Executive Summary of the NIH Listening Sessions on the Complement-ARIE Program Concept

Background:  

The National Institutes of Health (NIH) is conducting planning activities to inform a potential Common Fund research program called Complement-Animal Research In Experimentation (Complement-ARIE). The program aims to advance the development, standardization, validation, and use of new methods and approaches that will more accurately model human biology, known as New Approach Methodologies (NAMs). These NAMs are intended to more closely model human biology and would complement, or in some cases, replace traditional models, transforming the way we do basic, translational, and clinical sciences.

As part of the Complement-ARIE strategic planning activities, the NIH Common Fund hosted three public listening sessions in October 2023. These virtual events brought together key representatives from multiple sectors, including industry and academic partners, non-government organization (NGO) representatives, and U.S. government and international partners, to gain insight unique to their fields regarding current opportunities and roadblocks in NAMs development. Approximately 1,100 people registered, and over 550 participated across all sessions. Interest groups were also invited to submit written input via email to the Complement-ARIE planning team.  

Participants provided a range of feedback centered around five main topics:

• Limitations of animal models
• Current limitations of NAMs
• Potential future applications of NAMs
• Building confidence and validation of NAMs  
• Enabling widespread adoption of NAMs

This report summarizes the comments provided during the listening sessions and via written feedback. These summaries represent the opinions and perspectives of the listening session participants, which do not necessarily reflect the perspectives of NIH or the federal government or the goals or structure of the potential Complement-ARIE program. For the purposes of these discussions, NAMs encompass a broad range of human-relevant complex in vitro systems, multi-scale computational models, high-throughput screening systems, in chemico (cell-free) assays, and other innovations to better understand human physiology and disease. While model organisms have been an important tool in biological research, the use of metazoan models will not be considered for Complement-ARIE, including but not limited to zebrafish, fruit flies, roundworms, water fleas, and frog embryos.

Participant Discussion of Limitations of Animal Models  

Participants discussed the limitations of translating findings from animal studies to humans for diseases involving interactions between complex systems such as the immune, nervous, and gastrointestinal systems, as well as host-microbiome interactions. This includes deficiencies in animal models for autoimmune diseases, neurodevelopmental and neurodegenerative diseases, cognitive and psychiatric disorders, pregnancy and reproductive mechanisms, cancer, respiratory diseases, and metabolic disorders.

Differences in physiology, immune response, genetic diversity, xenobiotic metabolism, toxicokinetics, pharmacokinetics, and lifespan also limit the applicability of animal models to human health and disease. For example, short-lived animal models are insufficient in replicating human aging mechanisms and chronic diseases. Animal models do not sufficiently replicate how diseases develop and manifest in humans, and there are few animal models for rare diseases. Additionally, the limited genetic diversity and variability in animal models do not adequately represent human population diversity.

Animal models also do not capture the dynamic social and environmental factors that affect human health. For example, lab animals are housed in sterile and homogenous environments that do not mirror the complex environments humans experience, including social determinants of health.

These limitations can hinder drug development, as many drugs that are successful in pre-clinical testing in animals fail in human clinical trials.  

Animals also do not adequately model subjective measures such as pain management, psychological disorders, or disability recovery. Furthermore, animals are often expensive to obtain and maintain in laboratory settings.

Representatives from NGOs also noted the negative toll working with laboratory animals has on biomedical workers’ mental health and well-being, particularly when the animals are in pain. Participants from the NGO and the U.S. government and international partners sessions recommended NIH conduct a systematic assessment to identify animal models with poor translatability to inform future funding decisions for NAMs.

Participant Discussion of Current Limitations of NAMs

Like animal models, NAMs have limitations in replicating population diversity, complex human biology and disease, and dynamic environments. Technology constraints, lack of standardization in NAM protocols, and availability of harmonized datasets are also challenges.  

NAMs are currently deficient in modeling age-dependent diseases and the progression of chronic disorders over time. This is because in vitro NAMs are typically developed using young cells (i.e., induced pluripotent cells), which do not mimic mature cells in the human body. NAMs that model neurological disorders and behavioral endpoints are also inadequate due to the lack of long-term data to model the chronic and cumulative effects of aging on cells in vitro.  

Participants discussed gaps in the ability of NAMs to accurately mimic diseases involving communication between multiple cell types, tissues, or organ systems in the body. However, a systems approach may not always be required, and considering the context of use can help minimize connectivity between tissues as a limitation.  

NAM platforms do not represent dynamic, human-relevant exposures, including changes in the internal environment (e.g., hormone fluctuations) and the synergistic effects of multiple chemical exposures and social stressors in humans. NAMs are also limited in their ability to model the metabolism and pharmacokinetics of drugs. In addition, current limitations for in vitro to in vivo extrapolation analyses must be resolved to improve the confidence in NAMs results. High cost and low-throughput are also barriers to the predictive capability and widespread adoption of current NAMs. The lack of standardization regarding data harvesting, NAMs development, and validation endpoints makes reproducibility across labs and research organizations a continuing challenge.

Current NAMs do not represent population diversity or variability, particularly for groups traditionally underrepresented in health research. Participants called for a focus on community engagement to recruit diverse donors. Limited access to data, tissue banks, and cell lines derived from a diverse set of donors to develop NAMs is also a constraint.  

Participants noted several types of NAMs had specific limitations. For example, in silico NAMs are currently constrained by a lack of high-quality training data and insufficiencies in the high-performance computing technology required to model complex diseases.  

Generally, participants spoke about how a lack of standardization in the NAMs field complicates inter-laboratory collaboration and acceptance by end users. Furthermore, data standards are needed to facilitate the sharing and harmonization of NAMs data. Increasing scientific confidence in NAMs is also an issue, and participants in all sessions spoke about the need for frameworks to standardize how NAMs are developed, how data is collected, and how validation is conducted. A database is needed to catalog existing NAMs that includes the context of use, results, validation status, and best practices for each NAM. The database can build upon or coordinate with existing NAMs databases as well. Participants provided examples of existing databases as a reference, including the Microphysiology Systems Database and the European Commission Tracking System for Alternative Methods Towards Regulatory Acceptance, which track NAMs and their validation status.  

Finally, representatives from federal agencies expressed that there are currently no guidelines for how to properly cite NAMs in publications, often making it difficult to discover what work is being done with NAMs in what areas.  

Participant Discussion of Potential Future Applications of NAMs

Despite current limitations, NAMs have a wide-ranging potential to accelerate biomedical research discoveries by improving our understanding of the mechanisms driving disease. NAMs can use existing datasets from large human population studies and patient-derived induced pluripotent stem cells (iPSCs) to facilitate research on disease initiation, progression, prevention, and treatment. They allow for improved modeling of a breadth of health outcomes, including rare diseases, neurological disorders, cancers, wound healing, and chronic illnesses across the lifespan. The use of digital twins and virtual patient models are promising approaches that could achieve these advancements, as noted by representatives from the U.S. government, international groups, and NGOs.  

Participants in all sessions posited that in silico and in vitro approaches can facilitate the development of safer, more effective drugs. NAMs can be used to predict adverse clinical trial outcomes, test for potential adverse drug interactions, and generate more accurate drug metabolism profiles. Patient-derived iPSCs can enable a precision medicine approach, thus providing patients with personalized disease prevention and treatment options.

Additionally, NAMs can be used to conduct large-scale, high-throughput testing of chemicals to facilitate complex risk assessments, such as for large groups of chemicals like PFAS or for multi-dose exposures. Participants also expressed that NAMs have the potential to facilitate modeling and predictions for rapid response situations, such as infectious disease outbreaks.  

Participants noted that integrating data across various NAM platforms, anchoring NAMs in the whole-body context, and incorporating genetic and other types of diversity into NAMs will be critical to advancing the field.

Participant Discussion of Building Confidence and Validation of NAMs  

Barriers to validation and widespread acceptance of NAMs include uncertainty around validation requirements and processes, low confidence in NAMs data by regulators and end-users, lack of familiarity with NAMs data, and lack of support for validation studies.  

To build confidence in NAMs, participants called for a government-led effort to educate all partners on how to interpret NAMs data and the methods’ usefulness and limitations. Participants from all sessions agreed that creating opportunities for regulators, developers, and end-users to collaborate early and often is necessary to facilitate NAMs development, validation, and adoption. Furthermore, participants across sessions called for funding initiatives to support validation studies and inter-laboratory comparisons of NAMs assays.  

Representatives from academia and NGOs noted the need to set reasonable regulatory expectations for NAMs. It is critical that NAMs demonstrate relevance to human biology by reflecting key events along adverse outcome pathways. Some participants also noted the importance of validating NAMs against existing in vivo data to facilitate regulatory acceptance and uptake by end users.  

The development of a framework outlining validation requirements and steps is needed. This framework should be developed collaboratively, focus on the context of use, and be flexible enough to allow for cases when comparison to animal data is not possible or appropriate.  

Participants in all sessions recommended that the NIH invest in infrastructure to support NAMs validation, including tissue banks, data-sharing infrastructure, and a registry of NAM methods and data that have been accepted for regulatory purposes and their context of use.  

Participant Discussion of Enabling Widespread Adoption of NAMs

Achieving widespread adoption of NAMs will require collaboration among industry, academia, government, and regulatory agencies. These groups should work together to establish shared goals, achieve standardization, and share data and resources. Cooperation between NAMs developers and end users is also critical. Representatives from academia and industry noted that public-private partnerships for data sharing will be key to accelerating NAMs development and reducing end users’ uncertainty around NAMs.  

The preference for animal-based methods is another barrier to the widespread adoption of NAMs. Participants recommended an educational initiative aimed at grant reviewers, journal publishers, regulators, and end users to help reduce animal methods bias, demonstrate NAMs relevance to human biology, and build confidence in the methods. Standardization of NAMs data, relevant endpoints, and validation criteria is also needed to increase adoption and acceptance.  

Training scientists, particularly early-stage investigators, in NAMs is needed for adoption and continued growth in the field. Training should focus on NAMs development, use, limitations, and oversight. Participants recommended institutional training programs, center programs, and administrative supplements as approaches to train researchers while promoting collaboration.  

Access to costly NAMs infrastructure and disparate data is a major barrier to uptake. Creating a centralized tissue bank and data repository and leveraging existing initiatives, like the NIH All of Us Program, can help researchers access the resources needed to transition from traditional studies to NAMs research. Participants across sessions recommended the establishment of a center program to provide researchers access to shared core facilities and the interdisciplinary expertise necessary to stimulate NAMs adoption and progress.  

Collaborations and partnerships are also key to developing NAMs and the infrastructure and expertise needed to support the field. Interdisciplinary collaborations should bring together experts in materials science, computational science, toxicology, clinical research, and drug discovery. International partnerships will be key to building and supporting NAMs infrastructure, such as biobanking repositories, AI databases, and a data ecosystem. Funding opportunities are needed to incentivize scientists and institutions to undertake the substantial financial investment necessary to upgrade infrastructure to support NAMs research. Additionally, participants in the NGOs session noted that funding is needed to increase access to NAMs equipment and resources for Contract Research Organizations and researchers working outside the U.S. or Europe.  

Conclusion of Participant Views

NAMs offer many advantages over traditional models for replicating human biology and disease. Collaboration across interest groups and investment in training, data sharing, and research infrastructure is needed to advance the development, standardization, validation, and use of NAMs. NIH, in partnership with other U.S. government and international agencies, has a leadership role in planning, coordinating, and funding the initiatives that will encourage scientists to use NAMs and accelerate biomedical discoveries that can improve human health.