Ihre Browserversion ist veraltet. Wir empfehlen, Ihren Browser auf die neueste Version zu aktualisieren.

Latest News

June 5th, 2017: PAASP member participates in meeting organised by NIH director Francis Collins - read more

May 15th, 2017: New article about increasing value and reducing waste in stroke research - read more

April 25th, 2017: Vasilevsky et al. investigated data sharing policies of scientific journals - read more

Scientific data quality: Where self-correction needs to be facilitated

Quality of scientific data has recently received a lot of attention and, despite the negative press this subject receives, it is rightfully acknowledged that "Instances in which scientists detect and address flaws in work constitute evidence of success, not failure, because they demonstrate the underlying protective mechanisms of science at work"(Alberts et al., 2015 - here). 

Scientific practice should not be too much constrained by unnecessary policing that may have unwanted effects on freedom of scientific thought. However, there are some areas of applied science where intellectual success is tightly linked to financial success and therefore joint efforts of scientists, publishers, employing and funding organizations, and governmental agencies is required to ensure proper training and adherence to highest quality standards. 

PAASP focuses on one area of applied science - pharmaceutical research and development - where low robustness of preclinical data is one of the underlying causes of the overall poor productivity.

PAASP mission

Reduce operational risks in areas of applied biomedical science where research results have direct or indirect commercial value and have an impact on competition

 

Improving pre-clinical Research Quality by introducing standards to applied research will enhance robustness and trust in scientific theories. More importantly, this will also smoothen the way from excellent scientific hypotheses to increased clinical efficacy. By assessing and accrediting quality in pre-clinical research, we expect a reduction in clinical failure rates, and thereby, saving of valuable resources.

Who should invest into research quality?

Drug companies

… invest into the most expensive phases of drug R&D.

Clinical studies based on preclinical evidence lacking robustness are likely to fail.

Private and public funders

… support a great number of projects guided by their scientific excellence. 

Research quality assessment is a filter that can be applied to focus this investment on robust translatable research. 

Academic institutions

… may see mostly negative consequences of stronger focus on research quality.

However, long-term benefits outweigh short-term losses (costs, reduced productivity).

What does PAASP offer?

Webinar on the Reproducibility Crisis in Science

Martin presented a Webinar on the Reproducibility crisis in the Cohen Veterans Bioscience Webinar series.

A large fraction of published non-clinical research findings in the life sciences turns out to be non-reproducible. This wastes resources in research and undermines public trust in science, potentially putting public funding of such research in danger. The webinar will discuss the main reasons for lack of reproducibility with emphasis on inappropriate use of statistical approaches.

Two neglected aspects when discussing research quality

Scientific Excellence vs. Research Quality

Scientific excellence is the key to advance science and to develop novel drugs. However, scientific excellence does not guarantee that the conducted experiments deliver robust results. There are two primary reasons for that.

First, education in science does not always focus enough on the requirements for delivering of robust data (e.g. statistical power, blinding, randomization, etc.).

Second, excitement associated with a scientific hypothesis or conveyed by a scientific leader may introduce bias in study design, conduct, analysis and/or reporting.

Regulated vs. Non-regulated

In the drug discovery and development process, there are several steps that have adequate quality control and are covered by GxP policies (e.g. Good Laboratory Practice, GLP).

For non-regulated areas (most specifically, biology and pharmacology of drug discovery projects), GLP-like procedures would not be acceptable and may not even help to secure the quality of research. In fact, one may indeed imagine a lab running under GLP conditions but nevertheless still failing to design and execute robust studies.

Thus, for non-regulated areas of drug discovery, one needs to have a specialized set of Good Research Practice conditions that focus on study design, unbiased conduct, analysis and reporting. 

Quote of the month June:

"60% of the time, it works every time."

Anchorman: The legend of Ron Burgundy, 2004, American comedy film

Subscribe to our monthly Newsletter

Always stay informed about interesting news in the GRP field and achievements we make to advance the idea of more reproducible data in biomedical research.