Conflicts of interests, confidentiality and censorship in health risk assessment: the example of an herbicide and a GMO

Abstract

We have studied the long-term toxicity of a Roundup-tolerant GM maize (NK603) and a whole Roundup pesticide formulation at environmentally relevant levels from 0.1 ppb. Our study was first published in Food and Chemical Toxicology (FCT) on 19 September, 2012. The first wave of criticisms arrived within a week, mostly from plant biologists without experience in toxicology. We answered all these criticisms. The debate then encompassed scientific arguments and a wave of ad hominem and potentially libellous comments appeared in different journals by authors having serious yet undisclosed conflicts of interests. At the same time, FCT acquired as its new assistant editor for biotechnology a former employee of Monsanto after he sent a letter to FCT to complain about our study. This is in particular why FCT asked for a post-hoc analysis of our raw data. On 19 November, 2013, the editor-in-chief requested the retraction of our study while recognizing that the data were not incorrect and that there was no misconduct and no fraud or intentional misinterpretation in our complete raw data - an unusual or even unprecedented action in scientific publishing. The editor argued that no conclusions could be drawn because we studied 10 rats per group over 2 years, because they were Sprague Dawley rats, and because the data were inconclusive on cancer. Yet this was known at the time of submission of our study. Our study was however never attended to be a carcinogenicity study. We never used the word ‘cancer’ in our paper. The present opinion is a summary of the debate resulting in this retraction, as it is a historic example of conflicts of interest in the scientific assessments of products commercialized worldwide. We also show that the decision to retract cannot be rationalized on any discernible scientific or ethical grounds. Censorship of research into health risks undermines the value and the credibility of science; thus, we republish our paper.

The Seralini affair: degeneration of Science to Re-Science?

“…In an accompanying commentary, Séralini et al [28] state that all raw data used in regulatory assessments of GM crops and pesticides should also be made public. This point has also been raised by others [29].…”

“…Séralini et al [28] also point to another area where reform is needed, explaining that currently, GM crop and pesticide developers are allowed to submit to regulators, as evidence of safety of their products, data generated in their own laboratories or in contract laboratories under their supervision. Séralini et al [28] suggest that the conflicts of interest inherent in this arrangement can be avoided by having such research conducted by independent laboratories.…”

“…Séralini et al [28] suggest that the conflicts of interest inherent in this arrangement can be avoided by having such research conducted by independent laboratories. They suggest companies still pay for the research, at levels comparable to current costs, but public tender to carry out the research independent of the company that developed the product would recruit independent researchers.…”

“…The above issues, as well as issues raised in Séralini et al [28] point to structural biases in regulatory science processes that limit the ability of these processes to protect public, animal and ecosystem welfare. Rectifying this situation requires substantive changes in safety assessment policies.…”

Inclusive development and prioritization of review questions in a highly controversial field of regulatory science

“…Interpreting the resulting primary data has frequently been a subject of controversial debates augmented by the complexity and the diversity of test designs and the multitude of endpoints under investigation [1] as well as by contradicting results. Reviewing secondary data by academia and regulatory committees has frequently led to discussions about, among other, studies not (appropriately) considered, relative weight attached to data generated by different methods, bias, lack of transparency and to divergent conclusions [2][3][4][5][6][7][8].…”