Enhancing translational research success: The role of statistical innovation

Translational research plays a crucial role in ensuring the success of drug development. Its effectiveness lies in facilitating the seamless translation of scientific discoveries and knowledge from basic research into human clinical trials. This process involves bridging the gap between laboratory findings and their practical implementation in clinical practice, while ensuring their relevance, safety, and effectiveness. Translational research thus must be firmly grounded in rigorous scientific methods and evidence-based practices, which is essential to warrant thorough evaluations of the experimental medicine through preclinical studies, clinical trials, and real-world evidence studies. The success of translational research heavily relies on robust empirical evidence. However, it is widely acknowledged that translating basic scientific findings from the lab to practical applications, potential disease treatments or biomarkers (FDA-NIH Biomarker Working Group, 2016) presents significant challenges in the pharmaceutical industry. Despite ongoing efforts in academic and industry settings to address this problem, attrition rates in drug development remain high and issues surrounding reproducibility and translatability of preclinical findings to human applications persist. This limits the clinical impact and return on investment. This article discusses the role of recent statistical innovation in facilitating the success of translational research. Specifically, our discussion focuses on the topic of statistical methods to ensure replicability and reproducibility of preclinical studies, recommendation of preclinical usage of p-values, recent development of Bayesian modeling for animal to first-in-human translation, and AI/ML based pharmacological modeling for human trial prediction.