Re: Indirect response model

From: David H Salinger Date: May 16, 2007 technical Source: mail-archive.com
Dear Nishit, Jurgen is correct, you are using concentration (COP) as a forcing function. But, in ID 1101, for example, the COP has a value of 0 from TIME=0 to 840 and then values around 0.12 until TIME 1848. Unless this is what you had in mind, I would suggest two steps: 1. Include more time points for COP. These need not coincide with TIMEs were you have DV values. 2. Create a linear interpolation of COP to be used in the $DES block. One way to do this linear interpolation is to add two columns to your data file: PTME (previous time) and PCOP (previous concentration). Then, compute SLOPE = (COP-PCOP)/(TIME-PTME) and use a linear interpolation of conc: PCOP +SLOPE*(T-PTME) in place of COP in computing your COEF parameter (must be in $DES block). I hope this helps, David Salinger RFPK, Univ. of Washington
Quoted reply history
On Tue, 15 May 2007, Jurgen Bulitta wrote: > Dear Nishit, > > If I understand correctly, you are using concentration (COP) of > your drug as a time dependent covariate which is then used as > forcing function for your PD model. > > As concentrations change over time, you probably need the > CALLFL = 0 option ($PK CALLFL=0) to read in the concentration > at every new time. I would write out COP in $TABLE in order to > check, if COP changes over time as it should. > > This should work much better, but it will still give you a piecewise > constant concentration profile. This may cause numerical problems. > Instead, I would include the differential equations for your PK > model. This should give you better numerical stability and more > correct concentration predictions. You could start with reading in > the individual PK parameters (IPP approach, see reference below) > and then go to more complex PKPD analyses. > > You might try the MATRIX=S statement in $COV, if you like to get > the covariance step to work. > > Hope some of this works. > > Best regards > Juergen > > Reference: > Zhang, L., S. L. Beal, and L. B. Sheiner. 2003. Simultaneous vs. > sequential analysis for population PK/PD data I: best-case performance. > J Pharmacokinet Pharmacodyn 30:387-404. > > ----------------------------------------------- > Juergen Bulitta, PhD, Post-doctoral Fellow > Pharmacometrics, University at Buffalo, NY, USA > Phone: +1 716 645 2855 ext. 281, [EMAIL PROTECTED] > ----------------------------------------------- > > -----Ursprüngliche Nachricht----- > Von: "Modi, Nishit [ALZUS]" <[EMAIL PROTECTED]> > Gesendet: 15.05.07 18:31:57 > An: [EMAIL PROTECTED] > CC: [email protected] > Betreff: [NMusers] Indirect response model > > I am conducting a sequential pharmacokinetic-pharmacodynamic model. The > pharmacokinetic fits look good and I was using an indirect response model. The > PD model is that the drug inhibits clearance of the analyte (PD response), thus > one expects that the response increases with increasing drug (Model II). There > is a baseline measured (=Kfor/Kcl) and a dummy dose=1 unit given. It seems > despite trying various permutations of the model, eta1 seems to be very small > and no covariance step is conducted. The model and data for the first 3 > subjects are reproducted below. Any assistance would be appreciated. Note > that since conc (COP) are read in, the model only requires a single > differential equation. Any insight would be appreciated. > > Nishit > > $PROBLEM PD - ADVAN6 > > $DATA C:\PDDATA.CSV > > $INPUT ID TIME DV AMT=DOSE COP MDV > > ; data are subject ID, Time, DV=PD response, Amt (dummy dose of 1 inserted), > COP=plasma conc which drive PD model, MDV > > $SUBROUTINES ADVAN6 TOL=6 > > $MODEL > > COMP=(EFFECT, DEFDOSE, DEFOBS) > > $PK > > KFOR = THETA(1) > > KCL = THETA(2)*EXP(ETA(1)) > > IC50 = THETA(3) > > IMAX = THETA(4) > > F1 = KFOR/KCL > > COEF = IMAX*COP/(IC50+COP) > > $DES > > DADT(1) = KFOR-KCL*(1-COEF)*A(1) > > $ERROR > > W = F > > Y = F*EXP(ERR(1)) > > IPRED = F > > IRES = DV-IPRED > > IF (W.LE.0.) W=1 > > IWRES = IRES/W > > $THETA (0,0.3) > > $THETA (0, 0.003) > > $THETA (0,10) > > $THETA (0, 0.3, 1) > > $OMEGA 0.01 > > $SIGMA 0.5 > > $ESTIMATION METHOD=1 MAXEVAL=5000 PRINT=20 > > $COVR > > $TABLE ID TIME PRED IPRED IRES KFOR KCL IC50 IMAX > > NOPRINT ONEHEADER > > FILE=C:\PD.TAB > > 1001 0 . 1 0 1 > > 1001 0 98.3 . 0 0 > > 1001 168 90.6 . 122.44 0 > > 1001 840 92.8 . 183.69 0 > > 1002 0 . 1 0 1 > > 1002 0 105.1 . 0 0 > > 1002 840 88.5 . 61.253 0 > > 1002 842 106.7 . 106.8 0 > > 1002 844 122.1 . 116.4 0 > > 1002 1848 129.1 . 121.46 0 > > 1002 1850 160.4 . 212.63 0 > > 1002 1852 157.1 . 231.89 0 > > 1101 0 . 1 0 1 > > 1101 0 68.1 . 0 0 > > 1101 840 88.1 . 0.13884 0 > > 1101 842 105.5 . 0.12987 0 > > 1101 844 108.8 . 0.12147 0 > > 1101 1848 113.3 . 227.79 0 > > 1101 1850 62.6 . 379.54 0 > > 1101 1852 138.7 . 412.18 0
May 15, 2007 Nishit Modi Indirect response model
May 15, 2007 Jurgen Bulitta Re: Indirect response model
May 15, 2007 Sunny Chapel RE: Indirect response model
May 16, 2007 David H Salinger Re: Indirect response model
May 17, 2007 Jakob Ribbing RE: Indirect response model
May 28, 2007 Alison Boeckmann RE: Indirect response model