All is not well in the world of translational research.
It is not unusual for novel treatment strategies to fail in clinical trials, despite highly encouraging results in preclinical proof-of-concept studies. Typically, such "failures of translation" are blamed on the poor predictiveness of animal models. Often, however, the poor predictiveness of today's preclinical proof-of-concept studies is related not to limitations of the models but to investigator bias and a lack of scientific rigor. The resulting false-positive results only serve to mislead the field and impede medical progress. With the resurgence of translational research, it is useful to examine some of the problems that plague these studies and consider their solutions. With thoughtful planning, execution, and analysis, it is possible to generate reliable and predictive data from preclinical proof-of-concept studies, results that should more rapidly advance medical progress.
Unger
Ellis F
EF
Division of Cardiovascular and Renal Products, Office of New Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA. ellis.unger@fda.hhs.gov
D032761:Animal Experimentation / Q000592:standards*; D000818:Animals; D002318:Cardiovascular Diseases / Q000628:therapy*; D006801:Humans; D012107:Research Design / Q000592:standards*