Read-me


This repository serves as an addendum to the following scientific review paper published in AACR Clinical Cancer Research (2021):

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Cytokine release syndrome by T-cell-redirecting therapies: can we predict and modulate patient risk?

Arthur J Van De Vyver, Estelle Marrer-Berger, Ken Wang, Thorsten Lehr and Antje-Christine Walz

DOI: 10.1158/1078-0432.CCR-21-0470
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This repository contains three separate Berkeley-Madonna files that describe mechanistic models of in vitro and in vivo pharmacology of T-cell redirecting therapies in immune-oncology. 
This read-me file provides background information about the various models. The models are useful for simulation and hypothesis testing. To run the model files, a copy of Berkeley-Madonna (version 8.3.18 or higher) should be installed. 
The software is free, but a license is required in order to export graphs and tables generated by the simulations.

The code files have been provided in .txt and .mmd formats. The authors annotated the code where deemed necessary.

The following section provides the parameter values needed to reproduce the simulations depicted in figure 2 of the review paper. 
Simulations in the study were performed in Berkeley-Madonna (version 8.3.18; University of California, Berkeley). In each case, the integration method was Rosenbrock for solving stiff ordinary differential equations.



Figure 2B. Simulations based on the model from Van De Vyver et al. [1].
The model simulates T-cell activation within an in vitro experiment of tumor cells co-cultured with T-cells and incubated with a CD3-bispecific T-cell engager for 1 week. 
The extent of T-cell activation (CD25+CD8+ T-cells) with respect to the baseline value was simulated over a drug concentration range of 0.01 to 100 nmol/L. 
Model structure and model parameters were taken as reported in Van De Vyver et al. with the following modifications to the parameters for tumor antigen density and binding affinity:



High expression: 		200’000 antigen/tumor cell
Medium expression: 		50’000 antigen/tumor cell
Low expression: 		10’000 antigen/tumor cell

High affinity binder: 		Kd = 18 nM
Low affinity binder: 		Kd = 72 nM



Figure 2C. Simulations based on the model from Singh et al. [2]. 
Model structure and most parameter values are taken from the original paper.
The model simulates the outcome of an in vivo experiment in solid tumor xenograft mice treated with EGFR-CAR T-cells. 
The extent of IL6 release (taken as % of maximal cytokine concentration) is simulated in three separate cases with different extravasation rates (in the original paper called vascular to interstitial transmigration rate: Jtumor) towards the tumor:



Low accessibility: 		Jtumor = 2/h
Medium accessibility: 		Jtumor = 10/h
High accessibility: 		Jtumor = 50/h



Figure 2D-E. Simulations based on the model from Chen et al. [3]. 
Model structure and parameters values are taken from the original paper. 
Based on the model by Chen et al. we simulated systemic cytokine release following step up dosing. 
The patient receives an amount of dose A of a CD3-bispecific via IV bolus at start of treatment (day 0), followed by a 5-fold higher dose B after 7 days. 
For PK of the drug, we assumed a one compartmental model with a volume of distribution of 3.1L and a central clearance of 12.5 mL/h.



References



1.	Van De Vyver, A.J., et al., Predicting tumor killing and T-cell activation by T-Cell Bispecific antibodies as a function of target expression: combining in vitro experiments with systems modeling. Molecular Cancer Therapeutics, 2020: p. molcanther.0269.2020.
2.	Singh, A.P., et al., Development of a quantitative relationship between CAR-affinity, antigen abundance, tumor cell depletion and CAR-T cell expansion using a multiscale systems PK-PD model. mAbs, 2020. 12(1): p. 1688616-1688616.
3.	Chen, X., et al., A Modeling Framework to Characterize Cytokine Release upon T-Cell-Engaging Bispecific Antibody Treatment: Methodology and Opportunities. Clin Transl Sci, 2019. 12(6): p. 600-608.