I have often said that one way animal models are used in research and testing is to predict human response to drugs and disease. Some have challenged this. Consider the following from scientists and the media:
Hau, a scientist, stated in the Handbook of Laboratory Animal Science. Second Edition. Animal Models: "A third important group of animal models is employed as predictive models. These models are used with the aim of discovering and quantifying the impact of a treatment, whether this is to cure a disease or to assess toxicity of a chemical compound." (Hau 2003)
Michael F. Jacobson, executive director of the Center for Science in the Public Interest noted in 2008: “We must test animals to determine whether a substance causes cancer (Center For Science In The Public Interest 2008).” Similarly, Huff et al. observe: “Chemical carcinogenesis bioassays in animals have long been recognized and accepted as valid predictors of potential cancer hazards to humans” (Huff, Jacobson, and Davis 2008).
Ramesh Akkina, DVM, PhD, professor, Department of Microbiology, Immunology and Pathology at Colorado State University, Fort Collins, CO stated: "A major advantage with this in vivo system [genetically modified SCID mice] is that any data you get from SCID-hu mice is directly applicable to a human situation." (Anonymous 2008)
Ghose, in Nerve Graft Heals Paralysis In Rats written for The Scientist, reported on an article published in Nature (Alilain et al. 2011)about restoring nerve function in paralyzed rats. Ghose states. "The finding, published today (July 13) in Nature, suggests that a similar technique could one day be used to treat quadriplegics, who usually need artificial respirators to breathe. . . . The results give hope that a similar technique could eventually be used to restore breathing in quadriplegics. Because doctors use nerve grafts already, only chondroitinase ABC would need to be studied in humans, said Oswald Steward, the director of the Reeve-Irvine Research Center, who was not involved in the study. “I think it is something that could move forward to a clinical trial pretty quickly".”
James Gallagher of the BBC News wrote, on 21 July 2011, in Animal testing 'requires tighter regulation': "Introducing human material into animals has furthered medical research. Putting human breast tumour cells into mice has allowed researchers to test cancer drugs on human tissue."
If by medical research, Gallagher means the acquisition of more knowledge, he is correct. If he means that this knowledge has been important for medical advancement and the testing of drugs, the facts would disagree with him.
Lets compare the above to what the scientists from, or associated with, the pharmaceutical industry, and other scientists, say.
Enna and Williams 2009:
Many are now coming to the realization that, as in other therapeutic areas, the greatest limitation for identifying new drugs for treating cancer are the deficiencies in the animal models used for testing NCEs (Aggarwal et al., 2009). Yet despite their many limitations (Hackam & Redelmeier, 2006), these animal models remain a key element of translational medicine (Cozzi, Fraichard, & Thiam, 2008; Mankoff, Brander, Ferrone, & Marincola, 2004). Given this, it has been argued that a greater emphasis be placed on improving animal models of human disease rather than to emphasize the screening of chemical libraries for leads that are then tested in animal systems known to have limited predictive validity with respect to human illness. . . .
A major hurdle in the translational medicine undertaking is the fact that most preclinical animal models of disease generally lack predictive value with respect to the human condition under study. Indeed, the false positives that result from the present generation of animal assays are a major cause of NCE attrition in the clinic either because of lack of efficacy or the appearance of unacceptable side effects that were not detected preclinically. While there are notable, albeit retrospective, exceptions (Zambrowicz & Sands, 2003), this weakness in the conventional drug discovery process has not been resolved with the use of transgenic animals which themselves contribute additional confounds that further complicate data interpretation.
In therapeutic areas as diverse as pain (Rice et al., 2008), stroke (O'Collins et al., 2006), neurodegeneration (Lindner, McArthur, Deadwyler, Hampson, & Tariot, 2008), and substance abuse (Gardner, 2008), numerous agents that displayed substantial efficacy and safety in animal models, have failed in the clinic (Hackam & Redelmeier, 2006). Similarly, animal models used to interrogate the PK and PD effects of NCEs are in need of further refinement so they have predictive value with regard to human use. The poor translational record from animal models to humans has been attributed to poor preclinical methodologies (Green, 2008; Hackam, 2007; Perel et al., 2007), which include a lack of blinding and randomization, adequate powering/size, and an "optimization bias"; in that very often only positive results are reported. . . .
Other examples of a disconnect between animal data and human responses include a4B2 nicotinic agonists, like tebanicline (ABT-594) (Bannon et al., 1998). In this case, an NCE that was some 200 times more potent than morphine in various animal pain models had limited clinical efficacy due to side effects that were not detected in animal studies (Rueter, Honore, & Bitner, 2006). Moreover, clinical trials have indicated that the analgesic utility of TRPV1 antagonists, like SB-705498, are limited by effects on core temperature regulation (Caterina, 2008). (Enna and Williams 2009)
From Nature Reviews Drug Discovery 2011: "A suite of data suggest that cancer tends to be driven by diverse, rather than common, mutations, highlighting hurdles for targeted drug strategies. A genomic analysis of breast cancer tumours from 50 patients identified 1,700 mutations, of which only 3 occurred in more than 10% of patients, showed data presented at the American Association for Cancer Research (AACR) annual meeting. The study led oncologists and researchers alike to debate the difficulties of tackling cancers when patients' diseases are likely to have a unique genetic make-up." (Editors 2011)
Geerts, of In Silico Biosciences in 2009:
The tremendous advances in transgene animal technology, especially in the area of Alzheimer's disease, have not resulted in a significantly better success rate for drugs entering clinical development. Despite substantial increases in research and development budgets, the number of approved drugs in general has not increased, leading to the so-called innovation gap. While animal models have been very useful in documenting the possible pathological mechanisms in many CNS diseases, they are not very predictive in the area of drug development. . . .
It becomes increasingly clear that, despite a similar number of genes for humans and rodents, the expression of specific functional gene polymorphism can be dramatically different. . . . Almost all animal models for CNS diseases display part, but not all, of the pathology, often leading to erroneous conclusions with regard to the extrapolation of preclinical data to the clinical situation. In the field of psychiatry, 61 different animal models for schizophrenia have been described, 44 of these were based upon transgene technology; however, it is difficult to assess the predictability of these models for different patient populations. . . . The successful development of new innovative drugs for chronic CNS diseases is in jeopardy and new paradigms need to be explored. The current drug-discovery paradigm is based upon detection of activity and toxicity in animal models; however, these models show a rather limited predictability for the clinical situation. (Geerts 2009)(Emphasis added.)
Dirnagl and Lauritzen 2011: "A low reproducibility of experimental cerebrovascular research and problems in the translation of findings from animal experiments to successful treatment strategies in humans have precipitated investigations into the quality of preclinical research. Overall, deficits in the design, conduct, and reporting of preclinical research were found to be prevalent (Dirnagl, 2006; Dirnagl and Macleod, 2009; Fisher et al, 2009; Minnerup et al, 2010; Philip et al, 2009; Sena et al, 2007). In this issue, Vesterinen et al (2011) present a ‘Systematic survey of the design, statistical analysis, and reporting of studies published in the 2008 volume of the Journal of Cerebral Blood Flow and Metabolism’. Not surprisingly, this systematic analysis reveals indicators for deficiencies in original articles published in the Journal of Cerebral Blood Flow and Metabolism. Although this is the first such analysis in a neuroscience journal, it is quite clear that the deficits exposed are not specific to the Journal of Cerebral Blood Flow and Metabolism, one of the leading journals in the cerebrovascular field. In fact, another systematic analysis (Kilkenny et al, 2009) focusing on published biomedical research using laboratory animals in general reported very similar results." (Dirnagl and Lauritzen 2011)
So much for the controlling variables argument
The following is from John P. A. Ioannidis, writing in Scientific American, June 2011, and speaks for itself.
False positives and exaggerated results in peer-reviewed scientific studies have reached epidemic proportions in recent years. The problem is rampant in economics, the social sciences and even the natural sciences, but it is particularly egregious in biomedicine. Many studies that claim some drug or treatment is beneficial have turned out not to be true. We need only look to conflicting findings about beta-carotene, vitamin E, hormone treatments, Vioxx and Avandia. Even when effects are genuine, their true magnitude is often smaller than originally claimed.
The problem begins with the public’s rising expectations of science. Being human, scientists are tempted to show that they know more than they do. The number of investigators—and the number of experiments, observations and analyses they produce—has also increased exponentially in many fields, but adequate safeguards against bias are lacking. Research is fragmented, competition is fierce and emphasis is often given to single studies instead of the big picture.
Much research is conducted for reasons other than the pursuit of truth. Conflicts of interest abound, and they influence outcomes. In health care, research is often performed at the behest of companies that have a large financial stake in the results. Even for academics, success often hinges on publishing positive findings. The oligopoly of high-impact journals also has a distorting effect on funding, academic careers and market shares. Industry tailors research agendas to suit its needs, which also shapes academic priorities, journal revenue and even public funding. (Ioannidis 2011)
Alilain, W. J., K. P. Horn, H. Hu, T. E. Dick, and J. Silver. 2011. Functional regeneration of respiratory pathways after spinal cord injury. Nature 475 (7355):196-200.
Anonymous. 2008. Of Mice...and Humans. Drug Discovery and Development 11 (6):16-20.
Center For Science In The Public Interest. Longer Tests on Lab Animals Urged for Potential Carcinogens. CSPI 2008 [cited November 17. Available from http://www.cspinet.org/new/200811172.html.
Dirnagl, Ulrich, and Martin Lauritzen. 2011. Improving the Quality of Biomedical Research: Guidelines for Reporting Experiments Involving Animals. J Cereb Blood Flow Metab 31 (4):989-990.
Editors. 2011. News in brief. Nat Rev Drug Discov 10 (5):327-327.
Enna, S. J., and M. Williams. 2009. Defining the role of pharmacology in the emerging world of translational research. Advances in pharmacology 57:1-30.
Geerts, H. 2009. Of mice and men: bridging the translational disconnect in CNS drug discovery. CNS drugs 23 (11):915-26.
Hau, Jann. 2003. Animal Models. In Handbook of Laboratory Animal Science. Second Edition. Animal Models, edited by J. Hau and G. K. van Hoosier Jr. Boca Rotan: CRC Press.
Huff, J., M. F. Jacobson, and D. L. Davis. 2008. The limits of two-year bioassay exposure regimens for identifying chemical carcinogens. Environ Health Perspect 116 (11):1439-42.
Ioannidis, John P. A. 2011. An Epidemic of False Claims. Scientific American (June):16.