Traditionally, basic biomedical researchers were allowed to engage in their own intellectual pursuits both to advance knowledge for knowledge sake and in order to identify new targets for drugs in order to treat diseases. This was based, in large part, on the fact that such basic research worked out well for physics and chemistry. Many important advances in science were the result of basic research in the physical sciences and many of these discoveries led to new technologies that have benefitted humankind. Moreover, important discoveries about life in general have come about secondary to basic research in the life sciences. Occasionally these discoveries have also led to technologies or interventions that have benefitted humankind. But this model has broken down in the past few decades for two reasons. 1. Advances in the life sciences do not necessarily translate to advances in treatments for patients. 2. Basic research in biomedical science has transitioned from real basic research, to research masquerading as applied research using animal models. One needs look no further than drug development to see that such is the case.
Morgan et al of Pfizer (Morgan et al. 2012) reviewed the performance of 44 drugs from Pfizer for attrition in Phase II trials. Only 32% passed proof of concept (POC) in Phase II. They found that a majority of these drugs failed due to lack of efficacy. This is consistent with the findings of others.(Kola and Landis 2004; Arrowsmith 2011b; Paul et al. 2010) Efficacy involves drug targets and more and more targets are selected based on animal models. Time and again we are seing that what a drug does to a target in animal models is not the same as what it does to a human version of the target. This should not be surprising in light of complexity science. Morgan et al also concluded that the survival of new chemical entities (NMEs) was at its lowest during Phase II, “with small- and large molecule survival of 38% and 53%, respectively,” which was also consistent with the data from others.(DiMasi et al. 2010). It is also consistent with a report from the Centre for Medicines Research that included 16 companies that represented ~60% of global R&D. The report discovered that Phase II success rates for NMEs were ~28% from 2006–2007 but were only 18% from 2008–2009. (Arrowsmith 2011a). Of the drugs that passed POC, all had been tested in humans and “the pharmacological target was modulated as expected to elicit an effect.”(Morgan et al. 2012) In other words, human-based development worked and animal-based development did not.
One reason animal models fail is that the scientific approach known as reductionism does not work well for complex systems when searching for new drugs. Kevin Mullane of Profectus Pharma Consulting and Michael Williams of the Department of Molecular Pharmacology and Biological Chemistry, Feinberg School of Medicine, Northwestern University have written an article published in Drug Discovery Today.(Mullane and Williams 2012) The article acknowledges that advances have been made by using reductionism to study human disease and develop drugs to treat those diseases, just as I acknowledge this. However the main theme of the article is that Pharma and society are facing crisis in terms of developing new drugs that are safe, effective, and inexpensive. They cite the decrease in number of new chemical entities entering the market and late failure of many drugs in development, for example a success rate of only 5% for drugs that enter clinical trials (B. H. Munos and Chin 2011)and an 82% failure of drugs in Phase II proof of concept trials (Arrowsmith 2011a), to illustrate the problem.(B. Munos 2009; Pammolli et al. 2011) They note that this is ironic since the total investment in biomedical research in the US reached $150 billion in 2010 (B. H. Munos and Chin 2011) and the amount of knowledge regarding knowledge of life has also increased substantially in the recent past. Mullane and Williams state: “The difficulties in predicting drug efficacy from preclinical models have been of concern for more than two decades . . . Thus, novel findings apparently related to the systems and targets involved in disease causality; the delineation of the efficacy, selectivity and safety of NCEs; and the predictive relevance of biomarkers and animal model data to the human disease state, even when there is evidence for target engagement in humans, all frequently fail to enhance the success rate for new drug applications (NDAs).”
They then question the notion that advances in biomedical science equate to potential advances in drug development. I too have discussed this notion that more knowledge of the material universe ipso facto means more knowledge that is useful for patients (see here, here and here). They then discuss the very concepts I have been discussing in an attempt to understand why Pharma is in crisis:
As scientists active in TM [translational medicine] focus on defining appropriate criteria to improve decision-making and success at the preclinical and/or clinical interface, many of the basic paradigms in biomedical science that are key to these activities are being compromised. There is a failure to acknowledge the complexity of biology (Horrobin 2003; Walker 2011)to avoid confusing the simplistic, reductionist linearity of current approaches to biomedical research (Hogenesch and Ueda 2011; Kohl and Noble 2009). Added to this are significant concerns that the US research enterprise is now in crisis mode, coupled with a perception that the quality of scientific research has become ‘low’, approaching mediocrity (http://pipeline.corante.com/archives/2010/06/24/all_those_worthless_pape...) with ‘any paper, however bad, . . .[get-]. . .published’ owing to the pressures on peer review (http://www.guardian.co.uk/science/2011/sep/05/publish-perish-peerreview-...). The venture capitalist Bruce Booth has commented (http://lifescivc.com/2011/03/academic-biasbiotech-failures/) that at least 50% of published studies from academic laboratories could not be repeated in an industrial setting. The prestige of the investigator or the journal did not appear to impact these numbers. An analysis (Prinz et al. 2011) by Bayer of their internal efforts to replicate published new drug target data indicated that 65% could not be reproduced to such an extent that projects had to be abandoned. A similar analysis of company-driven research programs and their reproducibility by independent third parties has not been performed, but it may not differ substantially since concerns of translation and robustness of data highlight several broad issues related to data generation, relevance, quality and transparency. These include:
(i) An over-reliance on animal models of diseases that are poorly validated in the manner they are applied. Such models are ‘validated’ either because they provide a phenotypic behavior in response to a ‘gold standard’ drug, or they represent some pathophysiological phenomenon thought to be associated with the human disease state. In the former situation, the models can only be relied upon with any assurance to identify NCEs with the same mechanism of action, whereas the latter often represents an oversimplification of the disease, where absolute belief in a mechanism often trumps any contrary data, however robust the latter. Examples of this include the T helper 2 (Th2)/eosinophil model of asthma, the Non-obese diabetic (NOD) mouse in diabetes and the various animal models of stroke, that together, have led to over a 1000 failed compounds in the clinic. Difficulties in interpreting results from animal models are far from new. However, they remain a key part of hypothesis testing provided that newer data are integrated hierarchically and taken in context with other datasets to inform broadly the validity of the hypothesis being tested.
(ii) The intrinsic reductionism of molecular biology, where engineered cell lines bearing little resemblance to native systems (or the human species) are used to define disease pathophysiology . . . (Mullane and Williams 2012)
If researchers want to use animals to study the basic properties of life with no human treatments in mind, then they should be honest and tell society what they are doing. Society can then decide whether to fund such research or not. But lying about your research is immoral and lying about it in order to obtain taxpayer money is fraud. Furthermore, lying about it when the results that disprove your claims are available for all to see is a sign of either colossal arrogance or stupidity.
For more on this topic see our article: Is the use of sentient animals in basic research justifiable?
Arrowsmith, J. (2011a). Trial watch: Phase II failures: 2008-2010. [10.1038/nrd3439]. Nat Rev Drug Discov, 10(5), 328-329.
Arrowsmith, J. (2011b). Trial watch: Phase III and submission failures: 2008-2010. [10.1038/nrd3375]. Nat Rev Drug Discov, 10(2), 87-87.
DiMasi, J. A., Feldman, L., Seckler, A., & Wilson, A. (2010). Trends in risks associated with new drug development: success rates for investigational drugs. [Comparative Study
Research Support, Non-U.S. Gov't
Review]. Clinical Pharmacology and Therapeutics, 87(3), 272-277, doi:10.1038/clpt.2009.295.
Hogenesch, J. B., & Ueda, H. R. (2011). Understanding systems-level properties: timely stories from the study of clocks. [Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review]. Nature Reviews. Genetics, 12(6), 407-416, doi:10.1038/nrg2972.
Horrobin, D. F. (2003). Modern biomedical research: an internally self-consistent universe with little contact with medical reality? Nat Rev Drug Discov, 2(2), 151-154, doi:10.1038/nrd1012
Kohl, P., & Noble, D. (2009). Systems biology and the virtual physiological human. [Editorial
Research Support, Non-U.S. Gov't]. Molecular systems biology, 5, 292, doi:10.1038/msb.2009.51.
Kola, I., & Landis, J. (2004). Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov, 3(8), 711-715, doi:10.1038/nrd1470
Morgan, P., Graaf, P. H. V. D., Arrowsmith, J., Feltner, D. E., Drummond, K. S., Wegner, C. D., et al. (2012). Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Discovery Today, 17(9/10), 419-424.
Mullane, K., & Williams, M. (2012). Translational semantics and infrastructure: another search for the emperor’s new clothes? Drug Discovery Today, 17(9/10), 459-468.
Munos, B. (2009). Lessons from 60 years of pharmaceutical innovation. [Historical Article]. Nature reviews. Drug discovery, 8(12), 959-968, doi:10.1038/nrd2961.
Munos, B. H., & Chin, W. W. (2011). How to revive breakthrough innovation in the pharmaceutical industry. Science Translational Medicine, 3(89), 89cm16, doi:10.1126/scitranslmed.3002273.
Pammolli, F., Magazzini, L., & Riccaboni, M. (2011). The productivity crisis in pharmaceutical R&D. [10.1038/nrd3405]. Nat Rev Drug Discov, 10(6), 428-438.
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., et al. (2010). How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov, 9(3), 203-214, doi:nrd3078 [pii]
Prinz, F., Schlange, T., & Asadullah, K. (2011). Believe it or not: how much can we rely on published data on potential drug targets? [Comment
Letter]. Nature reviews. Drug discovery, 10(9), 712, doi:10.1038/nrd3439-c1.
Walker, M. J. (2011). The major impacts of James Black's drug discoveries on medicine and pharmacology. [Biography
Historical Article]. Trends in Pharmacological Sciences, 32(4), 183-188, doi:10.1016/j.tips.2011.02.001.