Extracting key information from existing data; another way to promote the 3Rs

The Network and the 3Rs

From its inception, one of the key commitments of the MRC National Mouse Genetics Network (NMGN) has been to lead the preclinical scientific community in adhering to the key principles of the 3Rs (replacement, reduction and refinement) in animal experimentation.

We have multiple strategies to achieve this goal, which among others include:

• fostering collaboration with scientists using other preclinical in vivo, ex vivo and in vitro models in our focus areas, to promote awareness and knowledge exchange resulting from diverse approaches,
• focusing on rigorous experimental planning and reproducibility of data,
• developing and curating a publicly available data platform to integrate and share our methods and experimental outcomes,
• creating a wide network of associates and involving them in our work through funding opportunities, project collaborations and meetings,
• establishing closer relationships with industry partners to allow seamless exchange of knowledge and awareness of requirements, and to accelerate the transition from preclinical research to therapeutic intervention.

A further key aspect of our focus is the integration and analysis of existing data collected over many years of preclinical and clinical research to extract as much useful information as possible, thereby avoiding unnecessary repetition of experimental work as well as gaining new insights.

DAR3T, a new NC3Rs infrastructure grant supported by the Network

Professor Crispin Miller, the NMGN’s data platform lead, has been using artificial intelligence and machine learning to group cancer patients into subsets according to the molecular and phenotypic characteristics of their tumours, and then to ‘disease position’ existing preclinical datasets against patient subgroups. This

enhances forward- and back-translation between mechanisms, models, and clinical applications. His work supports the concept that these apparently very different datasets can be pooled together to provide a more comprehensive understanding of clinical issues and to accelerate diagnostic and therapeutic interventions, while reducing the need to use animal models.

Crispin has been awarded £562,442 by the National Centre for the Replacement, Refinement and Reduction of Animal Research (NC3Rs) as part of the 2024 non-animal methods infrastructure grants supported by the Department for Science, Innovation and Technology (DSIT).

The goal of this funding is to provide a computational platform to support disease positioning and modelling—at scale—of cancer models data, with a focus on in silico, organoid and cell-line collections. Supported outputs will include the establishment of a well-curated, Findable Accessible Interoperable and Reusable (FAIR) collection of non-animal methods data, aligned to existing Genetically Engineered Mouse Models (GEMMs) and human tumours. This project will therefore promote the 3Rs, not only by demonstrating which non-animal models accurately recapitulate human disease, but also by making their data available in a form that supports the development of new in silico models, and downstream analysis by bioinformaticians, computational biologists and machine learners.

The full support of the Network

This project is fully aligned with the NMGN’s approach to animal research. The availability of GEMMs and their robust phenotype data, accessible through our clusters and the Mary Lyon Centre, will provide Crispin with invaluable datasets. These resources will enhance his platform’s ability to more accurately position all other models and patient data, achieving the level of integration and maximisation of output required to realise the clinical progress we are striving towards.

Owen Sansom (NMGN Director) said: “This new project complements the work of the NMGN data platform and of the Cancer cluster and we are excited to get further insight into how alternative models compare to our existing toolbox. We hope this will improve the quality of our new mouse models, while also contributing to reducing the number of necessary animal experiments”