Regulatory and commercial success of drug assets depend upon how well available data including mechanism of action, off-label uses, side-effects and subpopulation efficacy are analyzed and incorporated into the product strategy. Conducting such thorough analysis before committing funds into a trial can save sponsors more than a billion dollars as well as years of valuable time developing a drug. These analyses can be performed through the use of artificial intelligence (AI) and machine learning-driven platforms that identify sub-populations of patients in real world data as well as in clinical trial data for safety, efficacy and side-effects. 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, asset valuations and payer reimbursements.
Joining this webinar you will:
- Understand current payer, provider, regulatory and technology trends
- Learn how 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 a drug assets earlier
- Find out various R&D and Commercial applications of sub-population analysis