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All is not well in the world of translational research
1Division 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
Abstract:
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.
Insights
Novel treatments often fail in clinical trials due to flawed preclinical studies. Improving scientific rigor and reducing bias in proof-of-concept research can enhance translation and accelerate medical progress.
Area of Science:
- Biomedical Research
- Translational Science
- Drug Development
Background:
- Many novel therapeutic strategies fail in human clinical trials despite promising preclinical data.
- These
- failures of translation
- are frequently attributed to inadequate animal models.
- However, investigator bias and insufficient scientific rigor in preclinical proof-of-concept studies often lead to misleading false-positive results.
Discussion:
- Addressing investigator bias is crucial for improving the reliability of preclinical data.
- Implementing robust scientific rigor in study design, execution, and analysis is essential.
- False-positive results from biased studies impede scientific progress and waste resources.
Key Insights:
- Preclinical study limitations often stem from methodological flaws rather than model deficiencies.
- Enhanced rigor and unbiased execution are key to generating predictive preclinical data.
- Reliable preclinical data is vital for successful translation to clinical applications.
Outlook:
- Improving the quality of preclinical proof-of-concept studies can accelerate medical advancements.
- Thoughtful planning and execution are necessary to ensure the validity of translational research.
- More predictive preclinical data will lead to more effective treatments reaching patients faster.
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