Following the breakthrough announcement of the FDA in April 2025 to phase out animal requirements for monoclonal antibodies, this is the moment to highlight the growing regulatory, ethical, and translational relevance demands to reduce animal testing.
The Limitations of Traditional Animal Testing
Even though 90% of drugs appear safe and effective in traditional animal models (mammal models), they do not receive FDA approval in humans, mostly due to safety and/or efficacy issues [1]. Additionally, there are cases where mammal testing contradicts human outcomes, as some medications widely used in humans, like aspirin, may have never reached the market upon mammal testing. Yet, some compounds deemed safe in mammal models have proven lethal to humans [2]. Therefore, there is a growing realization that mammal models utilized for human health and disease research are insufficient.
Beyond efficacy concerns in drug discovery, mammal studies conducted for the safety evaluation of different compounds present substantial drawbacks, despite providing valuable data. The time and cost of long-term mammal models delay advancement in new drug development and safety identification for humans and the environment. Furthermore, the limited throughput of mammalian testing restricts the number of drugs/compounds that can be investigated.
Given these limitations, there is an emerging need for novel resource-effective approaches that can be integrated into decision-making earlier, not only limiting the cost for the companies but also positioning them to make better business decisions.
The emergence of New Approach Methodologies (NAMs)
As a result of these challenges, different alternative approaches have been developed, including Organism-on-a-Chip, in vitro human-based models together with organoids, and computational tools, often referred to as New Approach Methodologies (NAMs). The FDA and EPA recognize NAMs as a means to acquire “faster and more accurate human risk assessments” while reducing traditional animal models [3,4].
Innovative Platforms Leading the NAMs’ Thrive
Organism-on-a-Chip devices incorporate microfluidic flow and mechanical forces on a bioengineered chip, emulating the in vivo environment. In parallel, organoids, as 3D cell cultures that mimic the structure and function of human organs, were developed for the same purpose. Moreover, in silico approaches such as computational modeling, artificial intelligence (AI), and machine learning (ML) can leverage existing data to predict various biological responses.
Together, these advanced platforms can collectively evaluate key parameters, including safety, immune responses, and drug metabolism, with higher relevance to human biology.
FDA’s Roadmap: From Traditional Animal Testing to NAMs
Based on the FDA announcement, it will be crucial to implement an integrative strategy, and actions shall be taken to minimize traditional models use, including:
- Encouraging the submission of NAMs data in parallel with traditional methods by sponsors, building a repository of experience.
- Ensuring companies understand that less traditional testing will be required if NAMs data are validated.
- Investing the development of NAMs models through research collaborations with different venues.
- Investing in the development of NAMs models through research collaborations with different venues.
- Establishing NAM’s validation via retrospective analyses and prospective trials.
The ultimate long-term (3-5 years) goal of the FDA is to make traditional animal models the exception rather than the norm for pre-clinical safety and toxicity testing. This shift is possible since foundational research has been demonstrating the scientific validity of alternative approaches. Among these alternatives, noteworthy are alternative model organisms that bring unique advantages while maintaining scientific rigor.
Spotlight on Alternative Model Organisms as NAMs
These model organisms have already gained traction across multiple research fields [5] and present several key characteristics compared to traditional mammalian studies:
- Short lifespan and high reproductive rate → Faster testing and reduced costed.
- Transparent nature → Direct observation of developmental processes and toxic effects without the need for complex imaging techniques.
- Ease of cultivation → High-throughput screening and analysis.
- Non-sentient organisms → Part of the NAMs toolkit.
- High conservation with mammals and humans → Many genes and signaling pathways are highly conserved.
Leading Alternative Models in Toxicology Research
While these advantages apply broadly to alternative models, certain organisms have emerged as particularly valuable for toxicology applications. Below is a brief overview of the most widely adopted alternative model organisms and their specific contributions to toxicological research:
| Model organism | Applications in Toxicology |
|---|---|
| C. elegans (Caenorhabditis elegans) | Developmental & Reproductive Toxicity [6] Neurotoxicity [7] Environmental Toxicology [8] Predictive Toxicology [9] |
| Danio rerio (zebrafish) and its embryos | Developmental Toxicity [10] Drug Discovery Toxicology [11] Environmental Toxicology [12] |
| Drosophila melanogaster (fruit fly) | Developmental Toxicity [13] Neurotoxicity [14] Microplastic and Nanoplastic Toxicity [15] |
Transforming Toxicology: A Multi-Model Approach
How can the synergy of multi-model NAMs reduce mammal models by 50–80% in toxicology?
By integrating strategies that leverage NAMs, reliance on traditional mammalian testing could be minimized, while predictive accuracy could be enhanced.
Proposal for a Tiered Screening Pipeline
Tier 1: In silico predictions
Computational models that enable prioritizing compounds/chemicals based on structural similarity to compounds with known impact.
Tier 2: Human-based In Vitro Assays
Compounds/chemicals screening for mechanistic toxicity using human cell lines or organoids.
Tier 3: Alternative In Vivo Models
Additional screening with alternative model organisms focusing on the toxicity mechanisms mentioned in the Table above.
Tier 4: Targeted Mammalian Testing
Reserve mammal testing only for compounds/chemicals with ambiguous results and high regulatory concern.
Beyond Testing: Creating a Sustainable Framework
Of course, following the FDA guidelines for drug discovery, more actions should be implemented in toxicology research as well, including:
- Validation and Regulatory Collaboration: Validation studies across multiple research centers to standardize the alternative model organisms.
- Dynamic Feedback Loop: Continuously update predictive models with new data from NAMs and reduce mammalian testing, enhancing accuracy over time.
The Path Forward
All these steps can lead to the reduction of mammalian studies up to 80% based on the findings of Boyd, W. A. (2016)‘s study “Developmental Effects of the ToxCast™ Phase I and Phase II Chemicals in Caenorhabditis elegans and Corresponding Responses in Zebrafish, Rats, and Rabbits”. The presented study highlights reduced cost and time efficiency via automated high-content throughput platforms.
As the toxicology field continues to evolve, embracing NAMs represents not just a regulatory requirement but a scientific advancement that promises more human-relevant data, faster results, and more ethical research practices. The future of toxicology testing lies in these innovative approaches, and organizations that adapt quickly will be at the forefront of this scientific transformation.
References
[1] Marshall, L. J., Bailey, J., Cassotta, M., Herrmann, K., & Pistollato, F. (2023). Poor translatability of biomedical research using animals—a narrative review. Alternatives to Laboratory Animals, 51(2), 102-135.
[2] Bell, J. (2019). Aspirin killed the cat: animal research models do not always apply to humans. Expert Opinion on Drug Metabolism & Toxicology, 15(9), 683-685.
[3] FDA Science Board Subcommittee (2024). Potential Approaches to Drive Future Integration of New Alternative Methods for Regulatory Decision-Making.
[4] EPA (2024). Alternative Test Methods and Strategies to Reduce Vertebrate Animal Testing.
[5] Sandner, G., König, A., Wallner, M., & Weghuber, J. (2022). Alternative model organisms for toxicological fingerprinting of relevant parameters in food and nutrition. Critical reviews in food science and nutrition, 62(22), 5965-5982.
[6] Xiong, H., Pears, C., & Woollard, A. (2017). An enhanced C. elegans based platform for toxicity assessment. Scientific reports, 7(1), 9839.
[7] Sammi, S. R., Jameson, L. E., Conrow, K. D., Leung, M. C., & Cannon, J. R. (2022). Caenorhabditis elegans neurotoxicity testing: novel applications in the adverse outcome pathway framework. Frontiers in Toxicology, 4, 826488.
[8] Queirós, L., Pereira, J. L., Gonçalves, F. J. M., Pacheco, M., Aschner, M., & Pereira, P. (2019). Caenorhabditis elegans as a tool for environmental risk assessment: emerging and promising applications for a “nobelized worm”. Critical reviews in toxicology, 49(5), 411-429.
[9] Hunt, P., Sprando, R., Camacho, J., Welch, B. (2022). The C. elegans Model in Toxicity Testing. FDA.
[10] Bianchi, E., Bhattacharya, B., Bowling, A. J., Pence, H. E., Mundy, P. C., Jones, G., … & Bondesson, M. (2024). Applications of zebrafish embryo models to predict developmental toxicity for agrochemical product development. Journal of Agricultural and Food Chemistry, 72(32), 18132-18145.
[11] Cassar, S., Adatto, I., Freeman, J. L., Gamse, J. T., Iturria, I., Lawrence, C., … & Zon, L. I. (2019). Use of zebrafish in drug discovery toxicology. Chemical research in toxicology, 33(1), 95-118.
[12] Jayasinghe, C. D., & Jayawardena, U. A. (2019). Toxicity assessment of herbal medicine using zebrafish embryos: A systematic review. Evidence‐Based Complementary and Alternative Medicine, 2019(1), 7272808.
[13] Eom, H. J., Liu, Y., Kwak, G. S., Heo, M., Song, K. S., Chung, Y. D., … & Choi, J. (2017). Inhalation toxicity of indoor air pollutants in Drosophila melanogaster using integrated transcriptomics and computational behavior analyses. Scientific Reports, 7(1), 46473.
[14] Kaun, K. R., Devineni, A. V., & Heberlein, U. (2012). Drosophila melanogaster as a model to study drug addiction. Human genetics, 131, 959-975.
[15] Wang, C., & Shen, J. (2025). Deep learning-driven behavioral analysis reveals adaptive responses in Drosophila offspring after long-term parental microplastic exposure. Journal of Environmental Management, 376, 124502.
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