Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles

• Main Author: Attila A. Seyhan
• Format: Article
• Language: English
• Published: BMC 2019-11-01
• Series: Translational Medicine Communications
• Subjects: Translational research or medicine; Valley of death; Preclinical and clinical research; Reproducibility of research findings; Drug discovery and development
• Online Access: http://link.springer.com/article/10.1186/s41231-019-0050-7

Abstract A rift that has opened up between basic research (bench) and clinical research and patients (bed) who need their new treatments, diagnostics and prevention, and this rift is widening and getting deeper. The crisis involving the “translation” of basic scientific findings in a laboratory setting into human applications and potential treatments or biomarkers for a disease is widely recognized both in academia and industry. Despite the attempts that have been made both in academic and industry settings to mitigate this problem, the high attrition rates of drug development and the problem with reproducibility and translatability of preclinical findings to human applications remain a fact and the return on the investment has been limited in terms of clinical impact. Here I provide an overview of the challenges facing the drug development, and translational discordance with specific focus on a number of “culprits” in translational research including poor hypothesis, irreproducible data, ambiguous preclinical models, statistical errors, the influence of organizational structures, lack of incentives in the academic setting, governmental funding mechanisms, the clinical relevance of basic research, insufficient transparency, and lack of data sharing in research. I further provide some suggestions and new strategies that include some new aspects on open innovation models, entrepreneurship, transparency, and decision making to overcome each of the many problems during the drug discovery and development process and to more dynamically adjust for innovation challenges with broader scientific feedback.