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What strategies are being employed to accelerate product development and commercialization without compromising quality?

Raj Indupuri, Chief Executive Officer, eClinical Solutions

Reducing overall cycle times while maintaining or improving quality is a critical priority right now across the life sciences. The average total cost to bring a drug to market continues to heavily increase, with a recent study from the Tufts Center for the Study of Drug Development stating the number at over $2.1 billion, a result of the increasing complexity of clinical research. Both scientific and technological advancement have exploded in recent years, but this has also in many cases created an explosion of data types and data streams and protocol complexity, as well as silos, resulting in duplicative data processes. Data, technology innovation, and strategies focused on data oversight and a single source of truth for data can unlock efficiencies and enhance quality, taking this deluge of data and using source-agnostic ingestion and standardization capabilities to seamlessly connect data acquisition, data infrastructure, and analytics. Adopting AI and risk-based approaches to remove the manual and stepwise processes that persist in clinical research are critical steps for reducing timelines while addressing data quality at scale. It starts with having an interoperable, scalable architecture that sets the foundation for AI and generative AI (GenAI). 

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