Open position: aquatic risk modelling postdoc (3 years)

We are looking for a postdoctoral researcher to join Syngenta in Basel for a three-year project to develop a new digital tool. In this role you will be driving the development of the scientific model and the method for calculating risks to aquatic systems. This is an opportunity to apply state of the art science, work in an inter-disciplinary team and contribute to digital tools that make a difference in the real world. More details at the Syngenta Job website.

 

Robust, user-friendly, free, open-source Software: openGUTS v1.0

openGUTS standalone WINDOWS executable
openGUTS standalone WINDOWS executable

OpenGUTS is a user-friendly software to perform analyses with GUTS: the General Unified Threshold model for Survival. GUTS is the leading toxicokinetic-toxicodynamic framework for the endpoint survival (and other non-reversible all-or-nothing effects). This site hosts the software itself, as a standalone Windows executable, with manuals and background documentation.

 

Since openGUTS is (as the name implies) open source, also the source files can be obtained here.   We also provide (links to) further information on openGUTS and GUTS. The main features of the software are:

  1. Open and free: the software is open source and freely downloadable. The fully-functional Matlab version that served as the prototype is also available.
  2. User-friendly: fit models and derive confidence intervals without requiring user interaction (e.g., no need for starting values).
  3. Robust: always find the global optimum and relevant intervals, even for awkward data sets.
  4. Flexible: allow time-varying exposure, missing data, simultaneous fitting on multiple data sets, etc.
  5. Efficient: rapid screening of exposure profiles (e.g., FOCUS output) by batch processing.
  6. Supportive: the software follows the workflow as laid down in the 2018 EFSA opinion on TKTD models.

Toxicokinetic–Toxicodynamic Modeling of the Effects of Pesticides on Growth of Rattus norvegicus

Ecological risk assessment is carried out for chemicals such as pesticides before they are released into the environment. Such risk assessment currently relies on summary statistics gathered in standardized laboratory studies. However, these statistics extract only limited information and depend on duration of exposure. Their extrapolation to realistic ecological scenarios is inherently limited. Mechanistic effect models simulate the processes underlying toxicity and so have the potential to overcome these issues. Toxicokinetic–toxicodynamic (TK–TD) models operate at the individual level, predicting the internal concentration of a chemical over time and the stress it places on an organism. TK–TD models are particularly suited to addressing the difference in exposure patterns between laboratory (constant) and field (variable) scenarios. So far, few studies have sought to predict sublethal effects of pesticide exposure to wild mammals in the field, even though such effects are of particular interest with respect to longer term exposure. We developed a TK–TD model based on the dynamic energy budget (DEB) theory, which can be parametrized and tested solely using standard regulatory studies. We demonstrate that this approach can be used effectively to predict toxic effects on the body weight of rats over time. Model predictions separate the impacts of feeding avoidance and toxic action, highlighting which was the primary driver of effects on growth. Such information is relevant to the ecological risk posed by a compound because in the environment alternative food sources may or may not be available to focal species. While this study focused on a single end point, growth, this approach could be expanded to include reproductive output. The framework developed is simple to use and could be of great utility for ecological and toxicological research as well as to risk assessors in industry and regulatory agencies. (Chem. Res. Toxicol. 2019, 32, 11, 2281-2294, link)

 

Common ground between growth models of rival theories: a useful illustration for beginners

Dynamic energy budget theory (DEB) and the metabolic theory of ecology (MTE) both seek to quantify the processes of resource acquisition and use but differ in their underlying mechanisms and assumptions. Some in-depth comparisons of the theories have been conducted in the literature but require a level of knowledge that is likely to be beyond most newcomers to the topic. We reduce the theories to their simplest forms, their models for growth under optimal conditions, and present a side by side comparison of the model equations and key assumptions. This shows considerable overlap in how both theories characterise growth rate while also highlighting fundamental differences, such as MTE’s use of taxon specific parameters. Comparing DEB and MTE in this way provides an accessible platform to help beginners gain a better understanding from the existing literature. (link to paper)

 

Book: Modelling survival under chemical stress

Survival is a relevant endpoint for many questions related to the effects of chemicals in the environment. Making sense of mortality, as a process over time, requires mechanism-based models, known as toxicokinetic-toxicodynamic (TKTD) models. All published TKTD models for survival can now be viewed as members of an over-arching framework: GUTS.

 

Click on the book cover or here to view the e-book.

 

Preface

1 Introduction

2 Description of GUTS

3 Mathematical treatment

4 Case study: dieldrin in guppies

5 Case study: propiconazole in amphipods

6 Use cases

7 Ring test

8 Model evaluation

9 Outlook

Bibliography

Glossary

Appendices

Physiological modes of action across species and toxicants: the key to predictive ecotoxicology

Ecotoxicologists need tools to identify those combinations of man-made chemicals and organisms most likely to cause problems. In other words: which of the millions of species are at risk from pollution? And which of the tens of thousands of chemicals contribute most to the risk? We identified our poor knowledge on physiological modes of action (how a chemical affects the energy allocation in an organism), and how they vary across species and toxicants, as a major knowledge gap. We also find that the key to predictive ecotoxicology is the systematic, rigorous characterization of physiological modes of action because that will enable more powerful in vitro to in vivo toxicity extrapolation and in silico ecotoxicology. In the near future, we expect a step change in our ability to study physiological modes of action by improved, and partially automated, experimental methods. Once we have populated the matrix of species and toxicants with sufficient physiological mode of action data we can look for patterns, and from those patterns infer general rules, theory and models. (open access, link to paper at ESPI)