Lead PI Dr. Leon Barron (KCL), Post-doctoral researcher Dr. Tom Miller, co-I Dr Nic Bury (University of Suffolk, co-I Dr James MacRae (Crick Institute)
This project will generate groundbreaking knowledge on the subtle effects of pharmaceuticals in the environment on a model freshwater benthic invertebrate, Gammarus pulex. As excellent indicators of surface water quality, these species are consistently impacted by pharmaceuticals and their metabolites at the ng-ug/L level mainly via sewage treatment plant effluents. Non-lethal phenotype-level effects, metabolomics studies and analytical measurements of >60 pharmaceuticals in G. pulex will be combined to generate biologically-inspired artificial neural networks and/or support vector machine models for rapid prediction of ecotoxicity from molecular level changes. In particular, models will be used to (1) predict growth rate, feeding rate, ventilation and locomotion effects; (2) identify metabolic pathways affected by pharmaceuticals; and (3) reduce the number of animals required for ecotoxicity testing in the future. The project will house five work packages (WPs): (1) Bioanalytical methods for G. pulex; (2) Pharmaceutical exposures and non-lethal effect measurement; (3) Metabolomics of exposed G. pulex and pharmaceutical residue measurement in biota; (4) Machine learning methods to model metabolomics/chemical measurement datasets to predict sub-lethal effects and/or affected pathways; and (5) Bioevaluation of novel biomarkers of exposure to pharmaceuticals. Metabolite/chemical analysis will be performed using gas and liquid chromatography coupled to (high resolution) mass spectrometry. Correlations with phenotypic effects will be identified using, for example, principal component analysis, Volcano plots and Z-transformation to rapidly identify dependent biomarkers. Linkage to pharmaceutical exposure will be built-in to models via internal pharmaceutical concentrations. Lastly, and in reverse, the prediction of molecular level changes will be investigated from quantitative structure-activity relationships and phenotype data for biomarker discovery and read-across.