Meta menu:

From here, you can access the Emergencies page, Contact Us page, Accessibility Settings, Language Selection, and Search page.

Open Menu
Flipchart mit Charité 3R-Themen



Provide ongoing research projects with additional 3R support

Back to Overview

You are here:

Charité 3R funds three new projects - final decision by lot.

As part of the "Adding 3R Value" funding line, Charité 3R supports the expansion of three research projects already funded by other third-party funding sources such as the DFG or BMBF to include a 3R aspect that can be addressed with this additional funding. In the selected projects, the scientists are investigating, for example, the benefits of machine learning for the prediction of atrial fibrillation or developing an improved assessment system for the refinement of a pain therapy.

The intention of this funding line is to close funding gaps in the 3R field. This is because the core of a research project is primarily about answering a specific scientific question. In research projects in which animals are used, the implementation of the 3Rs principle must also be taken into account. The aim of this principle is to avoid animal experiments wherever possible (Replace), to reduce the number of animals used in experiments (Reduce) and to limit their suffering to the indispensable level (Refine). Within a single research project, the 3Rs could be advanced even further if targeted resources were made available for this purpose. The "Adding 3R Value" funding line creates this opportunity. It provides additional 3R support for projects that are already supported by other third-party funding sources. In this way, projects can focus on at least one of the 3Rs beyond the prescribed scope.

In the project "Replacement, Reduction, Refinement - Understanding Atrial Fibrillation Better with Machine Learning", researchers led by PD. Dr. F. Hohendanner want to develop a machine learning based tool to predict relapses of atrial fibrillation. Based on clinical and preclinical data, such as ECGs or 3D electroanatomical maps of the left atrium, the ML tool is expected to detect novel patterns, enabling more accurate predictions than can be made through traditional animal studies. Machine learning could help to find a therapy tailored to each patient, while improving the understanding of the disease and its underlying signaling pathways. The goal of the project is to develop and make available a computational tool for predicting atrial changes while avoiding animal testing. 

The aim of the project "Refining analgesia through combined and measurable scoring systems for inflammation, pain and inflammatory signals" led by Dr. Marina Kolesnichenko is to improve pain management in a mouse model of colitis, an inflammatory bowel disease. As part of this project, the researchers aim to develop a pipeline in which various parameters, such as analgesic efficacy or the degree of inflammation, are assessed according to a specific scoring system. For example, analgesic efficacy will be determined using the Mouse Grim Mass Scale (MIG) and the pain score. The degree of inflammation is determined using established histomorphological techniques and by quantifying the leukocytes in the tissue. The pipeline is designed to be adapted and transferred to other animal and disease models. The goal of this refinement effort is to enable the use of potentially pain-causing but important disease models through improved pain management.

The project, "Development of Full Spectrum Flow Cytometry Panels to Reduce Animal Numbers in Liver Cancer Immunotherapy Research," led by Dr. Linda Hammerich aims to develop an improved analytical method to assess the efficacy of new therapies against hepatocellular carcinoma (HCC). HCC is one of the most common cancers worldwide, with increasing mortality and limited treatment options. Because the disease is complex, preclinical research relies on animal models to develop new therapies. In their project, researchers are investigating immunotherapies in HCC mouse models using flow cytometry to analyze immunological changes and response to therapy. However, because conventional flow cytometry is limited by the number of parameters that can be analyzed in a single sample, a large number of animals are typically required to collect sufficient material and capture all subsets of immune cells.

Quality control and subsequent lottery procedure for equal selection
A total of 14 projects applied for Adding 3R value funding, of which three projects were selected for funding. The selection process took place in two stages: in the first step, the anonymized applications were evaluated by external reviewers with respect to predefined criteria. In the next step, 3 projects were selected by lot from the 12 best evaluated applications.

The application of the lottery procedure served the purpose of ensuring equality between thematically very different 3R approaches and increasing the chances of unusual approaches. In deciding on the lottery procedure, Charité 3R was guided by the Volkswagen Foundation, which introduced a partially randomized procedure in its Experiment! funding line and examined the effect in an accompanying project. Quality control by an upstream review panel is essential. 


Charité 3R Förderlinie "Adding 3R Vakue"


Charité 3R

Back to Overview