Translational Data and the Next Era of Drug Development

Despite unprecedented advances in drug discovery, the industry continues to face a persistent translational gap. Too many promising programs fail because early evidence does not reliably predict human biology or clinical outcomes. This white paper explores how human-relevant models, New Approach Methodologies (NAMs), AI-enabled analytics, and evolving regulatory frameworks can help organizations generate decision-grade translational evidence that supports better go/no-go decisions and reduces late-stage risk.

Download the white paper to learn how industry leaders are redefining translational strategies to improve prediction, reduce uncertainty, and accelerate the path to patients.

Key Takeaways:

•    Translational data must become decision-grade evidence
The challenge facing drug development is no longer generating more data. Success depends on producing evidence that is human-relevant, reproducible, interpretable, and capable of informing critical development decisions.

•    Human relevance matters more than speed
Industry leaders identified human relevance, predictive performance, and regulatory acceptability as the most important factors influencing development decisions, ranking them above operational speed or cost considerations.

•    Poor translation is most costly at proof of concept
While translational failures often originate in discovery and preclinical development, their greatest impact is felt during Phase II studies, where investments in time, capital, and resources are highest.

•    AI is an enabler, not a replacement
AI can help integrate complex biological and translational datasets, support biomarker identification, and refine decision-making. However, meaningful outputs still depend on high-quality, biologically relevant input data.

•    Regulatory acceptance is shifting toward totality of evidence
Emerging regulatory approaches increasingly focus on whether evidence reduces uncertainty for a specific development or regulatory decision rather than whether a model has achieved universal validation.