Animal Rights

Predicting Responses in Complex Systems. Part I

| by Dr Ray Greek

The foundation for my position that animal models cannot be predictive modalities for human response to drugs and disease is the fact that animals and humans are evolved complex systems. Therefore, when vivisection activists attempt to address the science of my position at all, they attempt to attack these facts. These retorts take various forms and I will address some of them in these two essays.

Retort #1. “Ray Greek uses the word predict in a way that is inconsistent with science.”

The prediction argument is the one that nonscientists find easiest to understand, both in terms of what it explicitly states and what it implies. Scientists and nonscientists have acknowledged that society supports using animals in research if and only if such use leads to safer drugs and better medical interventions. By that, society means that if a drug is tested on a rat and kills the rat, then the drug should not be given to humans because there is a high likelihood that the drug will also kill humans. This is so straightforward I can offer little to expand on the notion. Indeed, this is the basis for the requirements by the FDA and EPA that new drugs and other chemicals be tested on animals. In attempting to side-step this fact, vivisection activists commit the fallacy of equivocation and use the word predict in a different context. This is secondary to the fact that they cannot produce data in support of the position that society demands—that animal models can be used as predictive modalities for drugs testing and disease response.

The word predict and its various forms (prediction, predictive etc) can be used in essentially two ways in science. I. Scientists using the hypothetico-deductive method formulate a hypothesis in order to explain a phenomenon. This hypothesis suggests that various outcomes should be observed, and other outcomes absent, if the hypothesis is true. In other words, the hypothesis predicts that certain things will happen if the scientists that thought of the hypothesis are correct. Thus, the hypothesis contains predictions about future events, or events that occurred in the past but can be discovered in the future, and these predictions can be tested and either confirmed or falsified. If confirmed, then the hypothesis is stronger, more likely to be true, but if the predictions are falsified then the entire hypothesis may be falsified as well. The statement: “Hypotheses generate predictions,” is a true statement in science.

II. The second use of the word predict is in the context of a test or practice as opposed to a hypothesis. When scientists, physicians, or nonscientists for that matter, use a test or practice in an attempt to discover reality, this modality can be tested in order to determine how well it actually works. For example, if you are involved in a car accident and are taken to the ER complaining of shortness of breath, the physician will order an x-ray of your chest to make sure you do not have a pneumothorax (a collapsed lung). If you do have a pneumo, the physician might need to place a tube in your chest, a procedure that is not without risk. So you want the chest x-ray to have a very high predictive value for diagnosing pneumothorax, lest you have a chest tube placed inappropriately. You want the chest x-ray to be a predictive modality for diagnosing collapsed lungs. You are not interested in the ER physician generating a hypothesis, you want a definitive, or near definitive, way of determining what is wrong with you. This is one difference between applied and basic science.

Duncan Watts, PhD mathematics, authored the book Everything is Obvious*: Once You Know The Answer. An essay titled: “The Dream of Prediction: Why You Should Be Skeptical,” was taken from the book and published by Watts on Yahoo. Watts addresses the above, saying:

Human beings love to make predictions, whether about the movements of the stars, the gyrations of the stock market, or the upcoming season's hot color. Pick up the newspaper or browse through headlines on any given day, and you'll immediately encounter a mass of predictions -- so many, in fact, that you probably don't even notice them. What's a little puzzling about all these predictions, though, is that no one seems to know how accurate they are. Sure, when pundits get something right, they're usually happy to take credit for it ("I predicted the financial crisis," "I predicted Facebook would eclipse MySpace," etc.). But what if they were forced to write down every prediction they ever made and were then held to account for the ones they got wrong as well?  How accurate would they be?

As I have written numerous times, the way one judges how well a modality, be it a practice or test, functions at predicting outcomes (“how accurate would they be”) is by using the binomial classification table and formulas. This allows the practice or test to be evaluated for how well it predicts a positive outcome when the outcome really is positive (the x-ray reveals a collapsed lung when the patient really has a collapsed lung) along with how well it predicts a negative outcome when the outcome really is negative (the x-ray reveals no collapsed lung and in reality there is no collapsed lung). These values are called positive predictive value (PPV) and negative predictive value (NPV) and are taught to every medical student as well as students in college classes such as statistics. This is not only not controversial, it is standard practice for determining the value of everything from a blood test for cancer to how well drug sniffing dogs are at finding drug smugglers. Anytime a modality is used to approximate reality, and the reality is eventually known, PPV and NPV can be used to evaluate how well the modality performs. Chest x-rays and CT scans of the chest perform very well for diagnosing a pneumothorax while Ouija boards and Palm Readers perform very poorly for telling you what the future holds in store for you.

So . . . why would anyone conflate predictions that come from hypotheses with the predictive value (PPV and NPV) of a modality? As I said, society thinks there is an ethical cost to animal experimentation. (I happen to agree but this is not about my position it is about the position of society in general.) So society has said animal experimentation should be allowed under certain conditions but not others. The following support this. An editorial in Nature 2009: “Animal-research policies need to be guided by a moral compass—a concensus of what people find acceptable and unacceptable.” [1] Giles, writing in Nature:

In the contentious world of animal research, one question surfaces time and again: how useful are animal experiments as a way to prepare for trials of medical treatments in humans? The issue is crucial, as public opinion is behind animal research only if it helps develop better drugs. Consequently, scientists defending animal experiments insist they are essential for safe clinical trials, whereas animal-rights activists vehemently maintain that they are useless. [2]

As many uses of numerous animal species in basic research do not meet the above criteria, basic researchers need to persuade society that what they do really will lead to safe and effective drugs and other treatments hence the conflation. No conflation, no more grant money.

So . . . I do use the word predict in manner that is consistent with science and I differentiate between the two uses. I do not conflate, like the vivisection activists do.

Retort #2. “No one claims animal models are used as predictive models.”

In order to justify the use of animals in research in general, scientists routinely claim, explicitly or implicitly, that the animal model, in whatever form/species, is a predictive modality for humans. Consider the following. Dr. Keith Cheng of Penn State's College of Medicine stated: “Animal tests are necessary for some research, such as testing drugs for toxicity. It would be, in my opinion, improper to release drugs for human use without animal testing.” [3] Rigmor Thorstensson, Head of the Department of Virology, Immunology and Vaccinology, at SMI in Sweden wrote, in an article titled: “Medical research on apes is no ethical problem for me:” 

The ethical reasons against animal testing must be weighed against the evidence that more and more people across the globe can have access to effective drugs and vaccines. If these were tested in clinical trials without first undergoing animal testing large numbers of people risking their lives in such studies and the development could also be delayed catastrophic. . . . For me it is no ethical problem of using monkeys in experiments, it is the only way to produce an effective vaccine against the major global infectious diseases, HIV, tuberculosis and malaria. [4]

Heywood: “Animal studies fall into two main categories: predictive evaluations of new compounds and their incorporation into schemes designed to help lessen or clarify a recognised hazard.” [5] Robert Vassar of Northwestern University states: “Chronic dosing in mice and monkeys is necessary to show the efficacy and safety of the antibody before it’s taken into humans.” [6] The Council for International Organizations of Medical Sciences implies prediction when they state: “clinical testing must be preceded by adequate laboratory or animal experimentation to demonstrate a reasonable probability of success without undue risk.” [7] Ramesh Akkina, DVM, PhD, professor, Department of Microbiology, Immunology and Pathology at Colorado State University, Fort Collins, CO stated: “A major advantage with this in vivo system [genetically modified SCID mice] is that any data you get from SCID-hu mice is directly applicable to a human situation.” [8] Zbinden states: “The three main purposes of experimental toxicology are (1) determination of the toxicological spectrum in selected laboratory animal species; (2) extrapolation to other species and prediction of adverse effects in man; and (3) determination of safe levels of exposure.” [9]

Gad writes:

Biomedical sciences’ use of animals as models [is to] help understand and predict responses in humans, in toxicology and pharmacology . . . by and large animals have worked exceptionally well as predictive models for humans . . . Animals have been used as models for centuries to predict what chemicals and environmental factors would do to humans…. The use of animals as predictors of potential ill effects has grown since that time . . . If we correctly identify toxic agents (using animals and other predictive model systems) in advance of a product or agent being introduced into the marketplace or environment, generally it will not be introduced . . . The use of thalidomide, a sedative-hypnotic agent, led to some 10,000 deformed children being born in Europe. This in turn led directly to the 1962 revision of the Food, Drug and Cosmetic Act, requiring more stringent testing. Current testing procedures (or even those at the time in the United States, where the drug was never approved for human use) would have identified the hazard and prevented this tragedy. [10] (Emphasis added.)

Hau: “A third important group of animal models is employed as predictive models. These models are used with the aim of discovering and quantifying the impact of a treatment, whether this is to cure a disease or to assess toxicity of a chemical compound.” [11] Michael F. Jacobson, executive director of the Center for Science in the Public Interest noted in 2008: “We must test animals to determine whether a substance causes cancer.” [12] Similarly, Huff et al. observe: “Chemical carcinogenesis bioassays in animals have long been recognized and accepted as valid predictors of potential cancer hazards to humans.” [13]

The above could be easily multiplied. So . . . scientists do claim that animal models are used as predictive modalities. Not all scientists make this claim, but some do and that is what I have always claimed and the situation I have addressed when discussing the predictive value of animal models.


1.         Editorial: A slippery slope. Nature 2009, 462:699.

2.         Giles J: Animal experiments under fire for poor design. Nature 2006, 444:981.

3.         Q&A: Dr. Keith Cheng, researcher at Penn State's College of Medicine, shares views on using animals in scientific research []

4.         Thorstensson R: Medicinska försök på apor är inget etiskt problem för mig (Medical research on apes is no ethical problem for me). Infectious Diseases 2006, Kronikor.

5.         Heywood R: Clinical Toxicity--Could it have been predicted? Post-marketing experience. In Animal Toxicity Studies: Their Relevance for Man. Edited by CE Lumley, Walker S. Lancaster: Quay; 1990: 57-67

6.         Vassar R: Alzheimer's therapy: a BACE in the hand? Nat Med 2011, 17:932-933.

7.         Council for International Organizations of Medical Sciences (CIMOS): International ethical guidelines for biomedical research involving human subjects. Bulletin of medical ethics 2002:17-23.

8.         Anonymous: Of Mice...and Humans. Drug Discovery and Development 2008, 11:16-20.

9.         Zbinden G: Predictive value of animal studies in toxicology. Regul Toxicol Pharmacol 1991, 14:167-177.

10.       Gad S: Preface. In Animal Models in Toxicology. Edited by Gad S. Boca Rotan: CRC Press; 2007: 1-18

11.       Hau J: Animal Models. In Handbook of Laboratory Animal Science Second Edition Animal Models. Volume II. 2nd edition. Edited by Hau J, van Hoosier Jr GK. Boca Rotan: CRC Press; 2003: 1-9

12.       Longer Tests on Lab Animals Urged for Potential Carcinogens []

13.       Huff J, Jacobson MF, Davis DL: The limits of two-year bioassay exposure regimens for identifying chemical carcinogens. Environ Health Perspect 2008, 116:1439-1442.