[NMusers] Rounding errors with TRANSIT model
Dear All I'm trying to evaluate a transit model approach in an attempt to better describe early drug concentrations following intravenous injection (1 minute and 10 minute infusions) of a sedative-hypnotic drug. However, every minimisation attempt is terminated due to rounding errors (E=134). I've tried the usual strategies to overcome this e.g using final estimates of a terminated run as initial estimates for the next, changing number of sig dig requested, changing from a diagonal omega to a block omega, but nothing has been successful. I'd be very grateful for any suggestions of what I might try next :) Standard 3-comp and 4-comp mamillary models minise successful with these data, but so far no luck with any of the transit models, I have tried model with 1 to 6 transit compartments. An example control stream is shown below. I'm using NONMEM 7 with Intel Fortran. With thanks and all best wishes Ann $PROBLEM 3comp 2 transit $INPUT ID DOSE AMT RATE DUR TIME ORI DV EVID ART AGE WGT $DATA PKcen12LN.csv IGNORE=# $SUBROUTINES ADVAN9 TOL=3 $MODEL ;NCOMPS=9 COMP(CENTRAL, DEFOBS) ;1 COMP(PERIPH1) ;2 COMP(PERIPH2) ;3 COMP(TRANS1, DEFDOSE) ;4 COMP(TRANS2) ;5 ;COMP(TRANS3) ;6 ;COMP(TRANS4) ;7 ;COMP(TRANS5) ;8 ;COMP(TRANS6) ;9 $PK CL=THETA(1)*EXP(ETA(1)) Q2=THETA(2)*EXP(ETA(2)) Q3=THETA(3)*EXP(ETA(3)) V1=THETA(4)*EXP(ETA(4)) V2=THETA(5)*EXP(ETA(5)) V3=THETA(6)*EXP(ETA(6)) K10=CL/V1 K12=Q2/V1 K13=Q3/V1 K21=Q2/V2 K31=Q3/V3 S1=V1 IF (DUR.EQ.10) THEN ALAG4=THETA(7)*EXP(ETA(7)) ELSE ALAG4=THETA(8)*EXP(ETA(8)) ENDIF KTR=THETA(9)*EXP(ETA(9)) $DES ;DADT(1)=A(2)*K21 + A(3)*K31 - A(1)*(K10+K12+K13) ;DADT(2)=A(1)*K12 - A(2)*K21 ;DADT(3)=A(1)*K13 - A(3)*K31 DADT(1)=A(5)*KTR + A(2)*K21 + A(3)*K31 - A(1)*(K10+K12+K13) DADT(2)=A(1)*K12 - A(2)*K21 DADT(3)=A(1)*K13 - A(3)*K31 DADT(4)=-A(4)*KTR DADT(5)=A(4)*KTR - A(5)*KTR ;DADT(6)=A(5)*KTR - A(6)*KTR ;DADT(7)=A(6)*KTR - A(7)*KTR ;DADT(8)=A(7)*KTR - A(8)*KTR ;DADT(9)=A(8)*KTR - A(9)*KTR $ERROR W=1 IPRED= -2 IF (F.GT.0) IPRED=LOG(F) Y=IPRED + ERR(1) IRES=DV-IPRED IWRES=IRES/W $THETA (0, 725);CL $THETA (0, 238);Q2 $THETA (0, 2920) ;Q3 $THETA (0, 311);V1 $THETA (0, 38700) ;V2 $THETA (0, 39500) ;V3 $THETA (0.2167, 0.522,1) ;ALAG 10MIN $THETA (0.00833, 0.122, 1) ;ALAG 1MIN $THETA (0, 0.720) ;KTR $OMEGA BLOCK(6) 0.0959 ; ETA CL 0.00804 0.0211 ; ETA Q2 0.00631 0.00143 0.166 ; ETA Q3 -0.00506 0.00121 -0.0635 0.0491; ETA V1 0.00293 -0.00746 0.00708 -0.00315 0.0328 ; ETA V2 0.00836 0.00688 0.00144 -0.00497 0.00741 0.153 ; ETA V3 $OMEGA 0.217; ETA ALAG 10 $OMEGA 0.4 ; ETA ALAG 1 $OMEGA 0.0171 ; ETA KTR ;$OMEGA (0.0686) ; ETA CL ;$OMEGA (0.01) ; ETA Q2 ;$OMEGA (0.0251) ; ETA Q3 ;$OMEGA (0 FIX) ; ETA V1 ;$OMEGA (0.0328) ; ETA V2 ;$OMEGA (0 FIX) ; ETA V3 ;$OMEGA (0.170) ; ETA ALAG10 ;$OMEGA (1.37) ; ETA ALAG1 ;$OMEGA (0.00409); ETA KTR $SIGMA (0.0944) $ESTIMATION METHOD=1 PRINT=1 MAX= NOABORT SIG=3 ;POSTHOC INTER MSFO=msfo.outputfile ;$COVA $TABLE ID EVID AMT TIME IPRED IRES NOPRINT FILE=AllRecords.txt $TABLE ID CL Q2 Q3 V1 V2 V3 ETA1 ETA2 ETA3 ETA4 ETA5 ETA6 ETA7 ;ETA8 FIRSTONLY NOPRINT NOAPPEND FILE=FirstRecords.txt Ann ___ Ann Rigby-Jones PhD MRSC Research Fellow in Pharmacokinetics & Pharmacodynamics Peninsula College of Medicine & Dentistry N31, ITTC Phase 1 Tamar Science Park 1 Davy Road Derriford Plymouth PL6 8BX Tel: +44 (0) 1752 432014 ___
Re: [NMusers] Rounding errors with TRANSIT model
Ann Try ADVAN6 or ADVAN8 or ADVAN13 . Also, TOL=3 is too small. Increase it to 6-7 or even 9, and also change to NSIG=3 SIGL=9 ($EST step, as recommended in the guide). Also, at least initially, I would remove ETAs from the third compartment and from one of the parameters (Q2 or V2) of the second compartment. Do you really need both ALAG and transit compartment? They serve the same goal, to delay the input, so could be strongly correlated; you may see it on the POSTHOC plot of ETA7-ETA8 versus ETA9. I would try to remove ETAs from ALAG or KTR to check whether they are needed. If nothing helps, use UNCONDITIONAL on the $COV to get standard errors, and if they are reasonable, ignore the error, it may disappear on the next steps of the modeling process Leonid -- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web:www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel:(301) 767 5566 On 8/11/2010 5:57 AM, Ann Rigby-Jones wrote: Dear All I’m trying to evaluate a transit model approach in an attempt to better describe early drug concentrations following intravenous injection (1 minute and 10 minute infusions) of a sedative-hypnotic drug. However, every minimisation attempt is terminated due to rounding errors (E=134). I’ve tried the usual strategies to overcome this e.g using final estimates of a terminated run as initial estimates for the next, changing number of sig dig requested, changing from a diagonal omega to a block omega, but nothing has been successful. I’d be very grateful for any suggestions of what I might try next J Standard 3-comp and 4-comp mamillary models minise successful with these data, but so far no luck with any of the transit models, I have tried model with 1 to 6 transit compartments. An example control stream is shown below. I’m using NONMEM 7 with Intel Fortran. With thanks and all best wishes Ann $PROBLEM 3comp 2 transit $INPUT ID DOSE AMT RATE DUR TIME ORI DV EVID ART AGE WGT $DATA PKcen12LN.csv IGNORE=# $SUBROUTINES ADVAN9 TOL=3 $MODEL ;NCOMPS=9 COMP(CENTRAL, DEFOBS) ;1 COMP(PERIPH1) ;2 COMP(PERIPH2) ;3 COMP(TRANS1, DEFDOSE) ;4 COMP(TRANS2) ;5 ;COMP(TRANS3) ;6 ;COMP(TRANS4) ;7 ;COMP(TRANS5) ;8 ;COMP(TRANS6) ;9 $PK CL=THETA(1)*EXP(ETA(1)) Q2=THETA(2)*EXP(ETA(2)) Q3=THETA(3)*EXP(ETA(3)) V1=THETA(4)*EXP(ETA(4)) V2=THETA(5)*EXP(ETA(5)) V3=THETA(6)*EXP(ETA(6)) K10=CL/V1 K12=Q2/V1 K13=Q3/V1 K21=Q2/V2 K31=Q3/V3 S1=V1 IF (DUR.EQ.10) THEN ALAG4=THETA(7)*EXP(ETA(7)) ELSE ALAG4=THETA(8)*EXP(ETA(8)) ENDIF KTR=THETA(9)*EXP(ETA(9)) $DES ;DADT(1)=A(2)*K21 + A(3)*K31 - A(1)*(K10+K12+K13) ;DADT(2)=A(1)*K12 - A(2)*K21 ;DADT(3)=A(1)*K13 - A(3)*K31 DADT(1)=A(5)*KTR + A(2)*K21 + A(3)*K31 - A(1)*(K10+K12+K13) DADT(2)=A(1)*K12 - A(2)*K21 DADT(3)=A(1)*K13 - A(3)*K31 DADT(4)=-A(4)*KTR DADT(5)=A(4)*KTR - A(5)*KTR ;DADT(6)=A(5)*KTR - A(6)*KTR ;DADT(7)=A(6)*KTR - A(7)*KTR ;DADT(8)=A(7)*KTR - A(8)*KTR ;DADT(9)=A(8)*KTR - A(9)*KTR $ERROR W=1 IPRED= -2 IF (F.GT.0) IPRED=LOG(F) Y=IPRED + ERR(1) IRES=DV-IPRED IWRES=IRES/W $THETA (0, 725) ;CL $THETA (0, 238) ;Q2 $THETA (0, 2920) ;Q3 $THETA (0, 311) ;V1 $THETA (0, 38700) ;V2 $THETA (0, 39500) ;V3 $THETA (0.2167, 0.522,1) ;ALAG 10MIN $THETA (0.00833, 0.122, 1) ;ALAG 1MIN $THETA (0, 0.720) ;KTR $OMEGA BLOCK(6) 0.0959 ; ETA CL 0.00804 0.0211 ; ETA Q2 0.00631 0.00143 0.166 ; ETA Q3 -0.00506 0.00121 -0.0635 0.0491 ; ETA V1 0.00293 -0.00746 0.00708 -0.00315 0.0328 ; ETA V2 0.00836 0.00688 0.00144 -0.00497 0.00741 0.153 ; ETA V3 $OMEGA 0.217 ; ETA ALAG 10 $OMEGA 0.4 ; ETA ALAG 1 $OMEGA 0.0171 ; ETA KTR ;$OMEGA (0.0686) ; ETA CL ;$OMEGA (0.01) ; ETA Q2 ;$OMEGA (0.0251) ; ETA Q3 ;$OMEGA (0 FIX) ; ETA V1 ;$OMEGA (0.0328) ; ETA V2 ;$OMEGA (0 FIX) ; ETA V3 ;$OMEGA (0.170) ; ETA ALAG10 ;$OMEGA (1.37) ; ETA ALAG1 ;$OMEGA (0.00409) ; ETA KTR $SIGMA (0.0944) $ESTIMATION METHOD=1 PRINT=1 MAX= NOABORT SIG=3 ;POSTHOC INTER MSFO=msfo.outputfile ;$COVA $TABLE ID EVID AMT TIME IPRED IRES NOPRINT FILE=AllRecords.txt $TABLE ID CL Q2 Q3 V1 V2 V3 ETA1 ETA2 ETA3 ETA4 ETA5 ETA6 ETA7 ;ETA8 FIRSTONLY NOPRINT NOAPPEND FILE=FirstRecords.txt Ann ___ *Ann Rigby-Jones PhD MRSC* Research Fellow in Pharmacokinetics & Pharmacodynamics Peninsula College of Medicine & Dentistry N31, ITTC Phase 1 Tamar Science Park 1 Davy Road Derriford Plymouth PL6 8BX Tel: +44 (0) 1752 432014 ___
[NMusers] RE: Rounding errors with TRANSIT model
Ann I think your model is starting to be over-parameterised: 4 compartments plus lag plus transit (have you plotted the individual alag estimates as they have fairly big omegas?) I think if you cannot describe the data well with a 4 comp linear model, and are seeing differences in PK with different infusion lengths (is this why different ALAGs are being estimated?), it can mean nonlinearities: is there a reason to suspect nonlinear CL, or nonlinear distribution (perhaps worth looking at target mediated uptake models - see papers from Jusko group)? BW, Joe From: owner-nmus...@globomaxnm.com [owner-nmus...@globomaxnm.com] On Behalf Of Ann Rigby-Jones [ann.rigby-jo...@pms.ac.uk] Sent: 11 August 2010 10:57 To: 'nmusers@globomaxnm.com' Subject: [NMusers] Rounding errors with TRANSIT model Dear All I’m trying to evaluate a transit model approach in an attempt to better describe early drug concentrations following intravenous injection (1 minute and 10 minute infusions) of a sedative-hypnotic drug. However, every minimisation attempt is terminated due to rounding errors (E=134). I’ve tried the usual strategies to overcome this e.g using final estimates of a terminated run as initial estimates for the next, changing number of sig dig requested, changing from a diagonal omega to a block omega, but nothing has been successful. I’d be very grateful for any suggestions of what I might try next :) Standard 3-comp and 4-comp mamillary models minise successful with these data, but so far no luck with any of the transit models, I have tried model with 1 to 6 transit compartments. An example control stream is shown below. I’m using NONMEM 7 with Intel Fortran. With thanks and all best wishes Ann $PROBLEM 3comp 2 transit $INPUT ID DOSE AMT RATE DUR TIME ORI DV EVID ART AGE WGT $DATA PKcen12LN.csv IGNORE=# $SUBROUTINES ADVAN9 TOL=3 $MODEL ;NCOMPS=9 COMP(CENTRAL, DEFOBS) ;1 COMP(PERIPH1) ;2 COMP(PERIPH2) ;3 COMP(TRANS1, DEFDOSE) ;4 COMP(TRANS2) ;5 ;COMP(TRANS3) ;6 ;COMP(TRANS4) ;7 ;COMP(TRANS5) ;8 ;COMP(TRANS6) ;9 $PK CL=THETA(1)*EXP(ETA(1)) Q2=THETA(2)*EXP(ETA(2)) Q3=THETA(3)*EXP(ETA(3)) V1=THETA(4)*EXP(ETA(4)) V2=THETA(5)*EXP(ETA(5)) V3=THETA(6)*EXP(ETA(6)) K10=CL/V1 K12=Q2/V1 K13=Q3/V1 K21=Q2/V2 K31=Q3/V3 S1=V1 IF (DUR.EQ.10) THEN ALAG4=THETA(7)*EXP(ETA(7)) ELSE ALAG4=THETA(8)*EXP(ETA(8)) ENDIF KTR=THETA(9)*EXP(ETA(9)) $DES ;DADT(1)=A(2)*K21 + A(3)*K31 - A(1)*(K10+K12+K13) ;DADT(2)=A(1)*K12 - A(2)*K21 ;DADT(3)=A(1)*K13 - A(3)*K31 DADT(1)=A(5)*KTR + A(2)*K21 + A(3)*K31 - A(1)*(K10+K12+K13) DADT(2)=A(1)*K12 - A(2)*K21 DADT(3)=A(1)*K13 - A(3)*K31 DADT(4)=-A(4)*KTR DADT(5)=A(4)*KTR - A(5)*KTR ;DADT(6)=A(5)*KTR - A(6)*KTR ;DADT(7)=A(6)*KTR - A(7)*KTR ;DADT(8)=A(7)*KTR - A(8)*KTR ;DADT(9)=A(8)*KTR - A(9)*KTR $ERROR W=1 IPRED= -2 IF (F.GT.0) IPRED=LOG(F) Y=IPRED + ERR(1) IRES=DV-IPRED IWRES=IRES/W $THETA (0, 725);CL $THETA (0, 238);Q2 $THETA (0, 2920) ;Q3 $THETA (0, 311);V1 $THETA (0, 38700) ;V2 $THETA (0, 39500) ;V3 $THETA (0.2167, 0.522,1) ;ALAG 10MIN $THETA (0.00833, 0.122, 1) ;ALAG 1MIN $THETA (0, 0.720) ;KTR $OMEGA BLOCK(6) 0.0959 ; ETA CL 0.00804 0.0211 ; ETA Q2 0.00631 0.00143 0.166 ; ETA Q3 -0.00506 0.00121 -0.0635 0.0491; ETA V1 0.00293 -0.00746 0.00708 -0.00315 0.0328 ; ETA V2 0.00836 0.00688 0.00144 -0.00497 0.00741 0.153 ; ETA V3 $OMEGA 0.217; ETA ALAG 10 $OMEGA 0.4 ; ETA ALAG 1 $OMEGA 0.0171 ; ETA KTR ;$OMEGA (0.0686) ; ETA CL ;$OMEGA (0.01) ; ETA Q2 ;$OMEGA (0.0251) ; ETA Q3 ;$OMEGA (0 FIX) ; ETA V1 ;$OMEGA (0.0328) ; ETA V2 ;$OMEGA (0 FIX) ; ETA V3 ;$OMEGA (0.170) ; ETA ALAG10 ;$OMEGA (1.37) ; ETA ALAG1 ;$OMEGA (0.00409); ETA KTR $SIGMA (0.0944) $ESTIMATION METHOD=1 PRINT=1 MAX= NOABORT SIG=3 ;POSTHOC INTER MSFO=msfo.outputfile ;$COVA $TABLE ID EVID AMT TIME IPRED IRES NOPRINT FILE=AllRecords.txt $TABLE ID CL Q2 Q3 V1 V2 V3 ETA1 ETA2 ETA3 ETA4 ETA5 ETA6 ETA7 ;ETA8 FIRSTONLY NOPRINT NOAPPEND FILE=FirstRecords.txt Ann ___ Ann Rigby-Jones PhD MRSC Research Fellow in Pharmacokinetics & Pharmacodynamics Peninsula College of Medicine & Dentistry N31, ITTC Phase 1 Tamar Science Park 1 Davy Road Derriford Plymouth PL6 8BX Tel: +44 (0) 1752 432014 ___ This message may contain confidential information. If you
[NMusers] Expert Modeler position available at Novartis (Basel, Switzerland)
Expert Modeler position at Novartis Pharmaceuticals (Basel, Switzerland) The company: a global healthcare leader, Novartis has one of the most exciting product pipelines in the industry today. A pipeline of innovative products brought to life by diverse, talented, performance-driven people. All of which makes us one of the most rewarding employers in our field. Modeling and Simulation at Novartis: supports the optimal development of therapeutic drugs. Modeling & Simulation (M&S) integrates the principles of biology, pharmacology, and statistics to explain and predict the quantitative consequences of decisions, through the application of mathematical models. A dedicated programming group facilitates our activities by providing data extraction and integration. M&S is a Global Line Function which reports directly to the Global Head of Development. M&S is committed to provide excellence in quantitative support for informed decision-making throughout the organization, from Research through to Development, regulatory approval and the market place. The task: We are looking for an innovative and motivated quantitative scientist with a genuine interest in the utilization of integrated model based approaches to support projects throughout Research and Development. Ideal candidate: PhD with a minimum of 4 years relevant experience of applying M&S in the pharmaceutical industry. Knowledge and experience in modeling of both pharmacokinetic (PK) and efficacy/safety data. Understanding of model building and validation, including non linear mixed effect modeling. To apply, go to www.novartis.com (Careers) and the position has the following Job ID: 71063BR For more information please contact Bengt Hamrén (contact info below) or Colin Pillai (goonaseelan.pil...@novartis.com, +41 61 324 6025) Bengt Hamrén, PhD Novartis Pharma AG M&S Pharmacology CHBS, CHBS, WSJ-027.6.005 Novartis Pharma AG Forum 1 Novartis Campus CH-4056 Basel Switzerland Phone: +41 61 3245923 Fax: +41 61 3241246 Email : bengt.ham...@novartis.com <>
[NMusers] Baseline as a covariate
I'd like to get the group's opinion on something. I have a pharmacodynamic model and the baseline was shown to be a covariate on one of the model parameters. I was hoping to get general thoughts on the use of the baseline as a covariate. Is there a preference for using the observed baseline vs. NONMEM predicted baseline? And does your opinion change if you have a large residual error? Thanks Pete bonate Peter L. Bonate, PhD, FCP, FAAPS GlaxoSmithKline Clinical Pharmacology, Modeling, and Simulation 5 Moore Drive, 17.2259 Research Triangle Park, NC 27709 phone: 919-483-7534 fax: 919-483-8948 email: peter.l.bon...@gsk.com This is a long one but worth it... Scientists will never make as much money as business executives. There is now mathematical proof for this statement. Postulate 1: Knowledge is power postulate 2: Time is money According to the laws of physics, Work = Power(Eq. 1) Time Since, from the 2 postulates above, Knowledge = Power and Time = Money, we can substitute into the equation 1 and come up with Work = Knowledge Money Solving for money, Work = Money Knowledge Hence, there are 2 ways to make money. If we slug our guts out working, we can improve our money situation. This is the normal scientific approach. However the suits usually opt for the easier solution, i.e., the less you know, the more you'll make, regardless of the amount of work done. -Original Message- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Leonid Gibiansky Sent: Wednesday, August 11, 2010 9:01 AM To: Ann Rigby-Jones Cc: 'nmusers@globomaxnm.com' Subject: Re: [NMusers] Rounding errors with TRANSIT model Ann Try ADVAN6 or ADVAN8 or ADVAN13 . Also, TOL=3 is too small. Increase it to 6-7 or even 9, and also change to NSIG=3 SIGL=9 ($EST step, as recommended in the guide). Also, at least initially, I would remove ETAs from the third compartment and from one of the parameters (Q2 or V2) of the second compartment. Do you really need both ALAG and transit compartment? They serve the same goal, to delay the input, so could be strongly correlated; you may see it on the POSTHOC plot of ETA7-ETA8 versus ETA9. I would try to remove ETAs from ALAG or KTR to check whether they are needed. If nothing helps, use UNCONDITIONAL on the $COV to get standard errors, and if they are reasonable, ignore the error, it may disappear on the next steps of the modeling process Leonid -- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web:www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel:(301) 767 5566 On 8/11/2010 5:57 AM, Ann Rigby-Jones wrote: > Dear All > > I'm trying to evaluate a transit model approach in an attempt to better > describe early drug concentrations following intravenous injection (1 > minute and 10 minute infusions) of a sedative-hypnotic drug. However, > every minimisation attempt is terminated due to rounding errors (E=134). > I've tried the usual strategies to overcome this e.g using final > estimates of a terminated run as initial estimates for the next, > changing number of sig dig requested, changing from a diagonal omega to > a block omega, but nothing has been successful. > > I'd be very grateful for any suggestions of what I might try next J > Standard 3-comp and 4-comp mamillary models minise successful with these > data, but so far no luck with any of the transit models, I have tried > model with 1 to 6 transit compartments. An example control stream is > shown below. I'm using NONMEM 7 with Intel Fortran. > > With thanks and all best wishes > > Ann > > $PROBLEM 3comp 2 transit > > $INPUT ID DOSE AMT RATE DUR TIME ORI DV EVID ART AGE WGT > > $DATA PKcen12LN.csv IGNORE=# > > $SUBROUTINES ADVAN9 TOL=3 > > $MODEL > > ;NCOMPS=9 > > COMP(CENTRAL, DEFOBS) ;1 > > COMP(PERIPH1) ;2 > > COMP(PERIPH2) ;3 > > COMP(TRANS1, DEFDOSE) ;4 > > COMP(TRANS2) ;5 > > ;COMP(TRANS3) ;6 > > ;COMP(TRANS4) ;7 > > ;COMP(TRANS5) ;8 > > ;COMP(TRANS6) ;9 > > $PK > > CL=THETA(1)*EXP(ETA(1)) > > Q2=THETA(2)*EXP(ETA(2)) > > Q3=THETA(3)*EXP(ETA(3)) > > V1=THETA(4)*EXP(ETA(4)) > > V2=THETA(5)*EXP(ETA(5)) > > V3=THETA(6)*EXP(ETA(6)) > > K10=CL/V1 > > K12=Q2/V1 > > K13=Q3/V1 > > K21=Q2/V2 > > K31=Q3/V3 > > S1=V1 > > IF (DUR.EQ.10) THEN > > ALAG4=THETA(7)*EXP(ETA(7)) > > ELSE > > ALAG4=THETA(8)*EXP(ETA(8)) > > ENDIF > > KTR=THETA(9)*EXP(ETA(9)) > > $DES > > ;DADT(1)=A(2)*K21 + A(3)*K31 - A(1)*(K10+K12+K13) > > ;DADT(2)=A(1)*K12 - A(2)*K21 > > ;DADT(3)=A(1)*K13 - A(3)*K31 > > DADT(1)=A(5)*KTR + A(2)*K21 + A(3)*K31 - A(1)*(K10+K12+K13) > > DADT(2)=A(1)*K12 - A(2)*K21 > > DADT(3)=A(1)*K13 - A(3)*K31 > > DADT(4)=-A(4)*KTR > > DADT(5)=A(4)*KTR - A(5)*KTR > > ;DADT(6)=A(5)*KTR - A(6)*KTR > > ;DADT(7)=A(6)*KTR - A(7)*KTR > > ;DADT(8)=A(7)*KTR - A(8)*KTR > > ;DADT(9)=A(8)*KTR - A(9)*KTR > > $ERROR > > W=1 > > IPRED= -2 >
RE: [NMusers] Baseline as a covariate
Hi Pete, In this setting I generally try to first model the baseline response and perhaps pursue alternative structural model forms. For example, I may consider a multiplicative relationship rather than an additive relationship between baseline and placebo/drug effects. However, if the distribution of the baseline response is quite complex and not easy to model with a normal eta on baseline, and I'm concerned that I might be getting a biased estimate of the baseline response then I may consider treating the observed baseline as a covariate as a fall-back position. This question is similar to whether one uses observed or predicted concentrations when developing a PK/PD model. We generally prefer using predicted concentrations rather than observed concentrations to smooth out the measurement error (among other reasons as well). However, if we have a lot of lack-of-fit in developing a PK model then it may be preferable to use the observed concentrations. There is a tradeoff as to whether more bias is introduced due to lack-of-fit (e.g., poor estimation of the baseline response) or due to measurement error in using the observed baseline measurements as a covariate. If the residual variability due to lack-of-fit is considerably larger than measurement error, and you can't resolve the lack-of-fit, then you might consider using the observed baseline response as a covariate. Kind regards, Ken Kenneth G. Kowalski President & CEO A2PG - Ann Arbor Pharmacometrics Group, Inc. 110 E. Miller Ave., Garden Suite Ann Arbor, MI 48104 Work: 734-274-8255 Cell: 248-207-5082 Fax: 734-913-0230 ken.kowal...@a2pg.com -Original Message- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Peter Bonate Sent: Wednesday, August 11, 2010 11:22 AM To: 'nmusers@globomaxnm.com' Subject: [NMusers] Baseline as a covariate I'd like to get the group's opinion on something. I have a pharmacodynamic model and the baseline was shown to be a covariate on one of the model parameters. I was hoping to get general thoughts on the use of the baseline as a covariate. Is there a preference for using the observed baseline vs. NONMEM predicted baseline? And does your opinion change if you have a large residual error? Thanks Pete bonate Peter L. Bonate, PhD, FCP, FAAPS GlaxoSmithKline Clinical Pharmacology, Modeling, and Simulation 5 Moore Drive, 17.2259 Research Triangle Park, NC 27709 phone: 919-483-7534 fax: 919-483-8948 email: peter.l.bon...@gsk.com This is a long one but worth it... Scientists will never make as much money as business executives. There is now mathematical proof for this statement. Postulate 1: Knowledge is power postulate 2: Time is money According to the laws of physics, Work = Power(Eq. 1) Time Since, from the 2 postulates above, Knowledge = Power and Time = Money, we can substitute into the equation 1 and come up with Work = Knowledge Money Solving for money, Work = Money Knowledge Hence, there are 2 ways to make money. If we slug our guts out working, we can improve our money situation. This is the normal scientific approach. However the suits usually opt for the easier solution, i.e., the less you know, the more you'll make, regardless of the amount of work done. -Original Message- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Leonid Gibiansky Sent: Wednesday, August 11, 2010 9:01 AM To: Ann Rigby-Jones Cc: 'nmusers@globomaxnm.com' Subject: Re: [NMusers] Rounding errors with TRANSIT model Ann Try ADVAN6 or ADVAN8 or ADVAN13 . Also, TOL=3 is too small. Increase it to 6-7 or even 9, and also change to NSIG=3 SIGL=9 ($EST step, as recommended in the guide). Also, at least initially, I would remove ETAs from the third compartment and from one of the parameters (Q2 or V2) of the second compartment. Do you really need both ALAG and transit compartment? They serve the same goal, to delay the input, so could be strongly correlated; you may see it on the POSTHOC plot of ETA7-ETA8 versus ETA9. I would try to remove ETAs from ALAG or KTR to check whether they are needed. If nothing helps, use UNCONDITIONAL on the $COV to get standard errors, and if they are reasonable, ignore the error, it may disappear on the next steps of the modeling process Leonid -- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web:www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel:(301) 767 5566 On 8/11/2010 5:57 AM, Ann Rigby-Jones wrote: > Dear All > > I'm trying to evaluate a transit model approach in an attempt to better > describe early drug concentrations following intravenous injection (1 > minute and 10 minute infusions) of a sedative-hypnotic drug. However, > every minimisation attempt is terminated due to rounding errors (E=134). > I've tried the usual stra
Re: [NMusers] Translate ALAG1 and D1 in ADVAN2 to differential equations
Xiao, Try running a SIMONLY simulation first. If you are using an additive or additive + constant CV error model, simulation will sometimes generate negative or very tiny DV values. If this is the case, it will cause problems with fitting the model to the simulated data. Luann Xiao Hu wrote: Dear Luann, Andreas and others, Both parts are very clear to me now. Thank you very much! My problem was, when I tried to simulate the differential equation model with parameters from ADVAN2, it did not work. The error message is listed below. This was not an issue during the model switch when D1 was not included in ADVAN2. I then tried to derive the parameters from the differential equation model directly. But there were >10 error messages like below issued. Do you have any insight why this happens? 0PRED EXIT CODE = 1 0INDIVIDUAL NO. 1 ID= 2.001000E+03 (WITHIN-INDIVIDUAL) DATA REC NO. 18 THETA= 4.19E+00 1.22E+01 9.10E+00 1.07E+01 1.20E+01 8.89E+00 0.00E+00 1.50E-03 2.63E-01 2.28E+00 2.87E+00 NUMERICAL DIFFICULTIES WITH INTEGRATION ROUTINE. MAXIMUM NO. OF EVALUATIONS OF DIFFERENTIAL EQUATIONS, 100, EXCEEDED. Best regards, Shelley Xiao Hu (Shelley), Ph.D. Scientist, Development Pharmacokinetics & Disposition Biogen Idec, Inc. 14 Cambridge Center Cambridge, MA 02142 *Luann Phillips * 10-Aug-2010 01:29 PM Message Size: *6.6 KB* To Xiao Hu cc nmusers Subject Re: [NMusers] Translate ALAG1 and D1 in ADVAN2 to differential equations Xiao Hu, Part 1) NONMEM handles the input of all doses automatically via the dataset structure. Therefore, generally there is no need to initialize compartments or to code infusion rates in the $DES block. An exception to this rule is when you are dosing or modeling endogenous substances. Assuming your drug is not an endogenous substance, the differential equations for ADVAN2 with ALAG1 and D1 are shown below (using ADVAN6). Part 2) "I'm not very clear what's the meaning of D1 and KA when both are modeled." Based upon the ADVAN that you are using, NONMEM is putting the dose into the depot compartment as a constant rate infusion with a duration of D1 (hours, days, etc.). It starts the infusion into the depot compartment at time=ALAG1 (hours,days, etc.) after each dose. The dose is then transferred from the depot compartment to the central compartment using a first-order process (Ka 1/hr or 1/day, etc.) I hope this information helps, Luann Phillips - Example Code: $SUBROUTINES ADVAN6 TOL=5 $MODEL COMP=(DEPOT,DEFDOSE) COMP=(CENTRAL,DEFOBS) COMP=(PD1) etc. $PK KASC=THETA(4) VSC=THETA(5) CLSC=THETA(6) LGSC=THETA(8) MU_4=KASC MU_5=VSC MU_6=CLSC MU_8=THETA(11) SD=THETA(9) SIG=SD KA=EXP(MU_4+ETA(4)) V=EXP(MU_5+ETA(5)) CL=EXP(MU_6+ETA(6)) K=CL/V S2=V/1000; DOSE IN 1000 U, CONC in U, VOLUME IN mL ALAG1=LGSC*EXP(ETA(10)) D1=EXP(MU_8+ETA(8)) $DES DADT(1) = -KA*A(1) DADT(2) = KA*A(1) - K*A(2) DADT(3) = equations for PD cmt 1 etc. -- Xiao Hu wrote: > > Dear NMusers, > > I'm using ADVAN2 to model the PK of a drug. To best fit the profile, > the model includes ALAG1 and D1. For the next step, the ADVAN2 needs to > be translated into differential equation to include a PD compartment. > How should I write the differential equation for ALAG1 and D1? As you > can see, there is Ka in the ADVAN2 model. I'm not very clear what's the > meaning of D1 and KA when both are modeled. Any hint or previous link > would be appreciated. Thanks in advance! > > $SUBROUTINES ADVAN2 > $PK > >KASC=THETA(4) >VSC=THETA(5) >CLSC=THETA(6) >LGSC=THETA(8) > >MU_4=KASC >MU_5=VSC >MU_6=CLSC >MU_8=THETA(11) > >SD=THETA(9) >SIG=SD > >KA=EXP(MU_4+ETA(4)) >V=EXP(MU_5+ETA(5)) >CL=EXP(MU_6+ETA(6)) >K=CL/V >S2=V/1000; DOSE IN 1000 U, CONC in U, VOLUME IN mL >ALAG1=LGSC*EXP(ETA(10)) >D1=EXP(MU_8+ETA(8)) > > Final parameter estimates: > > $THETA >12 ;THETA5 >8.89 ;THETA6 >0.0015;THETA8 >0.263 ; THETA9 >2.87; THETA11 > > Best regards, > Shelley > > > Xiao Hu (Shelley), Ph.D. > Scientist, > Development Pharmacokinetics & Disposition > Biogen Idec, Inc. > 14 Cambridge Center > Cambridge, MA 02142
Re: [NMusers] Baseline as a covariate
Hi Peter, I assume from the question that the baseline is the baseline of your modeled PD measure, not of some other value (like the baseline weight), and that you model the actual PD measure, not change from the baseline. Then - it would be more logical to use the model-predicted baseline; - for the clinical applications it could be more useful to have observed predictor; - for simulations of future studies it could be more convenient to have everything in the model rather than use observed values (especially if you apply the model to different population where the baseline value could be shifted). I would try both versions to see the differences, and then use the one that is more convenient to use in the particular situation. Best regards, Leonid -- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web:www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel:(301) 767 5566 On 8/11/2010 11:21 AM, Peter Bonate wrote: I'd like to get the group's opinion on something. I have a pharmacodynamic model and the baseline was shown to be a covariate on one of the model parameters. I was hoping to get general thoughts on the use of the baseline as a covariate. Is there a preference for using the observed baseline vs. NONMEM predicted baseline? And does your opinion change if you have a large residual error? Thanks Pete bonate Peter L. Bonate, PhD, FCP, FAAPS GlaxoSmithKline Clinical Pharmacology, Modeling, and Simulation 5 Moore Drive, 17.2259 Research Triangle Park, NC 27709 phone: 919-483-7534 fax: 919-483-8948 email: peter.l.bon...@gsk.com
RE: [NMusers] Baseline as a covariate
Dear Dr. Bonate, Could you kindly elaborate on the structural model that is being used to characterize your PD system? In my limited experience, I have seen the behavior that you describe under 2 circumstances: a) Simple inhibitory Emax PD model with a baseline: Here patients with a higher baseline (worse disease state) displayed a more pronounced effect. There is greater margin/room for improvement for a patient starting out at a higher baseline and hence shows a better response (vs. another patient that starts out a lower baseline). I was able to model this behavior by putting an omega block between Emax and the baseline parameter Eo. It is then very easy to adjudicate the impact of Eo on the magnitude of effect in simulation mode. b) Indirect Response Model: Sun and Jusko [J Pharm Sci 88: 987 (1999)] have shown that "The baseline value may play an important role in affecting the extent of the response if its PD relationship can be described by a turnover Model. When other factors remain constant (e.g. Smax, SC50, kout), R0 controls the magnitude of the response." Is it possible that you're getting a spurious baseline covariate effect because of the choice of your structural model? I am also quite interested in hearing the general thoughts on the use of the baseline as a covariate from this forum. In fact I have seen yet another approach to this problem where the change from baseline was modeled as the PD endpoint. With this method the baseline is no longer a parameter in the model and observed baseline can then be used as a covariate. Does this method offer any benefit or do the statistical concerns around data transformation negate the benefits of this method. Thank-you, Mahesh -Original Message- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Ken Kowalski Sent: Wednesday, August 11, 2010 12:12 PM To: 'Peter Bonate'; nmusers@globomaxnm.com Subject: RE: [NMusers] Baseline as a covariate Hi Pete, In this setting I generally try to first model the baseline response and perhaps pursue alternative structural model forms. For example, I may consider a multiplicative relationship rather than an additive relationship between baseline and placebo/drug effects. However, if the distribution of the baseline response is quite complex and not easy to model with a normal eta on baseline, and I'm concerned that I might be getting a biased estimate of the baseline response then I may consider treating the observed baseline as a covariate as a fall-back position. This question is similar to whether one uses observed or predicted concentrations when developing a PK/PD model. We generally prefer using predicted concentrations rather than observed concentrations to smooth out the measurement error (among other reasons as well). However, if we have a lot of lack-of-fit in developing a PK model then it may be preferable to use the observed concentrations. There is a tradeoff as to whether more bias is introduced due to lack-of-fit (e.g., poor estimation of the baseline response) or due to measurement error in using the observed baseline measurements as a covariate. If the residual variability due to lack-of-fit is considerably larger than measurement error, and you can't resolve the lack-of-fit, then you might consider using the observed baseline response as a covariate. Kind regards, Ken Kenneth G. Kowalski President & CEO A2PG - Ann Arbor Pharmacometrics Group, Inc. 110 E. Miller Ave., Garden Suite Ann Arbor, MI 48104 Work: 734-274-8255 Cell: 248-207-5082 Fax: 734-913-0230 ken.kowal...@a2pg.com -Original Message- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Peter Bonate Sent: Wednesday, August 11, 2010 11:22 AM To: 'nmusers@globomaxnm.com' Subject: [NMusers] Baseline as a covariate I'd like to get the group's opinion on something. I have a pharmacodynamic model and the baseline was shown to be a covariate on one of the model parameters. I was hoping to get general thoughts on the use of the baseline as a covariate. Is there a preference for using the observed baseline vs. NONMEM predicted baseline? And does your opinion change if you have a large residual error? Thanks Pete bonate Peter L. Bonate, PhD, FCP, FAAPS GlaxoSmithKline Clinical Pharmacology, Modeling, and Simulation 5 Moore Drive, 17.2259 Research Triangle Park, NC 27709 phone: 919-483-7534 fax: 919-483-8948 email: peter.l.bon...@gsk.com This is a long one but worth it... Scientists will never make as much money as business executives. There is now mathematical proof for this statement. Postulate 1: Knowledge is power postulate 2: Time is money According to the laws of physics, Work = Power(Eq. 1) Time Since, from the 2 postulates above, Knowledge = Power and Time = Money, we can substitute into the equation 1 and come up with Work = Knowledge Money Solvi
RE: [NMusers] Baseline as a covariate
Hi Pete, I think the standard model most of us would use is baseline as a parameter in the model, and like other parameters it would have a variability between subjects. What you describe sounds like a covariance between the baseline parameter and some other parameter. We are used to include such covariances and I would suggest that to be the primary way to address it. If the distribution of baseline is truncated due to inclusion criteria or physiological limits, I suggest that you may use a semi-parametric IIV model (Peterson et al., Pharm Res. 2009 Sep;26(9):2174-85) If the relation is more complex than easily handled via a covariance, then I suggest that you use the baseline observation as a covariate with error (Dansirikul et al Pharmacokinet Pharmacodyn. 2008 Jun;35(3):269-83.) Best regards, Mats Mats Karlsson, PhD Professor of Pharmacometrics Dept of Pharmaceutical Biosciences Uppsala University Swedent regards, Mats Postal address: Box 591, 751 24 Uppsala, Sweden Phone +46 18 4714105 Fax + 46 18 4714003 -Original Message- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Peter Bonate Sent: Wednesday, August 11, 2010 5:22 PM To: 'nmusers@globomaxnm.com' Subject: [NMusers] Baseline as a covariate I'd like to get the group's opinion on something. I have a pharmacodynamic model and the baseline was shown to be a covariate on one of the model parameters. I was hoping to get general thoughts on the use of the baseline as a covariate. Is there a preference for using the observed baseline vs. NONMEM predicted baseline? And does your opinion change if you have a large residual error? Thanks Pete bonate Peter L. Bonate, PhD, FCP, FAAPS GlaxoSmithKline Clinical Pharmacology, Modeling, and Simulation 5 Moore Drive, 17.2259 Research Triangle Park, NC 27709 phone: 919-483-7534 fax: 919-483-8948 email: peter.l.bon...@gsk.com This is a long one but worth it... Scientists will never make as much money as business executives. There is now mathematical proof for this statement. Postulate 1: Knowledge is power postulate 2: Time is money According to the laws of physics, Work = Power(Eq. 1) Time Since, from the 2 postulates above, Knowledge = Power and Time = Money, we can substitute into the equation 1 and come up with Work = Knowledge Money Solving for money, Work = Money Knowledge Hence, there are 2 ways to make money. If we slug our guts out working, we can improve our money situation. This is the normal scientific approach. However the suits usually opt for the easier solution, i.e., the less you know, the more you'll make, regardless of the amount of work done. -Original Message- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Leonid Gibiansky Sent: Wednesday, August 11, 2010 9:01 AM To: Ann Rigby-Jones Cc: 'nmusers@globomaxnm.com' Subject: Re: [NMusers] Rounding errors with TRANSIT model Ann Try ADVAN6 or ADVAN8 or ADVAN13 . Also, TOL=3 is too small. Increase it to 6-7 or even 9, and also change to NSIG=3 SIGL=9 ($EST step, as recommended in the guide). Also, at least initially, I would remove ETAs from the third compartment and from one of the parameters (Q2 or V2) of the second compartment. Do you really need both ALAG and transit compartment? They serve the same goal, to delay the input, so could be strongly correlated; you may see it on the POSTHOC plot of ETA7-ETA8 versus ETA9. I would try to remove ETAs from ALAG or KTR to check whether they are needed. If nothing helps, use UNCONDITIONAL on the $COV to get standard errors, and if they are reasonable, ignore the error, it may disappear on the next steps of the modeling process Leonid -- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web:www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel:(301) 767 5566 On 8/11/2010 5:57 AM, Ann Rigby-Jones wrote: > Dear All > > I'm trying to evaluate a transit model approach in an attempt to better > describe early drug concentrations following intravenous injection (1 > minute and 10 minute infusions) of a sedative-hypnotic drug. However, > every minimisation attempt is terminated due to rounding errors (E=134). > I've tried the usual strategies to overcome this e.g using final > estimates of a terminated run as initial estimates for the next, > changing number of sig dig requested, changing from a diagonal omega to > a block omega, but nothing has been successful. > > I'd be very grateful for any suggestions of what I might try next J > Standard 3-comp and 4-comp mamillary models minise successful with these > data, but so far no luck with any of the transit models, I have tried > model with 1 to 6 transit compartments. An example control stream is > shown below. I'm using NONMEM 7 with Intel Fortran. > > With thanks and all best wi
