Another Effort by Animal Modelers to Avoid a Peer-Reviewed Debate

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On, 14 March 2019, Chris Magee of Understanding Animal Research posted an article titled: Ricky Gervais doesn't understand animal research. In it, Magee stated:

'The ability of the model to find a ‘true negative’ reaction is for dogs 94%, mice 95.6%, rats 97.9% and rabbits 99.2%. However, certain species make good or poor models for different types of compound and it was discovered long ago that if something was toxic in a rodent and non-rodent model then it would almost certainly be toxic in a human. This is why regulatory authorities require testing in two species before clinical trials can start.'

As the above is stated without evidence it can be dismissed without evidence. That having been said, around 90% of drugs that pass animal tests fail in clinical trials for the reasons of efficacy and toxicity, the very reason animal models are used in testing. (1-12) Furthermore, many drugs do make it through animal testing as described above only to make it to market and seriously harm humans. (13-18) University of Utah press release discusses (17): “Because of undetected toxicity problems, about a third of prescription drugs approved in the U.S. are withdrawn from the market or require added warning labels limiting their use.” (16) So not all toxicities are seen during clinical trials when 90% of all drugs fail. Therefore, the ultimate failure rate of drug that have passed animal tests and that were developed in animal models is greater than 90%.

Magee continues:

The second failure is the idea that drugs would then be dangerous in humans. Animals are used in testing to determine if a compound is safe enough to be given to human volunteers – no drug advances from the animal lab straight onto the shelves at the chemist. The animal test is not generally for determining whether it will be effective, just that it’s safe. Three rounds of testing in humans take place before the so-called ‘stage 4’ where the drug is rolled out to the wider human population.

This is controversial in drug development with some animal modelers claiming toxicity is performed simply to ensure safety in Phase I-III human clinical trials and others stating it is to ensure safety of humans in general. Regardless, the FDA is on record stating that 9 out of 10 new medicines fail to pass human/clinical trials because animal tests cannot predict outcomes in humans. [4]

The position that toxicity testing is only designed to protect people participating in first-in-man (FIM) trials is then followed by the claim that since so few people are injured during these trials animal models of toxicity must be effective. There are many problems with this logic. First, most of the time results from FIM trials are not released to the general public. The results are proprietary and Pharma does not want them released and, under current law, they have the right to not release them. Therefore, meta-analyses and systematic reviews that summarize the current scientific literature are of questionable value as it is the unpublished reports that would contain the damning evidence. So, Magee’s claim is an example of the fallacy of insufficient statistics and the animal modelers are using it to make a claim that is an example of the argument from ignorance.

Second, there are examples of people being harmed from FIM trials. 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.” (19) This brings us back to burden of proof. It is naïve or disingenuous to think the burden of proof for assessing FIM trials is on the public without all the data. If animal modelers want us to think that FIM are remarkably safe then show us all the data. As society does not have access to all the data, quotes like the above in Science are damning. And there have been disasters that went public.

Dresser 2009:

The traditional dose-escalation regimen is a cautious approach, designed to minimize risks to FIH trial participants. Risks are inevitable, however. A good assessment of the risks comes from a review of National Cancer Institute (NCI) phase I trials conducted from 1991-2002. Reviewers found that 15 percent of subjects in phase I trials of single chemotherapy agents experienced serious but nonfatal toxic events.95 Reviewers also found a toxicity-related death rate of .49 percent for FIH trials involving a single chemotherapy agent.96

95. Horstmann E, et al. Risks and Benefits of Phase I Oncology Trials, 1991 through 2002. New England Journal of Medicine 2005;352(9):895–904. [PubMed: 15745980]

96. Id. For another evaluation of risks and benefits in phase I oncology trials, see Roberts TG, et al. Trends in the Risks and Benefits to Patients with Cancer Participating in Phase I Clinical Trials. JAMA 292 (17)2004;:2130–2140.2140 [PubMed: 15523074] (20)

Chapman 2011:

A major factor complicating risk analysis in FIH trials is the difficulty of making accurate predictions from preclinical laboratory research on human tissues and animal studies of the likely effect of the investigational agent on humans. According to Rebecca Dresser, risk analysis based on preclinical research can fall short in three ways. It may fail to predict human risks, leading to adverse effects in human trials – one example being the TGN1412 trial. It may predict clinical benefits that then fail to materialize for human subjects. And it may predict nonexistent risks in humans with the result that a potentially useful agent is discarded. (Dresser 2009)

Extrapolating from laboratory and animal studies is a complex process under all circumstances, but even more so in proposed FIH trials which usually lack data from comparator studies in humans to help guide the analysis. Although an effort is usually made to choose species based on their similarities to the human biological response under study, there may not be appropriate animal models that accurately replicate the human disease. Moreover, there are significant differences between human and animal physiology. Given the limitations of animal models of many diseases and differences between human and animal physiology, toxicological studies in animals may be poor at predicting toxicity in humans. (Lavery 2011) For similar reasons, the ability to show proof-of-principle in preclinical research, whether in the in vitro or the animal studies, does not provide a therapeutic warrant for humans . . . (Anderson and Kimmelman 2010)

To lower risk in Phase I trials, subjects usually receive a low dose of the investigational agent, and if that level of exposure appears safe, then the dose is gradually increased with each cohort until investigators determine the maximum tolerated dose. This approach, however, is not risk-free, especially when the trials recruit seriously ill patients.

A review of all non-pediatric Phase I trials conducted by the National Cancer Institute between 1991 and 2002, a total of 460 trials involving 11,935 participants, about one quarter of which were FIH trials, found that 15 percent of subjects in trials of single chemotherapy agents experienced serious but nonfatal toxic events. There were 58 deaths (a death rate of .49 percent) that were determined to be at least possibly related to the treatment. The toxicity related death rate in these trials was 0.26 percent. Rates of response and toxicity varied among the various types of Phase 1 oncology trials but the data for FIH trials were not separately computed.(Horstmann et al. 2005) It is difficult to know whether these data can be generalized to other types of trials. (21)

But if the reason for animal testing is to keep volunteers safe in clinical trials, then they are failing as the two main reasons drugs fail in clinical trials are efficacy and safety. (22-31) Moreover, severe side effects are not detected in clinical trials due to the limited nature of Phase II and III clinical trials due to the fact that they are so expensive. (32, 33) This leads to drugs being released to the market and causing severe harm in patients.

Magee continues:

It’s illegal to use an animal if there’s an alternative,

This law is routinely ignored in reality as 1) regulatory agencies are comfortable with the status quo of using animals and 2) if they acknowledged how nonpredictive animal models are they would have to further acknowledge that using a magic 8-ball would do as good of a job. See Here's Why Vanda Pharmaceuticals Is Sinking Today.

Magee continues:

Testing on animals was mandated as compulsory by the Nuremburg Code after the Nazis, who were anti-vivisection, tested on humans instead.

I have refuted this many times. See The Nuremberg Code subverts human health and safety by requiring animal modeling.

Magee continues:

But it is also illegal not to test a potential new medicine using animals before it is given to human beings. It’s the law because it works to keep us safe and has done so for more than 50 years.

No, actually it hasn’t. The scientific literature is replete with scientists saying animal modeling does not work in terms of safety and efficacy. (19, 22-28, 30, 33-62)

Magee is right when he says it takes more words to correct errors than it took the person who wrote the errors: “it’s just so easy to throw out an ignorant comment and it somehow falls to the person with the facts to set the record straight.”

Magee continues:

Biology has been under assault from the same detractors for years – Young-Earth creationists, anti-vaxxers and anti-vivisectionists are not new, nor have their tactics changed over the past 150 years. There are always the false claims about efficacy, as if researchers would deliberately sabotage their own careers doing experiments that wouldn’t work. There’s the ‘follow the money’ argument that posits that the world’s regulators, academics, learned societies, industry bigwigs and all professional toxicologists have been duped by rat breeders wishing to sell them rats. There’s the false danger argument – did you know vaccines have mercury in them?!

This is a classic example of the fallacy of poisoning the well.

Magee continues:

Mr Gervais seemed to grasp this point during his now-ironically titled S.C.I.E.N.C.E. tour, but beliefs don’t trump facts. If you’re too busy for facts, try a shortcut and ask ‘What does every serious scientific institution in the world think?’ What does the Royal Society think? What does the FDA think?’ To be on the wrong side of this argument is literally to argue against cancer treatments, insulin and whatever’s coming next. This is far too important a moral question be this intellectually lazy about.

This uses the fallacies of appeal to fear and argument from authority. Every institute that profits from animal modeling, or has a different kind of vested interest, supports animal modeling. What a shock! And when Magee asks 'what does the FDA think?', remember the FDA acknowledges that 90% of new medicines do not reach human patients because animals cannot predict outcomes in humans. (4)

The empirical evidence overwhelmingly favors the position that animal modeling has no predictive value for human response to drugs and disease. The studies that claim otherwise are cherry-picked or misrepresented. But what really closes the case against animal modeling is Theory. Just as Darwin’s Theory of Evolution explained why creation was not necessary and Einstein’s Relativity Theory proved Newton’s physics was not completely accurate, so Trans-Species Modeling Theory (TSMT) explains why animals will never be of predictive value for human response to drugs and disease. (63) This is what animal modelers want to avoid debating in a fashion described below. Most members of the scientific community do not have a broad enough science background to understand TSMT and it is unreasonable to ask the general public to understand it. But, experts in the appropriate fields can and will understand it and this will end animal modeling as used for predictive models for humans.

Using fallacies, ignoring science, and relying on peoples’ cognitive biases have worked well for the animal model community: their scientifically unviable paradigm persists. As long as the animal model community can continue to use these tactics to mislead the unsuspecting and scientifically illiterate public, they will continue to make money from animal modeling. What is needed is a peer-reviewed debate as described in: Greek, Ray, and Lisa Kramer. 2019. "How to Evaluate the Science of Non-human Animal Use in Biomedical Research and Testing: A Proposed Format for Debate." In Animal Experimentation: Working Towards a Paradigm Change. Human-Animal Studies. Volume 22., edited by Kathrin Herrmann and Kimberley Jayne, 65-87. Boston: Brill.

FLOE in the UK has proposed this in Parliamentary EDM 66. If the animal model community is so confident that animals are necessary for medical science to advance, they should jump at the opportunity to have their position judged by experts in the various scientific fields upon which animal modeling is based on. But the animal modelers are not doing so.



1. Analysis. Counting the cost of failure in drug development: Pharmaceutical Technology; 2017 [updated June 19, 2017. Available from:

2. Bains W. Failure rates in drug discovery and development: will we ever get any better? Drug Discovery World. 2004(Fall):9-18.

3. BIO, Biomedtracker, Amplion. Clinical Development Success Rates 2006-2015: Biotechnology Innovation Organization; 2016 [updated 2016. Available from:,%20Biomedtracker,%20Amplion%202016.pdf.

4. FDA News. FDA Issues Advice to Make Earliest Stages Of Clinical Drug Development More Efficient: USFDA; 2006 [Available from:

5. Herrmann M. When products fail. Nat Biotechnol. 2001;19 Suppl:BE37-8.

6. Insel T. Post by Former NIMH Director Thomas Insel: Experimental Medicine: NIH/NIMH; 2012 [updated June 12, 2012. Available from:

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

8. Lowe D. Are Things Getting Any Better in the Clinic? : Science Translational Medicine; 2016 [updated June 2, 2016. Available from:

9. Lowe D. A New Look at Clinical Success Rates: Science Translational Medicine; 2018 [updated February 2, 2018. Available from:

10. Mohs RC, Greig NH. Drug discovery and development: Role of basic biological research. Alzheimer's & Dementia: Translational Research & Clinical Interventions. 2017;3(4):651-7.

11. Smietana K, Siatkowski M, Moller M. Trends in clinical success rates. Nat Rev Drug Discov. 2016;15(6):379-80.

12. Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2018:1-14.

13. General Accounting Office. FDA Drug Review: Postapproval Risks 1976-1985. . In: FDA, editor. Washington DC: GAO; 1990.

14. Editorial. A stronger role for science. Nat Rev Drug Discov. 2011;10(3):159-.

15. Park BK, Boobis A, Clarke S, Goldring CEP, Jones D, Kenna JG, et al. Managing the challenge of chemically reactive metabolites in drug development. Nat Rev Drug Discov. 2011;10(4):292-306.

16. The University of Utah. ‘Darwinian’ test uncovers an antidepressant’s hidden toxicity: University of Utah; 2014 [updated December 15, 2014. Available from:

17. Gaukler SM, Ruff JS, Galland T, Kandaris KA, Underwood TK, Liu NM, et al. Low-dose paroxetine exposure causes lifetime declines in male mouse body weight, reproduction and competitive ability as measured by the novel organismal performance assay. Neurotoxicol Teratol. 2015;47:46-53.

18. Downing NS, Shah ND, Aminawung JA, et al. Postmarket safety events among novel therapeutics approved by the us food and drug administration between 2001 and 2010. JAMA. 2017;317(18):1854-63.

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

20. Dresser R. First-in-human trial participants: not a vulnerable population, but vulnerable nonetheless. J Law Med Ethics. 2009;37(1):38-50.

21. Chapman AR. Addressing the Ethical Challenges of First-in-Human Trials. J Clinic Res Bioeth. 2011;2(4):113.

22. Young M. Prediction v Attrition Drug Discovery World. 2008(Fall):9-12.

23. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov. 2010;9(3):203-14.

24. Arrowsmith J. Trial watch: Phase III and submission failures: 2008 - 2010. Nat Rev Drug Discov. 2011;10(2):87-.

25. Arrowsmith J. Trial watch: Phase II failures: 2008-2010. Nat Rev Drug Discov. 2011;10(5):328-9.

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

27. Khanna I. Drug discovery in pharmaceutical industry: productivity challenges and trends. Drug Discovery Today. 2012;17(19-20):1088-102.

28. Morgan P, Graaf PHVD, Arrowsmith J, Feltner DE, Drummond KS, Wegner CD, et al. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Discovery Today. 2012;17(9/10):419-24.

29. Allison M. Reinventing clinical trials. Nat Biotech. 2012;30(1):41-9.

30. Shaffer C. Safety Through Sequencing: Drug Discovery & Development 2012 [updated January 1, 2012. Available from:

31. Harrison RK. Phase II and phase III failures: 2013–2015. Nature Reviews Drug Discovery. 2016;15:817.

32. Brigham and Women's Hospital. Nearly one in three drugs found to have safety concerns after FDA approval: ScienceDaily; 2017 [updated May 9, 2017. Available from:

33. van Meer PJK, Kooijman M, Gispen-de Wied CC, Moors EHM, Schellekens H. The ability of animal studies to detect serious post marketing adverse events is limited. Regulatory Toxicology and Pharmacology. 2012;64(3):345-9.

34. Litchfield JT, Jr. Symposium on clinical drug evaluation and human pharmacology. XVI. Evaluation of the safety of new drugs by means of tests in animals. Clin Pharmacol Ther. 1962;3:665-72.

35. Fletcher AP. Drug safety tests and subsequent clinical experience. J R Soc Med. 1978;71(9):693-6.

36. Heywood R. Target organ toxicity. Toxicol Lett. 1981;8(6):349-58.

37. Manson JM. Biological Considerations for Risk Assessment in Developmental Toxicology. In: McLachlan JA, Pratt RM, Markert CL, editors. Developmental Toxicology: Mechanisms and Risks Banbury Report 26: Cold Springs Harbor Laboratory; 1987. p. 307-22.

38. Sietsema WK. The absolute oral bioavailability of selected drugs. Int J Clin Pharmacol Ther Toxicol. 1989;27(4):179-211.

39. Eason CT, Bonner FW, Parke DV. The importance of pharmacokinetic and receptor studies in drug safety evaluation. Regul Toxicol Pharmacol. 1990;11(3):288-307.

40. Igarashi T. The duration of toxicity studies required to support repeated dosing in clinical investigation—A toxicologists opinion. In: C Parkinson NM, C Lumley, SR Walker, editor. CMR Workshop: The Timing of Toxicological Studies to Support Clinical Trials. Boston/UK: Kluwer; 1994. p. 67-74.

41. Igarashi T, Nakane S, Kitagawa T. Predictability of clinical adverse reactions of drugs by general pharmacology studies. J Toxicol Sci. 1995;20(2):77-92.

42. Lin JH. Species similarities and differences in pharmacokinetics. Drug Metab Dispos. 1995;23(10):1008-21.

43. Igarashi T, Yabe T, Noda K. Study design and statistical analysis of toxicokinetics: a report of JPMA investigation of case studies. J Toxicol Sci. 1996;21(5):497-504.

44. Li AP. Accurate prediction of human drug toxicity: a major challenge in drug development. Chem Biol Interact. 2004;150(1):3-7.

45. Dixit R, Boelsterli U. Healthy animals and animal models of human disease(s) in safety assessment of human pharmaceuticals, including therapeutic antibodies. Drug Discovery Today. 2007;12(7-8):336-42.

46. Spanhaak S, Cook D, Barnes J, Reynolds J. Species Concordance for Liver Injury. From the Safety Intelligence Program Board: BioWisdom Ltd; 2008 [updated July. Report]. Available from:

47. Geerts H. Of mice and men: bridging the translational disconnect in CNS drug discovery. CNS Drugs. 2009;23(11):915-26.

48. Collins FS. Reengineering Translational Science: The Time Is Right. Science Translational Medicine. 2011;3(90):90cm17.

49. Giri S, Bader A. Foundation review: Improved preclinical safety assessment using micro-BAL devices: the potential impact on human discovery and drug attrition. Drug Discovery Today. 2011;16(9/10):382-97.

50. Holmes AM, Solari R, Holgate ST. Animal models of asthma: value, limitations and opportunities for alternative approaches. Drug Discovery Today. 2011;16(15/16):659-70.

51. Turner M. Call to curb lab tests on dogs. Nature. 2011;474(7353):551.

52. Lowdell MW, Birchall M, Thrasher AJ. Use of compassionate-case ATMP in preclinical data for clinical trial applications. The Lancet. 2012;379(9834):2341.

53. Opar A. Overtaking the DILI Model-T. Nat Rev Drug Discov. 2012;11(8):585-6.

54. Bailey J, Thew M, Balls M. An Analysis of the Use of Dogs in Predicting Human Toxicology and Drug Safety. ATLA. 2013;41:335-50.

55. Bailey J, Thew M, Balls M. An Analysis of the Use of Animal Models in Predicting Human Toxicology and Drug Safety. ATLA. 2014;42(3):181-99.

56. Duyk G. Attrition and translation. Science. 2003;302(5645):603-5.

57. FDA. Innovation or Stagnation? Challenge and Opportunity on the Critical Path to New Medical Products: FDA; 2004 [updated 2004. Available from:

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

59. Lesko LJ, Woodcock J. Translation of pharmacogenomics and pharmacogenetics: a regulatory perspective. Nat Rev Drug Discov. 2004;3(9):763-9.

60. Mankoff SP, Brander C, Ferrone S, Marincola FM. Lost in Translation: Obstacles to Translational Medicine. J Transl Med. 2004;2(1):14.

61. McGee P. Breeding Better Animal Models. Drug Discovery & Development. 2006(April):18-23.

62. Schreiber SL, Shamji AF, Clemons PA, Hon C, Koehler AN, Munoz B, et al. Towards patient-based cancer therapeutics. Nat Biotech. 2010;28(9):904-6.

63. Greek R, Hansen LA. 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;113(2):231-253.