Speaker
Description
Scientific integrity is the cornerstone of progress in biomedical research. Nowhere is this more critical than in nonclinical settings. Reproducibility – the ability to consistently replicate findings across studies, laboratories and organisations is – is not just a technical requirement. It is a fundamental attribute that underpins trust. As nonclinical research continues to expand in scope and complexity, it is both a scientific and moral responsibility.
Nonclinical research and manufacturing environments present distinct challenges to reproducibility. Biological variability, evolving experimental models, and the diversity of data sources can introduce uncertainties that complicate both study design and interpretation. Constraints on sample sizes, limitations in animal models, and the pressure for rapid innovation further increase the risk of irreproducible results. In manufacturing, the translation of laboratory findings to scalable, reliable processes demands rigorous validation and ongoing monitoring, all while navigating shifting regulatory landscapes.
Artificial Intelligence (AI) and Machine Learning (ML) are transforming nonclinical statistics by enabling the analysis of complex, high-dimensional data and automating aspects of experimental design and quality control. These technologies hold immense promise for uncovering novel insights and driving efficiency. However, they also introduce new complexities that can threaten reproducibility if not carefully managed.
Transparent reporting is essential to enable independent verification, foster collaboration, and accelerate scientific progress. Clear documentation of methodologies, data sources, analytical decisions, and limitations allows others to reproduce results and build upon previous work.
Nonclinical statisticians have too often been viewed as on-demand specialists. This presentation will focus on how the rise of data-centric roles and powerful technologies opens the door to a more integrated, collaborative and strategic role for statisticians in drug development and manufacturing. In the era of big data and AI-driven research, statisticians can play a pivotal role in safeguarding methodological quality and ensuring that findings are not artifacts of flawed design or analysis.
53573503699