Do Animals Suffer From Human Diseases?


When I heard about a book called Zoobiquity: What Animals Can Teach Us About Health and the Science of Healing by Barbara Natterson-Horowitz and Kathryn Bowers, I knew it would just be a matter of time before vivisection activists latched on to the book’s theme. (My review of the book is here and a discussion I had regarding the book is here.) On August 22, 2013, Dr Dario Ringach published an essay titled: Do animals suffer from human diseases? The basis for the essay is, per Zoobiquity, that animals and humans suffer from “many of the same diseases.”

There is a kernel of truth this claim. Animals and humans are composed of the same elements, share conserved processes, and, in some cases, a body plan. The diseases they suffer from are caused by proliferation of cells, inflammation, viruses and so forth. But these commonalities mean essentially nothing in terms of response to disease. Even humans respond differently to the same drugs and diseases [1-9]; this is the basis for personalized medicine. It is the response to disease and drugs that physicians need to understand and studying animals does not help physicians understand what will and will not be safe or effective in their patients.

As I have stated many times, the reasons animal models offer little in terms of predictive value for humans lies in the fact that animals and humans are examples of evolved, complex adaptive systems. [10] Yes, we are all made of carbon and suffer from diseases with very general similarities but that is insufficient as Seok et al [11] demonstrated with mouse models of inflammation. Another recent example comes from research on cystic fibrosis (CF). Researchers have identified over 100 genetic errors that result in CF. [12] This means CF can have various manifestations. It also means scientists can design drugs for each mutation; one such drug already exists: ivacaftor. This new knowledge will also help potential parents learn whether a mutation they harbor can actually cause CF in their offspring. These slight genetic differences in terms of where the mutations are translate to important differences to patients with the disease.

In addition to differences in where the mutation is, background genes play an important role in terms of disease causation. Depending on the background genes, a mutation may have no effect or prove lethal. Van Regenmortel [13] writes the following:

In biology, only contributory causes can be identified because a multiplicity of background conditions or factors are always involved in bringing about an effect. Because of synergy and various interference phenomena, there is also no linear relationship between the magnitude of one causal factor and the magnitude of a biological effect Since any observed effect always results from the complex network of interactions and internal regulations that exist in every biological system, a single causal factor can never be presented as an explanation since it is not realistic to assume that the clause "other things being equal" is relevant when hundreds of background conditions contribute to an effect In non-linear dynamic systems, the notion of causality has very little explanatory value.

Likewise, Wagner states the following regarding complex systems:

Systems involving many interacting variables are at the heart of the natural and social sciences. Causal language is pervasive in the analysis of such systems, especially when insight into their behavior is translated into policy decisions. This is exemplified by economics, but to an increasing extent also by biology, due to the advent of sophisticated tools to identify the genetic basis of many diseases. It is argued here that a regularity notion of causality can only be meaningfully defined for systems with linear interactions among their variables. For the vastly more important class of nonlinear systems, no such notion is likely to exist. This thesis is developed with examples of dynamical systems taken mostly from mathematical biology. It is discussed with particular reference to the problem of causal inference in complex genetic systems, systems for which often only statistical characterizations exist. [14]

Differences in gene regulation and expression also play a huge role in disease response and these vary significantly among species. [15-18] All of these things together account for the fact that different species populate differnt niches. They also explain why animal models have no predictive value ofr humans. Even if a mouse could be genetically enginerred to be as human as possible, it would not be a similar to a human as other humans. As long as there are importat variations among humans in response to drugs and disease, thinking that animals and humans should suffer the same way from diseases that are seemingly identical is an intellectually naive position. Animals can serve as heuristics—serving to indicate or point out; stimulating interest as a means of furthering investigation—but not as predictive models.

In the final analysis, both the book Zoobiquity and Dr Ringach’s essay ignore current science and instead put forth notions better suited for a creation-centered universe. Evolution does not result in the same organ across species lines (once size is accounted for). Moreover, because of the superficiality and lack of current science exhibited by both, their statements are merely claims. They offer no evidence; rather they cite superficial similarities and assume that counts as scientific evidence. Superficially, all life is the same since we are all composed of stardust.

But animal models still receive billions in taxpayer dollars and Zoobiquity is a best seller on Amazon. Apparently, science doesn’t really matter.

Photo courtesy of NASA: X-ray: NASA/CXC/Rutgers/K.Eriksen et al.; Optical: DSS


1.         Bates, S., Progress towards personalized medicine. Drug Discovery Today, 2010. 15(3/4): p. 115-120.

2.         Belmaker, R., Y. Bersudsky, and G. Agam, Individual differences and evidence-based psychopharmacology. BMC Medicine, 2012. 10(1): p. 110.

3.         Koren, G., et al., Pharmacogenetics of morphine poisoning in a breastfed neonate of a codeine-prescribed mother. Lancet, 2006. 368(9536): p. 704.

4.         Serrano, D., et al., Efficacy of tamoxifen based on cytochrome P450 2D6, CYP2C19 and SULT1A1 genotype in the Italian Tamoxifen Prevention Trial. Pharmacogenomics J, 2011. 11(2): p. 100-107.

5.         Pirmohamed, M., Pharmacogenetics: past, present and future. Drug Discovery Today, 2011. 16(19/20): p. 852-861.

6.         Haiman, C.A., et al., Ethnic and racial differences in the smoking-related risk of lung cancer. N Engl J Med, 2006. 354(4): p. 333-42.

7.         Canto, J.G., et al., Association of Age and Sex With Myocardial Infarction Symptom Presentation and In-Hospital Mortality. JAMA: The Journal of the American Medical Association, 2012. 307(8): p. 813-822.

8.         Favoni, R.E. and A. Alama, Preclinical strategies targeted at non-small-cell lung cancer signalling pathways with striking translational fallout. Drug Discov Today, 2013. 18(1-2): p. 11-24.

9.         Klein, S. and S. Huber, Sex differences in susceptibility to viral infection, in Sex hormones and immunity to infection, S. Klein and C. Roberts, Editors. 2010, Springer-Verlag: Berlin. p. 93-122.

10.       Greek, R. and L.A. Hansen, Questions regarding the predictive value of one evolved complex adaptive system for a second: exemplified by the SOD1 mouse Progress in Biophysics and Molecular Biology, 2013: p.

11.       Seok, J., et al., Genomic responses in mouse models poorly mimic human inflammatory diseases. Proceedings of the National Academy of Sciences of the United States of America, 2013.

12.       Sosnay, P.R., et al., Defining the disease liability of variants in the cystic fibrosis transmembrane conductance regulator gene. Nat Genet, 2013.

13.       Van Regenmortel, M.H.V., Basic Research in HIV vaccinology is hampered by reductionist thinking. Frontiers in Immunology, 2012. 3.

14.       Wagner, A., Causality in Complex Systems. Biology and Philosophy, 1999. 14: p. 83-101.

15.       Kirschner, M.W. and J.C. Gerhart, The Plausibility of Life. 2006, New Haven: Yale University Press.

16.       Kasowski, M., et al., Variation in Transcription Factor Binding Among Humans. Science, 2010. 328(5975): p. 232-5.

17.       Lowe, C.B., et al., Three periods of regulatory innovation during vertebrate evolution. Science, 2011. 333(6045): p. 1019-24.

18.       Pai, A.A., et al., A Genome-Wide Study of DNA Methylation Patterns and Gene Expression Levels in Multiple Human and Chimpanzee Tissues. PLoS Genet, 2011. 7(2): p. e1001316.


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