The EU-IMI Project Intelligence-led Assessment of Pharmaceuticals in the Environment (iPiE)
Sunday 26 May, 13:00 – 17:30, Session Room 201
Attention: Registration required by 3 April!
Gerd Maack, German Environment Agency, Germany; Jason Snape, Astra Zeneca, UK; Alistair Boxall, University of York, UK; Reinhard Länge, Bayer AG, Germany; Anja Coors, ECT Oekotoxicologie GmbH, Germany
Active Pharmaceutical Ingredients (APIs) are biologically active compounds that are designed to interact with specific pathways and processes in target humans and animals. Concerns have therefore 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.
The challenge is to identify potential environmental risks of new APIs during the early stages of the development process, such that intelligent and efficient testing strategies can be defined. This can be realised by developing predictive models based on preclinical and clinical pharmacological and toxicological data for existing APIs or by using Quantitative Structure-Activity Relationships for environmental endpoints. For example, knowledge of the presence or absence of API targets across a wide range of taxa could be invaluable in identifying those organisms and life stages of organisms that are most likely to respond to exposure to an API and which should therefore be specifically targeted in the risk assessment process. Comparative biochemistry, genomics, and other “Omics” technologies also offer potential tools for early identification of APIs of potential concern, as well as the most sensitive and vulnerable species.
In addition to addressing potential environmental risks of APIs at an early stage of the development process, there also is a need to address the environmental risks associated with legacy APIs. More than 3000 APIs are currently in use and sufficient data to adequately assess environmental risks is only available for a small proportion of these. While it would be beneficial to understand the potential effects of the untested APIs, it would be a mammoth task to experimentally assess the hazards and environmental occurrence of all of these in a timely manner. Prioritisation approaches offer a potential solution to focus monitoring, testing, and research resources and to identify those APIs that are likely to pose the greatest risk in a particular situation. Ideally, these prioritisation approaches should not require extensive experimental testing but exploit a combination of the significant amount of data that companies have already generated on the fate and ecotoxicity of APIs, some of the predictive models described above, and targeted experimentation. For example, a lot of information is available from preclinical and clinical studies on the properties and effects of APIs which could be used to support predictions of the environmental impacts of an API.
The overall aim of the iPiE-project was to develop predictive frameworks that utilise information from existing datasets on environmental fate and effects of APIs, 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 was delivered through the following specific objectives:
- To review existing approaches for prioritisation and mode of action based intelligent testing of APIs to identify best practices and limitations in these approaches and to develop new and improved frameworks which are acceptable to potential end-users.
- To establish a database on the properties, environmental fate characteristics and ecotoxicity of APIs (and related compounds such as metabolites) and test species characteristics (e.g. presence/absence of API molecular targets, where available) and to align this database with the existing IMI eTOX database on toxicological properties of APIs.
- To develop methods for predicting external and internal exposure to APIs and related compounds in the natural environment for different scenarios based on data compiled in the database developed.
- To develop methods and models for predicting aquatic and terrestrial ecotoxicological responses to APIs and related compounds based on existing data compiled in the database developed.
- To validate the models, concepts and frameworks developed.
- To develop a software system, that supports intelligent testing and prioritisation of APIs in the environment. The system will be based on and be fully compatible with the IMI eTOXsys infrastructure for safety assessment of APIs.
The symposium aims to introduce the iPiE project in detail and to describe the software system, usable for the end-user free of charge. It will come in hand with a guidance on how the software system and associated predictive tools can be used in a) early development programmes for new compounds, and b) for prioritising legacy products for experimental testing.
This way the workshop will give a demonstration and a training session of the software system.
This symposium is organised by the SETAC Europe Pharmaceutical Interest Group.
|13:00||Welcome and Introduction|
|13:15||IPIE data base
What is in? How to use existing data? How to extend with new data (including spread sheets)
|13:45||Developed models (part 1):
Adsorption and distribution models
|14:15||Developed models (part 2):
Toxic ratio models
Drug target orthologs data base “Ecodrug”,
Fish plasma model
Implementation of models, how to access data base and models, connections between different model calculations (e.g. ePIE and phys-chem predictions)
|15:45||Application of models in prioritization and prediction framework|
|16:30||Hands on training on IPIEsys
How does IPIEsys work for users?
Participation is without any cost but registration is required by 20 March. You can register in combination with conference registration.