SETAC Europe 35th Annual Meeting

Time: 08:30 - 12:30 CEST
Room: TBD
Course level: Introductory

This training course will support attendees in understanding how the EU Joint Research Center (JRC) Safe and Sustainable by Design (SSbD) Framework promoted by the European Commission and the EU Green Deal can support the design and development of safer and sustainable chemicals and materials. To this end, the attendees will learn how to implement the framework through specific case studies designed and developed under the EU-funded projects SSbD4Chem, BIO-SUSHY and AlChemmiSStS, covering automotive, food packaging and coatings value chains. Leading experts from BioNanoNet (BNN), Wood K plus, Edelweiss Connect and ITENE will present specific case studies, including detailed explanations of the tools, procedures and methods that can be used to perform a complete assessment of a product or process, including in silico, new approach methodologies and software applications supporting SSbD data analysis. 

The training course is intended for researchers, regulatory bodies, product designers, developers and risk assessors at companies from sectors such as food, packaging, advanced materials, paints, coatings and textiles, interested in alternatives to PFAS and other restricted substances, who want to develop products that do not cause harm to human health and the environment. It will be helpful for attendees to have a background in materials science, toxicology, or computational chemistry; nevertheless, we will provide all participants with background material so that they can have a good learning experience regardless of their previous knowledge.

Learning Objectives and Outcomes

• To understand how to implement the SSbD Framework in 3 specific case studies 
• To get insights into the tools and methods that can be applied to conduct a proper SSbD assessment 
• To test specific tools 

Target Audience

The course is targeted at professionals, students and researchers with basic knowledge in Safety and Environmental Risk Assessment, including occupational and consumer health and safety. Regulatory bodies, and safety advisors from companies related to sectors such as food, packaging, paints and coatings are of special interest. 

Instructors

Carlos Fito (ITENE research centre), Pau Camilleri (ITENE), Arantxa Ballesteros (ITENE) and Ivana Burzic (wood-kplus).

Time: 08:30 - 17:30 CEST
Room: TBD
Course level: Intermediate

The presence of micropollutants in the environment is a global threat to environmental quality and biodiversity. A particular challenge is chemicals that are persistent, i.e., not eliminated by (bio)degradation processes, or that break down into persistent transformation products that accumulate in the environment. Therefore, accurate predictions of persistence and transformation product formation are direly needed by regulators to screen new and legacy chemicals for potential environmental harm, and by industry to consider safe and sustainable design principles early in the development of future chemicals. However, suitable algorithms are rare, partly due to a lack of large, standardized, and well-documented datasets for model training.

Here, we will introduce databases and prediction tools for contaminant biotransformation (e.g., enviPath, BioTransformer, CTS by US EPA, Rhea), discuss challenges, and familiarize the participants with the hands-on use of enviPath, a widely used, openly accessible online platform for accessing and publishing contaminant biotransformation data, as well as for pathway and persistence prediction. enviPath is currently the only fully open database in the field, and many other tools such as BioTransformer or CTS use enviPath in their prediction algorithms, which motivates a certain focus on this platform. We further present new avenues for linking biotransformation data with resources from systems biology and analytical chemistry.

Learning Objectives and Outcomes

This course aims to:

• Recap the fundamentals of microbial contaminant biotransformation, and the factors influencing kinetics and transformation pathways
• Provide an overview of algorithms and databases for contaminant biotransformation and their specific pros and cons
• Introduce envipath.org as an Open Research Data (ORD) platform for accessing and publishing biotransformation data, and as a tool for predicting biotransformation processes
• Enable participants to enter experimental data, and predict biotransformation pathways and persistence
• Demonstrate downstream applications of biotransformation predictions, including prediction of stable transformation products, coupling to analytical screening of transformation products, and elucidation of candidate genes and enzymes involved in contaminant biotransformation

Target Audience

The target audience for this course is broad. The course should be of interest to professionals (regulators and industry) working in chemical risk and hazard assessment, but also to students and scientists investigating chemical fate and hazard (analytical and environmental chemists, systems biologists). We would like to particularly motivate and enable those producing experimental biotransformation data to store and report their data in openly accessible, standardized formats, thus providing it to the scientific community for further downstream uses.

Instructors

Kathrin Fenner (Eawag), Jasmin Hafner (University of Zurich & Eawag), Sebastian Schmidt (Bayer AG) and Jörg Wicker (University of Auckland).

Time: 08:30 - 17:30 CEST
Room: TBD
Course level: Introductory

The course will introduce participants to different omic technologies and how to apply these in their own research with a focus on ecotoxicology and novel approach methodologies. Omics offers a unique way to understand how environmental factors interact with the organism. For example, epigenomics focuses on factors that interact with genome to influence gene expression and organismal development without changing the DNA sequence itself. Environmental omics can help identify novel biomarkers for environmental exposures and related effects, offering more sensitive and specific methods for early detection of negative impacts in populations caused by environmental factors. Its interdisciplinary nature, predictive capabilities and mechanistic understanding focus make it ideal as a novel approach methodology. It goes beyond traditional ecological or environmental assessments by offering mechanistic explanations for how certain exposures influence traits, development, and health at the molecular level. This approach allows for the assessment of how environmental changes impact populations, ecosystems, and human health, which is particularly relevant in understanding the effects of climate change, pollution, and habitat destruction.

Learning Objectives and Outcomes

This course aims to introduce attendants to the use of omic analysis in ecotoxicological studies. As specific objectives, it is expected that at the end of the course, attendants can:

• Introduction to challenges of using big data for regulatory risk assessment;
• Distinguish technologies available for epigenomics and transcriptomic research, their potential and limitations;
• Identify important aspects to consider when designing epigenetic/transcriptomic ecotoxicological experiments;
• Introduction to NAMs that integrate transcriptomics and data analysis options Data analysis pipelines for transcriptomics in non-model species (exercise using ExpressAnalyst);
• Transcriptomic dose-response analysis (using ExpressAnalyst);
• Case study using EcoToxChips (data analysis as an exercise);
• Understand the needs within omics and its use in a regulatory context.

Target Audience

Researchers in academia, R&D and government institutes who want to apply NAMs and want to gain more knowledge on the use of epigenomics as a proxy for long-term and chronic effects

Instructors

Jana Asselman (Ghent University), Joana Luísa Pereira (University of Aveiro), Laia Navarro-Martín (Institute of Environmental Assessment and Water Research) and Jessica Head (McGill University).

Time: 08:30 - 17:30 CEST
Room: TBD
Course level: Introductory

In silico approaches, considered as New Approach Methods (NAMs), represent a great potential for regulatory ecotoxicological hazard assessment. They also represent a promising alternative method in response to the need to reduce testing on vertebrates, time to market and cost compared to standard methods. This course will especially focus on QSAR and read across approaches used to predict substance acute and chronic aquatic ecotoxicity, biodegradability potential and bioconcentration factor. The interest in innovative and powerful mechanistic QSAR, which can provide a more complete mechanistic understanding of the features of the chemical which empirical experimental data alone often cannot, will be also presented. The aim of this course is to help the participants appreciate the strengths and weaknesses of the presented methods and the keys to identifying reliable and trustworthy in silico models. Therefore, emphasis will also be placed on the guidelines defined by competent authorities for the use of QSAR approaches that are relevant to the regulatory context and the development of Safe and Sustainable by Design substances, among others. Trainees will have the opportunity to put their new skills/knowledge into practice by testing different models during the session.

Learning Objectives and Outcomes

This course aims to:

• Understand the principles of in silico ecotoxicological hazard assessment.
• Discover and apply mechanistic QSAR and read-across for ecotoxicity, physicochemical, and fate in the environment predictions.
• Appreciate the reliability of predictions obtained from in silico approaches.
• Appreciate the role of in silico NAMs to achieve goals of 21st Century chemical sustainability

Target Audience

Students, academics, regulators, and environmental risk assessors. A basic knowledge of regulatory ecotoxicological studies (e.g. some of the OECD 202 series Guidelines) and a little physico-chemistry knowledge (water solubility, partition coefficient, volatility) would be an asset. No knowledge of statistical methodology or coding is required.

Instructors

Floriane Larras (KREATiS SAS), Paul Thomas (KREATiS) and Mark Cronin (Liverpool John Moores University).

Time: 08:30 - 17:30 CEST
Room: TBD
Course level: Introductory

Mechanistic effect models (MEMs) examine how chemicals impact individuals and ecological systems, including populations and communities. Their potential to support environmental risk assessment (ERA) and management is increasingly recognized, and significant experience has been gained in their application in recent years.

This training course introduces the main principles of effect modelling, model development, documentation and evaluation, important terminology and good modelling practice. It particularly addresses the state-of-art of effect modelling in the context of ERA of chemicals. Specifically, it focuses on organism-based models (TK-TD) and population models including their particular use for ERA. The participants can expect a combination of introductory presentations, example case studies that demonstrate the applicability of effect modelling in ERA, hands-on experience with one model application and Q & A sessions.

Learning Objectives and Outcomes

The aim is to introduce the participants to the world of mechanistic effect modelling and its applications in environmental risk assessments. The participants will learn about the basics of effect modelling to better understand its potential and gain first insights into model development, documentation, evaluation and application. They will get hands-on experience with model applications by exploring simple example cases and have the opportunity for questions and discussion. During the exercises, the participants will have the option to work on either an organism-based (TK-TD) or a population-based modelling example.

Target Audience

This course mainly aims at participants who are interested in a general introduction to mechanistic effect modelling and a first overview of how it can be used in the context of ERA. It is open to participants from any scientific background with an interest in applying effect modelling in the context of ecotoxicology. This includes students as well as professionals working in government agencies, industry, academia or NGOs who have little to no experience in this field.

Instructors

Oliver Jakoby (RIFCON GmbH), Judith Klein (Fraunhofer IME), Andreas Focks (Osnabrück University) and Amelie Schmolke (RIFCON GmbH). 

Time: 08:30 - 17:30 CEST
Room: TBD
Course level: Advanced

The open-source statistical environment R (http://www.r-project.org) is an extremely powerful and versatile statistical environment. Over the last two decades, there has been an amazing development in terms of added capabilities through extension packages, making it the preferred data analytic toolbox of statisticians and researchers in many applied sciences. Moreover, it encourages collaborative and reproducible research. It has become much easier to R since RStudio (http://www.rstudio.com) was introduced. RStudio is an integrated environment for R with a powerful syntax-highlighting editor and many other features, including integration of the R Markdown language has dramatically changed how R may interface with other languages and systems, allowing results from statistical analyses to be conveniently outputted in formats such as pdf, HTML, or MS word. Many recent advances in statistical approaches and techniques are only implemented in R or implemented to a larger extent in R compared to competing statistical software. This is particularly true when it comes to some statistical approaches commonly used in ecotoxicology (e.g., dose-response analysis).

Learning Objectives and Outcomes

In this short course, the primary focus will be on giving the participants practical hands-on experience and insights on using R for analyzing ecotoxicological data. Relevant recent statistical methodological advances and concepts will be touched upon. Specifically, analysis of variance and linear regression will be briefly revisited before introducing extensions to censored data and nonlinear regression and more general dose-response analysis, logistic and Poisson regression models as well as linear, logistic and nonlinear mixed-effects models. More advanced concepts such as robust standard errors, single-step multiplicity adjustments, benchmark dose estimation, and species sensitivity distributions will also be discussed if time permits.

Target Audience

The course is intended for PhD students as well as senior researchers and scientists in ecotoxicology and environmental sciences. An elementary understanding of statistical concepts is a prerequisite.

Instructors

Christian Ritz (University of Southern Denmark) and Signe M. Jensen (University of Copenhagen).

Time: 13:30 - 17:30 CEST
Room: TBD
Course level: Advanced

The current state of technology gives us unprecedented amounts of information on concentrations of substances in a large range of environmental media such as water, sediment, soil, air, plants and animal species, and human tissues. It is the job of environmental and human toxicologists to determine and communicate what these concentrations mean to wildlife and human health. This is a challenging job, that can result in unsatisfactory and vague conclusions such as concentrations are “elevated”, “of concern”, “a threat”, “a potential risk” etc. However, this fails to communicate what people really want to know - are these concentrations actually harming wildlife or people and what are the risks that these concentrations actually pose? There are of course established risk assessment methods but they are often hard to apply to real-world problems as the exposure and toxicological effects information are often poorly aligned; often limited to specific substances rather than mixtures; and in almost all cases concentration data or information is subject to uncertainty, doubt or perceived bias. These and other limitations of established risk assessment methods often cause risk assessments to be based on only a small fraction of the data available for risk evaluation and assessment. This approach runs counter to what risk assessments are all about and results in poor outcomes. The purpose of this course is to become familiar with a more inclusive risk assessment framework that takes advantage of the wide range of information that is often available on the exposure and toxicological effects of environmental pollutants. The technique that is discussed in this course is the use of chemical activity to determine or evaluate risks. Chemical activity is a well-established basic concept in environmental chemistry that is used to represent how “active” chemical substances are in their environmental state. “Active” means how well they interact with receptors, how they move and translocate, and what effect they can exert. Concentrations and dosages, which are normally used for environmental risk assessments of pollutants, are often difficult metrics for assessing the activity of substances in the environment.

Learning Objectives and Outcomes

In this course, you will learn how to:

• Express exposure and effects data in terms of chemical activities;
• Use chemical activities to conduct scientifically sound weight of evidence (WoE) analyses and risk assessments; and
• Graphically visualise chemical activity information from multiple species, exposure studies, or toxicity studies in Excel and/or R Studio to assess risk.

The course will further show examples of how activity-based risk assessments have been performed for certain POPs and PFAS and how these risk assessments have been perceived in judicial evaluations. 

Target Audience

Environmental toxicologists and chemists of all types because the subject matter applies to all involved in exposure and toxicological effects studies. The course material is especially useful for those involved in risk assessment, including academics, government and business-oriented scientists.

Instructors

Frank Gobas (Simon Fraser University), Barry Kelly (Meta Analytical Inc.), Katharine Fremlin (Simon Fraser University) and Sophia Hsu (Azimuth Consulting & Simon Fraser University).