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Animal Models and Asthma

The reasons animal models cannot predict human response to drugs and disease are to be found in a study of evolutionary biology and complex systems. As I have said many times, if someone wishes to understand this issue, she must study these areas. However, the empirical evidence is very strong and anyone willing to take the time to gather and study that evidence can come to the same conclusions. The problem with empirical evidence is that some will attempt to dismiss it by claiming that: “Those results only apply to that one area of research, they are not representative of a theme.” While there could be some truth to that, one must wonder how much truth, when study after study in field after field yields the same conclusion. Moreover, one must wonder why, in light of the fact animal models fail in so many areas, should they succeed in a slightly different area? The reasons, per evolutionary biology and complexity science, animal models fail in one area are the same reasons they fail in others. The same rules and facts apply.

Holmes, Solari (of GlaxoSmithKline), and Holgate, writing in a recent issue of Drug Discovery Today:


Asthma remains an area of considerable unmet medical need. Few new drugs have made it to the clinic during the past 50 years, with many that perform well in preclinical animal models of asthma, failing in humans owing to lack of safety and efficacy. The failure to translate promising drug candidates from animal models to humans has led to questions about the utility of in vivo studies and to demand for more predictive models and tools based on the latest technologies. . . .

Current strategies rely on cell- and animal-based assays during preclinical and clinical stages of drug development. The predictive power of these assays plays a considerable part in the lack of efficacy and safety seen in human trials of new drugs[1,2], including those developed to treat asthma. The scientific literature includes many examples of potential asthma therapies that have been developed on the basis of positive preclinical data, only for them to fail on the grounds of safety and/or efficacy in clinical trials (Box 1). The lack of predictive preclinical models of asthma is contributing to the paucity of efficacious asthma drugs . . . The US Food and Drug Administration (FDA)[3] and European Innovative Medicines Initiative (IMI)[4] have acknowledged the limitations of animal models as a major bottleneck in the development of efficacious and safe medicines across many therapeutic areas, including asthma. . . . Further variability is observed within species, with different strains of mouse exhibiting striking differences in the extent to which they develop allergic immunological responses to the same sensitizer.[5,6] Similar strain differences have also been observed in rats.[7,8]


The above is not subtle. Yet, no doubt some will raise the constant harangue: “What will we do if we do not experiment on animals?” Never mind that animal studies give results like the above, people with a vested interest will still set up the old false dichotomy of your dog or your child. Which bring me to the next story.

Scientists are now using computer technology to link gene expression patterns to medications that are already on the market. The thought being, that because medications act on many receptors and influence many genes, there may already be drugs available that would be efficacious for treating diseases other than the one they are currently being used for. The research is human-based. According to an NIH press release: “scientists drew their data from the NIH National Center for Biotechnology Information Gene Expression Omnibus, a publicly available database that contains the results of thousands of genomic studies on a wide range of topics, submitted by researchers across the globe. The resource catalogs changes in gene activity under various conditions, such as in diseased tissues or in response to medications.” The articles [9,10] describing the research can be accessed through the journal’s website.

No doubt the drugs were tested, and will be tested again, in animals but this begs the question whether animal models can predict human response. The answer is they cannot. Nevertheless, scientists will test the drugs again because that is the current way things are done. This illustrates another problem with using animal models. Not only are animal models misleading for human response to drugs and disease, not only has society lost cures because of animal models, but the continued use robs more modern and fruitful methods of research of money that could be used for worthwhile research.

Still, as long as people make money from the process, it will continue.


1.            Paul SM, Mytelka DS, Dunwiddie CT, et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov. Mar 2010;9(3):203-214.

2.            Kola I. The state of innovation in drug development. Clinical Pharmacology and Therapeutics. Feb 2008;83(2):227-230.

3.            FDA. Innovation or Stagnation? Challenge and Opportunity on the Critical Path to New Medical Products. 2004; Accessed July 15, 2011.

4.            Innovative Medicines Initiative. The Innovative Medicines Initiative (IMI) Strategic Research Agenda. 2006; Accessed August 19, 2011.

5.            Brewer JP, Kisselgof AB, Martin TR. Genetic variability in pulmonary physiological, cellular, and antibody responses to antigen in mice. Am J Respir Crit Care Med. Oct 1999;160(4):1150-1156.

6.            Shinagawa K, Kojima M. Mouse model of airway remodeling: strain differences. Am J Respir Crit Care Med. Oct 15 2003;168(8):959-967.

7.            Schneider T, van Velzen D, Moqbel R, Issekutz AC. Kinetics and quantitation of eosinophil and neutrophil recruitment to allergic lung inflammation in a brown Norway rat model. Am J Respir Cell Mol Biol. Dec 1997;17(6):702-712.

8.            Holmes AM, Solari R, Holgate ST. Animal models of asthma: value, limitations and opportunities for alternative approaches. Drug Discovery Today. 2011;16(15/16):659-670.

9.            Sirota M, Dudley JT, Kim J, et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Science Translational Medicine. Aug 17 2011;3(96):96ra77.

10.            Dudley JT, Sirota M, Shenoy M, et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Science Translational Medicine. Aug 17 2011;3(96):96ra76.


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