Projects

Neuroscience research is among the single largest animal research fields, with around 1.3 million animals used each year in the EU alone. In Switzerland, more than a quarter of experimental animal used in recent years were in neuroscience, and many undergoing experimental procedures of high severity. Despite the use of these numerous experimental animals, the overall success rate in therapy development for neurological diseases such as Alzheimer’s dementia and stroke is low compared to other fields. Bridging this translational gap is critical to advancing both science and the 3Rs of replacement, reduction, and refinement. Although there are different reasons for this gap, weak or inappropriate design of preclinical studies has been flagged as key driver. For instance, the selection of animal disease models is often made based on the resources available to that researcher rather than which model provides the best representation of the specific pathophysiological process under study. Yet there is a lack of a comprehensive resource to assist preclinical neuroscientists make informed decisions on experimental parameters during study planning. This costs many experimental animals with only modest translational relevance. The advent of complex cell culture models, such as 3D organoids or microphysiological systems, has brought prospects to partly replace animal experiments for drug testing. However, no systematic curation of the application of such models has demonstrated their translational value in comparison with respective animal models.

Our ambition is to systematically curate the neurological-psychiatric therapy development pipeline, including the use of animal and 3D culture models, by harnessing artificial intelligence and text analysis techniques. With this, our goal is to provide a comprehensive, up-to-date overview of translational success rates in neuroscience, including details of the experimental approaches used (such as animal models, therapies, candidate therapy classes and study quality) and to benchmark the performance of 3D culture models against respective animal models. Statistical modelling will be employed to identify potential experimental parameters being associated with successful translation. This evidence-based resource will be made available as “living” online data warehouse, named ANIMONE (ANImal MOdels in NEuroscience), to guide researchers in designing their own animal studies.

This biocuration, with clear transparency of the provenance of the information provided, will foster evidence-based practice among preclinical neuroscientists. With this, it will support the reduction and refinement of animal studies in neuroscience. Benchmarking animal models against 3D culture models can promote replacement of animal experiments. But our findings and developed resources can benefit additional animal welfare measures: if animal research is conducted to higher standards, through improved rigour and reporting, this enhances the ethical harm–benefit calculus by improving benefit. Furthermore, this resource and the methodology developed will encourage preclinical systematic reviews—a recognised approach to improving animal welfare. Finally, a richer understanding of translational success rates will foster a more informed discourse on the role of animal experiments among stakeholders in animal research and in the public sphere.

Details of the project, please find: here, 
https://www.crs.uzh.ch/en/research.html 
https://www.veterinary-practice.com/2022/funding-project-reduce-animal-testing-biomedical-research

Project lead: Benjamin Ineichen & Malcolm MacLeod (External Project Member)
Funding:Swiss National Science Foundation (SNSF): National Research Program 79 - "Advancing 3R - Animals, Research and Society"

Bayesian variable/model selection techniques can be used to find a prediction model for mortality at the intensive care unit (ICU) by assessing the effect of hospital-acquired pneumonia (HAP) and other possible variables (age, patient characteristics at day of admission, patient characteristics during ICU stay) on mortality at the ICU.

For more information and to participate please visit the project page.

Project lead: Leonhard Held

We develop novel clinical trial designs for the development of new antibiotic drugs. A particular focus is on the incorporation of historical information to reduce the number of patients needed in current trials.

For more information and to participate please visit the project page.

Project lead: Leonhard Held

The project aims to examine the relationship between CD4+ and CD8+ during HIV infection and the prognostic value of CD8+ additional to CD4+ for HIV disease progression. This relationship will be examined in treatment naive patients as well as for patients after starting a highly active antiretroviral therapy (HAART). The influence of different treatment regimens on the CD4+ and CD8+ counts will be investigated. In order to answer these questions, an methodological framework will be developed which extends and applies existing methods for longitudinal data.

For more information and to participate please visit the project page.

Project lead: Leonhard Held

This research proposal aims to develop novel statistical methodology for objective Bayesian model selection in generalized regression models. There is now a large literature on automatic and objective Bayesian model selection for the linear model, which unburden the statistician from eliciting manually the parameter priors for all models in the absence of substantive prior information (Berger and Pericchi, 2001). The g-prior, usually attributed to Zellner (1986) but already used by Copas (1983), is the standard choice for the regression coefficients. However, for generalized linear models and further extensions, there are computational and conceptual problems with the g-prior approach. Similarly, research on the appropriate prior distribution on the model space and the selection of the “best” model has been done mainly in the linear model, e. g. Scott and Berger (2006, 2010) and Barbieri and Berger (2004).
We will fill these gaps and will extend the scope of objective Bayesian model selection to generalized regression models.

For more information and to participate please visit the project page.

Project lead: Leonhard Held
Funding: Swiss National Science Foundation

This research project aims to develop novel statistical methodology for both retro- and prospective analysis of space-time data on infectious disease incidence. The new techniques will be applied in the particular context of space-time surveillance data, but important parts of the methodology can be used in a wider context.

Project lead: Leonhard Held
Funding: Swiss National Science Foundation

During infectious disease outbreaks such as the current coronavirus disease (COVID-19) pandemic, modern surveillance systems continuously produce detailed data on reported disease incidence. Typically, these data are available at various geographic resolutions and stratified by age and sex, leading to high-dimensional count time series. Statistical modelling approaches which can handle the heterogeneities and interdependencies in such data are a valuable tool to inform public health decision makers about disease dynamics, to evaluate the effect of intervention measures, and to provide probabilistic forecasts of disease spread. Important factors which need to be taken into account are social contact patterns, mechanisms of geographic spread, and possible underreporting, all of which can vary across regions, age groups, and time. The endemic-epidemic (in the following: EE) framework is an established flexible modelling framework for multivariate infectious disease surveillance counts. A robust, free, and easy-to-use implementation is provided in several R packages. To our knowledge, this is the only readily available implementation of a sophisticated and general model framework for age-stratified spatio-temporal surveillance data. In the past, the EE framework has mainly been used for seasonal diseases, but there is a clear need for general and well-implemented multivariate modelling tools also for acute outbreak situations like the current COVID-19 pandemic. The goal of this project is to extend the EE framework to further improve its applicability in such contexts. Specifically the extensions aim to better address the following aspects:

• Assessing the impact of underreporting due to asymptomatic and prodromal carriage and insufficient levels of testing
• Determining the role of different age groups and their contact patterns in transmission
• Providing impact estimates of control and mitigation strategies such as travel restriction and other social distancing policies.

This project will provide evidence to improve public health response and aid in decisions on optimal social control strategies, particularly when to initiate travel restrictions and social distancing measures, and improve situational awareness. For further information please visit the project page.

Project lead: Leonhard Held
Funding: Swiss National Science Foundation