Modeling 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.

A Mathematical Description of the Bone Marrow Dynamics during CAR T-Cell Therapy in B-Cell Childhood Acute Lymphoblastic Leukemia

“…While there are some studies involving solid tumors [26,27], most focus on hematological malignancies, given the success obtained in this group of cancers. There are general models of leukemia and lymphoma [28][29][30][31] and more specific applications in B-cell lymphoma [32], B-cell chronic lymphoblastic leukemia [33], T-cell ALL [34], and B-cell ALL [35,36].…”

Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy

“…Yet still, another treatment strategy could be patient preconditioning with Dexamethasone followed with delayed, and perhaps pulsed, CAR T-cell delivery. Recent simulated studies investigating preconditioning with chemotherapy [32] or targeted radionuclide therapy [33] followed with CAR T-cells suggests that combination pretreatment and time-delay approaches have clinical value, in particular for providing therapeutic dosages at lower total concentrations. Based on the duration of observable changes to the phase-space trajectory in Fig 5 , we suggest a time-delay of 2-3 Dexamethasone half-lives.…”

“…These same approaches can be naturally extended to inform and predict both pre-clinical and clinical applications of immunotherapies [18][19][20][21]. Most recently, such efforts have been applied to model and predict CAR T-cell therapies for leukemia [22,23], glioblastoma organoids and solid brains tumors [11], and combination therapies with radiotherapy [24] and chemotherapy [25].…”

“…Previous studies have utilized nonautonomous models to account for time-varying environmental conditions in generic predator-prey systems [33,34], for pulsed patient preconditioning in combination CAR T-cell and chemotherapy [25], and in the analysis of pharmacokinetic-pharmacodynamic tumor growth models with time-dependent perturbations due to anticancer agents (see chapter 7 in [35]). In the present model, we highlight the fact that the decaying dexamethasone concentration is modeled with a bounded and continuously differentiable function on the interval [0, ∞).…”

Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy

“…These same approaches can be naturally extended to inform and predict both pre-clinical and clinical applications of immunotherapies [ 18 – 21 ]. Most recently, such efforts have been applied to model and predict CAR T-cell therapies for leukemia [ 22 , 23 ], glioblastoma organoids and solid brains tumors [ 11 ], and combination therapies with radiotherapy [ 24 ] and chemotherapy [ 25 ].…”