Bayer halts nearly two-thirds of its target-validation projects because in-house experimental findings fail to match up with published literature claims, finds a first-of-a-kind analysis on data irreproducibility.
An unspoken industry rule alleges that at least 50% of published studies from academic laboratories cannot be repeated in an industrial setting, wrote venture capitalist Bruce Booth in a recent blog post. A first-of-a-kind analysis of Bayer’s internal efforts to validate ‘new drug target’ claims now not only supports this view but suggests that 50% may be an underestimate; the company’s in-house experimental data do not match literature claims in 65% of target-validation projects, leading to project discontinuation.
“People take for granted what they see published,” says John Ioannidis, an expert on data reproducibility at Stanford University School of Medicine in California, USA. “But this and other studies are raising deep questions about whether we can really believe the literature, or whether we have to go back and do everything on our own.”
For the non-peer-reviewed analysis, Khusru Asadullah, Head of Target Discovery at Bayer, and his colleagues looked back at 67 target-validation projects, covering the majority of Bayer’s work in oncology, women’s health and cardiovascular medicine over the past 4 years. Of these, results from internal experiments matched up with the published findings in only 14 projects, but were highly inconsistent in 43 (in a further 10 projects, claims were rated as mostly reproducible, partially reproducible or not applicable; see article online here). “We came up with some shocking examples of discrepancies between published data and our own data,” says Asadullah. These included inabilities to reproduce: over-expression of certain genes in specific tumour types; and decreased cell proliferation via functional inhibition of a target using RNA interference.
The unspoken rule is that at least 50% of the studies published even in top tier academic journals – Science, Nature, Cell, PNAS, etc… – can’t be repeated with the same conclusions by an industrial lab. In particular, key animal models often don’t reproduce. This 50% failure rate isn’t a data free assertion: it’s backed up by dozens of experienced R&D professionals who’ve participated in the (re)testing of academic findings.
For the pointer I thank Michelle Dawson.