Maximizing the potential of Treg-based therapies for transplant rejection via computational immune-modeling: effect of dose, timing, and distribution

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Abstract Regulatory T cells (Treg) are a promising target to treat transplant rejection but the optimal conditions for their effective clinical use still remain to be identified. Exploring all possible Treg conditions is unrealistic using conventional in vivo experimentation. Computational modeling provides an efficient and inexpensive solution to guide experimentation and the design of therapeutic protocols. Here, strategies for the adoptive transfer of Tregs are theorized using an experimentally-based mathematical model of the immune response to murine heart transplants. The model tracks populations of innate and adaptive immunity and proxies for pro- and anti-inflammatory factors within the graft and a representative lymph node. The model is used to predict the impact of different Treg adoptive transfer strategies on graft survival. In particular: timing of dosing, dosing rate, activation status of Tregs, and site of accumulation post-injection. Model results show that activated Tregs accumulating directly to the graft are most protective. 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 transfer. The model also 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. Thus, theoretical simulations are an invaluable tool that reveals unexpected treatment strategies for transplant patients that can be validated experimentally.