Breaking Down The Barriers To Animal-Free Research

Non-human animals have been used in scientific research for centuries to help us understand the human body and how it works. In theory, this seems logical: many animals have the same vital organs as we do and are physiologically and genetically similar to us. However, in practice, the seemingly small differences between our species can translate into vastly different responses to treatments.

Because animal models have such low translatability to humans, some treatments pass clinical trials when they shouldn’t, while others don’t reach the clinical trial stage when they should. For example, of the clinical trials testing new pharmaceuticals in humans (after passing animal testing), 40% to 50% fail because the drug isn’t effective. Another 20% to 30% fail because it’s toxic. And these failures aren’t just costly in terms of animal lives: bringing a new therapy to market costs an average of US$1.3 to US$4 billion.

New approach methodologies, or NAMs, are emerging as an alternative to testing on animals, with the potential to increase scientific knowledge and reduce costs and animal suffering. This review looks at why we should question animal experiments as the “gold standard” in research, what’s holding back the shift to NAMs, and how that shift could be sped up.

The Problems With Animal Models

The authors argue that genetic and physiological differences make animals poor models for human biomedical research.

Even with considerable genetic overlap, variation in gene regulation and expression can result in very different protein structures and functions between species. Take the enzyme CYP2A6, for example. CYP2A6 belongs to the enzyme superfamily responsible for the metabolism of around 80% of all drugs in humans, but it has different functions from the equivalent enzyme in mice, who share 85% of their genes with us.

Genetically similar animals can have translatability issues rooted in other biological differences. Rhesus macaques, for example, share close to 91% of their genes with us. But differences in immune system components mean that HIV/AIDS vaccines effective in non-human primates have yet to translate into an effective vaccine for humans.

These differences have major consequences: around 92% of drugs fail during clinical trials, mainly due to safety and efficacy issues in humans not predicted by animal data. This makes the use of the roughly 1.9 million mice and over 1,800 non-human primates in the U.K. in 2023 alone hard to justify.

It’s also important to take into account the effects of stress that research animals experience, which can influence experimental results and validity. For example, in one study on pain sensitivity in mice, environmental stressors accounted for 42% of experimental variability — meaning that nearly half of the differences in the data were explained by the animals’ stressful living conditions.

Stressors present in laboratory settings include:

• Living outside of their natural habitat
• Living in restrictive environments that prevent them from performing many natural behaviors
• Living among other stressed animals and observing them undergoing experimental procedures
• Noise and artificial light
• Frequent handling by people

These stressors not only make animals more prone to diseases, but can also lead to epigenetic changes that are passed on to their offspring, who also experience increased rates of disease.

The Promise Of New Approach Methodologies

The NAMs currently used in research fall into two main categories: in silico and in vitro models.

In silico techniques are mathematical models and computer simulations that use existing data to predict an experiment’s outcome — for example, checking whether a drug that’s already on the market could treat a different condition.

In vitro models, by contrast, use human cells grown outside the body to recreate small-scale versions of tissues, organs, or disease processes. These building blocks can be combined into more complex systems, like organ-on-a-chip and multi-organ-on-a-chip devices, which, as their names suggest, can model one organ or several working together. The table below breaks down the main types.

In silico and in vitro methods can also be combined with each other, showing promising results, such as effectively predicting the drug dosages that eliminate cancer cells with minimal impact on healthy cells. Thus, NAMs could have higher predictive power than animal testing, and could eventually replace it.

The Barriers To New Approach Methodologies

Despite the clear need to replace animal testing with less harmful and more effective methods, several issues are slowing the adoption of NAMs.

Currently, NAMs can be expensive and time-consuming to produce. They lack standardization and long-term cell cultures have the potential for genetic drift. In addition, some researchers are skeptical about adopting NAMs because they’re not familiar with them and don’t believe that NAMs are as reliable as animal testing. This skepticism is widespread: one survey found that 77% of U.S. researchers didn’t believe NAMs would be able to sufficiently replace animal testing in their own work, and a separate survey found that 71% of Dutch researchers using animals doubted replacement would be possible in the near future — though 40% said they’d support it if it were.

Many scientists also believe their research is less likely to be funded and published if they don’t include animal experiments. While publication of research with NAMs might actually be increasing, they still receive less funding overall than animal research. In the U.S., for example, the National Institutes of Health has pledged about $18 million a year to NAM development, compared to an estimated $19.6 billion of its budget spent on animal studies.

The Future Of Animal-Free Research

For animal advocates, this review offers an opportunity to raise awareness about the poor translatability of animal testing to humans and the encouraging potential of NAMs. Although the transition to (or at least the incorporation of) NAMs has already begun, it’s necessary to keep pushing for:

• Knowledge transfer between researchers to raise awareness and use of NAMs, including training opportunities and an online, open access NAM database
• Funding to develop, standardize, and validate a wider range of NAMs, which can then receive regulatory approval
• Collaboration between research institutions, industry, and government to accelerate NAM adoption

As advocates, it’s also crucial to keep calling for an end to animal use altogether since NAMs risk being adopted as an addition to animal testing rather than a replacement. Other than reducing animal suffering, the arguments for NAMs should emphasize their long-term cost-effectiveness, particularly through reduced failure rates during clinical trials, and their potential to provide more accurate, human-relevant data.