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

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

doi:10.3390/cancers13205171

Cited by 10 publications

(21 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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 [ 25 ] or targeted radionuclide therapy [ 24 ] 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: Discussionmentioning

confidence: 99%

“…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 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Brummer

Yang

et al. 2022

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

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 dexamethasone, making this a valuable model system for studying the rich dynamics of nonlinear biological processes with translational applications. We model the system as a nonautonomous, 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) dexamethasone destabilizes coexistence equilibria between CAR T-cells and tumor cells in a dose-dependent manner 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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Brummer

Yang

et al. 2022

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, we only consider the higher-order binding scenario of two CAR T-cells to one cancer cell. Solving for the contributions to the cancer and CAR T-cell populations due to binding dynamics results in, 18)- (23). See [30] for further development and analysis of the cell binding model.…”

Section: Car T-cell-cancer Cell Bindingmentioning

confidence: 99%

“…Expressions for how rate constants combine to contribute to the growth or death of the cancer cell and CAR T-cell populations are presented in Eqs. (18)-(23). See[30] for further development and analysis of the cell binding model.…”

mentioning

confidence: 99%

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

Brummer

Xella

Woodall

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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 implement the sparse identification of non-linear dynamics (SINDy) algorithm to discover cell-cell interaction dynamics in in vitro experimental data, derived from 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 cancer and CAR T-cell populations. 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-tumor cell binding models, and Allee effects in either of the CAR T-cell or cancer cell populations. As an application of data-driven model discovery, this work serves to identify biological interactions, and has the potential to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics.

show abstract

“…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].…”

Section: Mathematical Modeling Of Car T-cells Has Demonstrated Value In Quantitativelymentioning

confidence: 99%

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

Yang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 10 publications

References 26 publications

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

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

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

Contact Info

Product

Resources

About