Forest models have been constructed from physicochemical features for the same targets, giving similarly impressive performance statistics (93% correct predictions). A key difference between the models is interpretation of predictions-the structural alert models are transparent and easy to interpret, while Random Forest models can only identify the most important physicochemical features for making predictions. The two complementary models have been combined in a consensus model, improving performance compared to each individual model (94% correct predictions) and increasing confidence in predictions. Variation in model performance has been explained by calculating a modelability index (MODI), using Tanimoto coefficient between Morgan fingerprints to identify nearest neighbor chemicals. This work is an important step toward building confidence in the use of in silico tools for assessment of toxicity.
Collapse
Affiliation(s)
- Andrew J Wedlake
- Centre for Molecular Informatics, Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge , CB2 1EW , United Kingdom
| | - Maria Folia
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park , Sharnbrook , Bedfordshire , MK44 1LQ , United Kingdom
| | - Sam Piechota
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park , Sharnbrook , Bedfordshire , MK44 1LQ , United Kingdom
| | - Timothy E H Allen
- Centre for Molecular Informatics, Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge , CB2 1EW , United Kingdom.,MRC Toxicology Unit , University of Cambridge , Lancaster Road , Leicester LE19HN , United Kingdom
| | - Jonathan M Goodman
- Centre for Molecular Informatics, Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge , CB2 1EW , United Kingdom
| | - Steve Gutsell
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park , Sharnbrook , Bedfordshire , MK44 1LQ , United Kingdom
| | - Paul J Russell
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park , Sharnbrook , Bedfordshire , MK44 1LQ , United Kingdom
| |
Collapse
|
2
|
Nepelska M, Cronin M, Cubberley R, Dent M, Firman J, Fisher J, Kenna G, Mahony C, Nicol B, Piechota S, Przybylak K, Schepky A, Tozer S, Troutman J, Desprez B. Cosmetic Europe's long range science strategy – A non-animal safety assessment case study for phenoxyethanol, a cosmetic ingredient. Toxicol Lett 2018. [DOI: 10.1016/j.toxlet.2018.06.806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
3
|
|