Developing predictive biomarkers is one of the most powerful and challenging — frontiers in clinical research. For GSK’s COPD clinical discovery team, identifying patients with worsening emphysema required validating a complex multivariate biomarker model under tight sample limitations.

Partnering with MMS, GSK used KerusCloud®, an advanced simulation-guided study design platform, to generate realistic in silico data and test thousands of study designs virtually. The result? A statistically sound, sample-efficient model that delivered robust conclusions and reduced development risk.

This case study shows how KerusCloud® enables biomarker development teams to de-risk design decisions, optimize validation strategies, and make data-driven conclusions faster, smarter, and with fewer resources.

Key Benefits / What You’ll Learn:

• Simulate complex biomarker validation studies using synthetic data that mimics real-world patient variability.
• Reduce sample size needs by up to 70% while maintaining statistical robustness and confidence.
• Visualize performance outcomes instantly through heatmaps and key study metrics.
• Optimize multivariate models using data-driven insights for stronger clinical relevance.
• Accelerate decision-making and reduce the risk of failed or inconclusive studies.

Created in partnership with GSK’s Respiratory Therapeutic Area Unit and led by MMS statistical and modeling experts, this case study demonstrates real-world success applying KerusCloud® to complex biomarker-driven research.