Prediction of Combination Treatment Effect and Its Application in Oncology Trial Design

• Activity Number: 200 - Statistical Challenges and Opportunities for Expedited Oncology Drug Development
• Type: Topic-Contributed
• Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Linda Sun* and Cong Chen and Cai (Iris) Wu and Fang Liu and Yixin Ren and Leah Suttner and Xiaoyun (Nicole) Li
• Companies: Merck & Co., Inc. and Merck & Co., Inc. and Merck & Co., Inc. and Merck & Co., Inc. and Merck & Co., Inc. and Merck & Co., Inc. and Merck & Co.
• Keywords: Combination cancer therapy; Independent drug action; Prediction; Progression-free Survival; Duration of Response; Waterfall Plot

Abstract

An unprecedented number of new cancer targets are being developed in combination therapies. Early oncology development is strategically challenged in choosing the best combinations to move forward to late stage development. The most common early endpoints to be assessed in such decision-making include objective response rate, progression-free survival, duration of response and tumor size change. Researchers have long sought to find combinations of cancer drugs that might achieve synergy. However, while observed in some preclinical tumor models, synergistic effects are rarely seen in clinical trials. In fact, growing evidence in clinical trial data shows that the treatment effect of most approved combination therapies can be largely explained by the independent drug action (IDA) model at the patient level. In this talk, using IDA concept as a foundation, we introduce simple models to predict combination therapy efficacy for the early endpoints and discuss their statistical implications for trial design and monitoring. The discussion is enriched with real data examples. Such quantitative work facilitates efficient oncology drug development.