Failed clinical trials can cost sponsors more than a billion dollars, and waste years of time developing a drug that will never get to market. However, these losses can now be mitigated through the use of artificial intelligence (AI) and machine learning-driven platforms that identify sub-populations of patients within a clinical trial who could respond positively to a treatment. 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.
Joining this webinar you will:
- Learn how of sub-population analysis in trial design and precision medicine can add valuable insights across the development process
- Hear case studies from Oncology and see how the results of sub-population analysis could help sponsors make important decisions about the future of the trial earlier.
- Looking at CNS, how sub-population analysis can contribute to accelerate trial time and lead to better future drug development