As I have stated many times, the use of animal models persists because society is sold the myth that what happens in an animal is what will happen in humans. Despite the fact that this is verifiably not the case, society believes that by studying drug reactions and diseases in animals, scientists will be able to predict what a disease or drug will do in humans. (For example, see Animal Research: “Your Dog or Your Child”—Yet Again.)
An article appearing in Naturereveals that scientists have knocked out roughly 40% of the genes in the mouse genome. This is a tremendous scientific achievement. It will result in more knowledge about mice and perhaps living organisms in general. But note the claim in the press release:
The completed resource will power studies of gene activity in models of human disease. . . . This developing resource will be essential in our understanding of the role of genes in all mammals - including humans.
The function of a gene in a mouse is not indicative of its function in humans. Yet the vested interest groups spin this research in such a way as to encourage society to think exactly that.
In related news, scientists have discovered a hepatitis C-like virus, called canine hepatitis C virus (CHV), in dogs. Again, note the message from the press release:
Discovery of canine hepatitis C virus opens up new doors for research on deadly human pathogen. . . . The identification and characterization of this virus gives scientists new insights into how hepatitis C in humans may have evolved and provides scientists renewed hope to develop a model system to study how it causes disease. . . . Dr. Ian Lipkin, "The identification and characterization of CHV signals the advent of a new tractable animal model for hepatitis C. This discovery provides new tools for understanding how this virus causes disease, and will facilitate drug and vaccine research and development."
The article can be found here. This is like saying that since SIV infects monkeys, scientists can study monkeys and come up with a vaccine against HIV in humans. Once again, close does not count when trying to predict human response to drugs and disease.
And this from Drug Discovery & Development:
A new study from the University of North Carolina at Chapel Hill School of Medicine further validates the use of humanized BLT mice in the fight to block HIV transmission. The "BLT" name is derived from the fact that these designer mice are created one at a time by introducing human bone marrow, liver and thymus tissues into animals without an immune system of their own. Humanized BLT mice have a fully functioning human immune system and can be infected with HIV in the same manner as humans.
In the study published online Wednesday, May 18 in the Journal of Virology, Denton and colleagues provide data that validates humanized BLT mice as a preclinical experimental system that potentially can be used to develop and test the effectiveness of experimental HIV prevention approaches and topical microbicides. . . . "This animal model has great potential value for testing and predicting the HIV preventive benefits of the second generation of microbicide candidates that are aimed at preventing viral replication," Garcia said. "The results of these studies will help provide important information for current and future clinical trials."
All animals (including humans) are examples of complex systems. While it is true that complex systems are modular, these modules interact and the systems are robust. This means that scientists will not make a human out of a mouse, despite what the PR representatives say to improve the odds of getting grants or having society buy into and fund nonsense.
There exists much empirical evidence to refute the irrational claims above. However, more important is the scientific theory that places the evidence in context. If an overarching theory exists, then predictions can be made and some specific experiments abandoned because the theory explains why they will not yield the needed results. (For example, it does not make sense to work on developing a perpetual motion machine.) Theories are not scientific laws like the laws used in physics, for example Boyle’s law or the laws of thermodynamics. However, theories like the Germ Theory of Disease, the Theory of Evolution, and so forth offer scientists very good guidelines for predicting responses and outcomes. I have often said that an understanding of evolution, particularly evo devo, and complex systems offers a theory into which we can place the empirical evidence that convincingly shows that animal models cannot predict human response to drugs and disease. Trying to develop predictive animal models is doomed.
Many scientists who use animal models have demeaned the importance of theory, placing more importance on experimentation and the data that comes from experimentation. Some have even said that there are now laws in science. I strongly disagree with this interpretation of the philosophy of science.
Massimo Pigliucci writing a rebuttal in EMBO reports (Pigliucci 2009) about the “end of theory in science” (Anderson 2008) and about why correlation is sufficient and science no longer needs to look for what causes a phenomenon (a different but related issue to what I am discussing), stated:
But, if we stop looking for models and hypotheses, are we still really doing science? Science, unlike advertizing, is not about finding patterns—although that is certainly part of the process—it is about finding explanations for those patterns. (Pigliucci 2009) (Emphasis added.)
Pigliucci continues, addressing the claim that Craig Venter by finding new species in sea water, has advanced biology more than any other scientist of his generation:
But, as Anderson [the author whose article Pigliucci is rebutting] points out, “Venter can tell you almost nothing about the species he found. He doesn’t know what they look like, how they live, or much of anything else about their morphology. He doesn’t even have their entire genome. All he has is a statistical blip—a unique sequence that, being unlike any other sequence in the database, must represent a new species.” Which means that Venter has succeeded in generating a large amount of data—in response to a specific question, by the way: how many distinct, species-level genome sequences can be found in the oceans? This will surely provide plenty of food for thought for scientists, and a variety of ways to test interesting hypotheses about the structure of the biosphere, the diversity of bacterial life, and so on. But, without those hypotheses to be tested, Venter’s data are going to be a useless curiosity, far from being the most important contribution to science in this generation. (Emphasis added.)
There are some things that, while perhaps being useful, are not actually science; such as the accumulation of facts. Karl Pearson said, “The unity of all science consists alone in its method, not its material. . . . It is not the facts themselves which form science, but the method they are dealt with…” (Pearson 1892) Science and the accumulation of facts are not synonymous. Curd and Cover state:
…truth by itself cannot be sufficient as a characteristic of the goal of science. Why not? Because so many of the true statements we could make about the natural world have little or no scientific value. Imagine, for example, that a biologist wants to increase our store of scientific knowledge by counting the precise number of hairs on individual dogs at various times on various days, not to test a theory or experiment with a drug to prevent hair loss but simply to know the canine hair count for its own sake. Even if the information that the biologist collects is true it has negligible scientific value…Scientists are interested…in discovering truths about the world…in the form of general theories and laws with predictive power. These criteria of scientific excellence – generality and predictive power – and many others besides (such as explanatory power and simplicity) are among the cognitive values of science. They are not the same as truth. (Curd and Cover 1998)
I would add that even when more facts about nonhuman animals are generated, such as the mouse genome knock out studies, that alone does not guarantee that those facts will advance human medical care. In fact, the odds are decidedly against it.
The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all. (Anderson 2008)
Yet, science advances only if it can provide explanations, failing which, it becomes an activity more akin to stamp collecting.
Evolution and complex systems provide the explanation for why animal models fail to predict human response to drugs and disease. Granted there are many correlations among species but this is not the same as predicting responses. One can generate huge amounts of data from animal studies. Correlations can be found and in the old days mush was learned from these correlations. But today the questions have changed.
Some of this data from studying animals does in fact give society more knowledge about animals and maybe even life in general. There is merit to this. (See Is the use of sentient animals in basic research justifiable? for more.) But society is paying for research that will enlighten us about human diseases and how to treat those diseases. Based on statements from the researchers quoted above, an analysis of grant applications, and other such data, the researchers and their PR machines are committing fraud. If scientists want to generate data for the sake of more knowledge, then say so. But don’t commit fraud by saying this data will lead to cures or safer medications. As long as prosecutors fail to charge such researchers with fraud, they are saying that they do not care about the health and medical care of their children or anyone else.
Anderson, Chris. 2008. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired 16 (7).
Curd, Martin, and J. A. Cover. 1998. Philosophy of Science: Norton.
Pearson, Karl. The Grammar of Science (London, 1892; 3rd ed., 1911; reiss. Gloucester, Mass., 1969; 4th ed., E. S. Pearson, ed., London, 1937)
Pigliucci, Massimo. 2009. The end of theory in science? EMBO Rep 10 (6):534-534.