PAGE. Abstracts of the Annual Meeting of the Population Approach Group in Europe.
PAGE 25 (2016) Abstr 5827 [www.page-meeting.org/?abstract=5827]
Oral: Immune Response to Drug Treatment
Yuri Kosinsky(1), Kirill Peskov(1), Boris Shulgin(1), Oleg Stepanov(1), Veronika Voronova(1), Eric Masson(2), Gabriel Helmlinger(2)
(1)M&S Decisions LLC, (2)Quantitative Clinical Pharmacology, Early Clinical Development, AstraZeneca
Objectives: Immune cell pathways and their corresponding drug targets are the object of intense preclinical and clinical research. Complex kinetics, multiplicity of therapeutic targets, and potential for synergistic drug combinations all make the use of a quantitative systems pharmacology model (QSP) a necessity. An conceptual picture of immune responses to tumor cell progression and treatment has been described, based on a basic understanding of the immuno-oncology (IO) players and processes . A quantitative QSP model was developed, focusing on dynamic interactions between key immune cell types and cancer cells, soluble mediators, and IO checkpoint inhibitors within the tumor microenvironment. The main objective of this study was to properly incorporate and qualify, within the QSP model, the pleiotropic effects observed following CTLA-4 blockade mechanism.
Methods: The QSP model is represented by a system of ordinary differential equations. The core model was built and initially qualified based on in vivo mouse data published in multiple literature sources. Model parameters were calibrated based on levels of selected cytokines, immune cell counts in tumor and plasma, and tumour dynamics data (growth & shrinkage under experimental treatment). Model incorporation of anti-CTLA-4 treatment modulated mechanisms was performed based on preclinical studies from the literature as well [2,3].
Results: Several possible mechanisms linking CTLA-4 blockade to tumor growth inhibition effects were evaluated using outcomes from the QSP model. As a result, we identified the following mechanistic links, which are minimally necessary in order to correctly describe all published in vivo data on anti-CTLA-4 treatment: (1) increase in proliferation of CTLs and regulatory T lymphocytes (Tregs); (2) inhibition of the immuno-suppressive mechanism between Tregs and CTLs, which is most likely caused by the depletion of CTLA-4/CD80 interactions. Additionally, with these minimally necessary relations incorporated, a model-based sensitivity analysis was performed. It revealed a key set of parameters which may drive inter-animal variability in treatment outcome (tumor growth inhibition).
Conclusion: We developed an QSP model, which adequately describes regulatory dynamics of the IO multi-cell cycle, in experimental mouse models and in response to various anti-CTLA-4 inhibition therapies. Model-based simulations revealed key elements of CTLA-4-related regulatory mechanisms which affect tumor growth inhibition and parameters responsible for the observed inter-individual variability.