Sapounidou, M (2019) In Silico Classification of Industrial Chemicals Associated with Acute Aquatic Toxic Action Utilising Molecular Initiating Events. Doctoral thesis, Liverpool John Moores University.

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Abstract

Chemicals legislation requires the assessment of potential harm to humans and environmental species. There is a move away from testing industrial chemicals in animals to development and use of reliable non-testing methods towards Mode of Action (MOA) determination as an important part of understanding toxicity to aquatic organisms. However, current MOA classification approaches for acute aquatic toxicity endpoints are limited by their chemical and mechanistic domains. This thesis developed an in silico classification scheme for aquatic toxicology to enable grouping according to relevant mechanism of action. Over 6,200 publicly available toxicity data for nearly 5,000 chemicals and 10 aquatic species were collated and curated to form the basis of the analysis. In addition, mechanistic information was compiled and organised in three broad domains: narcotic, non-specific reactive and specific mechanisms of action. Where possible, the aquatic toxicology domains were organised around the Molecular Initiating Events (MIEs) of the relevant Adverse Outcome Pathway(s). Utilising the MIE allowed direct linkage between structural chemistry responsible for toxicity and the adverse outcome. Other considerations included information on the MIE target structure, i.e. the interaction, associated chemistry, taxonomic applicability and the sources and type of data (e.g. in silico, in vivo, in vitro) corresponding to MIE/MIE target of interest. Structural alerts were developed for each mechanism and were documented and evaluated using a MIE-centred set of criteria. Approximately 65 MIE and MIE targets relevant to the aquatic flora and fauna were identified. This knowledge extended greatly the classification scheme for non-specific reactive and specific mechanisms of toxicity beyond the currently applied schemes. The chemical structural criteria for class assignment along with transparency in data source and quality were coded in a workflow. This provides the user with an informed MIE prediction that can be used in the application of the AOPs to better understand chemical classification, predict toxicity and support interspecies risk assessment.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	AOP; Computational toxicology; Molecular Initiating Events; Aquatic toxicology
Subjects:	Q Science > QD Chemistry R Medicine > RA Public aspects of medicine > RA1190 Toxicology. Poisions
Divisions:	Pharmacy & Biomolecular Sciences
Date Deposited:	10 Oct 2019 10:06
Last Modified:	18 Oct 2022 13:42
DOI or ID number:	10.24377/LJMU.t.00011366
Supervisors:	Cronin, M, Madden, J and Gutsell, S
URI:	https://researchonline.ljmu.ac.uk/id/eprint/11366

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