Abstract
Thousands of pharmacology experiments are performed each day, generating hundreds of drug discovery programs, scientific publications, grant submissions, and other efforts. Discussions of the low reproducibility and robustness of some of this research have led to myriad efforts to increase data quality and thus reliability. Across the scientific ecosystem, regardless of the extent of concerns, debate about solutions, and differences among goals and practices, scientists strive to provide reliable data to advance frontiers of knowledge. Here we share our experience of current practices in nonclinical neuroscience research across biopharma and academia, examining context-related factors and behaviors that influence ways of working and decision-making. Drawing parallels with the principles of evidence-based medicine, we discuss ways of improving transparency and consider how to better implement best research practices. We anticipate that a shared framework of scientific rigor, facilitated by training, enabling tools, and enhanced data sharing, will draw the conversation away from data unreliability or lack of reproducibility toward the more important discussion of how to generate data that advances knowledge and propels innovation.
Highlights
Over the last 10 years, debate has raged about the quality of scientific evidence, expanding from a conversation among experts, amplified by systematic reviews and meta-analyses published in peer-reviewed journals, into a heated discussion splashed across mainstream press and social media
Nonclinical studies that support, for example, an investigational new drug (IND) filing or a clinical trial application (CTA), which gate studies in humans, are reviewed under quality control procedures; most safety studies must comply with regulations laid out by health authorities, whereas nonclinical efficacy studies are usually performed in a nonregulated environment
At least in part due to the diversity of nonclinical experiments, systematic reviews and metaanalyses are far less common in nonclinical phases than in clinical ones, and there are very few broadly accepted tools with which to assess nonclinical data quality (Hooijmans et al 2018; Sena et al 2014). Pioneering work in this area came from the stroke field, with nonclinical research guidelines and an assessment tool elaborated by STAIR, the Stroke Therapy Academic Industry Roundtable (Hooijmans et al 2014)
Summary
Over the last 10 years, debate has raged about the quality of scientific evidence, expanding from a conversation among experts, amplified by systematic reviews and meta-analyses published in peer-reviewed journals, into a heated discussion splashed across mainstream press and social media. At least in part due to the diversity of nonclinical experiments, systematic reviews and metaanalyses are far less common in nonclinical phases than in clinical ones, and there are very few broadly accepted tools with which to assess nonclinical data quality (Hooijmans et al 2018; Sena et al 2014). Pioneering work in this area came from the stroke field, with nonclinical research guidelines and an assessment tool elaborated by STAIR, the Stroke Therapy Academic Industry Roundtable (Hooijmans et al 2014) (https://www.thestair.org). We will consider which EBM principles can apply to nonclinical pharmacology work and how to strengthen our ability to implement best research practices, without limiting innovation that is urgently needed
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