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Kuznetsov M, Adhikarla V, Caserta E, Wang X, Shively JE, Pichiorri F, Rockne RC. Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595377. [PMID: 38826403 PMCID: PMC11142146 DOI: 10.1101/2024.05.22.595377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Targeted radionuclide therapy is based on injections of cancer-specific molecules conjugated with radioactive nuclides. Despite the specificity of this treatment, it is not devoid of side-effects limiting its use and is especially harmful for rapidly proliferating organs well perfused by blood, like bone marrow. Optimization of radioconjugates administration accounting for toxicity constraints can increase treatment efficacy. Based on our experiments on disseminated multiple myeloma mouse model treated by 225Ac-DOTA-daratumumab, we developed a mathematical model which investigation highlighted the following principles for optimization of targeted radionuclide therapy. 1) Nuclide to antibody ratio importance. The density of radioconjugates on cancer cells determines the density of radiation energy deposited in them. Low labeling ratio as well as accumulation of unlabeled antibodies and antibodies attached to decay products in the bloodstream can mitigate cancer radiation damage due to excessive occupation of specific receptors by antibodies devoid of radioactive nuclides. 2) Cancer binding capacity-based dosing. The rate of binding of drug to cancer cells depends on the total number of their specific receptors, which therefore can be estimated from the pharmacokinetic curve of diagnostic radioconjugates. Injection of doses significantly exceeding cancer binding capacity should be avoided since radioconjugates remaining in the bloodstream have negligible efficacy to toxicity ratio. 3) Particle range-guided multi-dosing. The use of short-range particle emitters and high-affinity antibodies allows for robust treatment optimization via initial saturation of cancer binding capacity, enabling redistribution of further injected radioconjugates and deposited dose towards still viable cells that continue expressing specific receptors.
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Affiliation(s)
- Maxim Kuznetsov
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Vikram Adhikarla
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Enrico Caserta
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Xiuli Wang
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, California, United States
| | - John E Shively
- Department of Molecular Imaging & Therapy, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States
| | - Flavia Pichiorri
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Russell C Rockne
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
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Adhikarla V, Awuah D, Caserta E, Minnix M, Kuznetsov M, Krishnan A, Wong JYC, Shively JE, Wang X, Pichiorri F, Rockne RC. Designing combination therapies for cancer treatment: application of a mathematical framework combining CAR T-cell immunotherapy and targeted radionuclide therapy. Front Immunol 2024; 15:1358478. [PMID: 38698840 PMCID: PMC11063284 DOI: 10.3389/fimmu.2024.1358478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/21/2024] [Indexed: 05/05/2024] Open
Abstract
Introduction Cancer 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. Methods Using 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. Results Therapy 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. Discussion Mathematical 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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Affiliation(s)
- Vikram Adhikarla
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Dennis Awuah
- Department of Hematology and Hematopoietic Cell Transplantation, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Enrico Caserta
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Megan Minnix
- Department of Molecular Imaging and Therapy, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Maxim Kuznetsov
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Amrita Krishnan
- Department of Hematology and Hematopoietic Cell Transplantation, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Jefferey Y. C. Wong
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, United States
| | - John E. Shively
- Department of Molecular Imaging and Therapy, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Xiuli Wang
- Department of Hematology and Hematopoietic Cell Transplantation, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Flavia Pichiorri
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Russell C. Rockne
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
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Mody H, Ogasawara K, Zhu X, Miles D, Shastri PN, Gokemeijer J, Liao MZ, Kasichayanula S, Yang TY, Chemuturi N, Gupta S, Jawa V, Upreti VV. Best Practices and Considerations for Clinical Pharmacology and Pharmacometric Aspects for Optimal Development of CAR-T and TCR-T Cell Therapies: An Industry Perspective. Clin Pharmacol Ther 2023; 114:530-557. [PMID: 37393588 DOI: 10.1002/cpt.2986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 06/26/2023] [Indexed: 07/04/2023]
Abstract
With the promise of a potentially "single dose curative" paradigm, CAR-T cell therapies have brought a paradigm shift in the treatment and management of hematological malignancies. Both CAR-T and TCR-T cell therapies have also made great progress toward the successful treatment of solid tumor indications. The field is rapidly evolving with recent advancements including the clinical development of "off-the-shelf" allogeneic CAR-T therapies that can overcome the long and difficult "vein-to-vein" wait time seen with autologous CAR-T therapies. There are unique clinical pharmacology, pharmacometric, bioanalytical, and immunogenicity considerations and challenges in the development of these CAR-T and TCR-T cell therapies. Hence, to help accelerate the development of these life-saving therapies for the patients with cancer, experts in this field came together under the umbrella of International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) to form a joint working group between the Clinical Pharmacology Leadership Group (CPLG) and the Translational and ADME Sciences Leadership Group (TALG). In this white paper, we present the IQ consortium perspective on the best practices and considerations for clinical pharmacology and pharmacometric aspects toward the optimal development of CAR-T and TCR-T cell therapies.
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Affiliation(s)
- Hardik Mody
- Clinical Pharmacology, Genentech, South San Francisco, California, USA
| | - Ken Ogasawara
- Clinical Pharmacology, Pharmacometrics, Disposition and Bioanalysis, Bristol Myers Squibb, Lawrence Township, New Jersey, USA
| | - Xu Zhu
- Quantitative Clinical Pharmacology, AstraZeneca, Boston, Massachusetts, USA
| | - Dale Miles
- Clinical Pharmacology, Genentech, South San Francisco, California, USA
| | | | - Jochem Gokemeijer
- Discovery Biotherapeutics, Bristol Myers Squibb, Cambridge, Massachusetts, USA
| | - Michael Z Liao
- Clinical Pharmacology, Genentech, South San Francisco, California, USA
| | | | - Tong-Yuan Yang
- Bioanalytical Discovery and Development Sciences, Janssen R&D, LLC, Spring House, Pennsylvania, USA
| | - Nagendra Chemuturi
- Clinical Pharmacology, DMPK, Pharmacometrics, Moderna, Inc., Cambridge, Massachusetts, USA
| | - Swati Gupta
- Development Biological Sciences, Immunology, AbbVie, Irvine, California, USA
| | - Vibha Jawa
- Clinical Pharmacology, Pharmacometrics, Disposition and Bioanalysis, Bristol Myers Squibb, Lawrence Township, New Jersey, USA
| | - Vijay V Upreti
- Clinical Pharmacology, Modeling & Simulation, Amgen, South San Francisco, California, USA
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Awuah D, Minnix M, Caserta E, Tandoh T, Adhikarla V, Poku E, Rockne R, Pichiorri F, Shively JE, Wang X. Sequential CAR T cell and targeted alpha immunotherapy in disseminated multiple myeloma. Cancer Immunol Immunother 2023:10.1007/s00262-023-03461-z. [PMID: 37209218 PMCID: PMC10361855 DOI: 10.1007/s00262-023-03461-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/01/2023] [Indexed: 05/22/2023]
Abstract
Multiple myeloma (MM) is still an incurable disorder despite improved antibody and cellular therapies against different MM antigens. Single targeted antigens have so far been ineffective against MM with most patients relapsing after initial response. Hence, sequential immunotherapies directed at different targets are expected to perform better than monotherapy alone. Here, we optimized and established in preclinical studies the therapeutic rationale of using targeted alpha therapy (TAT) directed against CD38 antigen (225Ac-DOTA-daratumumab) with CAR T cell therapy directed at CS1 antigen in a systemic MM model. The sequential therapies compared CAR T therapy followed by TAT to TAT followed by CAR T therapy. CAR T cell monotherapy increased median survival from 49 days (d) in untreated controls to 71d with a modest improvement to 89d for 3.7 kBq of TAT given 14d later. When CAR T was followed by 7.4 kBq of TAT 29d later, sequential therapy increased median survival from 47d in untreated controls to 106d, compared to 68d for CAR T monotherapy. When CAR T therapy was followed by untargeted alpha immunotherapy using 7.4 kBq of 225Ac-DOTA-trastuzumab (anti-HER2) antibody 29d later, there was only a slight improvement in response over CAR T monotherapy demonstrating the role of tumor targeting. TAT (7.4 kBq) followed by CAR T therapy was also effective when CAR T therapy was delayed for 21d vs 14d or 28d post TAT, highlighting the importance of timing sequential therapies. Sequential targeted therapies using CS1 CAR T or 225Ac-DOTA-CD38 TAT in either order shows promise over monotherapies alone.
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Affiliation(s)
- Dennis Awuah
- Department of Hematology, City of Hope Medical Center, Beckman Research Institute, Duarte, CA, 91010, USA
| | - Megan Minnix
- Department of Immunology and Theranostics, Beckman Research Institute, City of Hope, Duarte, CA, 91010, USA
| | - Enrico Caserta
- Department of Hematologic Malignancies Research Institute, City of Hope Medical Center, Duarte, CA, 91010, USA
| | - Theophilus Tandoh
- Department of Hematologic Malignancies Research Institute, City of Hope Medical Center, Duarte, CA, 91010, USA
| | - Vikram Adhikarla
- Division of Mathematical Oncology and Computational Systems Biology, Beckman Research Institute, City of Hope, Duarte, CA, 91010, USA
| | - Erasmus Poku
- City of Hope Medical Center, Duarte, CA, 91010, USA
| | - Russell Rockne
- Division of Mathematical Oncology and Computational Systems Biology, Beckman Research Institute, City of Hope, Duarte, CA, 91010, USA
| | - Flavia Pichiorri
- Department of Hematologic Malignancies Research Institute, City of Hope Medical Center, Duarte, CA, 91010, USA.
| | - John E Shively
- Department of Immunology and Theranostics, Beckman Research Institute, City of Hope, Duarte, CA, 91010, USA.
| | - Xiuli Wang
- Department of Hematology, City of Hope Medical Center, Beckman Research Institute, Duarte, CA, 91010, USA.
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Brummer AB, Xella A, Woodall R, Adhikarla V, Cho H, Gutova M, Brown CE, Rockne RC. Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables. Front Immunol 2023; 14:1115536. [PMID: 37256133 PMCID: PMC10226275 DOI: 10.3389/fimmu.2023.1115536] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/27/2023] [Indexed: 06/01/2023] Open
Abstract
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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Affiliation(s)
- Alexander B. Brummer
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
- Department of Physics and Astronomy, College of Charleston, Charleston, SC, United States
| | - Agata Xella
- Department of Hemtaology and Hematopoietic Cell Translation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Ryan Woodall
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Vikram Adhikarla
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Heyrim Cho
- Department of Mathematics, University of California, Riverside, Riverside, CA, United States
| | - Margarita Gutova
- Department of Stem Cell Biology and Regenerative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Christine E. Brown
- Department of Hemtaology and Hematopoietic Cell Translation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Russell C. Rockne
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
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Butner JD, Dogra P, Chung C, Pasqualini R, Arap W, Lowengrub J, Cristini V, Wang Z. Mathematical modeling of cancer immunotherapy for personalized clinical translation. NATURE COMPUTATIONAL SCIENCE 2022; 2:785-796. [PMID: 38126024 PMCID: PMC10732566 DOI: 10.1038/s43588-022-00377-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2023]
Abstract
Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.
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Affiliation(s)
- Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Department of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - John Lowengrub
- Department of Mathematics, University of California at Irvine, Irvine, CA, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
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Brummer AB, Yang X, Ma E, Gutova M, Brown CE, Rockne RC. Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy. PLoS Comput Biol 2022; 18:e1009504. [PMID: 35081104 PMCID: PMC8820647 DOI: 10.1371/journal.pcbi.1009504] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 02/07/2022] [Accepted: 01/12/2022] [Indexed: 12/14/2022] Open
Abstract
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. Bioengineering and gene-editing technologies have paved the way for advance immunotherapies that can target patient-specific tumor cells. One of these therapies, chimeric antigen receptor (CAR) T-cell therapy has recently shown promise in treating glioblastoma, an aggressive brain cancer often with poor patient prognosis. Dexamethasone is a commonly prescribed anti-inflammatory medication due to the health complications of tumor associated swelling in the brain. However, the immunosuppressant effects of dexamethasone on the immunotherapeutic CAR T-cells are not well understood. To address this issue, we use mathematical modeling to study in vitro dynamics of dexamethasone and CAR T-cells in three patient-derived glioblastoma cell lines. We find that in each cell line studied there is a threshold of tolerable dexamethasone concentration. Below this threshold, CAR T-cells are successful at eliminating the cancer cells, while above this threshold, dexamethasone critically inhibits CAR T-cell efficacy. Our modeling suggests that in the presence of high dexamethasone reduced CAR T-cell efficacy, or increased exhaustion, can occur and result in CAR T-cell treatment failure.
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Affiliation(s)
- Alexander B. Brummer
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
- * E-mail: (ABB); (CEB); (RCR)
| | - Xin Yang
- Department of Hematology and Hematopoietic Cell Translation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
| | - Eric Ma
- Department of Hematology and Hematopoietic Cell Translation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
| | - Margarita Gutova
- Department of Stem Cell Biology and Regenerative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
| | - Christine E. Brown
- Department of Hematology and Hematopoietic Cell Translation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
- * E-mail: (ABB); (CEB); (RCR)
| | - Russell C. Rockne
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
- * E-mail: (ABB); (CEB); (RCR)
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