A Mathematical Modeling Approach for Targeted Radionuclide and Chimeric Antigen Receptor-T Cell Combination Therapy

Adhikarla, Vikram; Awuah, Dennis; Brummer, Alex; Caserta, Enrico; Krishnan, Amrita; Pichiorri, Flavia; Minnix, Megan; Shively, John E.; Wong, Jeffrey Y.C.; Wang, Xiuli; Rockne, Russell C.

doi:10.20944/preprints202109.0300.v1

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Here we have chosen the model parameters within a group to vary by 50% from the group mean. This uncertainty level was chosen to capture expected variation in mice groups based on our previous experience ( 40 ); and is enough to demonstrate the differences in mean response to therapy between the groups while at the same time accommodating qualitatively reasonable fits to individual mice. In patients, the model can be personalized better by delivering multiple cycles of therapy and evaluating the patient response to individual doses to tailor the next dose or therapy sequence.…”

Section: Discussionmentioning

confidence: 99%

“…The framework of the combined mathematical model for TRT and CAR T-cell therapy dynamics is given by the set of differential equations as described below ( Figure 1 ) ( 40 ). This simplified model considers only the tumor cells, CAR-T cells, and action of the TRT.…”

Section: Methodsmentioning

confidence: 99%

“…We recently proposed a mathematical model for optimization of targeted radionuclide therapy with CAR-T cell therapy ( 40 ). The model considered the tumor response to targeted radionuclide and CAR-T cell therapies as monotherapies and preclinical data from each set of monotherapy experiments was used to characterize the model.…”

Section: Introductionmentioning

confidence: 99%

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Designing combination therapies for cancer treatment: application of a mathematical framework combining CAR T-cell immunotherapy and targeted radionuclide therapy

Adhikarla,

Awuah,

Caserta

et al. 2024

Front. Immunol.

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IntroductionCancer combination treatments involving immunotherapies with targeted radiation therapy are at the forefront of treating cancers. However, dosing and scheduling of these therapies pose a challenge. Mathematical models provide a unique way of optimizing these therapies. MethodsUsing a preclinical model of multiple myeloma as an example, we demonstrate the capability of a mathematical model to combine these therapies to achieve maximum response, defined as delay in tumor growth. Data from mice studies with targeted radionuclide therapy (TRT) and chimeric antigen receptor (CAR)-T cell monotherapies and combinations with different intervals between them was used to calibrate mathematical model parameters. The dependence of progression-free survival (PFS), overall survival (OS), and the time to minimum tumor burden on dosing and scheduling was evaluated. Different dosing and scheduling schemes were evaluated to maximize the PFS and optimize timings of TRT and CAR-T cell therapies. ResultsTherapy intervals that were too close or too far apart are shown to be detrimental to the therapeutic efficacy, as TRT too close to CAR-T cell therapy results in radiation related CAR-T cell killing while the therapies being too far apart result in tumor regrowth, negatively impacting tumor control and survival. We show that splitting a dose of TRT or CAR-T cells when administered in combination is advantageous only if the first therapy delivered can produce a significant benefit as a monotherapy. DiscussionMathematical models are crucial tools for optimizing the delivery of cancer combination therapy regimens with application along the lines of achieving cure, maximizing survival or minimizing toxicity.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Designing combination therapies for cancer treatment: application of a mathematical framework combining CAR T-cell immunotherapy and targeted radionuclide therapy

Adhikarla,

Awuah,

Caserta

et al. 2024

Front. Immunol.

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“…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.…”

Section: Potential Applications and Clinical Relevancementioning

confidence: 99%

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

Yang

et al. 2021

Preprint

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Chimeric antigen receptor (CAR) T-cell therapy is potentially an effective targeted immunotherapy for glioblastoma, yet there is presently little known about the efficacy of CAR T-cell treatment when combined with the widely used anti-inflammatory and immunosuppressant glucocorticoid, Dexamethasone. Here we present a mathematical model-based analysis of three patient-derived glioblastoma cell lines treated in vitro with CAR T-cells and Dexamethasone. Advanced in vitro experimental cell killing assay technologies allow for highly resolved temporal dynamics of tumor cells treated with CAR T-cells and Dexamethone, making this a valuable model system for studying the rich dynamics of nonlinear biological processes with translational applications. We model the system as a non-autonomous, two-species predator-prey interaction of tumor cells and CAR T-cells, with explicit time-dependence in the clearance rate of Dexamethasone. Using time as a bifurcation parameter, we show that (1) the presence of Dexamethasone destabilizes coexistence equilibria between CAR T-cells and tumor cells and (2) as Dexamethasone is cleared from the system, a stable coexistence equilibrium returns in the form of a Hopf bifurcation. With the model fit to experimental data, we demonstrate that high concentrations of Dexamethasone antagonizes CAR T-cell efficacy by exhausting, or reducing the activity of CAR T-cells, and by promoting tumor cell growth. Finally, we identify a critical threshold in the ratio of CAR T-cell death to CAR T-cell proliferation rates that predicts eventual treatment success or failure that may be used to guide the dose and timing of CAR T-cell therapy in the presence of Dexamethasone in patients.

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“…Predator-prey systems are a broad class of ordinary differential equations (ODEs) that aim to characterize changes in populations between two or more groups of organisms in which at least one survives via predation on another. Originally applied to the study of plant herbivory (20) and fishery monitoring (21) in the early 20 th century, predator-prey models have since become a workhorse of ecology, evolutionary biology, and most recently mathematical oncology (19,22). Importantly, predator-prey models underpin much of the computational modeling of CAR T-cell killing, particularly in the context of in vitro cell killing assays (7,23).…”

Section: Introductionmentioning

confidence: 99%

Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables

et al. 2023

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In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to a real biological system in order discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dynamics of CAR T-cell populations and cancer. Importantly, we show how the model terms can be interpreted biologically in relation to different CAR T-cell functional responses, single or double CAR T-cell-cancer cell binding models, and density-dependent growth dynamics in either of the CAR T-cell or cancer cell populations. We show how this data-driven model-discovery based approach provides unique insight into CAR T-cell dynamics when compared to an established model-first approach. These results demonstrate the potential for SINDy to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics.

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A Mathematical Modeling Approach for Targeted Radionuclide and Chimeric Antigen Receptor-T Cell Combination Therapy

Cited by 5 publications

References 17 publications

Designing combination therapies for cancer treatment: application of a mathematical framework combining CAR T-cell immunotherapy and targeted radionuclide therapy

Designing combination therapies for cancer treatment: application of a mathematical framework combining CAR T-cell immunotherapy and targeted radionuclide therapy

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

Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables

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