From Cold to Hot: Changing Perceptions and Future Opportunities for Quantitative Systems Pharmacology Modeling in Cancer Immunotherapy. Issue 5 (10th November 2022)

• Record Type: Journal Article
• Authors: Lemaire, Vincent; Bassen, David; Reed, Mike; Song, Roy; Khalili, Samira; Lien, Yi Ting (Kayla); Huang, Lu; Singh, Aman P.; Stamatelos, Spyros; Bottino, Dean; Hua, Fei
• Is Part Of: Clinical pharmacology & therapeutics. Volume 113:Issue 5(2023)
• Page Start: 963
• Page End: 972
• Publication Date: 2022-11-10
• Subjects: Pharmacology -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1002/cpt.2770
• Languages: English
• ISSNs: 0009-9236

Abstract

Abstract : Immuno‐oncology (IO) is a fast‐expanding field due to recent success using IO therapies in treating cancer. As IO therapies do not directly kill tumor cells but rather act upon the patients' own immune cells either systemically or in the tumor microenvironment, new and innovative approaches are required to inform IO therapy research and development. Quantitative systems pharmacology (QSP) modeling describes the biological mechanisms of disease and the mode of action of drugs with mathematical equations, which has significant potential to address the big challenges in the IO field, from identifying patient populations that respond to different therapies to guiding the selection, dosing, and scheduling of combination therapy. To assess the perspectives of the community on the impact of QSP modeling in IO drug development and to understand current applications and challenges, the IO QSP working group—under the QSP Special Interest Group (SIG) of the International Society of Pharmacometrics (ISoP)—conducted a survey among QSP modelers, non‐QSP modelers, and non‐modeling IO program stakeholders. The survey results are presented here with discussions on how to address some of the findings. One of the findings is the differences in perception among these groups. To help bridge this perception gap, we present several case studies demonstrating the impact of QSP modeling in IO and suggest actions that can be taken in the future to increase the real and perceived impact of QSP modeling in IO drug research and development.