Schropp2019 - Target-Mediated Drug Disposition Model for Bispecific Antibodies

View the 2019-10 Model of the Month entry for this model
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Model Identifier
BIOMD0000000788
Short description 
This model presents a general target-mediated drug disposition (TMDD) model for bispecific antibodies (BsAbs), which bind to two different targets on different cell membranes. The model includes four different binding events for BsAbs, turnover of the targets, and internalization of the complexes. In addition, a quasi-equilibrium (QE) approximation with decreased number of binding parameters is also present.
Format
SBML (L2V4)
Related Publication
  • Target-Mediated Drug Disposition Model for Bispecific Antibodies: Properties, Approximation, and Optimal Dosing Strategy.
  • Schropp J, Khot A, Shah DK, Koch G
  • CPT: pharmacometrics & systems pharmacology , 3/ 2019 , Volume 8 , Issue 3 , pages: 177-187 , PubMed ID: 30480383
  • Department of Mathematics and Statistics, University of Konstanz, Konstanz, Germany.
  • Bispecific antibodies (BsAbs) bind to two different targets, and create two binary and one ternary complex (TC). These molecules have shown promise as immuno-oncology drugs, and the TC is considered the pharmacologically active species that drives their pharmacodynamic effect. Here, we have presented a general target-mediated drug disposition (TMDD) model for these BsAbs, which bind to two different targets on different cell membranes. The model includes four different binding events for BsAbs, turnover of the targets, and internalization of the complexes. In addition, a quasi-equilibrium (QE) approximation with decreased number of binding parameters and, if necessary, reduced internalization parameters is presented. The model is further used to investigate the kinetics of BsAb and TC concentrations. Our analysis shows that larger doses of BsAbs may delay the build-up of the TC. Consequently, a method to compute the optimal dosing strategy of BsAbs, which will immediately create and maintain maximal possible TC concentration, is presented.
Contributors
Submitter of the first revision: Johannes Meyer
Submitter of this revision: Johannes Meyer
Modellers: Johannes Meyer

Metadata information

is (2 statements)
BioModels Database BIOMD0000000788
BioModels Database MODEL1907240003

isDescribedBy (1 statement)
PubMed 30480383

hasTaxon (1 statement)
Taxonomy Homo sapiens

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

isVersionOf (1 statement)
Curation status
Curated
Modelling approach(es)
ordinary differential equation model
Tags

Connected external resources

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Name Description Size Actions

Model files
Schropp2019.xml SBML L2V4 Schropp2019 - Target-Mediated Drug Disposition Model for Bispecific Antibodies 87.54 KB Preview | Download

Additional files
Schropp2019.cps COPASI file of Schropp2019 - Target-Mediated Drug Disposition Model for Bispecific Antibodies 120.38 KB Preview | Download
Schropp2019.sedml SED-ML file of Schropp2019 - Target-Mediated Drug Disposition Model for Bispecific Antibodies 1.72 KB Preview | Download

  • Model originally submitted by : Johannes Meyer
  • Submitted: Aug 12, 2019 10:32:33 AM
  • Last Modified: Aug 12, 2019 10:32:33 AM
Revisions
  • Version: 2 public model Download this version
    • Submitted on: Aug 12, 2019 10:32:33 AM
    • Submitted by: Johannes Meyer
    • With comment: Automatically added model identifier BIOMD0000000788
Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
C free

Bispecific Monoclonal Antibody 		50.0 nmol
R B

Receptor 		100.0 nmol
RC B

Receptor ; Bispecific Monoclonal Antibody ; macromolecular complex 		0.0 nmol
AD

Bispecific Antibody ; Bispecific Monoclonal Antibody ; Subcutaneous Route of Administration 		0.0 nmol
RC A

Receptor ; Bispecific Monoclonal Antibody ; macromolecular complex 		0.0 nmol
AP

Bispecific Antibody ; Bispecific Monoclonal Antibody ; peripheral blood 		0.0 nmol
Reactions
Reactions Rate Parameters
C_free + R_B => RC_B Central*k_on_2*C_free*R_B k_on_2 = 1.0 ml/(mol*s)
RC_A + R_B => RC_AB Central*k_on_3*RC_A*R_B k_on_3 = 1.0 ml/(mol*s)
=> RC_B + R_B; RC_AB Central*k_off_3*RC_AB k_off_3 = 0.01 1/ms
AD => Peripheral*k_a*AD k_a = 0.2 1/ms
=> C_free + R_B; RC_B Central*k_off_2*RC_B k_off_2 = 0.01 1/ms
=> RC_A + R_A; RC_AB Central*k_off_4*RC_AB k_off_4 = 0.01 1/ms
=> C_free + R_A; RC_A Central*k_off_1*RC_A k_off_1 = 0.01 1/ms
=> AP; C_free k_12*C_free*V k_12 = 0.1 1/ms; V = 3.0 l
C_free + R_A => RC_A Central*k_on_1*C_free*R_A k_on_1 = 10.0 ml/(mol*s)
Curator's comment:
(added: 12 Aug 2019, 10:32:22, updated: 12 Aug 2019, 10:32:22)
Reproduced plot of Figure 3B in the original publication. Model simulated and plot produced using COPASI 4.24 (Build 197).