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.
Chimeric antigen receptor (CAR) T‐cell subsets and immunophenotypic composition of the pre‐infusion product, as well as their longitudinal changes following infusion, are expected to affect CAR‐T cell expansion, persistence, and clinical outcomes. Herein, we sequentially evolved our previously described cellular kinetic‐pharmacodynamic (CK‐PD) model to incorporate CAR‐T cell product‐associated attributes by utilizing published preclinical and clinical datasets from two affinity variants (FMC63 and CAT19 scFv) anti‐CD19 CAR‐T cells. In step 1, a unified cell‐level PD model was used to simultaneously characterize the in vitro killing datasets of two CAR‐Ts against CD19+ cell lines at varying effector: target (E: T) ratios. In step 2, an augmented CK‐PD model for anti‐CD19 CAR‐Ts was developed, by integrating CK dataset(s) from two bioanalytical measurements (qPCR and flow cytometry) in cancer patients. The model described the differential in vivo expansion properties of CAR‐T cell subsets. The estimated expansion rate constant was ~1.12‐fold higher for CAR+CD8+ cells in comparison to CAR+CD4+ T cells. In step 3, the model was extended to characterize the disposition of four Immunophenotypic populations of CAR‐T cells, including stem‐cell memory (TSCM), central memory (TCM), effector memory (TEM) and effector (TEFF) cells. The model adequately characterized the longitudinal changes in immunophenotypes post anti‐CD19 CAR‐T cell infusion in acute lymphocytic leukemia paediatric patients. Polyclonality in the pre‐infusion product was identified as a categorical covariate influencing differentiation of immunophenotypes. In the future, this model could be leveraged a priori towards optimizing the composition of CAR‐T cell infusion product, and further understand the CK‐PD relationship in patients.
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