Modelling CAR T-cell Therapy with Patient Preconditioning

Abstract

The Federal Drug Administration (FDA) approved the first Chimeric Antigen Receptor T-cell (CAR T-cell) therapies for the treatment of several blood cancers in 2017, and efforts are underway to broaden CAR T technology to address other cancer types. Standard treatment protocols incorporate a preconditioning regimen of lymphodepleting chemotherapy prior to CAR T-cell infusion. However, the connection between preconditioning regimens and patient outcomes is still not fully understood. Optimizing patient preconditioning plans and reducing the CAR T-cell dose necessary for achieving remission could make therapy safer. In this paper, we test treatment regimens consisting of sequential administration of chemotherapy and CAR T-cell therapy on a system of differential equations that models the tumor-immune interaction. We use numerical simulations of treatment plans from within the scope of current medical practice to assess the effect of preconditioning plans on the success of CAR T-cell therapy. Model results affirm clinical observations that preconditioning can be crucial for some patients, not just to reduce side effects, but to even achieve remission at all. We demonstrate that preconditioning plans using the same CAR T-cell dose and the same total concentration of chemotherapy can lead to different patient outcomes due to different delivery schedules. Results from sensitivity analysis of the model parameters suggest that making small improvements in the effectiveness of CAR T-cells in attacking cancer cells, rather than targeting the recruitment and longevity of CAR T-cells, will significantly reduce the minimum dose required for successful treatment. Our modeling framework represents a starting point for evaluating the efficacy of patient preconditioning in the context of CAR T-cell therapy.

Immune checkpoint therapy modeling of PD-1/PD-L1 blockades reveals subtle difference in their response dynamics and potential synergy in combination

“…While these potentially curative cancer therapies are rapidly being developed and tested, a major barrier is the lack of quantitative models of their efficacy. To address this issue, mathematical modeling of cancer-immune interactions and of immunotherapy has been a topic of interest and primary significance [34,35,36,37,38,39,40,41]. Prior mathematical models of cancer-immune interactions [42,43] are mostly based on ordinary differential equations in combination with stochastic in-silico simulations [44,45].…”

Mechanistic Modeling of the Interplay Between Host Immune System, IL-7 and UCART19 Allogeneic CAR-T Cells in Adult B-cell Acute Lymphoblastic Leukemia

“…The relationship between CAR affinity, antigen abundance, tumor cell depletion, and CAR-T cell expansion were also investigated by Singh and colleagues ( 24 ). Owens and colleagues explored with their theoretical model the impact of preconditioning in the safety and efficacy of autologous CAR-T cell therapies ( 25 ). However, to our knowledge, no prior published models address the relationship between lymphodepletion and allogeneic CAR-T cell pharmacokinetics.…”