The target audience for this program includes researchers from academia and industry involved in basic, translational and clinical research, as well as clinicians and those from regulatory and funding agencies.
The “Combination Therapy: Using Novel Approaches to Identify Novel Biomarkers, Inform Patient Selection, and Design Trials” program helps SITC to advance the science and application of cancer immunotherapy by bringing together high-level experts to provide perspective and big-picture ideas on key issues surrounding combination therapies and clinical trial design and to highlight the latest work in these areas. The program will explore the use of artificial intelligence, such as machine learning, to predict patient outcomes and identify novel biomarkers. Application of statistical models to refine and optimize clinical trial design and regulatory issues surrounding clinical trials of combination therapies will also be addressed. Armed with a deeper understanding of these challenges and considerations surrounding combination therapies, attendees will be equipped to develop therapies that are cost-efficient, faster, safer, and more effective for patients.
At the conclusion of this activity, participants should be able to:
- Explain how artificial intelligence, machine learning and/or predictive models can be used to identify patients who will likely respond to early intervention
- Describe how statistical models can refine and optimize clinical trial design
- Address specific regulatory topics pertaining to clinical trials of combination therapy and explain how they affect trial design and/or interpretation of trial results
Combination Therapy: Using Novel Approaches to Identify Novel Biomarkers, Inform Patient Selection, and Design Trials is supported in part by grants from AstraZeneca Pharmaceuticals LP, Bristol Myers Squibb and Merck Sharp & Dohme, Corp., a subsidiary of Merck & Co., Inc. (as of October 13, 2022)