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Symposium Animal Free Innovations in Science

Report Symposium Animal Free Innovations in Science

On Monday 25 November 2019, a panel of eight speakers plus an audience of 275 students, researchers, clinicians and other industry professionals came together at the Vrije Universiteit Amsterdam (VU). The goal of this inspiring afternoon was to examine the alternatives to animal experiments in the medical field. Rector Magnificus of the VU, Vinod Subramaniam, introduced the symposium by emphasising the importance of this discussion both within the scientific community and within society at large.

“Animal experiments have been a very intrinsic part of our research for many years”, he said, “but we’re now trying to keep them at a minimum and replace them with alternatives wherever possible”. And what could some of these alternatives look like? Using big data to glean information, modelling complex disease structures and physiologies, honing new technologies such as stem cell research and organ-on-a-chip – all these have the potential to transform the way we innovate in medical science.
As academic institutions, the Amsterdam UMC and Vrije Universiteit Amsterdam have a responsibility to stay at the forefront of public discourse and to proactively participate in discussions about replacing animal testing with animal-free research methods. But crucially, the university needs to collaborate with others in the field – whether other institutions, medical research companies, commercial businesses and so on – to innovate on this controversial topic. Below is a brief recap of each of the presentations as well as the panel discussion at the symposium.


Prof. Dr. Paul Jennings, Chair of the Division of Molecular and Computational Toxicology at the VU, kicked off the presentations by taking the issue back to its roots: why do we use animals in the first place? “Scientists are responsible for protecting society from threats,” he said – including chemical pollution, viruses, bacteria and pharmaceuticals. He used the example of smallpox, which killed up to 500 million people in the 20th century alone, but which was eradicated in 1979 due to a series of vaccination campaigns in the 19th and 20th centuries. In another example, the widespread use of thalidomide to prevent morning sickness in pregnant women led to approximately 10,000 children being born with deformities until the drug was withdrawn. In both cases, the treatments were tested on humans, many of whom suffered as a consequence – leading to the question of whether the end justifies the means. Animal experimentation has therefore been being used as far back as 300 BC and is a cornerstone of modern medicine – it allows us to test on high-fidelity models that substitute for humans. Mice are used the most, being genealogically similar to humans but with short lifespans.

Jennings went on to look at one of the most popular alternatives: in vitro (cells cultured outside the body) and at the different types that exist: primary cells (taken directly from a donor and then cultured); cell lines (in which the carrier type can be changed); and stem cells (which keep the carrier type and genetic imprint of the person who donated them). Using in vitro technology has many benefits: it allows us to analyse the cell cycle, look at live cell imaging in real time, examine mitochondria and so on. Jennings concluded his presentation with a summary of the pros and cons of animal models vs.  human-based in vitro systems: while animals represent a complete physiological system, in vitro techniques do not. However, reactions to drugs are often different in humans than in animals, and in vitro methods may be cheaper, quicker and more efficient.


Prof. Dr. Sue Gibbs works for the Department of Molecular Cell Biology and Immunology at Amsterdam UMC, location VUmc and the Department of Oral Cell Biology at ACTA. She started her presentation by looking at the unmet medical needs for certain diseases that lack drugs and devices to treat them. Looking at the drug-development pipeline, improvement is needed at both the pre-clinical stage and the post-approval stage. “The current animal testing and in vitro testing are not good enough,” she said, emphasising that up to 90% of drugs in development don’t even pass the first stage of testing. To determine whether a substance is safe for human use, we need to identify target patients – and humans themselves represent the target models of the future.

Gibbs referred back to the cosmetics industry as an example of products that have already transitioned to animal-free testing since 2013. “Developments are moving fast, but the drugs industry is behind,” she said, pointing out that collaboration is still needed between different disciplines and organisations. And organotypic skin and mucosa models can be used to further this aim when it comes to allergies, fibrosis (burns and scars), melanoma and more. Gibbs gave several examples: scar tissue from a hot-water burn that can be cultured to resemble hypertrophic scars; melanoma cultures that can develop metastatic growth; and dental implants that enable us to examine the interaction between soft and hard tissue. In all cases, the aim is to create a long-term, stable culture for at least 28 days. Gibbs concluded by suggesting the future of skin science: skin and mucosa as part of multi-organs-on-chips innovations, creating a living microsystem for immunological assays.


Neuropsychiatric and neurological disorders represent 14% of global health issues – a higher percentage than cancer. This surprising figure was the introduction to Dr. Vivi Heine’s presentation looking at the treatments for brain disorders, which are by definition a typically human concern. While the causes of neurological disorders are diverse – ranging from genetics to injuries – we urgently need better treatments, and thus better disease models. Human brains are unique – we have far higher cognitive functions and more complex neuronal networks than animals’ brains have – which is why not all aspects of human neurological diseases can be recapitulated in animal models. This leads to misinterpretation of data and potentially failed clinical trials.

Induced pluripotent stem cells (iPSC) models have exploded over the last ten years. And in neuroscience, these have enabled us to create disease models representing the patients’ genetic makeup and to explore autologous cell populations for cell-replacement therapies. So far, different models have been created for different applications – such as autism, schizophrenia, Alzheimer’s and so on. But there are challenges: clinical variability, genomic variability, neuronal maturation, and self-organisation of neural structure. “We are developing a lot of different cellular models,” said Heine; “and we’re focusing on how to reach high-content models that can lead to drug development”. But there are still limits to iPSC technology in neuroscience: there are no such models for social impairment, communication, stereotypical behaviour, learning tasks or cognition. In short: there’s still a need for certain animal models in the neuroscience field.


While animal models are one way of analysing a biological system, big data and computational models are another. So said Prof. Dr. Bas Teusink in his presentation about the potential for big data and machine learning in biological systems, but also pleaded for computational modelling. He argued that the knowledge of biologists needs to be combined with that of computer scientists to understand disease mechanisms. He used an example of medium optimisation to show how studying bacterial cultures requires a huge number of experiments that each change different variables to reach a conclusion. But with metabolic modelling, fewer experiments are needed to find the optimum medium in a limited amount of time. In another example, he looked at the process for coming up with a vaccination for whooping cough: using a computational model, scientists discovered the production of compounds that were interfering with the immune system; they were able to optimise the process by suggesting alternatives for interfering nutrients.

“The next level is about making a model of a whole cell,” claimed Teusink. For example, a yeast cell has over 10,000 chemical reactions; through expressing the problem mathematically, we can map out exactly what it costs a cell to implement a certain flux. “Using mathematics to understand the problem allows you to change perspective,” he says. “It’s the constraints that are the important part – they determine which strategies are possible.” In summary, it’s not just big data that help in creating animal-free solutions – computational models are also equally important.


They keynote speaker for the symposium was Reyk Horland, VP Business Development at TissUse. He began by categorising the different systems: plate-based vs. chip-based, and single-organ vs. multi-organ. He then looked at why so many drug tests still fail, and proposed a solution: predicting substance performance through targeted multi-organ-chip (MOC) testing. TissUse develops automated on-chip testing of human organ models to achieve accurate, relevant results, with the eventual aim of delivering personalised medical treatments. The theory works by recreating organ functions on a micro-level: each smallest functional unit of an organ (e.g. the liver lobules in the liver) performs the same functions as the whole organ. It’s then possible to combine different organ models together to create multi-organ chips. MOCs are the size of a standard microscope slide, and even feature pulsatile flow that works a bit like the human heart.

Horland used the example of a bone-marrow organ model that aimed to replace animal testing in the context of bone marrow toxicity. “We tried to emulate and recreate bone marrow stem cells to create human bone marrow on a chip in 3D,” said Horland. “But in this case there was not enough proof of concept for the journal we wanted to publish in, so we also needed to use mice to prove it.” The challenges of MOC technology include how to reserve large numbers of cells for industry research, and how to translate the in vitro situation to the situation of an actual patient. Horland concluded his presentation with a look at an innovative solution to increase the reproducibility of data: automated systems can run up to 24 chips, with robots taking samples and carrying out the experiments. According to Horland, “the aim is to replace animal trials with these types of systems in the future”.


VP Research at UniQure, Dr. Pavlina Konstantinova started her presentation by explaining how gene therapy works: a capsid (a virus in which most of the factors have been removed) is added to an expression cassette to deliver genetic material to a patient to prevent or cure a disease by injecting it into the patient. Thus far, gene therapy is already in development for brain diseases involving neural degeneration or loss of brain mass like Alzheimer’s disease, Parkinson’s disease and Huntington’s disease. In the case of the latter, there is a clear transmission from parent to child of the mutant Huntingtin gene/protein, which results in degradation and death of cells. Gene therapy slows the progression of the disease via a one-time intracranial injection of AMT-130 into the patient’s striatum.

The traditional product development path uses a lot of animal experiments – injecting drugs into larger animals like minipigs, for example – due to regulatory requirements. “But we’re currently introducing patient cells and organoids to replace animals,” said Konstantinova. Using patient cells and organoids has many benefits: lower development time; fewer animals needed for preclinical research; lower costs and faster results than when using animals; and the ability to study possible unwanted effects directly in human cells. A year and a half ago, UniQure started a programme to take cells from donors and recapitulate mini-brains in a petri dish, with the aim of achieving personalised drug development. There are of course pros and cons, but when it comes to AMT-130 the regulators are starting to get on board, and the first patient in the US is scheduled to receive the drug in Q1 2020.


Of course, it’s not just brain disease that can benefit from in vitro technologies. “The heart is a brilliant organ which we’ve been trying to model using different strategies,” said Prof. Dr. Jolanda van der Velden in the opening of her presentation about innovative technologies to study, measure and mimic the human heart. A biobank of hearts was initiated back in 1989, when a global collaboration was started using heart tissue in different labs to study the heart. And since then, multi-disciplinary research has continued apace.

Van der Velden compared the heart to a light bulb: how much energy is needed for contraction in the heart? How much energy is transformed to enable it to function? Just like an LED bulb in comparison to an incandescent bulb, a healthy heart requires much less energy to function and is much more efficient than a failing heart. In genetic heart disease, this is caused by a mutant protein: if you’re able to add a normal protein, you’re able to correct the energy inefficiency. No animals were used to study efficiency of the diseased heart: it’s an example of studying the patient using state-of-the-art in vivo imaging and functional readouts in human cardiac tissue samples obtained during cardiac surgery. But there are of course limitations to studies in human: it’s not possible to take biopsies from people with a healthy heart, and there’s a need for maturation and validation of stem cell-derived human models. Plus, as Van der Velden pointed out, not all heart disease is caused by a genetic mutation – most heart failure cases are caused by factors such as obesity, type-2 diabetes and hypertension. This form of heart failure is much more complex to model than genetic heart disease, and we depend on animal models to study pathomechanisms and test new drugs. To get the maximum output from these animal studies, advances have been made in building high-throughput systems to measure the function of multiple cardiac muscle cells isolated from one heart. This advanced system contributes to lower the number of animals per experiment and as such represents an innovation to reduce animal use. “As researchers, we need to ask ourselves if there are animal-free alternatives during the design phase of our studies,” concludes Van der Velden.



To close the presentations at the symposium, Dasja Pajkrt, Principal Investigator Pediatric Infectious Diseases at Amsterdam UMC/AMC, outlined the ethical concerns and business opportunities/challenges when it comes to organoid technologies. After recapping the lengthy periods of time (12-24 years on average) to get a drug to market and the huge expense of doing so, she pointed out the potential benefits and applications of organoids. “Organoids are miniaturised versions of human organs produced in vitro and showing realistic microanatomy – which means they can be used to study diseases and their treatments in the laboratory.”  They deliver potential for personalised medicines, drug discovery and development, toxicology studies, regenerative medicine and so on.

But they also generate ethical questions. Not least, who owns the cells? “A biopsy of a cervical tumour was taken from Henrietta Lacks, a working-class African-American woman living near Baltimore” explained Pajkrt. “The cells were taken without her knowledge or permission, and they became the first human cells to grow well in a lab.” Given breakthroughs in creating mouse embryos, are human babies next? And at what stage do brain organoids have consciousness, feelings and rights? Stem cell technologies clearly pose ethical issues, but scientists are here to tell us what’s possible, not what’s right and wrong.

Meanwhile, the business challenges and opportunities mainly lie in patenting, with the key stakeholders being located in the US, Europe and Asia. Many of the patents in the field are inactive, and almost all are owned by scientific institutions – not by commercial organisations. It’s difficult for pharmaceutical companies to patent organoid technology because it’s unclear what the product is, and it’s hard to obtain tissue samples from patients. The only way to move forward is for commercial partners to work together with academic institutions. And indeed, multi-disciplinary and multi-stakeholder collaboration could be summed up as the theme of the symposium as a whole.


Following the eight presentations was a panel discussion moderated by Dr Katja Wolthers, clinical virologist at Amsterdam UMC/AMC, in which topics were proposed for debate – not only by the speakers but also by members of the audience.

Topic 1: It will never be possible to carry out biomedical research without some animal-testing, due to the complexity of pathophysiological processes.

Most participants believed that it will be possible at some point, although it will take different lengths of time in different research areas. By linking various data sets and disciplines, we can make this a reality. One audience member found it hard to imagine a world without any animal testing, but believes in using humans as the animal model of choice when it comes to in vivo cell cultures. A biosurgeon noted that in animal testing, a disease is introduced to an animal but it doesn’t take societal factors like obesity and smoking into account. We therefore need to extend our biobank. A member of the panel concluded that the question is not whether we can carry out biomedical research without animal testing – it’s how long this will take.

Topic 2: A lot of animal-testing is unnecessary and is due to lack of knowledge of alternatives.

Agreeing with the statement, one panel member suggested that advisory committees should be set up so that scientists can share knowledge and ask for advice about possible alternatives to animal testing. Another panel member suggested optimised imaging so that we can really measure in vivo systems – saying there’s still a lot to be gained here. It was noted that while a lot of animal testing may be unnecessary, regulators always require drugs to pass pre-clinical tests that involve animal testing. What’s more, the commercial sector pushes for it as a requirement for investment, and some medical journals refuse to publish papers about drugs unless they’ve been tested on animals. Scientists need to build these stakeholders – regulators, commercial parties, journal editors and so on – into their processes. Meanwhile, one panel member pointed out that the issue of lack of knowledge of alternatives is also an issue of a lack of data storage when it comes to past animal experiments, human trials and so on.

Topic 3: The private sector is not investing enough in animal-free innovation because it does not pay off.

This depends on the type of company – for example, the gene therapy sector is investing in animal-free innovation already. However, as one member of the panel noted, the best way to push the sector towards animal-free options is through funding. A lot of research is funded through pharmaceutical companies, so it’s important to change their objectives and raise the approval rate of funding animal-free research. Discussion turned to the cosmetics industry, in which animal testing became a political issue – it was not the private sector itself that pushed for change. But of course commercial parties must not be excluded from the discussion either – it’s all about public-private collaboration. And the situation is different here in Europe than it is in China and Indonesia, for example, where animal testing is increasing due to a lack of regulation. Plus, sectors like neuroscience are still heavily dependent on animal studies and are not yet ready to go animal-free. In short, it’s a complex area with many factors to take into account. 

Topic 4: The reason for slow progress of animal-free innovation is lack of funding.

Lots of funding has been liberated for animal-free innovation since the 1990s. There’s been significant progress on funding, but a slow uptake rate when it comes to the innovative technologies, concluded one panel member.

Topic 5: Every funding scheme which supports animal-test research should also require investment in animal-free research.

Most members of the panel believed this is already the case, claiming that every research proposal is required to state its argument for the necessity of animal testing, if this is indeed the case. Any experiment that is carried out on animals should only be done because there is no alternative. And there’s a differentiation to be made between efficacy redials and safety measurements. However, raising awareness is also important: the Heart Foundation, for example, does not have the expertise to be able to review applications in a consistent manner to assess whether animal testing is needed.

Topic 6: Ethical barriers to experiments involving humans prevent a decrease in animal testing.

 While one panel member asked whether we wanted to start experimenting on children, another pointed out that participating in a clinical trial – whether adult or child – is generally beneficial to the patient. Another panel member saw the issue in relation to big data: “Every human being is an experiment. We are all genetically different and have different lifestyles. The more we gather information, the more ‘experiments’ we are doing. There are no ethical barriers in these cases. But the question is who should hold and protect all of this data?” Meanwhile, the ethical implications of gene editing on babies were questioned: while it’s illegal to “design” babies, this can hamper innovation by limiting our ability to edit embryos. One audience member noted that there are differences in how medical scientists think about ethics and how other members of society think about ethics – and this can indeed create barriers to innovation.

Topic 7: Within ten years, big data and computational modelling will make animal testing obsolete.

The audience was split on this question, with the general consensus being that we will get there eventually – but it may not be within ten years. As one panel member pointed out, big data and computational modelling need to be coupled with biological data and clinical trials in order to come close. There is a lot of learning that needs to happen from each other, on both sides of the equation, to make this happen. Big data is not a solution on its own: we are limited by the amount of data we can store, by which questions we need to ask, by how different individuals are from each other, and by how many individuals exist. Data and modelling must go hand in hand to follow an iterative process of experimentation and deduction. And of course there are legal issues: how can we link all these data together and exchange them in a clinical environment without breaking confidentiality laws?

Text: Vicky Hampton
Photo’s: Peter Valckx
Read also the report Science Guide made of the symposium.