Zhu2015 - Combined gemcitabine and birinapant in pancreatic cancer cells - mechanistic PD model

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Model Identifier
BIOMD0000000669
Short description
Zhu2015 - combined gemcitabine and birinapant in pancreatic cancer cells - mechanistic PD model
Mechanistic mathematical model to illustrate the effectiveness of combination chemotherapy involving gemcitabine and birinapant against pancreatic cancer.

This model is described in the article:

Zhu X, Straubinger RM, Jusko WJ.
J Pharmacokinet Pharmacodyn 2015 Oct; 42(5): 477-496

Abstract:

Combination chemotherapy is standard treatment for pancreatic cancer. However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term Ψ was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, Ψ values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000669.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Format
SBML (L2V4)
Related Publication
  • Mechanism-based mathematical modeling of combined gemcitabine and birinapant in pancreatic cancer cells.
  • Zhu X, Straubinger RM, Jusko WJ
  • Journal of pharmacokinetics and pharmacodynamics , 10/ 2015 , Volume 42 , Issue 5 , pages: 477-496 , PubMed ID: 26252969
  • Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY, 14214, USA.
  • Combination chemotherapy is standard treatment for pancreatic cancer. However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term Ψ was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, Ψ values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies.
Contributors
Submitter of the first revision: Vijayalakshmi Chelliah
Submitter of this revision: administrator
Modellers: administrator, Vijayalakshmi Chelliah

Metadata information

is (2 statements)
BioModels Database MODEL1604270001
BioModels Database BIOMD0000000669

isDescribedBy (4 statements)
isInstanceOf (6 statements)
BioModels Database MODEL1604270001

hasPart (2 statements)
occursIn (1 statement)
Brenda Tissue Ontology pancreas


Curation status
Curated

Tags

Connected external resources

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Model files

BIOMD0000000669_url.xml SBML L2V4 representation of Zhu2015 - Combined gemcitabine and birinapant in pancreatic cancer cells - mechanistic PD model 132.43 KB Preview | Download

Additional files

BIOMD0000000669-biopax2.owl Auto-generated BioPAX (Level 2) 7.64 KB Preview | Download
BIOMD0000000669-biopax3.owl Auto-generated BioPAX (Level 3) 8.33 KB Preview | Download
BIOMD0000000669.m Auto-generated Octave file 10.88 KB Preview | Download
BIOMD0000000669.pdf Auto-generated PDF file 159.97 KB Preview | Download
BIOMD0000000669.png Auto-generated Reaction graph (PNG) 4.27 KB Preview | Download
BIOMD0000000669.sci Auto-generated Scilab file 154.00 Bytes Preview | Download
BIOMD0000000669.svg Auto-generated Reaction graph (SVG) 845.00 Bytes Preview | Download
BIOMD0000000669.vcml Auto-generated VCML file 900.00 Bytes Preview | Download
BIOMD0000000669.xpp Auto-generated XPP file 8.42 KB Preview | Download
BIOMD0000000669_urn.xml Auto-generated SBML file with URNs 132.30 KB Preview | Download
MODEL1604270001_mechanistic.cps Curated and annotated COPASI file. 127.58 KB Preview | Download
MODEL1604270001_mechanistic.sedml SED-ML file to produce a similar figure to figure 5J of the reference publication. Concentration of germcitabine and birinapant is set to 20nM and 500nM respectively with k_delay set to 18.6 as opposed to 36.8 and Sti_apo_g and Sti_apo_b set to 0 as values for these parameters could not be found. k_tau is also not used. If raw/non-scaled values of G1, S and G2 are plotted (that is, the 'species' values and not the scaled 'quantity' values) the simulated figure is much more similar to the figure in the reference publication. However, the y-axis is labelled as 'Cell Fraction' and supposedly scaled between 0-1. 5.48 KB Preview | Download

  • Model originally submitted by : Vijayalakshmi Chelliah
  • Submitted: Apr 27, 2016 3:54:18 PM
  • Last Modified: Feb 7, 2018 2:20:31 PM
Revisions
  • Version: 2 public model Download this version
    • Submitted on: Feb 7, 2018 2:20:31 PM
    • Submitted by: administrator
    • With comment: Current curated version of Zhu2015_mechanistic_ADAPT5
  • Version: 1 public model Download this version
    • Submitted on: Apr 27, 2016 3:54:18 PM
    • Submitted by: Vijayalakshmi Chelliah
    • With comment: Original import of Zhu2015_mechanistic_ADAPT5.txt

(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions unpublished model revision of this model will only be shown to the submitter and their collaborators.

Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
G2

M phase ; G2 phase ; PANC-1 cell ; G2 Phase Process
81656.0 mmol
R total

collection of specimens
236000.0 mmol
R other

PANC-1 cell
3540.0 mmol
G1

PANC-1 cell ; G1 Phase Process ; G1 phase
113516.0 mmol
R live

collection of specimens ; Cell Proliferation
220660.0 mmol
S

PANC-1 cell ; S Phase Process ; S phase
25488.0 mmol
R apo

PANC-1 cell ; Apoptosis
11800.0 mmol
Reactions
Reactions Rate Parameters
G2 = (((1-Inh_g)*k2*S-(1-Inh_3)*(1-Inh_b)*k3*G2)-(1+Sti_apo_g)*(1+Sti_apo_b)*k_apo*G2)-(1+Sti_other_g)*(1+Sti_other_b)*k_other*G2 (((1-Inh_g)*k2*S-(1-Inh_3)*(1-Inh_b)*k3*G2)-(1+Sti_apo_g)*(1+Sti_apo_b)*k_apo*G2)-(1+Sti_other_g)*(1+Sti_other_b)*k_other*G2 Inh_g = 0.874253042562929; Sti_apo_b = 0.0; Inh_3 = 0.483492347087237; k3 = 0.222; Inh_b = 0.135321100917431; k_apo = 0.00394; Sti_apo_g = 0.0; k_other = 2.97E-4; Sti_other_b = 2.75; k2 = 0.114; Sti_other_g = 1.34928284767356E-5
R_total = G1+S+G2+R_apo+R_other [] []
R_other = (1+Sti_other_g)*(1+Sti_other_b)*k_other*(G1+S+G2)-k_other*R_other (1+Sti_other_g)*(1+Sti_other_b)*k_other*(G1+S+G2)-k_other*R_other k_other = 2.97E-4; Sti_other_b = 2.75; Sti_other_g = 1.34928284767356E-5
G1 = ((2*(1-Inh_3)*(1-Inh_b)*k3*G2-(1-Inh_1)*(1-Inh_b)*k1*G1)-(1+Sti_apo_g)*(1+Sti_apo_b)*k_apo*G1)-(1+Sti_other_g)*(1+Sti_other_b)*k_other*G1 ((2*(1-Inh_3)*(1-Inh_b)*k3*G2-(1-Inh_1)*(1-Inh_b)*k1*G1)-(1+Sti_apo_g)*(1+Sti_apo_b)*k_apo*G1)-(1+Sti_other_g)*(1+Sti_other_b)*k_other*G1 Sti_apo_b = 0.0; k_apo = 0.00394; k_other = 2.97E-4; Sti_other_g = 1.34928284767356E-5; Inh_3 = 0.483492347087237; k3 = 0.222; Inh_b = 0.135321100917431; k1 = 0.357; Sti_apo_g = 0.0; Sti_other_b = 2.75; Inh_1 = 0.642088110341617
R_live = G1+S+G2 [] []
S = (((1-Inh_1)*(1-Inh_b)*k1*G1-(1-Inh_g)*k2*S)-(1+Sti_apo_g)*(1+Sti_apo_b)*k_apo*S)-(1+Sti_other_g)*(1+Sti_other_b)*k_other*S (((1-Inh_1)*(1-Inh_b)*k1*G1-(1-Inh_g)*k2*S)-(1+Sti_apo_g)*(1+Sti_apo_b)*k_apo*S)-(1+Sti_other_g)*(1+Sti_other_b)*k_other*S Inh_g = 0.874253042562929; Sti_apo_b = 0.0; Inh_b = 0.135321100917431; k1 = 0.357; k_apo = 0.00394; Sti_apo_g = 0.0; k_other = 2.97E-4; Sti_other_b = 2.75; k2 = 0.114; Sti_other_g = 1.34928284767356E-5; Inh_1 = 0.642088110341617
R_apo = (1+Sti_apo_g)*(1+Sti_apo_b)*k_apo*(G1+S+G2)-(1+Sti_apo_g)*(1+Sti_apo_b)*f1*k_apo*R_apo (1+Sti_apo_g)*(1+Sti_apo_b)*k_apo*(G1+S+G2)-(1+Sti_apo_g)*(1+Sti_apo_b)*f1*k_apo*R_apo Sti_apo_b = 0.0; k_apo = 0.00394; Sti_apo_g = 0.0; f1 = 0.467
Curator's comment:
(added: 07 Feb 2018, 14:13:17, updated: 07 Feb 2018, 14:13:17)
Similar figures of figure 5 of the reference publication have been produced with k_delay set to 18.6 as opposed to 38.6 as listed in table 2 of the reference publication. Additionally, the values of Sti_apo_g and Sti_apo_b were not found in the reference publication and were set to 0. The parameter k_tau, altough listed in table 2, was not used. The figures illustrate the fraction of live/proliferating cells in G0/G1 (blue), S (red) and G2/M (green) phases over a period of 100 hours. The top row represents the control case where both drug concentrations are set to 0. The effects of germcitabine, birinapant and combination treatment on cell cycle phase transition are illustrated in the second, third and bottom row respectively with increasing drug concentration from left to right. 20nm germcitabine + 500 nM birinapant treatment resulted in a large quantity of cells remaining in the S phase for an extended period of time, thus preventing transition to M phase. One discrepancy, for example, is that the red curve for figure 5J (bottom right) starts to decrease after approximately t=25 whereas in the reference publication it is monotonically increasing until approximately t=80. Despite the y-axis being labelled as 'Cell Fraction', if the raw/non-scaled values of G1, S and G2 are plotted, the curated figure will be more similar to the figure in the reference publication. The simulations were performed in COPASI V4.22 (Build 170) and figures were generated in MATLAB R2014.