I joined Dr. Larry Pease's laboratory as a fellow in 2001, where I studied the effect of a human IgM antibody on T cell activation. I am grateful to Larry—a remarkable scientist, an incredible human, an amazing mentor, and a valuable member of the Mayo Clinic—and the prestigious Mayo Clinic for providing me with a wonderful opportunity to be a part of their team. During my 9 years at Mayo, we showed that the antibody was capable of inducing immunostimulatory effects in both in vivo and in vitro experimental model systems.
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Recently, however, none of these previously identified effects stimulated by antibody binding was observed. Based on circumstantial observations, it was claimed that I fabricated an experiment designed to quantify the potency of the purified antibody. As a result, multiple papers were retracted, a clinical trial was canceled, and my appointment at the Mayo clinic...
I had an amazing PI, a fabulous institution filled with phenomenal people, and a dream career. I cannot contemplate causing harm in any form or fashion to any of them or to my own career or the Mayo Clinic. Indeed, there are alternative possibilities for the antibody's recent failure. It is well established that IgM antibodies constantly evolve by accumulating mutations, forming mutant species that fail to bind, bind inappropriately, or block the binding of the effective version of the antibody in the serum. These mutant forms of the antibody could have resulted in the precipitous drop in functional consequences of the antibody binding. Alternatively, the antibody's shelf life may simply have expired. In other words, the antibody may have begun to degrade, diminishing the concentration of active antibody below the effective dose concentration required to cause immunomodulatory effects. While we predicted a prolonged shelf life—more than 12 months—based on initial experiments, this may not reflect accurately the actual shelf life due to the frequency of freezing and thawing the antibody experienced over the course of many studies, for example.
Unfortunately, the data addressing these possibilities do not exist at this time, and despite my insistence that I did not falsify the data in question, I was found guilty of research misconduct in an investigation by the Mayo Clinic. As a result, I firmly believe that I have been added to science's blacklist, along with all the other accused—guilty or not—making it nearly impossible to find another position in science or education. But these individuals should not be so casually thrown aside, as their knowledge, scientific acumen, and experience are valuable resources to the scientific community, and can be applied in ways that do not threaten the credibility of the research of their new labs and institutions.
I am not seeking to relax the rules regarding the punishment of fraudulent scientists. Scientists who have deliberately manipulated data should be penalized. However, adopting this blanket approach can also cause bystander damage, harming not just the accused, but his or her immediate family, colleagues, institution, and the scientific community as a whole. I am confident that other dismissals have suffered similar fates, and offering an olive branch towards redemption of career can benefit the livelihood of such individuals while efficiently utilizing their potential in a cost-effective manner towards betterment of science.
Therefore, I suggest a weighted approach to be adopted before arriving at decisions regarding hiring a person accused of research misconduct—a risk-to-benefit ratio analysis of an individual's scientific know-how and the risk of being associated with his or her tainted record. Despite a finding of misconduct, a researcher's years of education and laboratory experience can be garnered towards productivity in activities such as grant and manuscript writing/editing, work presentations, curriculum designing, low risk laboratory management, laboratory work on blinded experiments, and intellectual contributions, where hiring such blacklisted people will have zero impact on the credibility of the data generated, the laboratory, and the institution. Importantly, these individuals can come at a cheaper cost while offering a PI-level of input without demanding a PI-level of salary. It only makes sense to use the already spent time on these individuals towards a positive outcome, as it may take so much more effort and time to generate an equally potent individual with similar capabilities.
Additionally, while the self-correcting nature of science is fool-proof, it can be time-consuming and science would benefit from a faster means of data verification process, as the frequency of misconduct accusations and convictions have increased significantly. In this regard, the development of a national or global facility that can support third-party data verification should be given a serious consideration. I hope to elaborate on this topic separately. Collectively, with a central facility to validate data and a more forgiving approach to those accused of data falsification, the effects of research misconduct can be mitigated and the consequences of such a finding on the scientific community can be minimized.
Suresh Radhakrishnan worked at the Mayo Clinic in Rochester, Minn., as a senior research associate until he was fired for misconduct in May 2010.
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