Hi, I prefer to code my peripheral compartment using
PERI = K23*A(2) - K32*A(3) DADT(1) = -KA*A(1) DADT(2) = KA*A(1) - K*A(2) - PERI DADT(3) = PERI helps avoinding errors and I tend to believe it saves some runtime :) Kind regards Sven 2016-12-12 13:02 GMT+01:00 Silber Baumann, Hanna < hanna.silber_baum...@roche.com>: > Jakob, Niels, > Thank you for finding the typo. That was the problem. I had 2 people > checking the code for me in addition to myself. Clearly sometimes, fresh > eyes is what is needed. > > Have a nice day all of you. > > -Hanna > > On Mon, Dec 12, 2016 at 11:58 AM, Jakob Ribbing < > jakob.ribb...@pharmetheus.com> wrote: > >> Hi Hanna, >> >> I did not check the whole model code, but could it be a typo in the rate >> for re-distribution that produces the difference? >> >> DADT(3) = K23*A(2) - *K23**A(3) >> >> Kind regards >> >> Jakob >> >> Jakob Ribbing, Ph.D. >> >> Senior Consultant, Pharmetheus AB >> >> >> Cell/Mobile: +46 (0)70 514 33 77 <+46%2070%20514%2033%2077> >> >> jakob.ribb...@pharmetheus.com >> >> www.pharmetheus.com >> >> >> Phone, Office: +46 (0)18 513 328 >> >> Uppsala Science Park, Dag Hammarskjölds väg 52B >> >> SE-752 37 Uppsala, Sweden >> >> >> *This communication is confidential and is only intended for the use of >> the individual or entity to which it is directed. It may contain >> information that is privileged and exempt from disclosure under applicable >> law. If you are not the intended recipient please notify us immediately. >> Please do not copy it or disclose its contents to any other person.* >> >> >> >> >> On 12 Dec 2016, at 10:13, Silber Baumann, Hanna < >> hanna.silber_baum...@roche.com> wrote: >> >> Dear nmusers, >> I have a data set which contains single and multiple ascending dose data. >> The model development was initially performed on the single dose data. >> I initially developed a model using ADVAN4 TRANS 2 (2 compartment linear >> model with oral administration) which I later reparameterized into ADVAN6. >> I expected to see some minor differences in parameter estimates, OFV etc >> due to the change in subroutine but was surprised to see large differences >> in both parameter estimates and OFV (+180 points) but also a significant >> improvement in overall fit (graphically) while the data was the same. With >> the ADVAN4 the model fit was particularly poor to parts of the multiple >> dose data, with the ADVAN6 the overall fit to all data was much improved. I >> was using NONMEM7.3 for the analysis. >> >> I guess the ADVAN4 model gets stuck in a local minima, but using the >> final estimates from the ADVAN6 model does not help. I would be grateful >> for an explanation of the reasons why this happens. >> >> I have included the two models below. >> Kind regards, >> Hanna Silber >> >> $PROBLEM PK with ADVAN4 >> >> $INPUT C ID TAD TIME AMT DV EVID CMT PTIM LDV DOSE BW BMI CLCR SEX AGE >> STUDY DAY BLQ >> >> $DATA nmpk05DEC16.csv IGNORE=@ >> >> $SUBROUTINES ADVAN4 TRANS4 >> >> $PK >> CL = THETA(1) * EXP(ETA(1)) >> V2 = THETA(2) * EXP(ETA(2)) >> KA = THETA(3) * EXP(ETA(3)) >> ALAG1 = THETA(6) * EXP(ETA(4)) >> Q = THETA(7) * EXP(ETA(5)) >> V3 = THETA(8) * EXP(ETA(6)) >> >> S2 = V2/1000 >> >> $ERROR >> IPRED = F >> W = SQRT(THETA(4)**2*IPRED**2 + THETA(5)**2) >> Y = IPRED + W*EPS(1) >> IRES = DV-IPRED >> IWRES = IRES/W >> >> $THETA >> (0,12.7) ;1 CL >> (0,275) ;2 V2 >> (0,3.06) ;3 KA >> (0, 0.12) ;4 Prop.RE (sd) >> (0, 0.0153) ;5 Add.RE (sd) >> (0,0.474) ;6 ALAG1 >> (0,26.3) ;7 Q >> (0,133) ;8 V3 >> >> $OMEGA BLOCK(2) 0.0747 ;1 IIV CL >> 0.0723 0.0942 ;2 IIV V2 >> $OMEGA >> 1.76 ;3 IIV KA >> 0.00166 ;4 IIV ALAG >> 0.036 ;5 IIV Q >> 0.0407 ;6 IIV V3 >> >> $SIGMA >> 1 FIX ; >> >> $EST METHOD=1 INTER MAXEVAL=9999 NOABORT SIG=3 PRINT=1 POSTHOC >> $COV >> ###################################################### >> >> $PROBLEM PK with ADVAN6 >> >> $INPUT C ID TAD TIME AMT DV EVID CMT PTIM LDV DOSE BW BMI CLCR SEX AGE >> STUDY DAY BLQ >> >> $DATA nmpk05DEC16.csv IGNORE=@ >> >> $SUBROUTINES ADVAN6 TOL=5 >> >> $MODEL >> COMP = (ABS) ;1 >> COMP = (CENT) ;2 >> COMP = (PER) ;3 >> >> $PK >> CL = THETA(1) * EXP(ETA(1)) >> V2 = THETA(2) * EXP(ETA(2)) >> KA = THETA(3) * EXP(ETA(3)) >> ALAG1 = THETA(6) * EXP(ETA(4)) >> Q = THETA(7) * EXP(ETA(5)) >> V3 = THETA(8) * EXP(ETA(6)) >> >> K=CL/V2 >> K23 = Q/V2 >> K32 = Q/V3 >> >> A_0(1) = 0 >> A_0(2) = 0 >> A_0(3) = 0 >> >> $DES >> DADT(1) = -KA*A(1) >> DADT(2) = KA*A(1) - K*A(2) - K23*A(2) + K32*A(3) >> DADT(3) = K23*A(2) - K23*A(3) >> >> $ERROR >> CONC = A(2)*1000/V2 >> IPRED = CONC >> IF(CONC.EQ.0) IPRED = 1 >> >> W = SQRT(THETA(4)**2*IPRED**2 + THETA(5)**2) >> Y = IPRED + W*EPS(1) >> IRES = DV-IPRED >> IWRES = IRES/W >> >> $THETA >> (0,12.1) ;1 CL >> (0,275) ;2 V2 >> (0,3.06) ;3 KA >> (0, 0.12) ;4 Prop.RE (sd) >> (0, 0.0153) ;5 Add.RE (sd) >> (0,0.474) ;6 ALAG1 >> (0,26.3) ;7 Q >> (0,133) ;8 V3 >> >> $OMEGA BLOCK(2) 0.0747 ;1 IIV CL >> 0.0723 0.0942 ;2 IIV V2 >> $OMEGA >> 1.76 ;3 IIV KA >> 0.00166 ;4 IIV ALAG >> 0.036 ;5 IIV Q >> 0.0407 ;6 IIV V3 >> >> $SIGMA >> 1 FIX ; >> >> $EST METHOD=1 INTER MAXEVAL=9999 NOABORT SIG=3 PRINT=1 POSTHOC >> $COV >> >> ############################### >> Data set example: >> C ID TAD TIME AMT DV EVID CMT PTIM LDV DOSE BW BMI CLCR SEX AGE STUDY DAY >> BLQ >> 0 11001 0 0 5 0 1 1 0 0 5 54.8 20.63 74.32657 0 44 1 1 0 >> 0 11001 0.5 0.5 0 1.94 0 2 0.5 0.662688 5 54.8 20.63 74.32657 0 44 1 1 0 >> 0 11001 1 1 0 14.6 0 2 1 2.681022 5 54.8 20.63 74.32657 0 44 1 1 0 >> 0 11001 1.5 1.5 0 22.4 0 2 1.5 3.109061 5 54.8 20.63 74.32657 0 44 1 1 0 >> 0 11001 2 2 0 18.1 0 2 2 2.895912 5 54.8 20.63 74.32657 0 44 1 1 0 >> 0 11001 2.5 2.5 0 15.4 0 2 2.5 2.734368 5 54.8 20.63 74.32657 0 44 1 1 0 >> 0 11001 3 3 0 16.3 0 2 3 2.791165 5 54.8 20.63 74.32657 0 44 1 1 0 >> 0 11001 4 4 0 15.5 0 2 4 2.74084 5 54.8 20.63 74.32657 0 44 1 1 0 >> 0 11001 6 6 0 11.9 0 2 6 2.476538 5 54.8 20.63 74.32657 0 44 1 1 0 >> 0 11001 8 8 0 11.5 0 2 8 2.442347 5 54.8 20.63 74.32657 0 44 1 1 0 >> 0 11001 12 12 0 7.71 0 2 12 2.042518 5 54.8 20.63 74.32657 0 44 1 1 0 >> 0 11001 16.017 16.017 0 8.71 0 2 16 2.164472 5 54.8 20.63 74.32657 0 44 1 >> 2 0 >> 0 11001 24 24 0 5.55 0 2 24 1.713798 5 54.8 20.63 74.32657 0 44 1 2 0 >> 0 11001 48 48 0 3.5 0 2 48 1.252763 5 54.8 20.63 74.32657 0 44 1 3 0 >> 0 11001 72 72 0 1.86 0 2 72 0.620576 5 54.8 20.63 74.32657 0 44 1 4 0 >> 0 11001 120.883 120.883 0 0.597 0 2 120 -0.51584 5 54.8 20.63 74.32657 0 >> 44 1 6 0 >> 0 11001 144.9 144.9 0 0.356 0 2 144 -1.03282 5 54.8 20.63 74.32657 0 44 1 >> 7 0 >> 0 11001 168.883 168.883 0 0.177 0 2 168 -1.73161 5 54.8 20.63 74.32657 0 >> 44 1 8 0 >> >> >> >> -- >> >> >> > > > -- > > > *Hanna Silber Baumann, PhD* > > Pharmacometrician > > Principal Scientist > Clinical Pharmacometrics, Clinical Pharmacology > > Roche Pharma Research and Early Development > > > Roche Innovation Center Basel > > > F. Hoffmann-La Roche Ltd > Grenzacherstrasse 124 > 4070 Basel > > Switzerland > > Phone +41 61 687 76 81 <+41%2061%20687%2076%2081> > > > Confidentiality Note: This message is intended only for the use of the > named recipient(s) and may contain confidential and/or proprietary > information. If you are not the intended recipient, please contact the > sender and delete this message. Any unauthorized use of the information > contained in this message is prohibited. > > _________________________________________________________________________ > >
