Finding Value In Failing Trials

When sponsors can find these populations and adapt the trial design accordingly, it can reduce the rate of trial failure, rescue a promising molecule for further development, and capture better primary and secondary endpoint data to support approval and payer valuations.

This paper explores how sponsors can leverage AI driven tools, such as IQVIA’s Sub-Population Optimization and Modeling Solutions (SOMS), throughout the trial lifecycle to reduce the risk of failure, while improving success rates, and identifying new opportunities in drug development.

One of the most effective ways to cut the cost of drug development is by reducing the rate of trial failure. SOMS platforms can provide the analytics and analysis necessary to achieve this goal, creating new commercial opportunities for sponsors, and bringing more lifesaving treatments to patients who need them.