Kosinsky2018 - Radiation and PD-(L)1 treatment combinations

  public model
Model Identifier
BIOMD0000000863
Short description
This is a quantitative systems pharmacology (QSP) model that describes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features. The model describes the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation under immunotherapeutic and radiological treatments.
Format
SBML (L2V4)
Related Publication
  • Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model.
  • Kosinsky Y, Dovedi SJ, Peskov K, Voronova V, Chu L, Tomkinson H, Al-Huniti N, Stanski DR, Helmlinger G
  • Journal for immunotherapy of cancer , 2/ 2018 , Volume 6 , Issue 1 , pages: 17 , PubMed ID: 29486799
  • M&S Decisions, Moscow, Russian Federation.
  • BACKGROUND:Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies. METHODS:A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx. RESULTS:The model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1. CONCLUSIONS:This study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.
Contributors
Submitter of the first revision: Johannes Meyer
Submitter of this revision: Johannes Meyer
Modellers: Johannes Meyer

Metadata information

hasTaxon (1 statement)
Taxonomy Mus musculus

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

Curation status
Curated
Modelling approach(es)
ordinary differential equation model
Tags
Immuno-oncology

Connected external resources

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Name Description Size Actions

Model files
Kosinsky2018.xml SBML L2V4 Representation of Kosinsky2018 - Radiation and PD-(L)1 treatment combinations 89.49 KB Preview | Download

Additional files
Kosinsky2018.cps COPASI file of Kosinsky2018 - Radiation and PD-(L)1 treatment combinations 111.57 KB Preview | Download
Kosinsky2018.sedml SED-ML file of Kosinsky2018 - Radiation and PD-(L)1 treatment combinations 3.28 KB Preview | Download

  • Model originally submitted by : Johannes Meyer
  • Submitted: Nov 14, 2019 2:21:45 PM
  • Last Modified: Nov 14, 2019 2:21:45 PM
Revisions
  • Version: 3 public model Download this version
    • Submitted on: Nov 14, 2019 2:21:45 PM
    • Submitted by: Johannes Meyer
    • With comment: Automatically added model identifier BIOMD0000000863
Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
U

C25832 		0.0 nmol
TVd

C25832 ; Tumor Volume 		0.0 nmol
TV

Tumor Volume 		15.0 nmol
PDL1

C96024 		0.0 nmol
dTeff

effector T cell 		0.0 nmol
nTeff

CL:0002420 		0.0 nmol
Reactions
Reactions Rate Parameters
=> U compartment*radiation_Dose*delta delta = 19.0; radiation_Dose = 0.0
TVd => compartment*mu*TVd mu = 0.1725 1/d
TV => ; dTeff compartment*(n_e*dTeff+d0)*TV n_e = 0.001 1/d; d0 = 0.01 1/d
PDL1 => compartment*kpdl*PDL1 kpdl = 1.0 1/d
TV => TVd; U compartment*TV*(alpha*radiation_Dose+0.2*alpha/(tau*delta^2)*U^2) alpha = 0.146; tau = 0.02 d; delta = 19.0; radiation_Dose = 0.0
=> TV; TV compartment*r*TV*(1-TV/TVmax) r = 0.4 1/d; TVmax = 2500.0 ul
=> PDL1; dTeff compartment*kpdl*dTeff/(Kpdl+dTeff) Kpdl = 478.0; kpdl = 1.0 1/d
dTeff => compartment*kapo*dTeff kapo = 2.0 1/d
nTeff => compartment*k_el*nTeff k_el = 0.2 1/d
U => compartment*tau*U tau = 0.02 d
Curator's comment:
(added: 14 Nov 2019, 14:21:35, updated: 14 Nov 2019, 14:21:35)
Reproduced plot of Figure 1(a,b,control) in the original publication. Model simulated using COPASI 4.24 (Build 197), plot produced using Wolfram Mathematica 11.3.