Early Survival Prediction Framework in CD19-Specific CAR-T Cell Immunotherapy Using a Quantitative Systems Pharmacology Model

Mueller‐Schoell, Anna; Puebla-Osorio, Nahum; Michelet, Robin; Green, Michael R.; Künkele, Annette; Huisinga, Wilhelm; Chasen, Beth; Neelapu, Sattva S.; Yee, Cassian; Kloft, Charlotte

doi:10.3390/cancers13112782

Cited by 26 publications

(34 citation statements)

References 65 publications

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“…Prediction of the late response of patients to CAR T-cell therapy during early treatment stage will greatly improve patient outcomes by guiding the treatment regimen that follows, especially for patients with acute disease progression. [36][37][38] Having demonstrated that our computational model accurately recapitulated the cellular dynamics of CAR T-cell therapy, we found that the response can be differentiated by the actual function level and dynamics of CAR T-cells in individual patients. Thus, we hypothesized Open access that such responses can be predicted using our computational immuno-oncology model with input of clinically measurable and available patient information related to CAR T-cell dynamics, such as the peak value and AUC7 (area under the curve from days 0 to 7) of CAR T-cells, at the early stage of CAR T-cell treatment.…”

Section: In Silico Prediction Of Late Response At Early Stage Of Car ...mentioning

confidence: 79%

Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse

Liu

Zhang

et al. 2022

J Immunother Cancer

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BackgroundAdaptive CD19-targeted chimeric antigen receptor (CAR) T-cell transfer has become a promising treatment for leukemia. Although patient responses vary across different clinical trials, reliable methods to dissect and predict patient responses to novel therapies are currently lacking. Recently, the depiction of patient responses has been achieved using in silico computational models, with prediction application being limited.MethodsWe established a computational model of CAR T-cell therapy to recapitulate key cellular mechanisms and dynamics during treatment with responses of continuous remission (CR), non-response (NR), and CD19-positive (CD19+) and CD19-negative (CD19−) relapse. Real-time CAR T-cell and tumor burden data of 209 patients were collected from clinical studies and standardized with unified units in bone marrow. Parameter estimation was conducted using the stochastic approximation expectation maximization algorithm for nonlinear mixed-effect modeling.ResultsWe revealed critical determinants related to patient responses at remission, resistance, and relapse. For CR, NR, and CD19+relapse, the overall functionality of CAR T-cell led to various outcomes, whereas loss of the CD19+antigen and the bystander killing effect of CAR T-cells may partly explain the progression of CD19−relapse. Furthermore, we predicted patient responses by combining the peak and accumulated values of CAR T-cells or by inputting early-stage CAR T-cell dynamics. A clinical trial simulation using virtual patient cohorts generated based on real clinical patient datasets was conducted to further validate the prediction.ConclusionsOur model dissected the mechanism behind distinct responses of leukemia to CAR T-cell therapy. This patient-based computational immuno-oncology model can predict late responses and may be informative in clinical treatment and management.

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Section: In Silico Prediction Of Late Response At Early Stage Of Car ...mentioning

confidence: 79%

Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse

Liu

Zhang

et al. 2022

J Immunother Cancer

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“…administration, whereas lower volumetric blood flow rate at the hepatic artery injection site near the tumor is beneficial for CAR T distribution to the hepatic tumor. As current investigation brought insights into the contribution of blood flow to CAR T distribution to solid tumors, future effort will strive to understand migration behavior of heterogenous CAR T subsets and phenotypes and the impact on its safety and efficacy [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Development of minimal physiologically-based pharmacokinetic-pharmacodynamic models for characterizing cellular kinetics of CAR T cells following local deliveries in mice

Tsai

Singh

Xia

et al. 2022

J Pharmacokinet Pharmacodyn

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Chimeric antigen receptor (CAR) T cell therapies have revolutionized the treatment of hematologic malignancies and have potentials for solid tumor treatment. To overcome limited CAR T cell infiltration to solid tumors, local delivery of CAR T cells is a practical strategy that has shown promising therapeutic outcome and safety profile in the clinic. It is of great interest to understand the impact of dosing routes on CAR T cell distribution, subsequent proliferation and tumor killing in a quantitative manner to identify key factors that contribute to CAR T efficacy and safety. In this study, we established mouse minimal physiologically-based pharmacokinetic (mPBPK) models combined with pharmacodynamic (PD) components to delineate CAR T cell distribution, proliferation, tumor growth, and tumor cell killing in the cases of pleural and liver tumors. The pleural tumor model reasonably captured published CAR T cellular kinetic and tumor growth profiles in mice. The mPBPK-PD simulation of a liver tumor mouse model showed a substantial increase in initial tumor infiltration and earlier CAR T cell proliferation with local hepatic artery delivery compared to portal vein and intravenous (i.v.) injections whereas portal vein injection showed little difference from i.v. administration, suggesting the importance of having the injection site close to tumor for maximal effect of non-systemic administration. Blood flow rate in the liver tumor was found to be a sensitive parameter for cellular kinetics and efficacy, indicating a potential role of tumor vascularization in the efficacy of CAR T cell therapies.

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“…The challenge of this approach is the requirement of enormous T cell data measured by flow cytometry for each kind/ species, as well as longitudinal data of tumor dynamics to capture the antigen-dependent T cell kinetics. A recent model by Mueller-Schoell et al 35 incorporated four phenotypes, including naïve, central memory, effector memory, and terminally differentiated effector T cells and enabled both T cell-mediated tumor killing as well as tumor-dependent T cell proliferation. 35 However, due to the sparse data from only 19 patients, several parameters, including tumor growth rate, homeostatic proliferation rate constants, and death rate constants, are unidentifiable, and only two parameters were allowed to consider IIV during fitting.…”

Section: Modeling and Simulation Strategies Of Actmentioning

confidence: 99%

Section: Modeling and Simulation Strategies Of Actmentioning

confidence: 99%

Clinical Pharmacology Perspectives for Adoptive Cell Therapies in Oncology

Huang¹,

Li²,

Liao³

et al. 2022

Clin Pharma and Therapeutics

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Adoptive cell therapies (ACTs) have shown transformative efficacy in oncology with five US Food and Drug Administration (FDA) approvals for chimeric antigen receptor (CAR) T‐cell therapies in hematological malignancies, and promising activity for T cell receptor T‐cell therapies in both liquid and solid tumors. Clinical pharmacology can play a pivotal role in optimizing ACTs, aided by modeling and simulation toolboxes and deep understanding of the underlying biological and immunological processes. Close collaboration and multilevel data integration across functions, including chemistry, manufacturing, and control, biomarkers, bioanalytical, and clinical science and safety teams will be critical to ACT development. As ACT is comprised of alive, polyfunctional, and heterogeneous immune cells, its overall physicochemical and pharmacological property is vastly different from other platforms/modalities, such as small molecule and protein therapeutics. In this review, we first describe the unique kinetics of T cells and the appropriate bioanalytical strategies to characterize cellular kinetics. We then assess the distinct aspects of clinical pharmacology for ACTs in comparison to traditional small molecule and protein therapeutics. Additionally, we provide a review for the five FDA‐approved CAR T‐cell therapies and summarize their properties, cellular kinetic characteristics, dose‐exposure‐response relationship, and potential baseline factors/variables in product, patient, and regimen that may affect the safety and efficacy. Finally, we probe into existing empirical and mechanistic quantitative techniques to understand how various modeling and simulation approaches can support clinical pharmacology strategy and propose key considerations to be incorporated and explored in future models.

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Early Survival Prediction Framework in CD19-Specific CAR-T Cell Immunotherapy Using a Quantitative Systems Pharmacology Model

Cited by 26 publications

References 65 publications

Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse

Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse

Development of minimal physiologically-based pharmacokinetic-pharmacodynamic models for characterizing cellular kinetics of CAR T cells following local deliveries in mice

Clinical Pharmacology Perspectives for Adoptive Cell Therapies in Oncology

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