Will Non-Animal Approaches Replace Some or All of Animal Testing?

Alternative methods of testing drugs have a long way to go toward building trust into a new norm

Louis Pasteur experimented on rabbits and sheep to first test new treatments against rabies or anthrax. By the mid 20th century this paradigm of animal before human testing had been established, and drug development ever since required, relied on animal model data, yet it remains costly, slow, and often unreliable. Over 92% of drugs that clear preclinical animal testing fail in human trials due to fundamental species differences¹. Meanwhile, the ethical debate surrounding animal use in biomedical research has prompted regulators and scientists to seek more humane, and at least equivalent or advanced alternatives.

This search has brought up New Approach Methodologies (NAMs), a cutting-edge set of technologies to reshape the way drugs are tested and approved. By reducing or circumventing altogether costly animal studies the hope is to accelerate and offer more predictive pathways to drug development or to repurposing existing drugs. These alternatives designed to improve safety and efficacy assessments include:

• Organs-on-a-Chip (OoC): Microfluidic devices that simulate human organ functions (e.g. lung, liver, kidney, skin), providing real-time insights into drug responses.
• 3D Cell Cultures and Organoids: Advanced tissue models that replicate human biology in vitro more accurately than traditional 2D cell cultures.
• Induced Pluripotent Stem Cells (iPSCs): Patient-derived adult somatic cells genetically programmed back to an embryonic stem cell state, which they can be used for disease modelling, basic research and cell therapy development, for example, enabling personalized medicine approaches.
• Artificial Intelligence (AI) and Machine Learning (ML): Data-driven models for predicting drug toxicity, interactions, and efficacy more efficiently than animal studies.
• In Silico Simulations: Virtual modeling techniques assessing drug behavior based on computational power rather than live testing.

Certainly, these tools can provide more reliable and relevant datasets. Animal models often fail to accurately predict drug responses in humans, leading to high failure rates in late-stage clinical trials. Their overall success chance also can be significantly increased by including biomarkers depending on therapeutic area². NAMs use human-derived cells, tissues, and AI-based predictions, making them more translatable to human biology.

Developing a new drug costs US$2.6 billion on average, with preclinical animal testing accounting for a significant portion³. NAMs, by contrast, enable high-throughput screening and rapid AI-driven predictions, cutting development time and expenses. For example, AI-powered drug discovery platforms can analyze millions of compounds in days, a process that would take years using traditional methods. Creating patient-specific models using iPSCs and organoids allow researchers to test drugs on a patient’s own cells before clinical trials. This personalized approach improves treatment accuracy and safety while reducing trial-and-error in drug prescriptions. Personalized drug testing could reduce adverse drug reactions after hospitalization, which account for over 100,000 deaths per year in the U.S. alone⁴.

Legislative and regulatory support for adopting non-animal alternatives is growing. In 2022 the U.S. FDA Modernization Act 2.0 was passed to allow drug developers the use of NAM-based models instead of animal testing⁵. Similarly, the European Medicines Agency (EMA) and REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) regulations actively promote alternative testing methods.

Nevertheless, several scientific, regulatory, and industry-related hurdles must be addressed before they can fully replace animal testing in drug development. Despite growing acceptance from agencies like the FDA and EMA, these alternatives still face regulatory uncertainty. Many existing approval frameworks were built around animal-based studies, meaning new guidelines and validated benchmarks for NAMs are still evolving. Lack of standardized protocols and guidelines makes it difficult for companies to navigate the approval process for NAM-based drug testing.

While NAMs outperform animal models in some areas, there are biological complexities difficult to replicate pertaining to:

• Systemic metabolism: Drug processing by liver and kidneys remains challenging to model entirely in vitro.
• Immune system interactions: Complex human immune responses cannot yet be fully mimicked using current NAM models.
• Long-term toxicity studies: NAMs primarily focus on short-term drug effects, while chronic exposure assessments are still underdeveloped.

If these hurdles can be overcome, NAMs promise long-term cost savings for drug development. However, transitioning from the established way is not straightforward; it involves substantial investments upfront in new laboratory infrastructure. R&D programs need to be overhauled to accommodate advanced cell-based assays and microfluidic technologies. Researchers still require training to interpret and make decisions based on data from AI-driven and non-animal models.

Automated systems that can improve reproducibility, robustness, and scalability of bioanalytical methods hold a key for continued refinement of NAMs. Alongside regulatory alignment and industry investments this will drive their wider adoption in the years ahead. From organs-on-a-chip, organoid technology and AI-powered drug discovery to human-derived cell cultures: they will be put to the test, if they can set a new gold standard for delivering faster, more cost-effective and ethically sound medicines.

References:

1.    Mak, IW, Evaniew, N, Ghert, M. Lost in translation: Animal models and clinical trials in cancer treatment. Am. J. Transl. Res., 2014, 6:114-118. PMID: 24489990
2.    Wong, CH, Siah, KW, Lo, AW. Estimation of clinical trial success rates and related parameters. Biostatistics, 2019, 20:273-286. DOI: 10.1093/biostatistics/kxx069
3.    DiMasi, JA, Grabowski, HG, Hansen, RW. Innovation in the pharmaceutical industry: New estimates of R&D costs. J. Health Econ., 2016, 47:20-33. DOI: 10.1016/j.jhealeco.2016.01.012
4.    Lazarou, J, Pomeranz, BH, Corey, PN. Incidence of adverse drug reactions in hospitalized patients: A meta-analysis of prospective studies. JAMA, 1998, 279:1200-1205. DOI: 10.1001/jama.279.15.1200
5.    Han, JJ. FDA Modernization Act 2.0 allows for alternatives to animal testing. Artif. Organs, 2023, 47:449-450. DOI: 10.1111/aor.14503