The best way to get an immediate grasp of the concepts presented by FOR LIFE ON EARTH is to read the books, brochures and leaflets that illustrate our science.
Animal Models in Light of Evolution is a science book that is written at a very advanced level for the highly trained scientist or medical specialist.
Thankfully the authors have written an excellent layman’s version of this seminal work, especially for the non-scientist, that outlines all the main scientific concepts governing the reasons why we do not rush our children and other relatives to the vet when they are sick. Please click to buy this very important> FAQS book:
Click here to read the latest sample of quotes from the pharmaceutical industry, who openly acknowledge the failings of animal experiments in their drug development process, and often write about this in the scientific literature.
This lecture at Toronto University is an excellent way to gain a grasp of FLOE’s illustration of up-to-date scientific principles that demonstrate why our companion animals are not capable of predicting responses for us. It underlines, in no uncertain terms, why the medicines of our family member animals instruct us to “keep away from children”.
This lecture is also a tremendous wake-up call to the reality of a deeply flawed, antiquated system that is now challenged by current scientific knowledge.
Compelling and entertaining, watching this film is a great introduction to our illustration of the science that governs this aspect of our health, and the lecture is delivered by co-author of Animal Models in Light of Evolution, Dr Ray Greek MD.
Personalised Medicine are treatments for the 21st century. This is an incredibly exciting field in which treatments are being designed specifically for you and you alone – for your unique genetic makeup. Today scientists understand that even identical twins differ entirely in diseases and treatment requirements. This makes an absolute nonsense of any animal model.
The persistent use of animal testing is holding back advances in personalized medicine by taking the lions share of resources – both financial and manpower – precious, finite resources for medical research which would otherwise focus entirely on targeting treatments that are tailor-made for individual patients who need help now!
To learn more about sophisticated human-relevant technologies available now please visit the excellent Speaking of Human-Based Research
EXAMPLES OF SUPPORTING DATA FROM EXPERTS ( * denotes References, please scroll down)
January 12, 2006, then U.S. Secretary of Health and Human Services Mike Leavitt:
Currently, nine out of ten experimental drugs fail in clinical studies because we cannot accurately predict how they will behave in people based on laboratory and animal studies. (FDA 2006)*
Sharp and Langer, writing in Science in 2011:
The next challenge for biomedical research will be to solve problems of highly complex and integrated biological systems within the human body. Predictive models of these systems in either normal or disease states are beyond the capability of current knowledge and technology.(Sharp and Langer 2011)*
In the April, 2010, issue of The Scientist
Mouse models that use transplants of human cancer have not had a great track record of predicting human responses to treatment in the clinic. It’s been estimated that cancer drugs that enter clinical testing have a 95 percent rate of failing to make it to market, in comparison to the 89 percent failure rate for all therapies . . . Indeed, “we had loads of models that were not predictive, that were [in fact] seriously misleading,” says NCI’s Marks, also head of the Mouse Models of Human Cancers Consortium . . .(Zielinska 2010)*
Dr. Richard Klausner, then-director of the National Cancer Institute: “The history of cancer research has been a history of curing cancer in the mouse . . . We have cured mice of cancer for decades—and it simply didn’t work in humans.” (Cimons, Getlin, and Maugh_II 1998)*
David F. Horrobin wrote in Nature Reviews Drug Discovery:
Does the use of animal models of disease take us any closer to understanding human disease? With rare exceptions, the answer to this question is likely to be negative. The reasoning is simple. An animal model of disease can be said to be congruent with the human disease only when three conditions have been met: we fully understand the animal model, we fully understand the human disease and we have examined the two cases and found them to be substantially congruent in all important respects . . . All the other animal models — including those of inflammation, vascular disease, nervous system diseases and so on — represent nothing more than an extraordinary, and in most cases irrational, leap of faith. We have a human disease, and we have an animal model which in some vague and almost certainly superficial way reflects the human disease. We operate on the unjustified assumption that the two are congruent, and then we spend vast amounts of money trying to investigate the animal model, often without bothering to test our assumptions by constantly referring back to the original disease in humans.
The problem of animal models is well known to the drug development community. Cook et al state: “Over many years now there has been a poor correlation between preclinical therapeutic findings and the eventual efficacy of these [anti-cancer] compounds in clinical trials (Johnson et al. 2001; Suggitt & Bibby 2005).
The development of antineoplastics (which inhibit or prevent the growth of malignant cells) is a large investment by the private and public sectors, however, the limited availability of predictive preclinical systems obscures our ability to select the therapeutics that might succeed or fail during clinical investigation.”(Cook, Jodrell, and Tuveson 2012) Singh and Ferrara echo this, stating: “Over 90% of phase 3 clinical trials in oncology fail to meet their primary endpoints despite encouraging preclinical and even early-stage clinical data. This staggering and sobering figure underscores the limitations of existing animal models for the evaluation of potential anticancer agents. The paucity of models is especially apparent with the advent of drugs that target the tumor milieu, or microenvironment, such as anti-angiogenics . . . immunotherapies and compounds directed against tumor-associated fibroblasts.”(Singh & Ferrara 2012)
Wittenburg and Gustafson agree, stating: “The current drug development pathway in oncology research has led to a large attrition rate for new drugs, in part due to a general lack of appropriate preclinical studies that are capable of accurately predicting efficacy and/or toxicity in the target population. . . . One of the most serious challenges currently facing pharmaceutical research of novel anti-cancer therapeutics is the lack of translation of efficacy and safety from preclinical models to human clinical trials, leading to a large attrition rate of investigational compounds. For new oncology drugs, only about 5% of investigational new drug applications submitted progress beyond the investigational phase due to a general lack of preclinical systems that can accurately predict efficacy and toxicity of new agents.”(Wittenburg & Gustafson 2011)
Caponigro and Sellers of the Novartis Institutes For BioMedical Research, Oncology Research and Oncology Translational Medicine stated in 2011: “Despite an improved understanding of the biology of cancer, and an unprecedented volume of new molecules in clinical trials, the number of highly efficacious drugs approved by the regulatory authorities remains disappointingly low. The significant attrition rate of drugs entering clinical trials comes at a high price. This price is paid primarily by the underserved patient and secondarily by the pharmaceutical and biotechnology community, which invests enormous resources perfecting a molecule only to watch it fail in humans . . .”(Caponigro & Sellers 2011)
Moreover, the major cost of drug development occurs during the clinical trials and the attrition rate during this stage is dreadful.(Unknown 2002; Shaffer 2012; Paul et al. 2010; Schachter 2007) Drugs entering Phase I trials have approximately a 9% chance of coming to market.(FDA 2004; Sarkar 2009; Editorial 2007; Paul et al. 2010) Of the drugs that advance to Phase III, less than 50% are marketed.(Arrowsmith 2011) The failure rate for oncology drugs is even higher.(Editorial 2011; Caponigro & Sellers 2011; Arrowsmith 2011; Begley & Ellis 2012) Only 5% of cancer drugs that have an Investigational New Drug Application (IND) eventually go to market.(Kummar et al. 2007) Lack of safety or efficacy accounts for approximately 90% of drug failures during clinical trials.(Kola & Landis 2004; Arrowsmith 2011). Both safety and efficacy determinations rely on animal models.To complicate matters further, the pipeline in Pharma is drying up and fewer drugs, especially new chemical entities (NCEs) are being marketed.(Editorial 2008; GBI Research 2011)
These unexplored assumptions are the fundamental flaws in any animal model scenario. The animal rights campaigners are justified in pointing out that there is little rationale for using animal models which frequently simply draw attention and funds away from the careful investigation of the human condition. The Castalian establishment is wrong in not drawing attention to the unjustified assumption of congruence in most cases of animal experimentation on disease models . . . What can be done to reduce the risk of isolated self-consistency? First, there must be a recognition that in the last analysis the human disease itself must be studied in human subjects. It is at least arguable that if we devoted as much effort to the human disease as we do to unvalidated models, then we might be much further forward in understanding. If we are to have any confidence our models are valid, then we must know at least as much about the diseases we investigate as the models we use. (Horrobin 2003)*
Kola and Landis wrote in Nature Reviews Drug Discovery:
The major causes of attrition in the clinic in 2000 were lack of efficacy (accounting for approximately 30% of failures) and safety (toxicology and clinical safety accounting for a further approximately 30%). The lack of efficacy might be contributing more significantly to therapeutic areas in which animal models of efficacy are notoriously unpredictive (Kola and Landis 2004)*
A Reuters article discuses a computer-based method for predicting drug toxicity. The chip would test for activation of genes and proteins in various human tissues:
“If things are going to fail, you want them to fail early,” Dr. Francis Collins, the director of the National Institutes of Health (NIH), told Reuters on Friday. “Now you’ll be able to find out much quicker if something isn’t going to work.”
Collins said a drug’s toxicity is one of the most common reasons why promising compounds fail. But animal tests — the usual method of checking a drug before trying it on humans — can be misleading. He said about half of drugs that work in animals may turn out to be toxic for people. And some drugs may in fact work in people even if they fail in animals, meaning potentially important medicines could be rejected.(Reuters 2011)*
The Editors of Nature Reviews Drug Discovery wrote in 2011: “Unpredicted drug toxicities remain a leading cause of attrition in clinical trials and are a major complication of drug therapy.” (Editors 2011)*
Robert Weinberg, of Massachusetts Institute of Technology, was quoted by Leaf in Fortune magazine as saying:
Weinberg explains. “And it’s been well known for more than a decade, maybe two decades, that many of these preclinical human cancer models have very little predictive power in terms of how actual human beings—actual human tumors inside patients—will respond . . . preclinical models of human cancer, in large part, stink . . . hundreds of millions of dollars are being wasted every year by drug companies using these [animal] models. (Leaf 2004)*
Leaf also quotes Homer Pearce, “who once ran cancer research and clinical investigation at Eli Lilly and is now research fellow at the drug company” as saying:
. . . that mouse models are “woefully inadequate” for determining whether a drug will work in humans. “If you look at the millions and millions and millions of mice that have been cured, and you compare that to the relative success, or lack thereof, that we’ve achieved in the treatment of metastatic disease clinically,” he says, “you realize that there just has to be something wrong with those models.” (Leaf 2004)*
Nature Medicine stated: “The complexity of human metastatic cancer is difficult to mimic in mouse models. As a consequence, seemingly successful studies in murine models do not translate into success in late phases of clinical trials, pouring money, time and people’s hope down the drain.”(Ellis & Fidler 2010; Van Dyke 2010) Caponigro and Sellers of the Novartis Institutes For BioMedical Research, Oncology Research and Oncology Translational Medicine stated in 2011: “Despite an improved understanding of the biology of cancer, and an unprecedented volume of new molecules in clinical trials, the number of highly efficacious drugs approved by the regulatory authorities remains disappointingly low. The significant attrition rate of drugs entering clinical trials comes at a high price. This price is paid primarily by the underserved patient and secondarily by the pharmaceutical and biotechnology community, which invests enormous resources perfecting a molecule only to watch it fail in humans . . .”(Caponigro & Sellers 2011)
In an editorial to two articles, Ellis and Fidler: “Preclinical models, unfortunately, seldom reflect the disease state within humans (Fig. 1).” (Ellis and Fidler 2010)*
Dr Sarkar, Director of Clinical Imaging, Medicines Development within Oncology R&D at GlaxoSmithKline stated in 2009:
High attrition rates, particularly at the late stage of drug development, is a major challenge faced by the entire pharmaceutical community. The average success rate from first in man to registration for all therapeutic areas combined is 11% (Kola and Landis 2004)*. For oncology, this is even lower at 5%. Approximately 59% of all oncology compounds that enter in Phase III of development undergo attrition (Kola and Landis 2004)*. In fact, the estimated cost of bringing a potential drug to the market has increased significantly and at the current cost growth rate the projected cost for a new drug approval (assuming the R&D was initiated in 2001) is $1.9 billion in 2013 (DiMasi, Hansen, and Grabowski 2003)*. (Sarkar 2009)*
Alan Oliff, former executive director for cancer research at Merck Research Laboratories in West Point, Pennsylvania stated in 1997: “The fundamental problem in drug discovery for cancer is that the [animal] model systems are not predictive at all.” (Gura 1997)*
Chabner and Roberts: “Fewer than 10% of new drugs entering clinical trials in the period from 1970 to 1990 achieved FDA approval for marketing, and animal models seemed unreliable in predicting clinical success . . .” (Chabner and Roberts 2005)*
Björquist et al. Drug Discovery World 2007:
Furthermore, the compound attrition rate is negatively affected by the inability to predict toxicity and efficacy in humans. These shortcomings are in turn caused by the use of experimental pre-clinical model systems that have a limited human clinical relevance . . . (Björquist and Sartipy 2007)*
Usha Sankar in The Scientist 2005:
The typical compound entering a Phase I clinical trial has been through roughly a decade of rigorous pre-clinical testing, but still only has an 8% chance of reaching the market. Some of this high attrition rate is due to toxicity that shows up only in late-stage clinical trials, or worse, after a drug is approved. Part of the problem is that the toxicity is assessed in the later stages of drug development, after large numbers of compounds have been screened for activity and solubility, and the best produced in sufficient quantities for animal studies. Traditionally, compounds are tested in two animal species – typically, the rat and the dog. But the process is far from ideal. Animal studies can be time-consuming, require large quantities of product, and still fail to predict a safety problem that can ultimately halt development . . . Rats and humans are 90% identical at the genetic level, notes Howard Jacob, cofounder of Wauwatosa, Wisconsin-based PhysioGenix. However, the majority of the drugs shown to be safe in animals end up failing in clinical trials. “There is only 10% predictive power, since 90% of drugs fail in the human trials” in the traditional toxicology tests involving rats, says Jacob. (Sankar 2005)*
Speaking of toxicity trials for new drugs in humans, an unnamed clinician quoted in Science stated, “If you were to look in [a big company’s] files for testing small-molecule drugs you’d find hundreds of deaths (Marshall 2000)*.” So much for animal testing to protect those undergoing clinical trials.
Chapman 2011: “. . . but other incidents of harm [besides TGN1412], even death, to participants in Phase I trials, some then known and other unpublicized, had taken place” (Chapman 2011)*
Handbook of Laboratory Animal Science Volume II Animal Models 1994:
It is impossible to give reliable general rules for the validity of extrapolation from one species to another. This…can often only be verified after the first trials in the target species (humans)…Extrapolation from animal models…will always remain a matter of hindsight…. [(Salén 1994)* p6]
>O’Collins et al., 2006 published a review article that revealed that of 1,026 putative neuroprotectants studied, the drugs that went to clinical trials were not more efficacious in animal studies then the ones passed over. (O’Collins et al. 2006)*
Björquist, Petter, and Peter Sartipy. 2007. Raimund Strehl and Johan Hyllner. Human ES cell derived functional cells as tools in drug discovery. Drug Discovery World (Winter):17-24.
Chabner, B. A., and T. G. Roberts, Jr. 2005. Timeline: Chemotherapy and the war on cancer. Nat Rev Cancer 5 (1):65-72.
Chapman, Audrey R. 2011. Addressing the Ethical Challenges of First-in-Human Trials. J Clinic Res Bioeth 2 (4):113.
DiMasi, J. A., R. W. Hansen, and H. G. Grabowski. 2003. The price of innovation: new estimates of drug development costs. J Health Econ 22 (2):151-85.
Editors. 2011. In this issue. Nat Rev Drug Discov 10 (4):239-239.
Ellis, L. M., and I. J. Fidler. 2010. Finding the tumor copycat. Therapy fails, patients don’t. Nat Med 16 (9):974-5.
FDA. 2010. FDA Issues Advice to Make Earliest Stages Of Clinical Drug Development More Efficient. FDA, June 18, 2009 2006 [cited March 7 2010]. Available from http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/2006/ucm108576.htm.
Gura, T. 1997. Cancer Models: Systems for identifying new drugs are often faulty. Science 278 (5340):1041-2.
Horrobin, D. F. 2003. Modern biomedical research: an internally self-consistent universe with little contact with medical reality? Nat Rev Drug Discov 2 (2):151-4.
Kola, I., and J. Landis. 2004. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3 (8):711-5.
Leaf, C. 2004. Why we are losing the war on cancer. Fortune (March 9):77-92.
Marshall, E. 2000. Gene therapy on trial. Science 288 (5468):951-7.
O’Collins, V. E., M. R. Macleod, G. A. Donnan, L. L. Horky, B. H. van der Worp, and D. W. Howells. 2006. 1,026 experimental treatments in acute stroke. Ann Neurol 59 (3):467-77.
Reuters. 2011. U.S. to develop chip that tests if a drug is toxic. Reuters, September 16 2011 [cited October 6 2011]. Available from http://www.msnbc.msn.com/id/44554007/ns/health-health_care/ – .To5AMnPaixF.
Salén, Jörn C W. 1994. Animal Models—Principles and Problems. In Handbook of Laboratory Science Volume II. Animal Models. 1st edition., edited by P. Svendsen and J. Hau. Boca Raton: CRC Press.
Sankar, U. 2005. The Delicate Toxicity Balance in Drug Discovery. The Scientist 19 (15):32.
Sarkar, Susanta K. 2009. Molecular imaging approaches. Drug Discovery World (Fall):33-38.
Sharp, Phillip A., and Robert Langer. 2011. Promoting Convergence in Biomedical Science. Science 333 (6042):527.
>Van Dyke, T. 2010. Finding the tumor copycat: approximating a human cancer. Nat Med 16 (9):976-7.
Zielinska, Edyta. 2010. Building a better mouse. The Scientist 24 (4):34-38.
THE HISTORICAL PERSPECTIVE
From 1847 -1878 French physician Claude Bernard established the modern use of laboratory animal models in experimental medicine for humans, which quickly grew to become the mainstay for twentieth century biomedical research despite its 130 year old, comparatively antiquated origin. To gain an insight into where science was during this period in the 19th century, the following sheds clear light:
‘Claude Bernard, the father of scientific physiology, believed that if medicine was to become truly scientific, it would have to be based on rigorous and controlled animal experiments. Bernard instituted a paradigm which has shaped physiological practice for most of the twentieth century. In this paper we examine how Bernard’s commitment to hypothetico-deductivism and determinism led to a) his rejection of the theory of evolution; b) his minimalization of the role of clinical medicine and epidemiological studies; and c) his conclusion that experiments on non-human animals were “entirely conclusive for the toxicology and hygiene of man”. We examine some negative consequences of Bernardianism for twentieth century medicine, and argue that physiology’s continued adherence to Bernardianism has caused it to diverge from the other biological sciences which have become increasingly infused with evolutionary theory’. For the full article please click here
THE LEGAL PERSPECTIVE
In 1938, the US Federal Food, Drug and Cosmetic Act first required some animal testing by law, and this became yet further enshrined in the 1946 Nuremberg Code, when scientific understanding was still, comparatively speaking, in its infancy. FOR LIFE ON EARTH regards AFMA/EFMA’s recently published paper to be a key item on our website. It expounds the international scientific evidence to date and places this within a legal and historical perspective. We ask every visitor to please take time to read this impressive report, ideal for witness testimony at any public hearing or legal challenge: The Nuremberg Code subverts human health and safety by requiring animal modeling. To understand where science was when this Nuremberg Code was established, and animal testing first became a requirement by law, the following excerpt from the above paper sheds a clear light (please note all references are at the bottom of this page):
‘At the time of the Nuremberg trials, medical science was very different than it is now. The structure of DNA had not been elucidated, scientists thought the poliovirus entered via the nose (it enters through the gut) , the notion of a magic bullet (that for every disease, or at least every infectious disease, a chemical existed that could interact with the single site causing the malady and thus cure the disease without harming the rest of the body) via Ehrlich and Salvarsan  was foremost in the minds of drug developers, the modern synthesis in evolution was brand new , and animals and humans seemed to be more or less the same except for humans having a soul [2,30,31]. There were no organ transplants, infectious diseases were still a major killer in the developed world, the fields of cognitive ethology and animal cognition were unheard of, and differences between ethnic groups [32-38] and sexes [39-43] in terms of disease and drug reactions had not yet been discovered. Physics was just beginning to cast off the shackles of determinism and reductionism but chaos and complexity theory was still on the horizon. It was a different world. People in the 1940s are to be excused for thinking that animals and humans would react more or less the same to drugs and disease. We will now bring the reader into the current scientific environment as it relates to our topic [30,44-49].’ For the full article please click here
27. Paul JR: A History of Poliomyelitis. New Haven: Yale University Press; 1971.
28. Ehrlich P, Hata S: Die experimentalle Chemotherapie der Spirillosen. Berlin: Springer; 1910.
29. Mayr E: What evolution Is. Basic Books 2002.
2. Elliot P: Vivisection in Historical Perspective. edn. In Vivisection and the Emergence of Experimental Medicine in Nineteenth Century France. Edited by Rupke N. New York: Croom Helm; 1987:48–77.
30. LaFollette H, Shanks N: Animal Experimentation: The Legacy of Claude Bernard. Int Stud Philos Sci 1994, 8(3):195–210.
31. Bernard C: An Introduction to the Study of Experimental Medicine. New York: Dover; 1957 (1865).
32. Cheung DS, Warman ML, Mulliken JB: Hemangioma in twins. Ann Plast Surg 1997, 38(3):269–274.
33. Couzin J: Cancer research. Probing the roots of race and cancer. Science 2007, 315(5812):592–594.
34. Gregor Z, Joffe L: Senile macular changes in the black African. Br J Ophthalmol 1978, 62(8):547–550.
35. Haiman CA, Stram DO, Wilkens LR, Pike MC, Kolonel LN, Henderson BE, Le Marchand L: Ethnic and racial differences in the smoking-related risk of lung cancer. N Engl J Med 2006, 354(4):333–342.
36. Spielman RS, Bastone LA, Burdick JT, Morley M, Ewens WJ, Cheung VG: Common genetic variants account for differences in gene expression among ethnic groups. Nat Genet 2007, 39(2):226–231.
37. Stamer UM, Stuber F: The pharmacogenetics of analgesia. Expert Opin Pharmacother 2007, 8(14):2235–2245.
38. Wilke RA, Dolan ME: Genetics and Variable Drug Response. JAMA: The Journal of the American Medical Association 2011, 306(3):306–307.
39. Holden C: Sex and the suffering brain. Science 2005, 308(5728):1574.
40. Kaiser J: Gender in the pharmacy: does it matter? Science 2005, 308(5728):1572.
41. Simon V: Wanted: women in clinical trials. Science 2005, 308(5728):1517.
42. Wald C, Wu C: Of Mice and Women: The Bias in Animal Models. Science 2010, 327(5973):1571–1572.
43. Willyard C: HIV gender clues emerge. Nat Med 2009, 15(8):830.
44. LaFollette H, Shanks N: Animal models in biomedical research: some epistemological worries. Public Aff Q 1993, 7(2):113–130.
45. LaFollette H, Shanks N: Brute Science: Dilemmas of animal experimentation. London and New York: Routledge; 1996.46. Shanks N, Greek R: Animal Models in Light of Evolution. Boca Raton: Brown Walker;
47. Shanks N, Greek R, Greek J: Are animal models predictive for humans? Philos Ethics Humanit Med 2009, 4(1):2.
48. Greek R, Greek J: Is the use of sentient animals in basic research justifiable? Philos Ethics Humanit Med 2010, 5:14.
49. Greek R, Shanks N, Rice MJ: The History and Implications of Testing Thalidomide on Animals. The Journal of Philosophy, Science & Law 2011, 11.
Since 1847 – 1878, the period when Claude Bernard first established the modern use of animal experiments, a massive financial infrastructure has arisen – built on the animal model – upon which very many research centres, universities and scientists now rely. This well established and vast financial aspect is entirely out of step with current understanding of science. Moreover, a realistic appreciation of this finance is key to understanding why experiments on animals continue.
We will all be aware that there have been times when industry has supported dangerous or environmentally damaging practices, such as smoking and the erosion of the ozone layer. Respected books have documented the rise of such practices, which attach more importance to profit than developing scientific knowledge. One such book is Science Money and Politics by Daniel Greenberg, reviewed here by Steven Shapin: “Like many scientists, he (Greenberg) is fiercely critical of many aspects of current financial, political and ethical arrangements bearing on the conduct of American science, arrangements which, if unchecked, have the capacity to undermine the integrity and authority of scientific knowledge”. Also recommended are the books Trust Us! We’re Experts and Toxic Sludge Is Good For You, both by Rampton and Stauber.
The scientific value of using animals to predict human response to drugs and disease has, in fact, never existed. However, a massive financial system – creating its vested interest which has grown up around this now disproven system – is key to understanding why this practice persists, and what it will realistically take to dismantle. For more on this please click here.
Senior scientists involved in medical research are speaking out about this financial aspect and the pressure placed on them to blur the distinction between “basic” science research – that does not make any claim to be relevant for humans – and “applied” research that is funded on the premise that it will lead to cures for human disease i.e will lead to “clinical translation”. Senior investigator and Director of Research of the Samuel Lunenfeld Research Institute, Dr Jim Woodgett comments on this, following an article in Nature:
‘When we publish our studies in mouse models, we are encouraged to extrapolate to human relevance. This is almost a requirement of some funding agencies and certainly a pressure from the press in reporting research progress. When will this enter the clinic? The problem is an obvious one. If the scientific (most notably, biomedical community) does not take ownership of the problem, then we will be held to account. If we break the “contract” with the funders (a.k.a. tax payers), we will lose not only credibility but also funding. Dr Woodgett concludes his comment with the following: Building only on solid foundations was a principle understood by the ancient Greeks and Egyptians yet we are building castles on the equivalent of swampland. No wonder clinical translation fails so often’
For more examples of the above, please visit Supporting Data from Experts