Purpose. Regulatory T cells (Treg), endogenous regulators of the immune system, have a significant therapeutic potential for prevention of transplant rejection. Experimental evidences however, indicate a need to identifying the optimal conditions for an effective clinical use of these cells. Exploring all possible Treg conditions is unrealistic using conventional in vivo experimentation. Computational modeling provides an efficient and inexpensive method to guide experimentation and the design of therapeutic strategies. Here, strategies for the adoptive transfer of Tregs are theorized using an experimentally-based mathematical model of the immune response to murine heart transplants.

Methods. We recently developed a theoretical model of mouse heart transplantation that tracks populations of innate and adaptive immunity and proxies for pro- and anti-inflammatory factors within the graft and a representative draining lymph node. Here, this model is used to predict the impact of different Treg dosing strategies on graft survival. The timing of dosing, dosing rate, activation status of Tregs, and site of accumulation post-injection are assessed using the model.

Results. Model results suggest that activated Tregs accumulating directly to the graft (rather than lymph nodes) are most effective at extending graft survival. The administration day of a single dose of Tregs has an unexpected impact on graft survival: delaying administration to POD2 or POD3 is more effective than peri-transplant adoptive transfer. The model suggests that distributing a total dose over multiple days exerts a more protective effect than administering the same dose amount on a single day. Finally, the model predicts that a delayed dose at a lower rate can have a greater impact on graft survival than multiple doses at the same rate, suggesting that the timing of doses is most important.

Conclusion. Overall, the breadth of hypothetical conditions that the model can simulate reveals unexpected strategies (difficult to identify via conventional experimentation) that can then be validated experimentally. In this way, mathematical modeling can become an invaluable tool to accelerate the identification of effective treatment strategies for transplant patients.

CITATION INFORMATION: Raimondi G, Arun A, Dorabiala O, Arciero J. Maximizing the Potential of Treg-Based Therapies for Transplant Rejection via Computational Immune-Modeling: Effect of Dose, Timing, and Distribution. Am J Transplant. 2017;17 (suppl 3).

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