Supporting data from experts

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. (FDA 2006)

Sharp and Langer, writing in Science in 2011:

The next challenge for biomedical research will be to solve problems of highly complex and integrated biological systems within the human body. Predictive models of these systems in either normal or disease states are beyond the capability of current knowledge and technology.(Sharp and Langer 2011)

In the April, 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 . . .(Zielinska 2010)

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.” (Cimons, Getlin, and Maugh_II 1998)

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. (Horrobin 2003)

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 (Kola and Landis 2004)

A Reuters article discuses a computer-based method for predicting drug toxicity. The chip would test for activation of genes and proteins in various human tissues:

“If things are going to fail, you want them to fail early,” Dr. Francis Collins, the director of the National Institutes of Health (NIH), told Reuters on Friday. “Now you’ll be able to find out much quicker if something isn’t going to work.”

Collins said a drug’s toxicity is one of the most common reasons why promising compounds fail. But animal tests — the usual method of checking a drug before trying it on humans — can be misleading. He said about half of drugs that work in animals may turn out to be toxic for people. And some drugs may in fact work in people even if they fail in animals, meaning potentially important medicines could be rejected.(Reuters 2011)

The Editors of Nature Reviews Drug Discovery wrote in 2011: “Unpredicted drug toxicities remain a leading cause of attrition in clinical trials and are a major complication of drug therapy.” (Editors 2011)

Robert Weinberg, of Massachusetts Institute of Technology, was quoted by Leaf in Fortune magazine as saying:

Weinberg explains. “And it’s been well known for more than a decade, maybe two decades, that many of these preclinical human cancer models have very little predictive power in terms of how actual human beings—actual human tumors inside patients—will respond . . . preclinical models of human cancer, in large part, stink . . . hundreds of millions of dollars are being wasted every year by drug companies using these [animal] models. (Leaf 2004)

Leaf also quotes Homer Pearce, “who once ran cancer research and clinical investigation at Eli Lilly and is now research fellow at the drug company” as saying:

. . . that mouse models are “woefully inadequate” for determining whether a drug will work in humans. “If you look at the millions and millions and millions of mice that have been cured, and you compare that to the relative success, or lack thereof, that we’ve achieved in the treatment of metastatic disease clinically,” he says, “you realize that there just has to be something wrong with those models.” (Leaf 2004)

In an editorial introduction to one article by Ellis and Fidler and another by Van Dyke (Van Dyke 2010), the editors of Nature Medicine stated:

The complexity of human metastatic cancer is difficult to mimic in mouse models. As a consequence, seemingly successful studies in murine models do not translate into success in late phases of clinical trials, pouring money, time and people’s hope down the drain. (Ellis and Fidler 2010)

Ellis and Fidler: “Preclinical models, unfortunately, seldom reflect the disease state within humans (Fig. 1).” (Ellis and Fidler 2010)

Dr Sarkar, Director of Clinical Imaging, Medicines Development within Oncology R&D at GlaxoSmithKline stated in 2009:

High attrition rates, particularly at the late stage of drug development, is a major challenge faced by the entire pharmaceutical community. The average success rate from first in man to registration for all therapeutic areas combined is 11% (Kola and Landis 2004). For oncology, this is even lower at 5%. Approximately 59% of all oncology compounds that enter in Phase III of development undergo attrition (Kola and Landis 2004). In fact, the estimated cost of bringing a potential drug to the market has increased significantly and at the current cost growth rate the projected cost for a new drug approval (assuming the R&D was initiated in 2001) is $1.9 billion in 2013 (DiMasi, Hansen, and Grabowski 2003). (Sarkar 2009)

Alan Oliff, former executive director for cancer research at Merck Research Laboratories in West Point, Pennsylvania stated in 1997: “The fundamental problem in drug discovery for cancer is that the [animal] model systems are not predictive at all.” (Gura 1997)

Chabner and Roberts: “Fewer than 10% of new drugs entering clinical trials in the period from 1970 to 1990 achieved FDA approval for marketing, and animal models seemed unreliable in predicting clinical success . . .” (Chabner and Roberts 2005)

Björquist et al. Drug Discovery World 2007:

Furthermore, the compound attrition rate is negatively affected by the inability to predict toxicity and efficacy in humans. These shortcomings are in turn caused by the use of experimental pre-clinical model systems that have a limited human clinical relevance . . . (Björquist and Sartipy 2007)

Usha Sankar in The Scientist 2005:

The typical compound entering a Phase I clinical trial has been through roughly a decade of rigorous pre-clinical testing, but still only has an 8% chance of reaching the market. Some of this high attrition rate is due to toxicity that shows up only in late-stage clinical trials, or worse, after a drug is approved. Part of the problem is that the toxicity is assessed in the later stages of drug development, after large numbers of compounds have been screened for activity and solubility, and the best produced in sufficient quantities for animal studies. Traditionally, compounds are tested in two animal species – typically, the rat and the dog. But the process is far from ideal. Animal studies can be time-consuming, require large quantities of product, and still fail to predict a safety problem that can ultimately halt development . . . Rats and humans are 90% identical at the genetic level, notes Howard Jacob, cofounder of Wauwatosa, Wisconsin-based PhysioGenix. However, the majority of the drugs shown to be safe in animals end up failing in clinical trials. “There is only 10% predictive power, since 90% of drugs fail in the human trials” in the traditional toxicology tests involving rats, says Jacob. (Sankar 2005)

Speaking of toxicity trials for new drugs in humans, an unnamed clinician quoted in Science stated, “If you were to look in [a big company’s] files for testing small-molecule drugs you’d find hundreds of deaths (Marshall 2000).” So much for animal testing to protect those undergoing clinical trials.

Chapman 2011: “. . . but other incidents of harm [besides TGN1412], even death, to participants in Phase I trials, some then known and other unpublicized, had taken place” (Chapman 2011)

Handbook of Laboratory Animal Science Volume II Animal Models 1994:

It is impossible to give reliable general rules for the validity of extrapolation from one species to another. This…can often only be verified after the first trials in the target species (humans)…Extrapolation from animal models…will always remain a matter of hindsight…. [(Salén 1994) p6]

O’Collins et al., 2006 published a review article that revealed that of 1,026 putative neuroprotectants studied, the drugs that went to clinical trials were not more efficacious in animal studies then the ones passed over. (O’Collins et al. 2006)


Björquist, Petter, and Peter Sartipy. 2007. Raimund Strehl and Johan Hyllner. Human ES cell derived functional cells as tools in drug discovery. Drug Discovery World (Winter):17-24.

Chabner, B. A., and T. G. Roberts, Jr. 2005. Timeline: Chemotherapy and the war on cancer. Nat Rev Cancer 5 (1):65-72.

Chapman, Audrey R. 2011. Addressing the Ethical Challenges of First-in-Human Trials. J Clinic Res Bioeth 2 (4):113.

Cimons, Marlene, Josh Getlin, and Thomas H. Maugh_II. 2010. Cancer Drugs Face Long Road From Mice to Men  1998 [cited Nov 8 2010]. Available from

DiMasi, J. A., R. W. Hansen, and H. G. Grabowski. 2003. The price of innovation: new estimates of drug development costs. J Health Econ 22 (2):151-85.

Editors. 2011. In this issue. Nat Rev Drug Discov 10 (4):239-239.

Ellis, L. M., and I. J. Fidler. 2010. Finding the tumor copycat. Therapy fails, patients don’t. Nat Med 16 (9):974-5.

FDA. 2010. FDA Issues Advice to Make Earliest Stages Of Clinical Drug Development More Efficient. FDA, June 18, 2009 2006 [cited March 7 2010]. Available from

Gura, T. 1997. Cancer Models: Systems for identifying new drugs are often faulty. Science 278 (5340):1041-2.

Horrobin, D. F. 2003. Modern biomedical research: an internally self-consistent universe with little contact with medical reality? Nat Rev Drug Discov 2 (2):151-4.

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

Leaf, C. 2004. Why we are losing the war on cancer. Fortune (March 9):77-92.

Marshall, E. 2000. Gene therapy on trial. Science 288 (5468):951-7.

O’Collins, V. E., M. R. Macleod, G. A. Donnan, L. L. Horky, B. H. van der Worp, and D. W. Howells. 2006. 1,026 experimental treatments in acute stroke. Ann Neurol 59 (3):467-77.

Reuters. 2011. U.S. to develop chip that tests if a drug is toxic. Reuters, September 16 2011 [cited October 6 2011]. Available from – .To5AMnPaixF.

Salén, Jörn C W. 1994. Animal Models—Principles and Problems. In Handbook of Laboratory Science Volume II. Animal Models. 1st edition., edited by P. Svendsen and J. Hau. Boca Raton: CRC Press.

Sankar, U. 2005. The Delicate Toxicity Balance in Drug Discovery. The Scientist 19 (15):32.

Sarkar, Susanta K. 2009. Molecular imaging approaches. Drug Discovery World (Fall):33-38.

Sharp, Phillip A., and Robert Langer. 2011. Promoting Convergence in Biomedical Science. Science 333 (6042):527.

Van Dyke, T. 2010. Finding the tumor copycat: approximating a human cancer. Nat Med 16 (9):976-7.

Zielinska, Edyta. 2010. Building a better mouse. The Scientist 24 (4):34-38.