(B-60) Mechanistic Computational Modeling of IL-2 and IL-2 Immunocytokines

Introduction:: Interleukin-2 (IL-2) stimulates the survival, activation, and expansion of T lymphocytes. Due to its critical role in immune function, the IL-2 cytokine has been FDA-approved for the treatment of certain metastatic cancers and used clinically for the treatment of autoimmune conditions such as type 1 diabetes and for the prevention of transplant rejection. However, the off-target effects of IL-2 and its vanishingly short half-life have hampered clinical progress. To circumvent the therapeutic shortcomings of natural cytokines, our lab has tethered IL-2 to anti-IL-2 antibodies to form immunocytokines, which enhance target specificity and significantly prolong serum persistence of IL-2. To advance therapeutic translation, we are building a computational pharmacological model that mechanistically characterizes the activity of IL-2 and IL-2-based immunocytokines.


Materials and Methods:: We built and validated a computational mechanistic model of IL-2 and two IL-2 immunocytokines that incorporated ligand-receptor binding, trafficking dynamics, and signaling in two cell types (effector T cells and regulatory T cells). The model included over a dozen molecules, approximately 30 parameters, and close to 40 ordinary differential equations. The model was parameterized using extensive experimental data when available, and physiological estimates when necessary. The level of IL-2 signaling induction (represented in the model as ligand-receptor binding) was used as a predictor of downstream signaling and validated against experimental measurements of signaling induced by IL-2 and IL-2-based immunocytokines.


Results, Conclusions, and Discussions:: Based on initial model simulations, the relative difference in signal activation between cell types stimulated by IL-2 could be explained by the ligand-receptor binding affinity differences between those cell types; however, affinity disparities could not explain why experimental activity readouts (for both cell types) took place at IL-2 doses well below what would be expected based on ligand-receptor binding affinities alone. Using parameter optimization, we were able to identify binding parameters that could reproduce the experimental data. However, this method was not parsimonious, as it required each combination of ligand and cell type to be independently optimized and therefore did not produce a consistent set of broadly applicable parameters. Instead, we introduced an intermediate signaling step between the ligand-receptor activation and the signaling readout, using a Hill function to permit amplification of the receptor activation. This transformation allowed for the previously-measured binding affinities to reproduce the observed cell-type-specific and ligand-specific responses to IL-2 and IL-2-based immunocytokines using a consistent set of parameters. We are next translating the mechanistic model to a computational pharmacological model to simulate IL-2 and IL-2-based immunocytokines as therapeutics in the body, in order to help accelerate therapeutic regimen development. Further iteration between experimentation and modeling will make the computational mechanistic model more robust and predictive. Better mechanistic understanding of immunocytokines presents a new paradigm for the translation of safe and effective cytokine-based therapeutics, which has the potential to accelerate progress on treatment of conditions such as transplant intolerance, autoimmune diseases, and cancer.


Acknowledgements (Optional): :

References (Optional): :