Active pharmaceutical ingredients (APIs) can be released to the natural environment during the manufacturing process, following use by patients or when unused medicines are disposed of. As APIs are biologically active compounds concerns have been raised about the potential effects of APIs in the environment on human and environmental health. Over the past 15 years, a substantial amount of work has been done to determine the occurrence, fate, effects, and resulting risks of APIs in the environment and regulatory schemes have been developed requiring environmental risk assessments of all new APIs. However for most APIs currently in use, limited data are available on environmental risks and for new APIs, the testing regimes may not always be optimum. The aim of this project therefore is to develop frameworks that utilise information from toxicological studies, pharmacological mode of action and in silico models to support more intelligent environmental testing of pharmaceuticals in development and to prioritise legacy pharmaceuticals for full environmental risk assessment and/or environmental (bio) monitoring. The aim will be delivered through a number of specific objectives: 1) to review existing approaches for prioritisation and mode of action based intelligent testing of APIs and develop improved frameworks; 2) to establish a high quality database on the properties, environmental fate characteristics and ecotoxicity of APIs; 3) to develop methods for estimating external and internal exposure to APIs for different scenarios; 4) to develop methods and models for predicting ecotoxicological responses to APIs; 5) to validate the developed models, concepts and frameworks using targeted experiments; and 6) to develop a software system to support intelligent testing and prioritisation of APIs in the environment. These objectives will be delivered by a world leading consortium comprising experts in data management and computational modelling, pharmacology, ecotoxicology, environmental chemistry, predictive (eco)toxicology, prioritisation and intelligent testing methodologies and environmental risk assessment.
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