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Animal Models, Personalized Medicine, and the Cost of Drugs

One reason new drugs are so expensive is that a vast majority fail to make it to market, thus the ones that do must make up the cost for those that do not. Giri and Bader 2011:


The pharmaceutical industry is facing an increased financial burden owing to a high attrition rate at the post-marketing stage. Despite technological and biological advances, the process of drug development from preclinical testing into the clinical setting remains lengthy (>12 years) and expensive (>US$800 million) for a single drug (Paul et al. 2010) Moreover, it is an uncertain and inefficient process because only one drug out of ~5000-10 000 drug compounds reaches the market after preclinical testing (Hughes et al. 2011). Preclinical research costs ~US$16 million and takes in the region of two years for most pharmaceutical companies (Paul et al. 2010). According to a 2006 survey of pharmaceutical companies, hepatotoxicity was ranked first in terms of adverse drug reactions [ and for withdrawal of a drug from the market (Kaplowitz 2005), probably because the liver is the central organ for drug metabolism. More than 50% of chemical entities that enter clinical trials fail because of efficacy or safety issues (Kola and Landis 2004).


Catherine Shaffer, Contributing Editor of Drug Discovery & Development – wrote on January 01, 2012: “Drug development is an extremely costly endeavor. Estimates of the total expense of advancing a new drug from the chemistry stage to the market are as high as $2 billion. Much of that cost is attributable to drug failures late in development, after huge investments have been made. Drugs are equally likely to fail at that stage for safety reasons, as for a lack of efficacy, which is often well-established by the time large trials are launched.”(Shaffer 2012)  She goes on to say that genomic sequencing tools might solve this problem.

Pharma has very few predictive tools. Historically animal models have been the most trusted tool, but scientists in and out of Pharma have acknowledged that animal models are not predictive for humans and that they are responsible for much of the increase in cost. Not only do animal models fail to weed out or at least identify the drugs that will injure some patients, they also derail drugs that would have been good treatments for patients. Lazzarini et al. 2006: “Drugs which were unsuccessful in animal models were not used in clinical osteomyelitis, with few exceptions. Teicoplanin and linezolid were successful in the treatment of osteomyelitis in clinical trials, despite being completely inactive in two animal model studies of staphylococcal osteomyelitis. Therefore, the value of animal models as predictors of failure should also be carefully assessed.”(Lazzarini et al. 2006) 

Bendtsen and Møller discussing animal models of portal hypertension and the pharmacologic interventions thereof: “In conclusion, the results of this experimental study emphasize that pharmacologic hemodynamic effects are [animal] model specific, and one should therefore be cautious to draw firm conclusions as to the efficacy in humans based on animal studies.”(Bendtsen and Moller 2008) 

Spedding et al writing in Nature Reviews Drug Discovery 2005: “Animal models often cannot be transposed to Phase I and Phase II clinical testing, and Phase I/II clinical testing is often not transposable to Phase III trials and the general population.”(Spedding et al. 2005) 

All of this gets us back to personalized medicine and human-based testing and research. Since humans differ genetically from each other, a drug that harms me may cure you and vice versa. A study out of Stanford University Medical Center reports that women feel pain more intensely than men in almost all disease categories. The study was based on an evaluation of electronic medical records and is not perfect but, if the results are replicated, will be yet another example of differences between the sexes. An abstract of the article can be viewed here and will be published in The Journal of Pain.

Personalized gene-based treatments may increase the survival and cure rates of glioblastoma, a form of brain cancer. Researcher Nicola Serao stated: “Our research suggests you can't treat all patients the same. For example, we found gene expression patterns that have different, and sometimes opposite, relationships with survival in males and females and concluded that treatments affecting these genes will not be equally effective. Personalized therapy dependent on gender, race and age is something that is possible today with our advanced genomic tools.” The study is published in BMC Medical Genomics.

Along the same lines, acute lymphoblastic leukemia (ALL) is more common in Hispanic children than children from other ethnicities. Hispanic children are also more likely to die from ALL. Researchers at St Jude’s studied humans and discovered that eight variants of the gene ARID5B are responsible for this.(Xu et al. 2012) The article is published in the Journal of Clinical Oncology .

In 2011, the FDA approved the first genomics-derived drug for lupus—Benlysta. Currently, drugs with genetic information their prescribing labels include warfarin, trastuzumab, panitumumab, abacavir, azathioprine, carbamazepine, clopidogrel, cetuximab, and irinotecan.(Hudson 2011) Many other drugs and diseases are now “personalized.”

Pharma cannot remain solvent under the current drug development template and society cannot afford more expensive drugs. One place to start, in terms of fixing the problem, is to acknowledge that animal and humans are complex systems with different evolutionary trajectories and to accept all that this implies.


Bendtsen, F., and S. Moller. 2008. Pharmacological effects are model specific in animal models of portal hypertension. Hepatol Int 2 (4):397-8.

Hudson, Kathy L. 2011. Genomics, Health Care, and Society. New England Journal of Medicine 365 (11):1033-1041.

Hughes, J. P., S. Rees, S. B. Kalindjian, and K. L. Philpott. 2011. Principles of early drug discovery. British Journal of Pharmacology 162 (6):1239-1249.

Kaplowitz, N. 2005. Idiosyncratic drug hepatotoxicity. Nat Rev Drug Discov 4 (6):489-99.

Kola, I., and J. Landis. 2004. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3 (8):711-5.

Lazzarini, L., K. A. Overgaard, E. Conti, and M. E. Shirtliff. 2006. Experimental osteomyelitis: what have we learned from animal studies about the systemic treatment of osteomyelitis? J Chemother 18 (5):451-60.

Paul, S. M., D. S. Mytelka, C. T. Dunwiddie, C. C. Persinger, B. H. Munos, S. R. Lindborg, and A. L. Schacht. 2010. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov 9 (3):203-14.

Shaffer, Catherine. 2012. Safety Through Sequencing. Drug Discovery & Development January 1, 2012 2012 [cited January 30 2012]. Available from

Spedding, M., T. Jay, J. Costa e Silva, and L. Perret. 2005. A pathophysiological paradigm for the therapy of psychiatric disease. Nat Rev Drug Discov 4 (6):467-76.

Xu, Heng, Cheng Cheng, Meenakshi Devidas, Deqing Pei, Yiping Fan, Wenjian Yang, Geoff Neale, Paul Scheet, Esteban G. Burchard, Dara G. Torgerson, Celeste Eng, Michael Dean, Frederico Antillon, Naomi J. Winick, Paul L. Martin, Cheryl L. Willman, Bruce M. Camitta, Gregory H. Reaman, William L. Carroll, Mignon Loh, William E. Evans, Ching-Hon Pui, Stephen P. Hunger, Mary V. Relling, and Jun J. Yang. 2012. ARID5B Genetic Polymorphisms Contribute to Racial Disparities in the Incidence and Treatment Outcome of Childhood Acute Lymphoblastic Leukemia. Journal of Clinical Oncology.


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