Complement-ARIE Landscape Analysis

Please view the complete analysis here: Complement-ARIE Landscape Analysis

To ensure that the Complement Animal Research In Experimentation (Complement-ARIE) program is focused on the areas of science with the greatest need, and which present the best opportunities for human-based model development, a landscape analysis was required to collect information on ongoing efforts in the NAMs space. 

The landscape analysis is intended to provide a foundation on which to better define the scope of Complement-ARIE and inform upon coordination with existing programs. It includes a survey of in vitro, in chemico, and in silico approaches that have the potential to improve understanding of human health and disease mechanisms, reduce reliance on animal models, and make the use of animals more efficient. To ensure a rapid and comprehensive approach, we leveraged generative artificial intelligence (GenAI) and other computational methods, supplemented with subject matter expertise. In addition, a survey was presented of the requirements for data associated with and generated by NAMs to make the data Findable, Accessible, Interoperable, and Reusable (FAIR) and AI-ready. This survey includes considerations for a suitable data ecosystem and analysis of currently available infrastructure, including existing data centers and repositories, that can be leveraged.

Accordingly, this analysis focused on describing existing efforts, and highlighting gaps, challenges, and opportunities in the following primary areas of developing human-based models of health and disease:

• In vitro models (e.g., cell lines and organoids)
• In silico models (e.g., multiscale models and digital twins)
• In chemico cell-free models (e.g., biocomputers and high-throughput receptor-ligand screens)
• FAIRness of data needed to train, interpret, and use
  NAMs (FAIR = findable, accessible, interoperable, reusable) (e.g., findability/accessibility of datasets, data annotation and interoperability, artificial intelligence (AI)-readiness of training data, data ecosystem infrastructure requirements)

In these areas, the following questions are addressed:

• Current and past systematic efforts (e.g., Multiscale Modeling Consortium and Tissue Chip program) to develop and refine NAMs, including both success stories, scientific and technical challenges, and roadblocks to wider adoption. This includes, e.g., current efforts by the Food and Drug Administration (Regulatory Science Tools program) and the National Science Foundation (Reproducible Cells and Organoids initiative), as well as other federal agencies and non-federally supported initiatives (as relevant).
• Opportunities to validate mature NAMs to support their regulatory use and market adoption. This includes thinking beyond the NAM itself to see how it can be meaningfully leveraged in research and/or industrial settings.
• Requirements for data associated with and generated by NAMs to make the data Findable, Accessible, Interoperable, and Reusable (FAIR) and AI-ready. This includes considerations for a suitable ecosystem, as well as analysis of currently available infrastructure that can be leveraged without building new data centers or dedicated data repositories.
• The likely impact of NAMs on complementing and streamlining animal research, including methods to evaluate potential economic benefits.