Lee2017 - Paracetamol first-pass metabolism PK model

  public model
Model Identifier
BIOMD0000000947
Short description
Authors developed a microfluidic gut-liver co-culture chip that aims to reproduce the first-pass metabolism of oral drugs. The study suggests the possibility of reproducing the human PK profile on a chip, contributing to accurate prediction of pharmacological effect of drugs.
Format
SBML (L2V4)
Related Publication
  • 3D gut-liver chip with a PK model for prediction of first-pass metabolism.
  • Lee DW, Ha SK, Choi I, Sung JH
  • Biomedical microdevices , 11/ 2017 , Volume 19 , Issue 4 , pages: 100 , PubMed ID: 29116458
  • Department of Chemical Engineering, Hongik University, Seoul, 121-791, Korea.
  • Accurate prediction of first-pass metabolism is essential for improving the time and cost efficiency of drug development process. Here, we have developed a microfluidic gut-liver co-culture chip that aims to reproduce the first-pass metabolism of oral drugs. This chip consists of two separate layers for gut (Caco-2) and liver (HepG2) cell lines, where cells can be co-cultured in both 2D and 3D forms. Both cell lines were maintained well in the chip, verified by confocal microscopy and measurement of hepatic enzyme activity. We investigated the PK profile of paracetamol in the chip, and corresponding PK model was constructed, which was used to predict PK profiles for different chip design parameters. Simulation results implied that a larger absorption surface area and a higher metabolic capacity are required to reproduce the in vivo PK profile of paracetamol more accurately. Our study suggests the possibility of reproducing the human PK profile on a chip, contributing to accurate prediction of pharmacological effect of drugs.
Contributors
Submitter of the first revision: Matthew Roberts
Submitter of this revision: Krishna Kumar Tiwari
Modellers: Matthew Roberts, Krishna Kumar Tiwari

Metadata information

is (2 statements)
BioModels Database BIOMD0000000947
BioModels Database MODEL1803050002

isDescribedBy (1 statement)
PubMed 29116458

hasTaxon (1 statement)
Taxonomy Homo sapiens

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

hasPart (1 statement)
isVersionOf (1 statement)
occursIn (2 statements)
Brenda Tissue Ontology CACO-2 cell
Brenda Tissue Ontology Hep-G2 cell

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

Connected external resources

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Name Description Size Actions

Model files
Lee2017_Paracetamol_Metabolism.xml SBML L2V4 representation of Lee2017 - Paracetamol first-pass metabolism PK model 49.22 KB Preview | Download

Additional files
Evans2005.sedml SEDML file for the model 2.66 KB Preview | Download
Lee2017_Paracetamol_Metabolism.cps COPASI file 61.82 KB Preview | Download
figure.jpg Attempt at reproducing figure 6. 23.32 KB Preview | Download

  • Model originally submitted by : Matthew Roberts
  • Submitted: May 21, 2018 3:41:48 PM
  • Last Modified: May 12, 2020 5:29:11 AM
Revisions
  • Version: 5 public model Download this version
    • Submitted on: May 12, 2020 5:29:11 AM
    • Submitted by: Krishna Kumar Tiwari
    • With comment: Automatically added model identifier BIOMD0000000947
  • Version: 3 public model Download this version
    • Submitted on: May 21, 2018 3:41:48 PM
    • Submitted by: Matthew Roberts
    • With comment: Uploaded COPASI file and curated figure to facilitate the curation process in the future

(*) 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
C para Basolateral HepG2

paracetamol ; D00217 		5.0 μmol
C glu Basolateral HepG2

D00217 ; paracetamol ; Glucuronide 		1.0E-15 μmol
C sulf Apical

paracetamol sulfate 		1.0E-15 μmol
C sulf Basolateral HepG2

paracetamol sulfate 		1.0E-15 μmol
C para Apical

D00217 ; paracetamol 		2500.0 μmol
C para Caco 2

paracetamol ; D00217 		1.0E-15 μmol
C glu Caco 2

Glucuronide ; paracetamol ; D00217 		1.0E-15 μmol
C sulf Caco 2

paracetamol sulfate 		1.0E-15 μmol
C glu Apical

paracetamol ; D00217 ; Glucuronide 		1.0E-15 μmol
Reactions
Reactions Rate Parameters
C_para__Basolateral___HepG2_ = ((P_para*Ai*(C_para_Caco_2-C_para__Basolateral___HepG2_)-Mp_s_HepG2*C_para__Basolateral___HepG2_*V_basol)-Mp_g_HepG2*C_para__Basolateral___HepG2_*V_basol)/V_basol ((P_para*Ai*(C_para_Caco_2-C_para__Basolateral___HepG2_)-Mp_s_HepG2*C_para__Basolateral___HepG2_*V_basol)-Mp_g_HepG2*C_para__Basolateral___HepG2_*V_basol)/V_basol V_basol = 380.0; Mp_g_HepG2 = 0.59; Mp_s_HepG2 = 0.35; P_para = 103.8; Ai = 0.33
C_glu__Basolateral___HepG2_ = (P_glu*Ai*(C_glu_Caco_2-C_glu__Basolateral___HepG2_)+Mp_g_HepG2*C_para__Basolateral___HepG2_*V_basol)/V_basol (P_glu*Ai*(C_glu_Caco_2-C_glu__Basolateral___HepG2_)+Mp_g_HepG2*C_para__Basolateral___HepG2_*V_basol)/V_basol P_glu = 58.9; V_basol = 380.0; Mp_g_HepG2 = 0.59; Ai = 0.33
C_sulf_Apical = (-1)*P_sulf*Ai*(C_sulf_Apical-C_sulf_Caco_2)/V_api (-1)*P_sulf*Ai*(C_sulf_Apical-C_sulf_Caco_2)/V_api P_sulf = 49.9; V_api = 500.0; Ai = 0.33
C_sulf__Basolateral___HepG2_ = (P_sulf*Ai*(C_sulf_Caco_2-C_sulf__Basolateral___HepG2_)+Mp_s_HepG2*C_para__Basolateral___HepG2_*V_basol)/V_basol (P_sulf*Ai*(C_sulf_Caco_2-C_sulf__Basolateral___HepG2_)+Mp_s_HepG2*C_para__Basolateral___HepG2_*V_basol)/V_basol P_sulf = 49.9; V_basol = 380.0; Mp_s_HepG2 = 0.35; Ai = 0.33
C_para_Apical = (-1)*P_para*Ai*(C_para_Apical-C_para_Caco_2)/V_api (-1)*P_para*Ai*(C_para_Apical-C_para_Caco_2)/V_api P_para = 103.8; V_api = 500.0; Ai = 0.33
C_para_Caco_2 = (((P_para*Ai*(C_para_Apical-C_para_Caco_2)-P_para*Ai*(C_para_Caco_2-C_para__Basolateral___HepG2_))-Mp_s_caco*C_para_Caco_2*V_caco)-Mp_g_caco*C_para_Caco_2*V_caco)/V_caco (((P_para*Ai*(C_para_Apical-C_para_Caco_2)-P_para*Ai*(C_para_Caco_2-C_para__Basolateral___HepG2_))-Mp_s_caco*C_para_Caco_2*V_caco)-Mp_g_caco*C_para_Caco_2*V_caco)/V_caco Mp_s_caco = 14.9; Mp_g_caco = 17.6; V_caco = 0.33; P_para = 103.8; Ai = 0.33
C_glu_Caco_2 = ((P_glu*Ai*(C_glu_Apical-C_glu_Caco_2)-P_glu*Ai*(C_glu_Caco_2-C_glu__Basolateral___HepG2_))+Mp_g_caco*C_para_Caco_2*V_caco)/V_caco ((P_glu*Ai*(C_glu_Apical-C_glu_Caco_2)-P_glu*Ai*(C_glu_Caco_2-C_glu__Basolateral___HepG2_))+Mp_g_caco*C_para_Caco_2*V_caco)/V_caco P_glu = 58.9; Mp_g_caco = 17.6; V_caco = 0.33; Ai = 0.33
C_sulf_Caco_2 = ((P_sulf*Ai*(C_sulf_Apical-C_sulf_Caco_2)-P_sulf*Ai*(C_sulf_Caco_2-C_sulf__Basolateral___HepG2_))+Mp_s_caco*C_para_Caco_2*V_caco)/V_caco ((P_sulf*Ai*(C_sulf_Apical-C_sulf_Caco_2)-P_sulf*Ai*(C_sulf_Caco_2-C_sulf__Basolateral___HepG2_))+Mp_s_caco*C_para_Caco_2*V_caco)/V_caco P_sulf = 49.9; Mp_s_caco = 14.9; V_caco = 0.33; Ai = 0.33
C_glu_Apical = (-1)*P_glu*Ai*(C_glu_Apical-C_glu_Caco_2)/V_api (-1)*P_glu*Ai*(C_glu_Apical-C_glu_Caco_2)/V_api P_glu = 58.9; V_api = 500.0; Ai = 0.33
Curator's comment:
(added: 12 May 2020, 05:28:50, updated: 12 May 2020, 05:28:50)
Figure 6 a,c,d,e,f are match and figure 6b is also very similar. Model encoded in COPASI and plot generated using MATLAB.