How significant are your data? The need for a culture shift

  • Martin C Michel
  • Published 2014 in Naunyn-Schmiedeberg's Archives of Pharmacology


There is growing concern about a lack of reproducibility of findings from experimental studies in the life sciences. For example, investigators at Bayer Healthcare found only a quarter of 67 seminal studies to be reproducible (Prinz et al. 2011). Similarly, investigators at Amgen attempted to replicate 53 studies in basic cancer biology but were able to reproduce only 6 despite often cooperating with the original investigators (Begley and Ellis 2012). Many academic researchers have made the same experience, and lack of reproducibility apparently does not depend on the origin of the lab reporting the finding, i.e., academia vs. pharmaceutical industry, or the reputation of the journal publishing them. Mathematical models propose explanations why a large fraction of published results are false (Ioannidis 2005). The lack of reproducibility of a relevant percentage of seminal findings is worrisome as it will lead to a major waste in time and resources for those basing their own research on published findings which may turn out to be false. In acade-mia, it may derail the early career of young scientists who waste time chasing irreproducible findings and end up empty-handed. For more senior scientists, it may cause problems in obtaining future grants which can be particularly painful in times where research funding becomes ever harder to obtain. Where academic research is funded by governments or charities , it also constitutes a waste of taxpayer money or that donated for charitable purposes and, in the long run, will undermine the willingness of governments and charities to support research. In the pharmaceutical industry, lack of reproducibility also leads to a waste of resources as invalid targets may be pursued, which further drives up the already escalating costs of drug discovery and development (Prinz et al. 2011). For example, a number of major drug companies have stopped or significantly reduced their investment into central nervous system drug research; in many cases, the main argument was that preclin-ical data failed to translate into clinical efficacy. Among many factors potentially implicated in the failed translation (including poor or no target engagement), irreproducible data are a major contributor. For example, there were a number of compounds reported to be effective in a mouse model of the amyotrophic lateral sclerosis but, when these findings were rigorously re-tested under very well-defined conditions, none of the original compounds showed any benefit (Scott et al. 2008). Lack of reproducibility also raises ethical problems as it …

DOI: 10.1007/s00210-014-1044-7

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@article{Michel2014HowSA, title={How significant are your data? The need for a culture shift}, author={Martin C Michel}, journal={Naunyn-Schmiedeberg's Archives of Pharmacology}, year={2014}, volume={387}, pages={1015-1016} }