Dear All, I am new to the field of POP-PK. We are developing a POP-PK model for our in-house drug. The conc. are in ng/mL and dose is in mg. The drug is administered orally and follows 1-compartment model. It has a short half-life of 2-3 h. We have developed the basic model using rich data from serial sampling of healthy subjects.
-We have determined the Bayesian estimates of CL and V. But I think, we
are overestimating these values. If we determine CL , using
non-compartmental analysis (rich data )we are getting lower estimates
(800 L/h), while using NONMEM (using same rich data), we are getting
quite high estimates (mean for 25 mg- 4000 L/hr).
Kindly give your inputs on further modifying this model to get an
irreducible model and also validating the model.
Thanking you in anticipation.
Thanks and regards
Tausif Ahmed, Ph.D.
Senior Research Scientist
Metabolism and Pharmacokinetics Department,
Ranbaxy Research Lab., India
Model:
PROB Oral RSd-RMD-PK01-healthy study
$DATA RSD_PK01ctdata.csv IGNORE=C
$INPUT ID TIME CONC=DV DOSE AMT AGE WT MDV
$SUBROUTINES ADVAN2 TRANS2; One Compartment Linear Model
$PK
TVCL=THETA(1) ; mean clearance
CL=TVCL*EXP(ETA(1))
TVV=THETA(2) ; mean central volume
V=TVV*EXP(ETA(2))
K=CL/V ;reparameterization required
S1=V/1000
KA=THETA(3)
$THETA (0, 100) ; clearance estimate (L\hr)
(0, 100) ; volume estimate (L)
(0, 1.5) ; Absorption rate constant
$OMEGA .16 .16 ; forty percent cv
$SIGMA .16 ; forty percent cv
$ERROR
IPRED=F
Y=F+ERR(1)
$ESTIMATION METHOD=0 MAXEVAL=9999 PRINT=5 POSTHOC SIGDIGITS=4
$COVARIANCE
$TABLE ID TIME DV DOSE CL V ETA1 ETA2 AMT AGE WT MDV
NOPRINT ONEHEADER FILE=CS1_oralRSDRMDPK01.PAR
$SCAT CONC VS TIME
OUTPUT
MINIMUM VALUE OF OBJECTIVE FUNCTION
************************************************** 6971.242
**************************************************
************************************************************************
************************************************
********************
******************** FINAL PARAMETER ESTIMATE
************************************************************************
************************************************
THETA - VECTOR OF FIXED EFFECTS PARAMETERS *********
TH 1 TH 2 TH 3
4.97E+03 8.02E+03 1.70E-01
OMEGA - COV MATRIX FOR RANDOM EFFECTS - ETAS ********
ETA1 ETA2
ETA1
+ 2.57E+00
ETA2
+ 0.00E+00 1.14E+00
SIGMA - COV MATRIX FOR RANDOM EFFECTS - EPSILONS ****
EPS1
EPS1
+ 4.72E+02
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