A Model-Based Investigation of Cytokine Storm for T-Cell Therapy

Hopkins, Brooks; Tucker, Matthew S.; Pan, Yiming; Fang, Nan; Huang, Zuyi

doi:10.1016/j.ifacol.2018.09.039

Cited by 15 publications

(12 citation statements)

References 9 publications

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“…Mathematical models have been developed to describe the complex interaction between the activated neutrophils/monocyte and pro inflammatory cytokines in bacteria‐induced sepsis, but none of these models can be used to investigate the CRS and CART cells in human subjects without major modifications . Recently, several mathematical models have been developed to describe the biologics of the CART . However, no clinical data are used to develop and validate these models, and, therefore, the ability of these models in describing the observed kinetics of cytokines and CART is questionable.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, several mathematical models have been developed to describe the biologics of the CART . However, no clinical data are used to develop and validate these models, and, therefore, the ability of these models in describing the observed kinetics of cytokines and CART is questionable.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, further investigation is needed to elucidate the quantitative relationship of CRS and the DB/CART dose in order to design an optimal dosing strategy for the prevention of the CRS. Recently, several mathematical models have been developed to provide theoretical speculation for the biologic of CART therapy, but none of these models has been used to describe the kinetic profiles of pro inflammatory cytokine and CART observed in patients with cancer treated with CART . Hence, in this study, we develop the first quantitative systems pharmacology (QSP) model that can describe the observed kinetic profiles of CART, DB, and pro inflammatory cytokines in a patient with advanced chronic lymphocytic leukemia (CLL) treated with anti‐CD19 CART therapy.…”

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confidence: 99%

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Quantitative Systems Pharmacology Model of Chimeric Antigen Receptor T‐Cell Therapy

Hardiansyah

2019

Clinical Translational Sci

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Chimeric antigen receptor T‐cell (CART) therapy is a new and promising cancer therapy. However, severe toxicity due to cytokine release syndrome (CRS) in CART‐treated patients highlighted the possible danger of this new therapy. Disease burden and CART doses are the potential factors associated with CRS but the detail relationships between these factors and the severity of the CRS remain largely unknown. In this study, the quantitative systems pharmacology (QSP) approach is used to quantify the complex relationships among CART doses, disease burden, and pro inflammatory cytokines in human subjects and to gain relevant insights into the determinant of clinical toxicity/efficacy in development of CART therapy. The expansion of CART and elimination of B cells are more highly correlated with disease burden than the administered CART doses. To our best knowledge, this is the first QSP model that can describe the observed clinical data from CART‐treated patients with cancer. This QSP model is a valuable tool for deepening our understanding of how the mechanism of action connects to the clinical outcomes and, therefore, may serve as an important model‐based platform to guide the development and personalized dosing of the CART therapy.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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Quantitative Systems Pharmacology Model of Chimeric Antigen Receptor T‐Cell Therapy

Hardiansyah

2019

Clinical Translational Sci

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“…Several computational models have recently been developed to investigate aspects of CAR T-cell therapy including cytokine release syndrome toxicity management (Hopkins et al, 2018;Stein et al, 2017Stein et al, , 2019, mechanisms of CAR T-cell activation (Harris et al, 2018;Rohrs et al, 2019), and how factors including CAR T-cell dose, donor-dependent T-cell differences, cancer cell proliferation, and target antigen expression contribute to the overall effectiveness of CAR T-cell therapy (Hardiansyah and Ng, 2019;Kimmel et al, 2019;Rodrigues et al, 2019;Sahoo et al, 2020). Here we formulate a model that allows us to investigate how the interplay between specific preconditioning plans and CAR T-cell dosage affects patient outcomes.…”

Section: Mathematical Modeling Of Cancer Treatmentmentioning

confidence: 99%

Modelling CAR T-cell Therapy with Patient Preconditioning

Owens

Božić

2020

Preprint

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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.

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“…As CAR T-cell therapy is a newly advanced treatment modality, relatively few studies have utilized computational modelling to understand and improve this cell-based therapy. Recently computational models have been developed to investigate cytokine release syndrome for toxicity management [15][16][17], effect of cytokine release syndrome on CAR T-cell proliferation [18], and mechanisms of CAR T-cell activation [19,20], dosing strategies [21]. However, it remains an open challenge how to use mathematical modeling to study and ultimately predict dynamics of CAR T-cell mediated cancer cell killing with respect to CAR T-cell dose, cancer cell proliferation, target antigen expression, and how these factors contribute to the overall effectiveness of CAR T-cell therapy.…”

Section: Introductionmentioning

confidence: 99%

Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data

Sahoo¹,

Yang²,

Abler³

et al. 2019

Preprint

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Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of hematological cancers and is currently being investigated for solid tumors including high-grade glioma brain tumors. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumors. In this work we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor t-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, CAR T-cell proliferation and exhaustion, and CAR T-cell persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyzer to parameterize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted as compared to higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumor growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumor characteristics including tumor growth rate and antigen level to maximize therapeutic benefit.Statement of SignificanceWe utilize a mathematical model to deconvolute the nonlinear contributions of CAR T-cell proliferation and exhaustion to predict therapeutic efficacy and dependence on CAR T-cell dose and target antigen levels.

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A Model-Based Investigation of Cytokine Storm for T-Cell Therapy

Cited by 15 publications

References 9 publications

Quantitative Systems Pharmacology Model of Chimeric Antigen Receptor T‐Cell Therapy

Quantitative Systems Pharmacology Model of Chimeric Antigen Receptor T‐Cell Therapy

Modelling CAR T-cell Therapy with Patient Preconditioning

Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data

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