Traditional or Alternative: That is the New Question

Recent advances in non-animal alternatives are putting drug developers on a learning curve to determine the right or right combination of models to advance therapies before testing in humans

Two years after the US passed the FDA Modernization Act 2.0¹, the US Food and Drug Administration announced plans to phase out animal testing requirements for monoclonal antibodies and other drugs. Clearly, this is putting so-called New Approach Methodologies (NAMs),^2,3 despite their infancy, on the map in pre-clinical steps of drug discovery and development. 

Historically, animal testing has been strictly regulated by IACUC guidelines and agencies like the FDA and the European Medicines Agency (EMA). With societal pressure mounting to abandon research animal use out of ethical concerns, some existing non-animal models can at least complement drug assessments in the earliest stages of development.

Yet, these in vitro, AI and in silico options have to be systematically validated before fully substituting animal models. Conducting biomedical research can be balanced with animal welfare. The National Toxicology Program introduced by the U.S. Department of Health and Human Services (HHS) already lists 3R methods being evaluated or considered acceptable within the regulatory framework. However, this overview of suggested alternatives lacks completion. With decades of published animal model data the drug development industry will still need to rely on some animal testing in the near-term. When selecting between these traditional models and the next set of non-animal alternatives, among the many factors to consider are three key ones:

1.    Scientific Validity and Relevance to Human Biology: Most commonly mice or rats have been widely used due to their closer genetic similarities (up to 85%) to humans. However, species differences can limit their predictive accuracy, so larger animal species even more resembling humans in terms of their genetic and physiological makeup are relied on for studying complex neurological diseases and immune responses. Zebrafish present advantages when evaluating developmental biology and drug screening because of their transparent embryos and rapid reproduction. While animal models generally provide insights into whole-organism interactions, they can fall short in fully replicating human physiology or disease mechanisms. Non-animal approaches like 3D organ chips, human-derived cell cultures, bioengineered tissues and AI-based simulations, can sometimes produce more human-relevant datasets. However, for systemic metabolism or immune responses, animal models may still be needed and useful⁴, whereas for mimicking human-specific cellular responses, non-animal models are increasingly viable.

2.    Operational Costs: Animal studies are expensive and time-consuming due to housing, care, and regulatory compliance. Non-animal approaches, such as in vitro models or computational simulations, can reduce costs and streamline early stages of drug discovery and development. AI-based predictions combined with high-throughput in vitro screening and organoid cultures can quickly help identifying promising lead candidates. For toxicity testing organ-on-a-chip and human-based cellular assays can be predictive but could miss systemic human toxicities. Therefore, traditional rodent and primate models may still be necessary, but they should be complemented with alternative methods as these mature to reduce reliance on animal testing.

3.    Technological Advancements and Availability: Virtual control groups (VCGs), which are already used in clinical testing, are moving to the pre-clinical space as well. These can supplement animals traditionally used as controls with computer generated datasets simulating the behavior of live control groups in toxicology studies under the tested experimental conditions. Besides that, organoid technology and AI-driven drug screening, are rapidly improving, though their widespread adoption depends on validation, availability, and integration into existing research frameworks. Evaluating the validity and accessibility of these alternatives takes time but is essential for data-based decision-making. If using well-established, readily available models is a priority, traditional methods may be preferred fallback, while non-animal alternatives are not yet the standard.

For decades animal models were the sole gateway for therapies to find their way into the clinic. However, from 3D models like organoids and organ chips to artificial intelligence, induced pluripotent stem cells and computational modeling, science is changing the notion of how to conduct drug development in the future. How quickly things change could depend upon finding fully validated models that work in real-world settings and ideally harmonized for global markets. 

With alternative tools aiming to replace animals altogether or reduce their numbers, measurable endpoints are still needed for the same decisions that come from nonclinical studies. A challenge will be in the comparability of NAM results with traditional datasets, requiring standardized readouts, definitions and reproducible data generation⁵. With regulatory acceptance it will be crucial to stay updated on evolving guidelines for incorporating alternative methods, where they can lower the risk of later clinical trial failures. Reaching towards animal free drug testing is desirable, but the path is complex and long and be driven by the need to ensure patient safety.

References:
1.    Adashi EY, O’Mahony DP, Cohen IG. The FDA Modernization Act 2.0: Drug testing in animals is rendered optional. Am. J. Med., 2023, 136:853-854. doi: 10.1016/j.amjmed.2023.03.033
2.    Sewell F, Alexander-White C, Brescia S, et al. New approach methodologies (NAMs): identifying and overcoming hurdles to accelerated adoption. Toxicol Res (Camb), 2024, 13:tfae044. doi: 10.1093/toxres/tfae044.
3.    Shenton J, Bousnina I, Oropallo, et al. Opportunities and insights from pharmaceutical companies on the current use of new approach methodologies in nonclinical satefy assessment. Drug Disco Today, 2025, 30:104328. doi: 10.1016/j.drudis.2025.104328.
4.    Mukherjee P, Roy S, Ghosh D, Nandi SK. Role of animal models in biomedical research: a review. Lab Anim Res, 2022, 38:18. doi: 10.1186/s42826-022-00128-1.
5.    Blum J, Brüll M, Hengstler JG, et al. The long way from raw data to NAM-based information: Overview on data layers and processing steps. In: ALTEX. Springer, 2025, 42:167-180. doi: 10.14573/altex.2412171

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