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BIOMD0000000238 - Overgaard2007_PDmodel_IL21

 

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Reference Publication
Publication ID: 17009101
Overgaard RV, Holford N, Rytved KA, Madsen H.
PKPD model of interleukin-21 effects on thermoregulation in monkeys--application and evaluation of stochastic differential equations.
Pharm. Res. 2007 Feb; 24(2): 298-309
Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersens Plads, Building 321, Room 015, Kongens Lyngby 2800, Denmark. ruvo@novonordisk.com  [more]
Model
Original Model: CellML logo
Submitter: Vijayalakshmi Chelliah
Submission ID: MODEL0911110000
Submission Date: 12 Nov 2009 14:01:50 UTC
Last Modification Date: 31 Mar 2014 12:03:25 UTC
Creation Date: 12 Nov 2009 14:29:18 UTC
Encoders:  Catherine Lloyd
   Vijayalakshmi Chelliah
   Rune Viig Overgaard
set #1
bqbiol:isVersionOf Gene Ontology circadian temperature homeostasis
bqbiol:hasPart Taxonomy Macaca fascicularis
set #2
bqbiol:isVersionOf OMIM 605384
Gene Ontology temperature homeostasis
set #3
bqbiol:hasProperty Mathematical Modelling Ontology MAMO_0000046
Notes

This a model from the article:
PKPD model of interleukin-21 effects on thermoregulation in monkeys--application and evaluation of stochastic differential equations.
Overgaard RV, Holford N, Rytved KA, Madsen H. Pharm Res.2007 Feb;24(2):298-309. PUBMED,
Abstract:
PURPOSE: To describe the pharmacodynamic effects of recombinant human interleukin-21 (IL-21) on core body temperature in cynomolgus monkeys using basic mechanisms of heat regulation. A major effort was devoted to compare the use of ordinary differential equations (ODEs) with stochastic differential equations (SDEs) in pharmacokinetic pharmacodynamic (PKPD) modelling. METHODS: A temperature model was formulated including circadian rhythm, metabolism, heat loss, and a thermoregulatory set-point. This model was formulated as a mixed-effects model based on SDEs using NONMEM. RESULTS: The effects of IL-21 were on the set-point and the circadian rhythm of metabolism. The model was able to describe a complex set of IL-21 induced phenomena, including 1) disappearance of the circadian rhythm, 2) no effect after first dose, and 3) high variability after second dose. SDEs provided a more realistic description with improved simulation properties, and further changed the model into one that could not be falsified by the autocorrelation function. CONCLUSIONS: The IL-21 induced effects on thermoregulation in cynomolgus monkeys are explained by a biologically plausible model. The quality of the model was improved by the use of SDEs.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

Model
Publication ID: 17009101 Submission Date: 12 Nov 2009 14:01:50 UTC Last Modification Date: 31 Mar 2014 12:03:25 UTC Creation Date: 12 Nov 2009 14:29:18 UTC
Mathematical expressions
Rules
Rate Rule (variable: Metabolic rate) Rate Rule (variable: Temperature) Rate Rule (variable: Bound Receptor) Assignment Rule (variable: tprime)
Assignment Rule (variable: heat conductance baselinevalue) Assignment Rule (variable: T_day) Assignment Rule (variable: M_day) Assignment Rule (variable: Priming)
Assignment Rule (variable: T_night) Assignment Rule (variable: M_night_baseline) Assignment Rule (variable: M_night) Assignment Rule (variable: circadian rhythm)
Assignment Rule (variable: f2_drug) Assignment Rule (variable: heat conductance) Assignment Rule (variable: X1) Assignment Rule (variable: X2)
Assignment Rule (variable: X3) Assignment Rule (variable: Kf) Assignment Rule (variable: Ks) Assignment Rule (variable: gNsTs1)
Assignment Rule (variable: gNsTs2) Assignment Rule (variable: gNsTs3) Assignment Rule (variable: gNfTf1) Assignment Rule (variable: gNfTf2)
Assignment Rule (variable: gNfTf3) Assignment Rule (variable: Slow Effect) Assignment Rule (variable: Fast Effect)  
Physical entities
Compartments Species
COMpartment      
Global parameters
Metabolic rate Temperature Bound Receptor Slow Effect
Fast Effect Priming ambient temperature basiline temperature
temperature difference kinc tdose1 tdose2
tdose3 circadian rhythm t_day t_night
tprime day_length rate constant Metabolism specific heat constant
heat conductance pEtot kR AMT_dose
pEf1 pEs1 pEf2 pEs2
pEf3 pEs3 f2_drug T_day
T_night heat conductance baselinevalue M_b M_day
M_night t_prime alpha delta_high_dose
M_night_baseline gNsTs1 gNsTs2 gNsTs3
gNfTf1 gNfTf2 gNfTf3 No. of transit compartment (slow)
No. of transit compartment (fast) mean total delay (slow) mena total delay (fast) X1
X2 X3 Kf Ks
Reactions (0)
Rules (27)
 
 Rate Rule (name: M) d [ Metabolic rate] / d t= (-km)*(M-M_c)
 
 Rate Rule (name: T) d [ Temperature] / d t= c^(-1)*(M-k*(T-T_a))
 
 Rate Rule (name: BR) d [ Bound Receptor] / d t= f_prime*(E_slow+E_fast)*(1-BR)-kR*BR
 
 Assignment Rule (name: tprime) tprime = time*3600*1-floor(time*3600*1/day_length)*day_length
 
 Assignment Rule (name: kb) heat conductance baselinevalue = M_b/(T_b-T_a)
 
 Assignment Rule (name: T_day) T_day = T_b+delta_T/2
 
 Assignment Rule (name: M_day) M_day = (kb+kinc*(T_day-T_b))*(T_day-T_a)
 
 Assignment Rule (name: f_prime) Priming = delta_high_dose*(1+exp((-alpha)*(time-(tdose1+t_prime))))^(-1)
 
 Assignment Rule (name: T_night) T_night = T_b-delta_T/2
 
 Assignment Rule (name: M_night_baseline) M_night_baseline = (kb+kinc*(T_night-T_b))*(T_night-T_a)
 
 Assignment Rule (name: M_night) M_night = (1-f_prime)*M_night_baseline+f_prime*M_day
 
 Assignment Rule (name: M_c) circadian rhythm = piecewise(M_night, (tprime/3600 >= t_night) && (tprime/3600 < t_day), M_day)
 
 Assignment Rule (name: f2_drug) f2_drug = 0
 
 Assignment Rule (name: k) heat conductance = kb+kinc*(T-T_b*(1+pEtot*BR))+f2_drug
 
 Assignment Rule (name: X1) X1 = (time-tdose1)/24
 
 Assignment Rule (name: X2) X2 = (time-tdose2)/24
 
 Assignment Rule (name: X3) X3 = (time-tdose3)/24
 
 Assignment Rule (name: Kf) Kf = Nf/Tf
 
 Assignment Rule (name: Ks) Ks = Ns/Ts
 
 Assignment Rule (name: gNsTs1) gNsTs1 = piecewise(Ks^Ns/6*exp((-Ks)*X1)*X1^(Ns-1), X1 > 0, 0)
 
 Assignment Rule (name: gNsTs2) gNsTs2 = piecewise(Ks^Ns/6*exp((-Ks)*X2)*X2^(Ns-1), X2 > 0, 0)
 
 Assignment Rule (name: gNsTs3) gNsTs3 = piecewise(Ks^Ns/6*exp((-Ks)*X3)*X3^(Ns-1), X3 > 0, 0)
 
 Assignment Rule (name: gNfTf1) gNfTf1 = piecewise(Kf^Nf/6*exp((-Kf)*X1)*X1^(Nf-1), X1 > 0, 0)
 
 Assignment Rule (name: gNfTf2) gNfTf2 = piecewise(Kf^Nf/6*exp((-Kf)*X2)*X2^(Nf-1), X2 > 0, 0)
 
 Assignment Rule (name: gNfTf3) gNfTf3 = piecewise(Kf^Nf/6*exp((-Kf)*X3)*X3^(Nf-1), X3 > 0, 0)
 
 Assignment Rule (name: E_slow) Slow Effect = AMT_dose*pEs2*(gNsTs1+gNsTs2+gNsTs3)
 
 Assignment Rule (name: E_fast) Fast Effect = pEf2*(gNfTf1+gNfTf2+gNfTf3)
 
   Spatial dimensions: 3.0  Compartment size: 1.0
Global Parameters (56)
 
   Metabolic rate
Value: 3.5
 
   Temperature
Value: 38.785
 
   Bound Receptor  
 
   Slow Effect
Value: NaN
 
   Fast Effect
Value: NaN
 
   Priming
Value: NaN
 
   ambient temperature
Value: 21.0
Constant
 
   basiline temperature
Value: 38.0
Constant
 
   temperature difference
Value: 1.57
Constant
 
   kinc
Value: 0.0258
Constant
 
   tdose1
Value: 24.0
Constant
 
   tdose2
Value: 72.0
Constant
 
   tdose3
Value: 120.0
Constant
 
   circadian rhythm
Value: NaN
 
   t_day
Value: 17.5
Constant
 
   t_night
Value: 6.73
Constant
 
   tprime
Value: NaN
 
   day_length
Value: 86400.0
Constant
 
   rate constant Metabolism
Value: 1.1375
Constant
 
   specific heat constant
Value: 3.47
Constant
 
   heat conductance
Value: NaN
 
   pEtot
Value: 0.144
Constant
 
   kR
Value: 5.35
Constant
 
   AMT_dose
Value: 3.0
Constant
 
   pEf1
Value: 1.0
Constant
 
   pEs1
Value: 0.2
Constant
 
   pEf2
Value: 3.57
Constant
 
   pEs2
Value: 2.43
Constant
 
   pEf3
Value: 8.0
Constant
 
   pEs3
Value: 50.0
Constant
 
   f2_drug
Value: NaN
 
   T_day
Value: NaN
 
   T_night
Value: NaN
 
   heat conductance baselinevalue
Value: NaN
 
   M_b
Value: 3.0
Constant
 
   M_day
Value: NaN
 
   M_night
Value: NaN
 
   t_prime
Value: 45.12
Constant
 
   alpha
Value: 0.2229166
Constant
 
   delta_high_dose
Value: 1.0
Constant
 
   M_night_baseline
Value: NaN
 
   gNsTs1
Value: NaN
 
   gNsTs2
Value: NaN
 
   gNsTs3
Value: NaN
 
   gNfTf1
Value: NaN
 
   gNfTf2
Value: NaN
 
   gNfTf3
Value: NaN
 
   No. of transit compartment (slow)
Value: 4.0
Constant
 
   No. of transit compartment (fast)
Value: 4.0
Constant
 
   mean total delay (slow)
Value: 2.45
Constant
 
   mena total delay (fast)
Value: 0.368
Constant
 
   X1
Value: NaN
 
   X2
Value: NaN
 
   X3
Value: NaN
 
   Kf
Value: NaN
 
   Ks
Value: NaN
 
Representative curation result(s)
Representative curation result(s) of BIOMD0000000238

Curator's comment: (updated: 07 Dec 2009 16:23:26 GMT)

The model reproduces figure 3 of the reference publication. There is a slight inconsistency between the graph obtained by the model and that is presented in the paper. The model uses the ODE parameters given in Table 1, which presents the parameter estimates and their standard errors from the model. The author suggested that, the parameter estimates might have changed slightly from the final model, and that could be the reason of the slight variation in the graph obtained by this model and that is presented in the paper.
In the model time is defined in hours. The model was simulated for 200hrs, and for making the curation figure consistent with that of the paper, hours was converted to days. The model was simulated using SBML odeSolver. It was also tested in Copasi.

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