DDMODEL00000004: DeWinter_2006_diabetes

  public model
Short description:
A mechanism-based disease progression model for comparison of long-term effects of pioglitazone, metformin and gliclazide on disease processes underlying Type 2 Diabetes Mellitus
PharmML 0.8.x (0.8.1)
  • A mechanism-based disease progression model for comparison of long-term effects of pioglitazone, metformin and gliclazide on disease processes underlying Type 2 Diabetes Mellitus.
  • de Winter W, DeJongh J, Post T, Ploeger B, Urquhart R, Moules I, Eckland D, Danhof M
  • Journal of pharmacokinetics and pharmacodynamics, 6/2006, Volume 33, Issue 3, pages: 313-343
  • LAP&P Consultants BV, Leiden, The Netherlands.
  • Effective long-term treatment of Type 2 Diabetes Mellitus (T2DM) implies modification of the disease processes that cause this progressive disorder. This paper proposes a mechanism-based approach to disease progression modeling of T2DM that aims to provide the ability to describe and quantify the effects of treatment on the time-course of the progressive loss of beta-cell function and insulin-sensitivity underlying T2DM. It develops a population pharmacodynamic model that incorporates mechanism-based representations of the homeostatic feedback relationships between fasting levels of plasma glucose (FPG) and fasting serum insulin (FSI), and the physiological feed-forward relationship between FPG and glycosylated hemoglobin A1c (HbA1c). This model was developed on data from two parallel one-year studies comparing the effects of pioglitazone relative to metformin or sulfonylurea treatment in 2,408 treatment-naïve T2DM patients. It was found that the model provided accurate descriptions of the time-courses of FPG and HbA1c for different treatment arms. It allowed the identification of the long-term effects of different treatments on loss of beta-cell function and insulin-sensitivity, independently from their immediate anti-hyperglycemic effects modeled at their specific sites of action. Hence it avoided the confounding of these effects that is inherent in point estimates of beta-cell function and insulin-sensitivity such as the widely used HOMA-%B and HOMA-%S. It was also found that metformin therapy did not result in a reduction in FSI levels in conjunction with reduced FPG levels, as expected for an insulin-sensitizer, whereas pioglitazone therapy did. It is concluded that, although its current implementation leaves room for further improvement, the mechanism-based approach presented here constitutes a promising conceptual advance in the study of T2DM disease progression and disease modification.
Paolo Magni
Context of model development: Mechanistic Understanding; Clinical end-point;
Discrepancy between implemented model and original publication: However, because the original code was not available, this implementation might be different from the one used in the original publication in model parts that are not described in detail. In addition, initial value expressions for FPG0 and FSI0 are derived from the differential equations at the steady-state (Eq.1-2 in the paper), since the reported expressions (Eq.6-7 in the paper) contain an error: from Eq.7 S0=22.5/(FPG0*FSI0) should be obtained instead of S0=FPG0*FSI0/22.5.);
Model compliance with original publication: Yes;
Model implementation requiring submitter’s additional knowledge: No;
Modelling context description: Effective long-term treatment of Type 2 Diabetes Mellitus (T2DM) implies modification of the disease processes that cause this progressive disorder. This paper proposes a mechanism-based approach to disease progression modeling of T2DM that aims to provide the ability to describe and quantify the effects of treatment on the time-course of the progressive loss of beta-cell function and insulin-sensitivity underlying T2DM. It develops a population pharmacodynamic model that incorporates mechanism-based representations of the homeostatic feedback relationships between fasting levels of plasma glucose (FPG) and fasting serum insulin (FSI), and the physiological feed-forward relationship between FPG and glycosylated hemoglobin A1c (HbA1c). This model was developed on data from two parallel one-year studies comparing the effects of pioglitazone relative to metformin or sulfonylurea treatment in 2,408 treatment-naïve T2DM patients. It was found that the model provided accurate descriptions of the time-courses of FPG and HbA1c for different treatment arms. It allowed the identification of the long-term effects of different treatments on loss of beta-cell function and insulin-sensitivity, independently from their immediate anti-hyperglycemic effects modeled at their specific sites of action. Hence it avoided the confounding of these effects that is inherent in point estimates of beta-cell function and insulin-sensitivity such as the widely used HOMA-%B and HOMA-%S. It was also found that metformin therapy did not result in a reduction in FSI levels in conjunction with reduced FPG levels, as expected for an insulin-sensitizer, whereas pioglitazone therapy did. It is concluded that, although its current implementation leaves room for further improvement, the mechanism-based approach presented here constitutes a promising conceptual advance in the study of T2DM disease progression and disease modification;
Modelling task in scope: estimation;
Nature of research: Clinical research & Therapeutic use;
Therapeutic/disease area: Endocrinology;
Annotations are correct.
This model is not certified.
  • Model owner: Paolo Magni
  • Submitted: Sep 25, 2014 5:20:22 PM
  • Last Modified: Oct 10, 2016 9:56:13 PM
Revisions
  • Version: 7 public model Download this version
    • Submitted on: Oct 10, 2016 9:56:13 PM
    • Submitted by: Paolo Magni
    • With comment: Update MDL syntax to the version 1.0 and R script to SEE version 2.0.0. Code automatically generated for NONMEM and MONOLIX
  • Version: 6 public model Download this version
    • Submitted on: Jul 16, 2016 4:24:05 PM
    • Submitted by: Paolo Magni
    • With comment: Updated model annotations.
  • Version: 3 public model Download this version
    • Submitted on: Dec 11, 2015 3:32:36 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.
  • Version: 1 public model Download this version
    • Submitted on: Sep 25, 2014 5:20:22 PM
    • Submitted by: Paolo Magni
    • With comment: Import of DeWinter_2006_diabetes

Name

Generated from MDL. MOG ID: dewinter2006_mog

Independent Variables

T

Function Definitions

additiveError:realadditive:real=additive

Covariate Model: cm

Continuous Covariates

STEP
TREAT

Parameter Model: pm

Random Variables

eta_EFFvm_mdl.ID~Normal2mean=0var=pm.OMEGA_EFF
eta_RBvm_mdl.ID~Normal2mean=0var=pm.OMEGA_RB
eta_RSvm_mdl.ID~Normal2mean=0var=pm.OMEGA_RS
eta_B0vm_mdl.ID~Normal2mean=0var=pm.OMEGA_B0
eta_S0vm_mdl.ID~Normal2mean=0var=pm.OMEGA_S0
eta_FRHB0vm_mdl.ID~Normal2mean=0var=pm.OMEGA_FRHB0
eps_RES_FSIvm_err.DV~Normal2mean=0var=pm.SIGMA_RES_FSI
eps_RES_FPGvm_err.DV~Normal2mean=0var=pm.SIGMA_RES_FPG
eps_RES_HBvm_err.DV~Normal2mean=0var=pm.SIGMA_RES_HB

Population Parameters

K_OUT_FPG
K_OUT_HB
POP_FRHB0
POP_B0
POP_RB_G
POP_S0
POP_RS_G
EF_B_G
EF_S_M
EF_S_P
BETA_RB_P
BETA_RB_M
BETA_RS_P
BETA_RS_M
RES_FSI
RES_FSI30
RES_FPG
RES_HB
OMEGA_EFF
OMEGA_RB
OMEGA_RS
OMEGA_B0
OMEGA_S0
OMEGA_FRHB0
COV_B0_S0
COV_B0_FRHB0
COV_S0_FRHB0
SIGMA_RES_FSI
SIGMA_RES_FPG
SIGMA_RES_HB
TRT_M={1ifcm.TREAT=00otherwise
TRT_P={1ifcm.TREAT=10otherwise
TRT_G={1ifcm.TREAT=20otherwise

Individual Parameters

SHIFT_EF_B_G=pm.EF_B_Gpm.eta_EFF
SHIFT_EF_S_M=pm.EF_S_Mpm.eta_EFF
SHIFT_EF_S_P=pm.EF_S_Ppm.eta_EFF
FRHB0=pm.POP_FRHB0pm.eta_FRHB0
B0=pm.POP_B0pm.eta_B0
S0=pm.POP_S0pm.eta_S0
RB=pm.POP_RB_G1+pm.TRT_Ppm.BETA_RB_P+pm.TRT_Mpm.BETA_RB_M+pm.eta_RB
RS=pm.POP_RS_G1+pm.TRT_Ppm.BETA_RS_P+pm.TRT_Mpm.BETA_RS_M+pm.eta_RS
K_OUT_FSI=1
BB0=11+pm.B0
SS0=11+pm.S0
FSI0=-53.5pm.BB0+53.5pm.BB02+4522.5pm.BB0pm.SS02
FPG0=22.5pm.SS0pm.FSI0
HB0=pm.FRHB0pm.FPG0
K_IN_FSI=5pm.K_OUT_FSI
K_IN_FPG=22.5pm.K_OUT_FPG
K_IN_HB=pm.FRHB0pm.K_OUT_HB

Random Variable Correlation

coveta_B0eta_S0=pm.COV_B0_S0
coveta_B0eta_FRHB0=pm.COV_B0_FRHB0
coveta_S0eta_FRHB0=pm.COV_S0_FRHB0

Structural Model: sm

Variables

BB=11+pm.B0+pm.RBT365
SS=11+pm.S0+pm.RST365
EF_B=1+pm.TRT_Gcm.STEPpm.SHIFT_EF_B_G
EF_S=1+pm.TRT_Mcm.STEPpm.SHIFT_EF_S_M+pm.TRT_Pcm.STEPpm.SHIFT_EF_S_P
TFSI=sm.EF_Bsm.BBsm.FPG-3.5pm.K_IN_FSI-sm.FSIpm.K_OUT_FSIFSIT=0=pm.FSI0
TFPG=pm.K_IN_FPGsm.EF_Ssm.SSsm.FSI-sm.FPGpm.K_OUT_FPGFPGT=0=pm.FPG0
THB=sm.FPGpm.K_IN_HB-sm.HBpm.K_OUT_HBHBT=0=pm.HB0
logFSI=lnsm.FSI
logFPG=lnsm.FPG
logHB=lnsm.HB
RES_FSI_IND={pm.RES_FSIifsm.FSI30pm.RES_FSI2+pm.RES_FSI3020.5otherwise

Observation Model: om1

Continuous Observation

Y1=sm.logFSI+additiveErroradditive=sm.RES_FSI_IND+pm.eps_RES_FSI

Observation Model: om2

Continuous Observation

Y2=sm.logFPG+additiveErroradditive=pm.RES_FPG+pm.eps_RES_FPG

Observation Model: om3

Continuous Observation

Y3=sm.logHB+additiveErroradditive=pm.RES_HB+pm.eps_RES_HB

External Dataset

OID
nm_ds
Tool Format
NONMEM

File Specification

Format
csv
Delimiter
comma
File Location
Simulated_winter2006_data.csv

Column Definitions

Column ID Position Column Type Value Type
ID
1
id
int
TIME
2
idv
real
DV
3
dv
real
STEP
4
covariate
real
TREAT
5
covariate
real
ORIG
6
dvid
int
EVID
7
undefined
real

Column Mappings

Column Ref Modelling Mapping
ID
vm_mdl.ID
TIME
T
DV
{om1.Y1ifORIG=1om2.Y2ifORIG=2om3.Y3ifORIG=3
STEP
cm.STEP
TREAT
cm.TREAT

Estimation Step

OID
estimStep_1
Dataset Reference
nm_ds

Parameters To Estimate

Parameter Initial Value Fixed? Limits
pm.K_OUT_FPG
0.021
false
pm.K_OUT_HB
0.0272
false
pm.POP_FRHB0
0.82
false
pm.POP_B0
0.635
false
pm.POP_RB_G
0.178
false
pm.POP_S0
1.38
false
pm.POP_RS_G
0.245
false
pm.EF_B_G
1.115
false
pm.EF_S_M
0.699
false
pm.EF_S_P
0.649
false
pm.BETA_RB_P
-2.24
false
pm.BETA_RB_M
-2.82
false
pm.BETA_RS_P
0.567
false
pm.BETA_RS_M
1.01
false
pm.RES_FSI
0.2985
false
pm.RES_FSI30
0.6124
false
pm.RES_FPG
0.1277
false
pm.RES_HB
0.0438
false
pm.OMEGA_EFF
0.13
false
pm.OMEGA_RB
0.0125
false
pm.OMEGA_RS
0.00805
false
pm.OMEGA_B0
0.967
false
pm.OMEGA_S0
0.519
false
pm.OMEGA_FRHB0
0.0158
false
pm.COV_B0_S0
-0.48
false
pm.COV_B0_FRHB0
-0.053
false
pm.COV_S0_FRHB0
-0.0185
false
pm.SIGMA_RES_FSI
1
true
pm.SIGMA_RES_FPG
1
true
pm.SIGMA_RES_HB
1
true

Operations

Operation: 1

Op Type
generic
Operation Properties
Name Value
algo
focei

Step Dependencies

Step OID Preceding Steps
estimStep_1
 
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