Molecular docking and geographical information systems as tools to assess the potential impact of veterinary medicines on non-target organisms and the environment

Veterinary medicines (VMs) from agricultural industry can enter the environment in a number of ways. This includes direct exposure through aquaculture, accidental spillage and disposal, and indirect entry by leaching from manure or runoff after treatment. Many compounds used in animal treatments have ecotoxic properties that may have chronic or sometimes lethal effects when they come into contact with non-target organisms. VMs enter the environment in mixtures, potentially having additive effects. Traditional ecotoxicology tests are used to determine the lethal and sometimes reproductive effects on freshwater and terrestrial organisms. However, organisms used in ecotoxicology tests can be unrepresentative of the populations that are likely to be exposed to the compound in the environment. Most often the tests are on single compound toxicity but mixture effects may be significant and should be included in ecotoxicology testing. This work investigates the use, measured environmental concentrations (MECs) and potential impact of sea lice treatments on salmon farms in Scotland. Alternative methods for ecotoxicology testing including mixture toxicity, and the use of in silico techniques to predict the chronic impact of VMs on different species of aquatic organisms were also investigated. The Scottish Environmental Protection Agency (SEPA) provided information on the use of five sea lice treatments from 2008-2011 on Scottish salmon farms. This information was combined with the recently available data on sediment MECs for the years 2009-2012 provided by SEPA using ArcGIS 10.1. In depth analysis of this data showed that from a total of 55 sites, 30 sites had a MEC higher than the maximum allowable concentration (MAC) as set out by SEPA for emamectin benzoate and 7 sites had a higher MEC than MAC for teflubenzuron. A number of sites that were up to 16 km away from the nearest salmon farm reported as using either emamectin benzoate or teflubenzuron measured positive for the two treatments. There was no relationship between current direction and the distribution of the sea lice treatments, nor was there any evidence for alternative sources of the compounds e.g. land treatments. The sites that had MECs higher than the MAC could pose a risk to non-target organisms and disrupt the species dynamics of the area. There was evidence that some marine protected sites might be at risk of exposure to these compounds. To complement this work, effects on acute mixture toxicity of the 5 sea lice treatments, plus one major metabolite 3-phenoxybenzoic acid (3PBA), were measured using an assay using the bioluminescent bacteria Aliivibrio fischeri. When exposed to the 5 sea lice treatments and 3PBA A. fischeri showed a response to 3PBA, emamectin benzoate and azamethiphos as well as combinations of the three. In order to establish any additive effect of the sea lice treatments, the efficacy of two mixture prediction equations, concentration addition (CA) and independent action ii(IA) were tested using the results from single compound dose response curves. In this instance IA was the more effective prediction method with a linear regression confidence interval of 82.6% compared with 22.6% of CA. In silico molecular docking was carried out to predict the chronic effects of 15 VMs (including the five used as sea lice control). Molecular docking has been proposed as an alternative screening method for the chronic effects of large animal treatments on non-target organisms. Oestrogen receptor alpha (ERα) of 7 non-target bony fish and the African clawed frog Xenopus laevis were modelled using SwissModel. These models were then ‘docked’ to oestradiol, the synthetic oestrogen ethinylestradiol, two known xenoestrogens dichlorodiphenyltrichloroethane (DDT) and bisphenol A (BPA), the antioestrogen breast cancer treatment tamoxifen and 15 VMs using Auto Dock 4. Based on the results of this work, four VMs were identified as being possible xenoestrogens or anti-oestrogens; these were cypermethrin, deltamethrin, fenbendazole and teflubenzuron. Further investigation, using in vitro assays, into these four VMs has been suggested as future work. A modified recombinant yeast oestrogen screen (YES) was attempted using the cDNA of the ERα of the zebrafish Danio rerio and the rainbow trout Oncorhynchus mykiss. Due to time and difficulties in cloning protocols this work was unable to be completed. Use of such in vitro assays would allow for further investigation of the highlighted VMs into their oestrogenic potential. In conclusion, VMs used as sea lice treatments, such as teflubenzuron and emamectin benzoate may be more persistent and have a wider range in the environment than previously thought. Mixtures of sea lice treatments have been found to persist together in the environment, and effects of these mixtures on the bacteria A. fischeri can be predicted using the IA equation. Finally, molecular docking may be a suitable tool to predict chronic endocrine disrupting effects and identify varying degrees of impact on the ERα of nine species of aquatic organisms.

Methods, Models & Systems for Dealing with Some Business, Management & Engineering Problems

This keynote presentation will report some of our research work and experience on the development and applications of relevant methods, models, systems and simulation techniques in support of different types and various levels of decision making for business, management and engineering. In particular, the following topics will be covered. Modelling, multi-agent-based simulation and analysis of the allocation management of carbon dioxide emission permits in China (Nanfeng Liu & Shuliang Li Agent-based simulation of the dynamic evolution of enterprise carbon assets (Yin Zeng & Shuliang Li) A framework & system for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps: a big data perspective (Jin Xu, Zheng Li, Shuliang Li & Yanyan Zhang) Open innovation: intelligent model, social media & complex adaptive system simulation (Shuliang Li & Jim Zheng Li) A framework, model and software prototype for modelling and simulation for deshopping behaviour and how companies respond (Shawkat Rahman & Shuliang Li) Integrating multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making (Shuliang Li & Jim Zheng Li) A Web-based hybrid intelligent system for combined conventional, digital, mobile, social media and mobile marketing strategy formulation (Shuliang Li & Jim Zheng Li) A hybrid intelligent model for Web & social media dynamics, and evolutionary and adaptive branding (Shuliang Li) A hybrid paradigm for modelling, simulation and analysis of brand virality in social media (Shuliang Li & Jim Zheng Li) Network configuration management: attack paradigms and architectures for computer network survivability (Tero Karvinen & Shuliang Li)