Though difficult to quantify due to poor reporting requirements, every year an estimated 192 million animals are used for scientific purposes worldwide, of which approximately 80 million are used directly for scientific procedures. As well as being used directly for experimentation, within science they are also used for their tissues, organs and breeding of both genetically modified and non-genetically modified animals in order to replenish lab animal stocks (of which a surplus is normally bred and killed).1 Within experimentation, animals are used widely, particularly in the fields of toxicology (including toxicity of environmental exposures and new drugs), genomics and basic physiological and biological research. The most commonly used animals worldwide are rodents, fish and birds, with other species including dogs, cats, non-human primates and farmed animals also used.2
While there is still some controversy regarding the necessity of animal experiments for medical progress, the consensus in the scientific and non-scientific community alike is this: animals used for science live miserable lives. They are housed in small pens or cages with little enrichment, often isolated, and subjected to painful tests that can lead directly to disability and death. For animals who survive the testing process, they are almost always euthanised afterwards.
The common belief about animal experiments is that they are a necessary evil, required for safe progress in human medicine; that in order to relieve human suffering, animals must unfortunately be harmed. Though there is no denying animal (not to mention human) experimentation provided fundamental insights into basic biology and physiology, and contributed to notable drug discovery in the past, more and more scientists are recognising the fundamental flaws associated with animal testing.
The main reason for this is that, simply, animals do not make good models to demonstrate human diseases. Though mice share 85% of genetic material with humans, and primates 99%, all animals are biologically and physiologically different, including humans. They do not develop all diseases the same way humans do, and it is not known if artificially inducing these diseases in animals is an accurate way of mimicking the complex, multifactorial process that occurs when humans develop these diseases. Further, even if the disease in question could be perfectly produced in a non-human animal, the way animals respond to biological stimuli is not always the same as a human would. This is particularly the case in the artificial environments of labs, where animals are often stressed – in fact, it has been shown that even changing noise exposure or housing can affect the way animals respond in experiments.3
All of these reasons contribute to the unfortunate reality that only ~10% of drugs that pass preclinical testing eventually get market approval.4 In fact, it has been shown that in drug development, approximately 25% will fail due to safety concerns in humans (even though most have been deemed safe in animals), and approximately 45% will fail due to efficacy issues. The remaining 30% mainly fail due to “commercial viability”.5 Further, of the drugs that fail for toxicity reasons after they have been approved and marketed, it is estimated that animal testing would have only predicted this in 20-50% of cases – in other words, the probability of getting a true positive result using animal experiments in this context is, at best, the same as flipping a coin (a much cheaper and less cruel endeavour).6, 7
As well as animal tests producing false negative results in regards to toxicity, there are also concerns about how many false positive results are produced in regards to efficacy, leading to unnecessary resources being spent exploring treatment options that will go on to fail in humans. Many new drugs and vaccines, particularly in the fields of oncology (cancer) and neurology (strokes, degenerative nerve diseases and acquired brain injuries), have seemed extremely promising in animal trials but then gone on to fail in humans in late stages of trials due to a lack of efficacy.3
Possibly even more worrying is the potential harm from false results that have either shown toxicity in animals when there would have been no toxicity in humans (think something as simple as chocolate) or have shown a lack of effect in animals when there would have been an effect in humans. These harms are much more difficult to quantify than those discussed above, but the potential losses to medical discovery that may have occurred are possibly more detrimental to human health than the harms we can see. Real life examples include the commonly used anti-inflammatory drugs COX2 inhibitors and breast cancer drug tamoxifen, both of which play key roles in the management of human disease but showed toxic effects in animals during the animal experimentation phase, only succeeding to approval through chance and determination of the researchers.8
Animal testing could possibly have been argued for in a world where there were no alternatives, but given modern technological advances it makes little sense to continue investing resources into a method that, much of the time, simply does not work.
| Description | Pros | Cons | |
| In-vitro experiments using human cells9 | “Test-tube” experiments with human cell culturesEg. Human liver cells to see how a drug would be metabolised | · Can study cellular response in controlled experimental environment· Findings specific to humans | · Not a whole systems model – cannot look at interactions between organs· Cells don’t always behave the same way when taken out of intra-organ environment |
| Microphysiological systems such as “organs on chips”10 | “Chips” containing human cells, linked by a blood substitute, that can be exposed to various physiological stressors (eg. toxins, chemicals, drugs) through the use of a research system similar to a computer | · Findings specific to humans· Can link up multiple “organs on chips” to create a “body-on-chip” and monitor multi-organ interactions· Preliminary findings indicate more accurate than animal models | · Industry/ regulatory body hesitancy |
| Genomics (and other “omics”)11 | Modifying human cell lines genetically to provide an alternative substrate to test chemicals on in the form of an in-vitro experiment | · Findings specific to humans· Initially requires additional resources to develop the genetically altered cell lines, but more efficient than animal experiments once developed | · Not a whole systems model – cannot look at interactions between organs· Industry/ regulatory body hesitancy |
| “In silico” approaches 10, 12 | Computer simulation or modelling techniques that perform an experiment or parts of an experiment using pre-existing knowledge about chemicals and biological systems. Also includes data mining techniques using existing databases of chemicals to predict outcomes about similar substances. | · Can test large amounts of chemicals with various biological endpoints quickly and relatively cheaply· Largely better accuracy than animal studies | · Unable to entirely replace animal experimentation alone (would need to be combined with other alternative testing techniques)· Requires sufficient IT skills for modelling – for this reason regulatory bodies may prefer the more “familiar” and easier to understand animal testing |
| Safe human experiments eg. microdosing13, clinical imaging | Microdosing: uses drug dosing in human volunteers large enough to be detected in blood, but too small to cause adverse reactions. Clinical imaging: uses recent developments in medical imaging (such as fMRI and PET) to detect responses in the body or study organ activity. | · Human-specific results | · Pharmacokinetic profile may be different when dose is increased· Possible difficulties in converting microdosing into therapeutic dosing (ie. unable to dissolve large quantity of drug, etc)· Industry/ regulatory body hesitancy |
| Epidemiological/ observational studies | Observing humans to see the effect an exposure has on an outcome (eg. smoking/tobacco and lung cancer) | · Often provides high-quality evidence regarding human-specific disease | · Time consuming and resource intensive |
A common argument against the use of alternatives is that there is currently no single one that can fully replace the whole-system model of an animal experiment. While this may be the case, it is based on the assumption that having a whole-system model is better than combining multiple different tests, regardless of how accurate each method is in predicting the outcome of interest. Instead, we should be working out how best to combine these tests in order to draw conclusions that are inherently more robust than we are currently finding.
Despite the development of many technologies that could, in combination, possibly replace animal testing, there has not been a corresponding decline in the amount of animal tests being conducted.1 This is because it has proven to be extremely difficult to get a novel testing method approved for use without also using a concurrent animal testing method. In this way, regulatory body hesitancy, largely due to entrenchment in familiarity and bureaucratic delays, has proved to be an even bigger hold-up in the quest to find alternatives to animal testing than the actual discovery of the alternatives themselves.14
Proponents of animal testing will often state that there are methodological errors in animal experiments that cause the poor concordance of results between animals and humans. Whilst it is always helpful to have a robust study design, it is likely that the use of animals as models of human diseases is fundamentally flawed as a basic concept, regardless of how robust an experimental process is used. Given that new and exciting technologies have already proven to be valid as components in the testing process, there is little excuse for the current regulatory hold-ups that are limiting their ability to be used practically. Though animal testing may not yet be ready to be phased out completely, it is unacceptable that more resources are not being allocated to the development and validation of alternatives, especially considering the very real potential for them to be more accurate and resource-efficient than current animal testing paradigms.
1. Taylor K, Alvarez LR. An Estimate of the Number of Animals Used for Scientific Purposes Worldwide in 2015. Alternatives to Laboratory Animals. 2019;47(5-6):196-213.
2. Meigs L, Smirnova L, Rovida C, Leist M, Hartung T. Animal testing and its alternatives – the most important omics is economics. (1868-596X (Print)).
3. Akhtar A. The flaws and human harms of animal experimentation. Camb Q Healthc Ethics. 2015;24(4):407-19.
4. Van Norman Gail A. Drugs, Devices, and the FDA: Part 1. JACC: Basic to Translational Science. 2016;1(3):170-9.
5. Kaitin KI, editor. Causes of clinical failures vary widely by therapeutic class, phase of study. 2013.
6. van Meer PJK, Kooijman M, Gispen-de Wied CC, Moors EHM, Schellekens H. The ability of animal studies to detect serious post marketing adverse events is limited. Regulatory Toxicology and Pharmacology. 2012;64(3):345-9.
7. Leenaars CHC, Kouwenaar C, Stafleu FR, Bleich A, Ritskes-Hoitinga M, De Vries RBM, et al. Animal to human translation: a systematic scoping review of reported concordance rates. Journal of Translational Medicine. 2019;17(1):223.
8. Follow the yellow brick road. Nature Reviews Drug Discovery. 2003;2(3):167-.
9. Doke SK, Dhawale SC. Alternatives to animal testing: A review. Saudi Pharmaceutical Journal. 2015;23(3):223-9.
10. Van Norman Gail A. Limitations of Animal Studies for Predicting Toxicity in Clinical Trials. JACC: Basic to Translational Science. 2020;5(4):387-97.
11. Johansson H, Lindstedt M, Albrekt A-S, Borrebaeck CAK. A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests. BMC Genomics. 2011;12(1):399.
12. Taylor K, Rego Alvarez L. Regulatory drivers in the last 20 years towards the use of in silico techniques as replacements to animal testing for cosmetic-related substances. Computational Toxicology. 2020;13:100112.
13. Watts G. Animal testing: is it worth it? BMJ. 2007;334(7586):182-4.14. Taylor K. Recent Developments in Alternatives to Animal Testing. Leiden, The Netherlands: Brill; 2019. p. 585-609.