Ivana Bozic, PhD, Discusses the Efficacy of Patient Preconditioning for CAR-T Therapy

Ivana Bozic, PhD, Assistant Professor, Department of Applied Mathematics, University of Washington, Seattle, Washington, highlights her study which evaluated the efficacy of patient preconditioning in the context of CAR T-cell therapy.


Hi. My name is Ivana Bozic. I'm an Assistant Professor in the Department of Applied Mathematics at the University of Washington in Seattle.

CAR T‑cell therapy has shown remarkable success against leukemias and lymphomas, but many questions still remain. For example, why is this therapy typically not at all effective in solid cancers?

Even in leukemias and lymphomas, many patients still do not respond or do not have durable responses. Even patients who do respond and have durable responses oftentimes can suffer very serious side effects.

It is also important to note that CAR T‑cell therapy is typically preceded by a preconditioning regimen, lymphodepleting chemotherapy, in order to improve efficacy and to reduce side effects.

What we wanted to do in this study, together with Katherine Owens, was to understand what contributes to the success of CAR T therapy and if there are some things that are close to the current clinical practice that could improve its success, especially as it relates to the interplay of preconditioning chemotherapy and CAR T‑cell therapy.

In order to understand this better, we developed a mathematical dynamical systems model that models this interaction between tumor cells, endogenous T‑cells, CAR T‑cells, and lymphodepleting chemotherapy.

We used numerical simulations of treatment plans from within the scope of current medical practice to assess the effects of preconditioning plans on the success of CAR T therapy.

What we (found) is that our model results affirm clinical observations that preconditioning can be crucial in most patients, and not just to reduce side effects, but to achieve remission at all.

Most importantly, we demonstrate that preconditioning plans that use the same CAR T‑cell dose at the same total concentration of chemotherapy can lead to very different patient outcomes due to different delivery schedules of chemotherapy and CAR T‑cell therapy.

For example, delivering the same total dose of chemotherapy over five days versus over three days can mean the difference between successful and unsuccessful outcome in some patients in our model. What also appears to be crucial is the length of the rest period, so between the end of the chemotherapy preconditioning and the CAR T‑cell injection.

That time seems to be critical, especially for patients with faster‑growing tumors, where we show in our model that shorter rest periods are typically better and can lead to successful therapy in the cases where sometimes if the rest period is a few days longer, this would lead to the failure of CAR T therapy.

We were also interested in understanding what are some critical things that could be improved in CAR T‑cell therapy that could lead to larger improvements in the effectiveness of this therapy.

We performed sensitivity analysis of the model parameters. It suggests that making small improvements in how effective CAR T‑cells are in attacking cancer cells can significantly reduce the minimum dose required for successful treatment.

We think of our modeling framework as a starting point, an inexpensive first step, for evaluating the efficacy of patient preconditioning in the context of CAR T‑cell therapy. We believe that some of our findings, especially those regarding delivery schedules of chemotherapy and the length of the rest period, warrant further experimental and perhaps also clinical investigation.

We plan to continue working on understanding CAR T‑cell therapy better and especially would like to understand the interplay between tumor cells and CAR T‑cells in solid tumors, where we believe that the role of three‑dimensional space is crucial. We are now extending our model to account for space as well.

Thank you and I hope you found this interesting.

2 + 3 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
Stay in the know.
OncNet Newsletter