From clear reporting to better research models

The Researchers' View of Scientific Rigor-Survey on the Conduct and Reporting of In Vivo Research

Reproducibility in animal research is alarmingly low, and a lack of scientific rigor has been proposed as a major cause. Systematic reviews found low reporting rates of measures against risks of bias (e.g., randomization, blinding), and a correlation between low reporting rates and overstated treatment effects. Reporting rates of measures against bias are thus used as a proxy measure for scientific rigor, and reporting guidelines (e.g., ARRIVE) have become a major weapon in the fight against risks of bias in animal research. Surprisingly, animal scientists have never been asked about their use of measures against risks of bias and how they report these in publications. Whether poor reporting reflects poor use of such measures, and whether reporting guidelines may effectively reduce risks of bias has therefore remained elusive. To address these questions, we asked in vivo researchers about their use and reporting of measures against risks of bias and examined how self-reports relate to reporting rates obtained through systematic reviews. An online survey was sent out to all registered in vivo researchers in Switzerland (N = 1891) and was complemented by personal interviews with five representative in vivo researchers to facilitate interpretation of the survey results. Return rate was 28% (N = 530), of which 302 participants (16%) returned fully completed questionnaires that were used for further analysis. According to the researchers' self-report, they use measures against risks of bias to a much greater extent than suggested by reporting rates obtained through systematic reviews. However, the researchers' self-reports are likely biased to some extent. Thus, although they claimed to be reporting measures against risks of bias at much lower rates than they claimed to be using these measures, the self-reported reporting rates were considerably higher than reporting rates found by systematic reviews. Furthermore, participants performed rather poorly when asked to choose effective over ineffective measures against six different biases. Our results further indicate that knowledge of the ARRIVE guidelines had a positive effect on scientific rigor. However, the ARRIVE guidelines were known by less than half of the participants (43.7%); and among those whose latest paper was published in a journal that had endorsed the ARRIVE guidelines, more than half (51%) had never heard of these guidelines. Our results suggest that whereas reporting rates may underestimate the true use of measures against risks of bias, self-reports may overestimate it. To a large extent, this discrepancy can be explained by the researchers' ignorance and lack of knowledge of risks of bias and measures to prevent them. Our analysis thus adds significant new evidence to the assessment of research integrity in animal research. Our findings further question the confidence that the authorities have in scientific rigor, which is taken for granted in the harm-benefit analyses on which approval of animal experiments is based. Furthermore, they suggest that better education on scientific integrity and good research practice is needed. However, they also question reliance on reporting rates as indicators of scientific rigor and highlight a need for more reliable predictors.

Mechanistic, translational, quantitative pain assessment tools in profiling of pain patients and for development of new analgesic compounds

Background

Mechanistic, translational, human experimental pain assessment technologies (pain bio markers) can be used for: (1) profiling the responsiveness of various pain mechanisms and pathways in healthy volunteers and pain patients, and (2) profiling the effect of new or existing analgesic drugs or pain management procedures. Translational models, which may link mechanisms in animals to humans, are important to understand pain mechanisms involved in pain patients and as tools for drug development. This is urgently needed as many drugs which are effective in animal models fail to be efficient in patients as neither the mechanisms involved in patients nor the drugs’ mechanistic actions are known.

Aim

The aim of the present topical review is to provide the basis for how to use mechanistic human experimental pain assessment tools (pain bio markers) in the development of new analgesics and to characterise and diagnose pain patients. The future aim will be to develop such approaches into individualised pain management regimes.

Method

Experimental pain bio markers can tease out mechanistically which pain pathways and mechanisms are modulated in a given patient, and how a given compound modulates them. In addition, pain bio markers may be used to assess pain from different structures (skin, muscle and viscera) and provoke semi-pathophysiological conditions (e.g. hyperalgesia, allodynia and after-sensation) in healthy volunteers using surrogate pain models.

Results

With this multi-modal, multi-tissue, multi-mechanism pain assessment regime approach, new opportunities have emerged for profiling pain patients and optimising drug development. In this context these technologies may help to validate targets (proof-of-concept), provide dose-response relationships, predicting which patient population/characteristics will respond to a given treatment (individualised pain management), and hence provide better understanding of the underlying cause for responders versus non-responders to a given treatment.

Conclusion

In recent years, pain bio markers have been substantially developed to have now a role to play in early drug development, providing valuable mechanistic understanding of the drug action and used to characterise/profile pain patients. In drug development phase I safety volunteer studies, pain bio marker scan provide indication of efficacy and later if feasible be included in clinical phase II, III, and IV studies to substantiate mode-of-action.

Implications

Refining and optimizing the drug development process ensures a higher success rate, i.e. not discarding drugs that may be efficient and not push non-efficient drugs too far in the costly development process. Mechanism-based pain bio markers can help to qualify the development programmes and at the same time help qualifying them by pain profiling (phenotyping) and recognising the right patients for specific trials. The success rate from preclinical data to clinical outcome may be further facilitated by using specific translational pain bio-markers. As human pain bio markers are getting more and more advanced it could be expected that FDA and EMA in the future will pay more attention to such mechanism-related measures in the approval phase as proof-of-action.