Animal testing has long been a divisive subject among researchers, philosophers and consumers. The argument in its favor is that the benefits that come from using animals as our models for testing greatly outweigh any drawbacks, and that we are continually providing major human health benefits by doing so. But how true is that previous statement?
Recent research and not-so-recent research argue that animals are just too different from us to be accurate biological models. Over 90% of drugs that are proven safe and effective during their animal models end up failing in human clinical trials. And there have been examples of times where drugs that were proven to be tolerable and non-harmful to animals could end up extremely harmful for humans.
Until December 2022, the FDA required preclinical tests on two species of non-human animals before the drugs can enter human clinical trials. However, a 2014 study that analyzed over 2,000 drugs found that animal testing was highly inconsistent when it came to predicting toxic responses in humans. It’s difficult to even do the study exactly the same way again and get the same result. Doing the exact same study on three identical sets of mice can result in different behaviors each time. Little things like what the animals were fed could largely alter the results as well.
It’s also been found that the standard laboratory conditions that we’ve been using could be affecting results. The standard lab mouse cage makes its inhabitants sick and even increases their risk of death. One of the most interesting cases is that of a study done by Jeffrey Mogil. He studies pain perception and, along with his collaborators, was filming mice before and after they were injected with a pain-inducing chemical. This footage would be used to develop their “mouse grimace scale” to measure their level of pain. The mice wouldn’t begin displaying a response until he’d leave. When they did further experiments, they found that if a man was in the room, or even a shirt that a man had worn was in the room, the mice would show higher thresholds for pain. So, alongside ethical questions, there are also many practical ones. How does one account for every single variable that might affect an animal who senses things that we possibly do not, like smells to trace for our noses, ultrasounds and magnetic objects?
Seventy-five percent of drug research costs are currently going towards unsuccessful human trials. Could this be because our primary screening system does not appear to be reliable or replicable? These kinds of concerns have started prompting changes in the world of animal testing. As we slowly progress, technologically speaking, we are creating more sophisticated methods that replicate actual human cells — methods that do not rely on animals. There is now such a thing as “organ-on-a-chip” technology, in which a collection of cells can replace or mimic an entire organ. These methods are generally also considerably less expensive, faster and more effective. Because of this, people are shifting away from animal testing.
In 2019, the U.S. Environmental Protection Agency announced that by 2035, it would eliminate all mammal testing and will no longer be funding such studies, either. Their administrator also announced 4.25 million dollars in funding given to Johns Hopkins University, Vanderbilt University and its medical center, Oregon State University, and the University of California to develop better non-animal alternatives. The EPA says that the total number of animals that they currently use is between 20,000 and 100,000 a year.
There’s no doubt that science did make advances through the use of animal testing, but it’s time for more efficient and reliable means of researching potentially life-saving drugs, environmental impacts or cosmetics. It might not be something that happens right away; researchers need time to be absolutely certain that non-animal alternatives work just as well, if not better, than the real thing. If all goes to plan, though, it might be possible that the future of animal testing is that there isn’t one.