FDA to Phase Out Animal Models
Since 1983, animal testing has been mandatory for safety reasons (Han, 2023), but recently, the FDA Modernization Act 2.0 (2022) ended the mandatory testing requirement. Animal models have been the gold standard for preclinical research for a decade (Domínguez-Oliva et al., 2023). These models have been the bridge between lab discoveries and human trials, providing a necessary stepping stone. Certain strengths have made animal models indispensable, but ethical, biological, and technological reasons are driving the need for changes to be made.
How diseases progress over time is difficult to replicate in isolated or computer modeled cell, which is why animal organ systems are highly favored. Diseases often affect more than one system, and a single isolated cell cannot replicate this (Mukherjee et al., 2022). An example of this would be the immune, endocrine, and nervous systems interacting in autoimmune or metabolic disorders (Han, 2023). Observing systemic effects allows for understanding of side effects (Mukherjee et al., 2022).
Another reason why animal models are so useful is that many animals share significant genes with humans. According to Domínguez-Oliva et al. (2023), mice share about 85% of their genome with humans. This allows for the creation of transgenic models, where certain genes can be inserted or altered to study the results. This is so helpful for cancer, diabetes, and cardiovascular research (Domínguez-Oliva et al., 2023).
The fact that animals have a shorter lifespan is another reason why animal models are used. The shorter lifespans allow scientists to track a disease from birth to death, which is once again hard to replicate in an isolated cell (Han, 2023). Researchers can observe long-term effects of treatments, toxicology, and progression over time.
Although animal testing is helpful, it raises a big ethical dilemma. There is a growing concern about the pain, distress, and suffering in animals that are tested on (Robinson et al., 2019). This has led to the adoption of the 3Rs principle: Replacement, Reduction, and Refinement. Replacement is the avoidance or replacement of animals in studies. Reduction is if an animal must be used, researchers should use the minimum amount to obtain the necessary results. Lastly, refinement is the process of minimizing any pain, suffering, or distress an animal may feel during testing (Robinson et al., 2019).
The translation crisis is another reason why animal testing is being phased out. According to Domínguez-Oliva et al. (2023), about ninety percent of drugs pass animal testing but then fail in human clinical trials. This means that even with genetic similarity, gene expression may differ (Domínguez-Oliva et al., 2023). A simple example of this could be how Penicillin is safe in humans but toxic to guinea pigs.
In summary, animal models have been an important part of biomedical research for decades. This has provided critical insight into disease progression, treatment effects, and drug safety. At the same time, ethical concerns and the limitations of translating results from animals to humans highlight the ongoing need for careful consideration and responsible research practices. By balancing the strengths of animal models with attention to ethical standards and scientific accuracy, researchers continue to use these models as a vital bridge between laboratory discoveries and human applications. Animal testing has long been a key point of biomedical research and drug development. However, due to ethical concerns and scientific limitations, there has been a growing shift towards New Approach Methodologies (NAM). These new methods aim to reduce or replace animal testing with more human-relevant systems. Organoids, organs-on-chips, and computational models such as AI are some examples of NAM (Kwon, 2026). Although these technologies offer a more accurate prediction of human responses, they are still limited in their ability to fully replicate how complex living organisms are. As a result, NAM represents a promising but incomplete alternative to animal testing.
One of the most notable advancements in NAMs is the development of organoids and microfluidic organs-on-chip using human-derived induced pluripotent stem cells (iPSCs). iPSCs are adult cells that have been reprogrammed into a state similar to a stem cell, allowing them to differentiate into various specialized cells. Scientists use these cells to create 3D organoids, which are miniature versions of human organs, that can replicate key structural and functional features (Kwon, 2026). Similarly, organs-on-chips are small devices that contain human cells. These models provide a more accurate representation of human biology compared to animal models. According to Kwon (2026), these systems can even support clinical trials where patient-specific cells are used to test drug responses. For example, a liver-on-a-chip model was shown to detect drug-induced liver toxicity with 87% accuracy, which helped identify harmful compounds that had previously been missed during animal testing (Kwon, 2026). This demonstrates how human-based systems can improve the prediction of drug safety and effectiveness.
In addition to biological models, computational approaches and generative AI are changing how researchers evaluate toxicity. These systems analyze large datasets compiled from human studies, experiments, and chemical structures to predict how substances will affect the body (Kwon, 2026). For instance, a computational model made for skin sensitization used data from approximately 430 chemicals and was able to accurately predict the likelihood of allergic reactions in humans (Kwon, 2026). Similarly, an AI model known as AnimalGAN was trained on data from thousands of animal studies and used to simulate virtual experiments on 100,000 “digital” rats (Kwon, 2026). This model successfully predicted liver toxicity for drugs with similar chemical structures, demonstrating the potential of AI to replace certain forms of animal testing. These computational tools are especially valuable because they allow researchers to screen large numbers of compounds quickly and cost-effectively, reducing the need for live-animal experimentation while still generating meaningful safety data (Kwon, 2026).
Despite these advantages, current NAMs face important limitations. One of the main challenges is that many of these systems are simplistic, meaning they focus on specific cells or tissues rather than the interactions of the entire organism (Kwon, 2026). While organoids and organs-on-chips recreate certain aspects of human biology, they cannot fully replicate processes that involve multiple organ systems working together. For example, drug metabolism often requires coordination between the liver, kidneys, and circulatory system, which is difficult to reproduce in isolated models. Furthermore, these technologies struggle to capture long-term processes such as aging, hormonal regulation, and endocrine system interactions (Kwon, 2026).
Additionally, Kwon (2026) highlights that some organ-on-chip models include only a limited number of cell types, such as a kidney chip that represents just one of many cell types found in a real kidney. Technical barriers further limit the use of NAMs, including challenges in validation and standardization, which are necessary for regulatory approval (Kwon, 2026). Moreover, certain aspects of biology, such as behavior and cognition, cannot yet be replicated outside a living organism, meaning that animal testing is still required in some areas.
In conclusion, New Approach Methodologies are reshaping the future of biomedical research by offering more ethical and human-relevant alternatives to animal testing. Organoids, organs-on-chips, and AI-driven computational models provide improved accuracy in predicting human responses and could potentially reduce the use of animals in scientific studies (Kwon, 2026). However, their current limitations, particularly in modeling complex systemic interactions and long-term biological processes, prevent them from fully replacing animal testing at this time. As research continues to advance, these technologies are likely to play an increasingly important role in drug development and safety testing, moving science closer to a future where animal testing is minimized.
Works Cited
Robinson, N. B., Krieger, K., Khan, F. M., Huffman, W., Chang, M., Naik, A., Yongle, R., Hameed, I., Krieger, K., Girardi, L. N., & Gaudino, M. (2019). The current state of animal models in research: A review. International Journal of Surgery, 72, 9–13. https://doi.org/10.1016/j.ijsu.2019.10.015
Domínguez-Oliva, A., Hernández-Ávalos, I., Martínez-Burnes, J., Olmos-Hernández, A., Verduzco-Mendoza, A., & Mota-Rojas, D. (2023). The Importance of Animal Models in Biomedical Research: Current Insights and Applications. Animals, 13(7), 1223. https://doi.org/10.3390/ani13071223
Mukherjee, P., Roy, S., Ghosh, D., & Nandi, S. K. (2022). Role of animal models in biomedical research: A review. Laboratory Animal Research, 38(1), 18. https://doi.org/10.1186/s42826-022-00128-1
Han, J. J. (2023). FDA Modernization Act 2.0 allows for alternatives to animal testing. Artificial Organs, 47(3), 449–450. https://doi.org/10.1111/aor.14503