Clinical trials bridge the gap between discovery and patient care, but high failure rates, unpredictable patient responses and data variability continue to challenge even the most promising therapies. By connecting biological insight with clinical design, researchers can better predict therapeutic response, refine patient selection criteria and reduce costly trial inefficiencies. Biomarkers grounded in strong translational evidence enable smarter inclusion and exclusion parameters, while data models ensure that insights flow seamlessly from discovery to development.

Despite record R&D spending in drug development, the failure rate of clinical trials remains stubbornly high. Only about 10% of drug candidates that enter clinical trials ultimately reach the market and a single Phase III failure can cost hundreds of millions of dollars. A major contributor to these failures is the lack of robust translational alignment between preclinical research and clinical execution. Variability in biospecimen quality, patient heterogeneity and inconsistent data interpretation compound these challenges.

Integrating biomarker discovery with strong translational data models creates a more precise, data-driven approach that speeds development, improves reproducibility and boosts subpopulations most likely to respond to a therapy, thereby helping to define inclusion and exclusion criteria that reflect biological reality rather than broad demographic assumptions. Biomarkers can also help with early efficacy or safety endpoints, allowing researchers to make data-driven decisions earlier in the process.