Hanson2016 - Toxicity Management in CAR T cell therapy for B-ALL

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This model provides an in silico mathematical platform to explore the interactions between chimeric antigen receptor-modified T cells, inflammatory toxicitiy, and the tumour burdens of individual patients.
Related Publication
  • Toxicity Management in CAR T cell therapy for B-ALL: Mathematical modelling as a new avenue for improvement
  • Shalla Hanson, David Robert Grimes, Jake P. Taylor-King, Pravnam I. Warman, Ziv Frankenstein, Artem Kaznatcheev, Michael J. Bonassar, Vincent L. Cannataro, Zeinab Y. Motawe, Ernesto A. B. F. Lima, Sungjune Kim, Marco L. Davila, Arturo Araujo
  • Preprint , 4/ 2016 , DOI: 10.1101/049908
  • Department of Mathematics, Duke University, Durham, NC, USA
  • Advances in genetic engineering have made it possible to reprogram individual immune cells to express receptors that recognise markers on tumour cell surfaces. The process of re-engineering T cell lymphocytes to express Chimeric Antigen Receptors (CARs), and then re-infusing the CAR-modified T cells into patients to treat various cancers is referred to as CAR T cell therapy. This therapy is being explored in clinical trials - most prominently for B Cell Acute Lymphoblastic Leukaemia (B-ALL), a common B cell malignancy, for which CAR T cell therapy has led to remission in up to 90% of patients. Despite this extraordinary response rate, however, potentially fatal inflammatory side effects occur in up to 10% of patients who have positive responses. Further, approximately 50% of patients who initially respond to the therapy relapse. Significant improvement is thus necessary before the therapy can be made widely available for use in the clinic. To inform future development, we develop a mathematical model to explore interactions between CAR T cells, inflammatory toxicity, and individual patients’ tumour burdens in silico. This paper outlines the underlying system of coupled ordinary differential equations designed based on well-known immunological principles and widely accepted views on the mechanism of toxicity development in CAR T cell therapy for B-ALL - and reports in silico outcomes in relationship to standard and recently conjectured predictors of toxicity in a heterogeneous, randomly generated patient population. Our initial results and analyses are consistent with and connect immunological mechanisms to the clinically observed, counterintuitive hypothesis that initial tumour burden is a stronger predictor of toxicity than is the dose of CAR T cells administered to patients. We outline how the mechanism of action in CAR T cell therapy can give rise to such non-standard trends in toxicity development, and demonstrate the utility of mathematical modelling in understanding the relationship between predictors of toxicity, mechanism of action, and patient outcomes.
Submitter of the first revision: Johannes Meyer
Submitter of this revision: Johannes Meyer
Modellers: Johannes Meyer

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hasProperty (1 statement)
Mathematical Modelling Ontology Ordinary differential equation model

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Hanson2016.xml SBML L2V4 Representation of Hanson2016 - Toxicity Management in CAR T cell therapy for B-ALL 75.37 KB Preview | Download

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Hanson2016.cps COPASI file of Hanson2016 - Toxicity Management in CAR T cell therapy for B-ALL 114.93 KB Preview | Download
Hanson2016.sedml SED-ML file of Hanson2016 - Toxicity Management in CAR T cell therapy for B-ALL 4.25 KB Preview | Download

  • Model originally submitted by : Johannes Meyer
  • Submitted: Oct 25, 2019 10:39:49 AM
  • Last Modified: Oct 25, 2019 10:39:49 AM
  • Version: 2 public model Download this version
    • Submitted on: Oct 25, 2019 10:39:49 AM
    • Submitted by: Johannes Meyer
    • With comment: Automatically added model identifier BIOMD0000000837
: Variable used inside SBML models

Species Initial Concentration/Amount
C m

cytotoxic T cell
10.0 item
C e

cytotoxic T cell
10.0 item
H e

helper T cell
10.0 item

1600.0 item

inflammatory response
140.0 item

300.0 item
H m

helper T cell
10.0 item
Reactions Rate Parameters
C_m => ; B, H_e, Inflam compartment*a_1*B*C_m*(1+a_2*H_e)*Inflam^2/(Inflam^2+b^2) b = 800.0; a_1 = 4.0E-7; a_2 = 2.0
=> C_e; B, C_m, Inflam, H_e compartment*2^n*a_1*B*C_m*Inflam^2/(Inflam^2+b^2)*(1+a_2*H_e) n = 6.0; b = 800.0; a_1 = 4.0E-7; a_2 = 2.0
H_e => compartment*d_4*H_e d_4 = 0.004
=> L compartment*p_2 p_2 = 0.4
L => compartment*d_5*L d_5 = 2.0E-4
Inflam => compartment*d_2*Inflam d_2 = 1.5
=> L; L compartment*r_4*L*Lymphocyte_Term r_4 = 0.1; Lymphocyte_Term = 0.0
=> B; B compartment*r_1*B*(1-B/k) k = 4800.0; r_1 = 0.003
C_e => compartment*d_3*C_e d_3 = 0.004
=> H_m; H_m compartment*r_3*H_m*Lymphocyte_Term Lymphocyte_Term = 0.0; r_3 = 0.1
Curator's comment:
(added: 25 Oct 2019, 10:39:38, updated: 25 Oct 2019, 10:39:38)
Reproduced plot of Figure 3 in the original publication. Model simulated and plots produced using COPASI 4.24 (Build 197).