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Basic Science and Prediction

I thank LifeScientist for continuing our discussion.

I do not think I implied that “basic research rests on immediate returns in the form of technological advances or new treatments.” I thought I stated very plainly “Basic research, by definition, must stand on the premise of knowledge for knowledge sake.” I also said, that basic research using animals is something society is not comfortable with unless it leads to treatments and cures. So I am perplexed as to how LifeScientist can suggest I implied otherwise. Let me state again, basic research is valuable and should be funded. However, it should not be funded because of promises it makes regarding cures and treatments. Nor is society comfortable using sentient animals in basic research and as Nature said, animal based research must revolved around “a concensus of what people find acceptable and unacceptable (1).” There is no contradiction here. Basic research can easily continue without using animals and without claims of curing human disease.

Once again, I agree that the division of research into basic and applied can be problematic but the division between using animals as predictive models and using them in other circumstances is easy. To maintain that any research on animals or in vitro research might someday lead to a cure is not helpful. Funding is limited and if a project is going to be funded on the basis of someday maybe something will happen then all grants are equal. There is a difference between good basic science research and not so good. But that difference is not going to revolve around which animal-model project is predictive for humans. Neither are researchers going to distinguish themselves by appealing to society that their particular basic research project using animals will predict human response to drugs or disease and therefore society should not be uncomfortable with it. Promising or implying cures in an NIH grant application based on using animals as predictive models is fraud.

As I have stated many times, Dr Shanks and my issue with using animal models focuses on using them as predictive models for human disease and drug response. LifeScientist states animal models are predictive and that most scientists agree with him that animal use is necessary. Consider the following:

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. (2)


Dixit uses a variation on a famous real estate phrase to explain what scientists are looking for: "Prediction, prediction, prediction is everybody's call, everybody's desire. We all want to predict safety and efficacy early so that we have fewer and fewer drugs failing so we can reduce the cost of drug development. It's not the cost of developing a successful drug; it's the cost of having unsuccessful drugs." (3)

Hurko in Drug Discovery World Spring 2000:

We have become very efficient in finding compounds that are safe and effective in laboratory animals, however, for novel targets, we still are not very efficient in identifying drugs that work in people. Most do not. (4)

 In the April 1, 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 . . .

Nature Biotechnology 2010:

The low predictive value of mouse cancer models for human disease is a major challenge for cancer research. Whereas human tumors develop from individual cells in the context of normal tissue, cancer research mostly relies on models employing xenografts or carrying oncogenic mutations throughout the whole animal or tissue. (5)

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. (6)

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.

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. (7)

 In a Newsweek piece on primate experimentation Animal-based research advocate Michael Conn is quoted:

"There is a degree of truth to the argument that animals are not a good model for humans," says Conn, who is not affiliated with OSU. "But it's also true that adults are not the best model for children and men are not the best model for women. The only perfect model for you is you, but obviously we can't test every substance on every individual. (8)

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 (9)

Referring to the results when using human cells, Palfreyman, Charles and Blander state: “They are clearly superior to those obtained from animals.” (10

Much of LifeScientist’s two essays responding to my April 19 post revolve around what prediction means. To a degree in my blogs on Opposing Views and much more thoroughly in Animal Models in Light of Evolution, I have explained what prediction means and the difference between the lay use, where astrology can claim to be predictive and the scientific use, which involves mathematical formulas. In biomedical science when a scientist claims a modality (like animal models) is predictive for humans, proof is required. Animal models have been tested for their predictive ability for human response to drugs and disease and they have failed. (See Animal Models in Light of Evolutionand the following articles (11-38) for more on prediction. I apologize for the number of references in this blog, but as long as LifeScientist and others challenge the notion that animal models cannot predict human response to drugs and disease, I will by necessity need to use the references to prove my point. It will be mush easier for the interested reader to just read Animal Models in Light of Evolution.)

Finally, as I have said numerous times, I do not see the prediction issue being settled (or even much more light being shed) in these blogs. I suggest that Dr Ringach and or LifeScientist debate me on this issue at UCLA and that the debate involve unbiased scientists who can moderate, judge, and weigh in on this issue. I participated in their panel discussion based on promises that a debate would happen. It is not happening.


1.            Editorial, Nature462, 699 (2009).

2.            FDA. (FDA, 2006), vol. 2010, pp. FDA News Release.

3.            P. McGee, Drug Discovery & Development, 18 (2006).

4.            O. Hurko, Drug Discovery World, 63 (2000).

5.            M.E., Nature Biotechnology28, vii (2010).

6.            M. Cimons, J. Getlin, T. H. Maugh_II, “Cancer Drugs Face Long Road From Mice to Men,” LA Times,  1998.

7.            D. F. Horrobin, Nat Rev Drug Discov2, 151 (Feb, 2003).

8.            J. Interlandi. (Newsweek, 2009), vol. 2009.

9.            I. Kola, J. Landis, Nat Rev Drug Discov3, 711 (Aug, 2004).

10.            M. G. Palfreyman, V. Charles, J. Blander, Drug Discovery WorldFall, 33 (2002).

11.            E. J. Calabrese, Drug Metab Rev15, 505 (1984).

12.            E. J. Calabrese, Principles of Animal Extrapolation.  (CRC Press, 1991).

13.            J. M. Collins, Chem Biol Interact134, 237 (May 16, 2001).

14.            A. P. Fletcher, J R Soc Med71, 693 (Sep, 1978).

15.            S. Garattini, Crit Rev Toxicol16, 1 (1985).

16.            D. G. Hackam, BMJ334, 163 (Jan 27, 2007).

17.            D. G. Hackam, D. A. Redelmeier, JAMA296, 1731 (Oct 11, 2006).

18.            R. Heywood, Toxicol Lett18, 83 (Aug, 1983).

19.            R. Heywood, in Animal Toxicity Studies: Their Relevance for Man, CE Lumley, S. Walker, Eds. (Quay, Lancaster, 1990),  pp. 57-67.

20.            T. Igarashi, in CMR Workshop: The Timing of Toxicological Studies to Support Clinical Trials, N. M. C Parkinson, C Lumley, SR Walker, Ed. (Kluwer, Boston/UK, 1994),  pp. 67-74.

21.            T. Igarashi, S. Nakane, T. Kitagawa, J Toxicol Sci20, 77 (May, 1995).

22.            T. Igarashi, T. Yabe, K. Noda, J Toxicol Sci21, 497 (Dec, 1996).

23.            Y. Igarashi, “Report from the Japanese Pharmaceutical Manufacturers Association 1994 Seiyakukyo data.”.

24.            J. I. Johnson et al., Br J Cancer84, 1424 (May 18, 2001).

25.            A. Knight, Altern Lab Anim35, 641 (Dec, 2007).

26.            A. Knight, J. Bailey, J. Balcombe, Altern Lab Anim34, 19 (Feb, 2006).

27.            T. Koppanyi, M. A. Avery, Clin Pharmacol Ther7, 250 (Mar-Apr, 1966).

28.            T. Lindl, M. Voelkel, R. Kolar, ALTEX22, 143 (2005).

29.            T. Lindl, M. Völkel, R. Kolar, ALTEX23, 111 (2006).

30.            J. T. Litchfield, Jr., Clin Pharmacol Ther3, 665 (Sep-Oct, 1962).

31.            C. Lumley, in Animal Toxicity Studies: Their Relevance for Man, C. Lumley, S. Walker, Eds. (Quay, 1990),  pp. 49-56.

32.            P. Perel et al., BMJ334, 197 (Jan 27, 2007).

33.            D. Salsburg, Fundam Appl Toxicol3, 63 (Jan-Feb, 1983).

34.            N. Shanks, R. Greek, Animal Models in Light of Evolution.  (Brown Walker, 2009).

35.            N. Shanks, R. Greek, J. Greek, Philos Ethics Humanit Med4, 2 (Jan 15, 2009).

36.            N. Shanks, R. A. Pyles, Philos Ethics Humanit Med2, 4 (2007).

37.            C. Spriet-Pourra, M. Auriche, (Eds), SCRIP Reports.  (PJB, 1994).

38.            R. J. Wall, M. Shani, Theriogenology69, 2 (Jan 1, 2008).


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