The NIH Common Fund is beginning a strategic planning process for a potential Complement-Animal Research In Experimentation (Complement-ARIE) program concept to catalyze the development, standardization, validation, and use of New Approach Methodologies (NAMs) that will more accurately model human outcomes. As part of the NIH’s Complement-ARIE strategic planning working group, the Office of Strategic Coordination (Common Fund) intends to publish a prize competition announcement to solicit entries for new methods and approaches that would complement, make more efficient, or, in some cases, replace traditional animal models, transforming the way biomedical researchers conduct basic, translational, and clinical sciences.
Interdisciplinary collaboration drives creativity and innovation, and teams of people coming from different disciplines and backgrounds can create out-of-the-box solutions that would otherwise never be imagined. By integrating various perspectives towards developing solutions, researchers can develop NAMs that provide high-quality, reproducible findings that can address significant biomedical questions with the highest relevance to human biology.
This Notice is being provided to inform potential solvers about the upcoming opportunity to compete in the Complement-ARIE Challenge. Publication of the Challenge announcement and launch of the submission portal are planned for November 7, 2023. Potential applicants are encouraged to sign up for the Complement-ARIE listserv to receive updates about the Challenge. The estimated submission deadline for submissions is January 11, 2024.
The NIH Common Fund intends to award a total prize purse of up to $1,000,000 which will be awarded to up to 20 winners (up to $50,000 each). To be eligible to win prizes, participants must be led by a Team Leader who is a citizen or permanent resident of the United States or be an entity incorporated in and maintaining a primary place of business in the United States. Additional information about the planned prize competition is included below.
The 21st century has been a time of expeditious technological advancement, with increasing use of new and evolving methods including gene editing, artificial intelligence (AI), induced pluripotent stem cells (iPSCs), and advances in organoid culture that are fundamentally changing the way biomedical science is done. These technologies and other advances, collectively referred to as New Approach Methodologies (NAMs) hold tremendous promise in understanding fundamental biology and advancing human health. While traditional animal models continue to be vital to advancing scientific knowledge, NAMs offer unique strengths that, when utilized singly or in combination, can expand the toolbox for researchers to answer previously difficult or unanswerable biomedical research questions.
Historically, certain methods in biomedical research have encountered limitations in their ability to emulate human physiological responses accurately. As the field progresses toward personalized medicine, there is an evident need for advanced research methods capable of a closer approximation to human outcomes. NAMs offer potential solutions to these challenges, and the NIH is keen on exploring innovative proposals that can enhance the current state of biomedical research.
While the introduction of a novel method is a significant feat, this challenge seeks more than just innovation. It aims to find solutions that bridge the existing gaps in biomedical research. Winning entries will not only showcase a novel approach but will also demonstrate its potential impact in the real world. A deep understanding of human biology, the relevance of the innovation in the current research landscape, and its scalability are factors that will be pivotal in the evaluation process. In essence, the challenge is not just about creating a solution, but about crafting the foreseeable future of biomedical research.
Participation in the Complement-Animal Research In Experimentation Challenge is open to citizens or permanent residents of the United States, or in the case of a private entity, be incorporated in or maintain a primary place of business in the United States. Full eligibility criteria will be available in the challenge announcement.
Prize Competition Details
The Complement-ARIE Challenge seeks to propel the development and refinement of human-centric New Approach Methodologies (NAMs), by developing approaches that complement or replace animal models. Solutions must include next-generation innovation in NAMs for complex in vitro human-derived cell- or tissue-based models, in silico multi-scale systems, in chemico approaches to emulate human biology, and/or integrated NAMs with associated Findable, Accessible, Interoperable, and Reusable (FAIR) datasets and AI-engines. Solutions must demonstrate substantial advancement from the current state-of-the art models and technologies.
Challenge solvers will submit a 15-page whitepaper demonstrating:
Innovation in In Vitro Modeling: Develop advanced cell- or tissue-based models that allow for accurate representations of human physiological processes. These models should be capable of replicating intricate biological reactions, thereby serving as effective platforms for testing, analysis, and prediction. Some capabilities of these solutions are (but are not limited to):
- Demonstrate the ability to model effects of human population diversity.
- Demonstrate advanced model complexity to incorporate at least two of the following: vascular, neural, immune, and/or microbiome components.
- Incorporate vascular-like structures for better nutrient and oxygen distribution in 3D models.
- Incorporate complex immune function in MPS systems for emulating immune-related pathways in inflammatory and autoimmune conditions.
- Standardize functional assays to assess the physiological relevance of the models.
- Personalized Tissue Engineering: Utilize patient-derived cells to develop personalized tissue models.
- Crispr-Cas9 Integration: Use gene-editing techniques like CRISPR to introduce specific mutations into your in vitro models to study disease mechanisms.
Powering In Silico Models and Simulations: Harness the capabilities of computational tools to create models that can simulate biological responses. These in silico models, backed by robust algorithms, should be capable of analyzing complex datasets, predicting possible outcomes, and aiding in decision-making processes in biomedical research. Some capabilities of these solutions are (but are not limited to):
- Simulate health and model key disease states or pathological changes (e.g., chronic inflammation, mitochondrial dysfunction, mutation accumulation, etc.)
- Ability to obtain real-world clinical data and insights to fine-tune simulations, improving their predictive accuracy.
- Utilize generative models to create synthetic biological systems or pathways for more robust testing and validation.
- Demonstrate the ability to model the effects of human population diversity.
- Develop computational models that can accurately simulate metabolic reactions in cells, providing insights into disease pathways.
- Virtual Clinical Trials: Use simulations to predict how human populations might respond to a new drug or treatment, enabling more informed clinical trial designs.
Exploration of In Chemico Systems: Venture into the realm of cell-free systems that allow researchers to study molecular interactions outside the confines of cellular environments. These systems should provide a clear window into the intricate dance of molecules, enabling the study of human-based NAMs to advance the human relevance in biomedical research. Some capabilities of these solutions are (but are not limited to):
- Ability to capture and control dynamic epigenetic, biochemical, and genetic changes.
- Include high-throughput assays to query biologically relevant molecular properties
- Incorporate advanced analytical methods to assess the full range of metabolic and proteomic changes in reaction conditions.
- Include cell-free expression systems as alternatives to production of biologics including those with non-natural functionalities.
- Implement nanotechnology to manipulate molecular interactions at an incredibly small scale.
NAM Integration: The challenge emphasizes integration of NAMs, either across models or into other platforms. The idea is to create a cohesive solution that leverages multiple methodologies for a comprehensive insight into human biology. Some capabilities of these solutions are (but are not limited to):
- Development of patient and/or population-level digital twins
- Integration across in vitro, in silico, and in chemico approaches
- Integration with diagnostic platforms for informed decision making in the clinical setting (i.e. learning healthcare systems)
- Application of AI/ML to develop predictive models
- Implement AI-based decision support systems that can analyze data from various NAMs to assist clinicians and researchers in making more informed decisions.
Each submission should be a detailed document that sheds light on the chosen methodology’s design, its potential applications, and a rationale for its feasibility. It’s crucial to elucidate the challenges encountered during the development phase, the strategies employed to overcome them, and the innovations introduced. Any datasets included should align with the FAIR principles, ensuring they are easily findable, accessible, interoperable, and reusable. If AI or Machine Learning (ML) models are employed, their design, accuracy metrics, and utility should be clearly detailed. To address these hurdles, the transformative NAMs proposed under this initiative must demonstrate substantial advancement compared to current approaches in challenge areas by developing innovative and integrative approaches that emulate/mimic complex biological structures/processes. Those may include but are not limited to, multi-cell/tissue type constructs that recapitulate organ complexity or multi-tissue/organ assemblies that allow for the interrogation of communication mechanisms between cell/tissue/organs in distant locations in the human body. Solutions should further enable the study of human-based NAMs to advance human relevance in biomedical research. For this challenge competition, solutions that include small animal models and other non-mammalian species will be considered out of scope.
Examples of general challenge areas for all NAMs in need of testable and feasible solutions include, but are not limited to:
- Representing diversity in human populations in modeling disease or health outcomes (e.g., diverse genetic ancestries, age groups, sex as a biological variable (SABV), socioeconomic status, health disparities, social determinants of health (SDOH)
- Modeling complex human physiology (e.g. pregnancy, development, metabolic, immune, neurosensory) and characterizing long-term, systemic, and developmental health effects of environmental and drug exposures
- Diseases and health-related areas of research that need alternative models for basic, translational, or clinical research within NIH mission (e.g., rare diseases and cancers, psychiatry-ic disorders, ophthalmology, pediatrics, aging, reproductive health, infectious diseases, neuroscience and behavior research, drug development, dose-ranging and toxicology studies)
- Development of systems that are not yet well defined (e.g. neuroscience and behavior research)
- Testing in complex systems for non-clinical data needed for first-in-human or first-in-population trials (e.g., dose-ranging, toxicology)
- Developing, standardizing, and manufacturing of platforms for in vitro, in chemico, and in silico approaches and the integration of such platforms for advancing NAMs for regulatory acceptance.
- Validation and standardization of NAMs to enable robust, reliable, and reproducible research
- Global harmonization approaches to facilitate widespread use and adoption of NAMs across the many stakeholders (e.g., academia, industry, regulatory agencies)
The Office of Strategic Coordination intends to conduct this Challenge under the authority provided by Section 24 of the Stevenson-Wydler Technology Innovation Act of 1980 (15 U.S.C. 3719), as added by the America COMPETES Reauthorization Act of 2010 (Pub. L. 111-358).