Have the principles of experimental medicine become obsolete in the era of big data?
Authors: Leon G. Fine
Journal: Kidney International (2017)

“By a marvelous compensation, science, in humbling our pride, proportionally increases our power.”—Claude Bernard (1865)1Bernard C. Introduction a l’etude de la medicine experimentale. Bailliere Bros, Paris: J-B Bailliere e fils, New York, NY1865Google Scholar Writing in 1865, Claude Bernard tells the following anecdote about kidney function1Bernard C. Introduction a l’etude de la medicine experimentale. Bailliere Bros, Paris: J-B Bailliere e fils, New York, NY1865Google Scholar, 2Bernard C. An introduction to the study of experimental medicine. The Macmillan Co, New York, NY1927Google Scholar:“One day, rabbits from the market were brought into my laboratory. They were put on the table where they urinated, and I happen to observe that their urine was clear and acid. This fact struck me, because rabbits, which are herbivora, generally have turbid and alkaline urine; while on the other hand carnivora, as we know, have clear and acid urine. This observation of acidity in the rabbits’ urine gave me an idea that these animals must be in the nutritional condition of carnivora. I assumed that they had probably not eaten for a long time, and that they had be transformed by fasting, into veritable carnivorous animals, living on their own blood. Nothing was easier than to verify this preconceived idea or hypothesis by experiment. I gave the rabbits grass to eat; and a few hours later, their urine became turbid and alkaline. I then subjected them to fasting and after twenty-four hours or thirty-six hours at the most, their urine again became clear and strongly acid; then after eating grass, their urine became alkaline again.” Bernard repeated the experiment a number of times and obtained the same results in another species, the horse. He confirmed his hypothesis by feeding meat to rabbits, which promptly acidified their urine. Here we see the essence of Claude Bernard’s principles of experimental medicine: he formulated a hypothesis, which posited that kidney function in all fasting animals reflects the nutritional condition of carnivora; he designed an experiment to test the hypothesis; he confirmed the findings in another species; and finally, he designed an experiment that sought “counterproof” to potentially disprove the hypothesis. The experimental results enabled him to draw a conclusion that supported the initial hypothesis, namely that the kidneys of all fasting animals behave as if they are feeding on meat. In his now classical text “An Introduction to the Study of Experimental Medicine” of 1865, Claude Bernard outlined the principles of experimental medicine that are applicable to the study of human health and disease.1Bernard C. Introduction a l’etude de la medicine experimentale. Bailliere Bros, Paris: J-B Bailliere e fils, New York, NY1865Google Scholar, 2Bernard C. An introduction to the study of experimental medicine. The Macmillan Co, New York, NY1927Google Scholar (The quotations that follow are all drawn from this work.) His fundamental goal was to understand the mechanisms that underlie the biological and medical phenomena in a rigorous and unbiased manner. The anecdote above frames the essence of these principles. Bernard compared “observation” with “experimentation.” He asserted that an idea that is born from an observation must be controlled by an experiment. Experimenters make judgments by comparing at least 2 facts. Often an intentional action is replaced by an accidental finding, which requires the observer to view it as another simple fact rather than as a mistake or failed experiment. Working on such unanticipated facts becomes the métier of the talented experimenter. In human research, facts derived from a patient’s interactions with a physician make the physician an “observer.” However, to justify the designation of “experimenter,” the physician would have to apply methods to alter the observed natural phenomenon, namely to produce an induced observation. Thus, this reasoning would be the same for a basic scientist as it would be for an experimental physiologist or physician. Bernard denigrated those who make “generalization their specialty,” where an idea is often formulated as a “truth” by a “scholastic” investigator; he asserted that a more modest experimenter would pose the idea as a question and proceed from partial to more general truths, without stating that any absolute truth has been grasped. Therefore, the experiment is the mediator between the objective and subjective. A hypothesis is an idea that is based on prior observations. The more facts are accumulated, the more reasonable it is to suppose that they support (but never prove) the existence of a principle. Inherent in this logic is the need for counterproof, that is, seeking a way to disprove a hypothesis. Bernard emphasized that the tendency to cite a “failed experiment” is pervasive in biological and human research and argued that provided that the facts are appropriately collected with precision, a finding that contradicts an expectation requires an explanation rather than being discarded as a failure. Bernard boldly claimed that statistics are irrelevant to experimentation. His reasoning was that the best science is based on “doubt,” which is something that statisticians avoid by assigning a probability or likelihood to an outcome. This is of relevance when considering the place of “big data” in understanding disease mechanisms, which is considered below. Described above is a philosophy of “hypothesis-driven” research. Central to Claude Bernard’s thinking was the unity of the experimental method, which links prior observations to a reasonable hypothesis. Unrelated facts and undocumented hypotheses had no value in his eyes. He believed that formulation of a novel hypothesis, no matter how improbable, was of far greater importance than the simple gathering of information or the continued “wisdom” of those who do not conduct experiments, which inevitably is antiscientific. We have entered an era of research on human disease, in which we place emphasis on discerning patterns within enormous datasets.3Luo J. Gopukumar D. Zhao Y. Bif data application in biomedical research and health care: a literature review.Biomed Inform Insights. 2016; 8: 1-10Crossref PubMed Google Scholar Such patterns are expected to demonstrate associations between genomics and other “omics” and specific phenotypes in human diseases. However, this approach presupposes that there is no need for a hypothesis to be tested or a specific question to be asked. It is argued that the new generation of commodity hardware, widespread instrumentation, and open source software, coupled with information derived from electronic medical records, may diminish the need for expensive clinical trials and identify targets for new drugs, patient responses to drugs, and new classifications of disease. This revolution, which is largely genomics driven, may embrace datasets from millions of patients. This approach, by its very nature, is hypothesis free and will provide probabilistic outcomes to identify causes and effects. The mantra “Let’s see what we come up with without wasting time generating time-consuming hypotheses” is a mentality that is driving many research organizations and companies. So how does one acquire new knowledge about mechanisms of disease using this approach and are the principles espoused by Claude Bernard still relevant? A sober answer would be that the analysis of large volumes of data coupled with the most sophisticated phenotyping will, indeed, indicate associations and probabilities; however, an association per se will not nail down a mechanism until this has been specifically explored by experimentation. However, these increasingly strong associations should generate more highly focused novel hypotheses that, in turn, will drive more focused experimentation. Thus, biomedical research is shifting from a “hypothesis-driven” culture to a “hypothesis-generating” culture. Indeed, the hypotheses that are being formulated on the basis of unbiased “big data” will increasingly become more focused and precise than ever before. If anything, hypothesis-driven research has been invigorated by an ever-increasing depth to questions and hypotheses that are currently being addressed. It is clear that the principles of experimental medicine espoused by Bernard remain relevant today. We have entered a “hypothesis-generating” era, which will continue to lead to “hypothesis-driven” experimentation.