From: Vichapat Tharanon <*vichapat.t*>

Date: Wed, 10 Jul 2019 01:52:13 +0700

Dear Luann,

"How did you determine that ALT, HGB, and TB should be in the model

for TVCL?" I have determined these covariates as significant covariates by

stepwise forward selection and backward elimination. Sincerely, I really

concerned that I answered you in the right way?

kind regards,

Pete

On Wed, Jul 10, 2019 at 1:23 AM Luann Phillips <luann

wrote:

*> Hi Vichapat,
*

*>
*

*> I was just asking which method that you had used. Some people do use the
*

*> baseline value for the whole dataset even for long studies . I disagree
*

*> with this method. I think time-varying covariate values should always be
*

*> used for long study periods. It sounds like the way you built your data is
*

*> fine.
*

*> The ctl stream looks the same whether you use stationary or time-varying
*

*> covariates. When NM is fitting the model, it uses which ever covariate
*

*> value is available.
*

*>
*

*> As the differential equation solver in NONMEM steps from TIME=N to
*

*> TIME=N+1, the covariate value from TIME=N+1 is used (see example below)
*

*>
*

*> Example:
*

*>
*

*> TIME=0 AMT=40 DV=. COVAR=55
*

*> TIME=11.833 AMT=. DV=25 COVAR=55
*

*> TIME=12 AMT=40 DV=. COVAR=55
*

*> TIME=23.833 AMT=. DV=30 COVAR=55
*

*> TIME=24 AMT=40 DV=. COVAR=60
*

*> TIME=35.833 AMT=. DV=25 COVAR=60
*

*> TIME=36 AMT=40 DV=. COVAR=60
*

*> TIME=47.833 AMT=. DV=27 COVAR=60
*

*> etc.
*

*>
*

*> As NONMEM steps from TIME=0 to TIME=11.833 the value of COVAR=55 from the
*

*> TIME=11.833 record is used
*

*> As NONMEM steps from TIME=11.833 to TIME=12 the value of COVAR=55 from the
*

*> TIME=12 record is used
*

*> As NONMEM steps from TIME=12 to TIME=23.833 the value of COVAR=55 from the
*

*> TIME=23.833 record is used
*

*> As NONMEM steps from TIME=23.833 to TIME=24 the value of COVAR=60 from the
*

*> TIME=24 record is used
*

*> As NONMEM steps from TIME=24 to TIME=35.833 the value of COVAR=60 from the
*

*> TIME=35.833 record is used
*

*> As NONMEM steps from TIME=35.833 to TIME=36 the value of COVAR=60 from the
*

*> TIME=36 record is used
*

*> As NONMEM steps from TIME=36 to TIME=47.833 the value of COVAR=60 from the
*

*> TIME=47.833 record is used
*

*> etc.
*

*>
*

*> If the value of COVAR=55 for all records NONMEM still works the same way,
*

*> it's just that the value of COVAR will never change.
*

*>
*

*> So in your case,
*

*> TVCL=THETA(1)*EXP(THETA(4)*(ALT/388))*((HGB/10.50)**THETA(5))*((TB/4.7)**THETA(6))
*

*> changes value every time that ALT, HGB, or TB changes value.
*

*> The ETA(1) value remains the same for all observations within an
*

*> individual but CL will still change with time because ALT, HGB, and TB
*

*> change with time.
*

*>
*

*> How did you determine that ALT, HGB, and TB should be in the model for
*

*> TVCL?
*

*>
*

*> Luann
*

*>
*

*>
*

*> ------------------------------
*

*> *From: *"Vichapat Tharanon" <vichapat.t *

*> *To: *"Luann Phillips" <Luann.Phillips *

*> *Cc: *"nmusers" <nmusers *

*> *Sent: *Tuesday, July 9, 2019 1:53:18 PM
*

*> *Subject: *Re: [NMusers] Is it possible that IIV (%CV) of final model was
*

*> higher than IIV of base model?
*

*>
*

*> Dear Luann,
*

*>
*

*> (1) My data file was recorded with covariates values changed
*

*> each times in according to the lab monitored. Hence, I think I have
*

*> time-vary covariates in datafile. Now, I use normal control stream to model
*

*> these data. So, you suggested me to put a new value on a record with
*

*> matching date and then retain forward to the next covariate sample. From
*

*> this suggestion, let me confirm that I should have one column for baseline
*

*> covariate (1st Lab monitoring) and another column for exact covariates
*

*> recorded on that day?
*

*>
*

*> (2) Then, how could I code the control stream for the covariate
*

*> model with time-varying covarites? (sorry that I have never get into it)
*

*>
*

*> (3) Btw, I have one doubtful question about stationary
*

*> covariates on the data file. Is it possible to model the PPKs of the drugs
*

*> with stationary covariates. I mean that is it rationale to use
*

*> only one value of each covariates in the model wheres the
*

*> concentration+dose were dynamic especially if the study period take quite
*

*> long time.
*

*>
*

*> Thank you so much for your reply, valued comments and
*

*> suggestions.
*

*>
*

*> Kind regards,
*

*> Vichapat
*

*>
*

*>
*

*> On Tue, Jul 9, 2019 at 8:58 PM Luann Phillips <luann *

*> wrote:
*

*>
*

*>> Vichapat,
*

*>>
*

*>> Your ctl stream appears to be correct. To model with time-varying
*

*>> covariates involves a change in the database.
*

*>> (A) Did you use the covariate values at the time of each patient's first
*

*>> dose (ie, baseline values) in the data?
*

*>> or
*

*>> (B) Did you use the covariate values each time that they were collected?
*

*>>
*

*>> (A) is stationary covariates and (B) is time-vary covariates.
*

*>>
*

*>> To include time-varying covariates in the data, put the new value on a
*

*>> record with a matching date and then retain forward to the next covariate
*

*>> sample.
*

*>>
*

*>> Please be aware that the dosing and sample time assumptions (which
*

*>> sometimes are required) will also add to unexplained variability. I would
*

*>> look at plot of the data prior to running any models and exclude any
*

*>> concentrations that look very wrong (ie, collected at a peak instead of a
*

*>> trough). Perform the modeling and then try re-including the 'wrong'
*

*>> concentrations to show the impact to the model but I would still make the
*

*>> final model the one excluding those concentrations.
*

*>>
*

*>> Luann
*

*>>
*

*>> ------------------------------
*

*>> *From: *"Vichapat Tharanon" <vichapat.t *

*>> *To: *"Luann Phillips" <Luann.Phillips *

*>> *Sent: *Monday, July 8, 2019 10:06:06 PM
*

*>> *Subject: *Re: [NMusers] Is it possible that IIV (%CV) of final model
*

*>> was higher than IIV of base model?
*

*>>
*

*>> Dear Luann,
*

*>>
*

*>> Thank you so much for your valued suggestions. I greatly
*

*>> appreciated it. By the way, The suggestion given me that mean I should use
*

*>> "Time varying covariates" on the model? I am really new with NONMEM, If
*

*>> you do not mind helping me. Could you suggest me how to code the control
*

*>> file for that model in right way. I really know that my request may disturb
*

*>> you, but I do not know how to start it. Thank you in advance.
*

*>>
*

*>> Best regards,
*

*>>
*

*>> PS, This is my original control file for final model. There are 1170
*

*>> Tacrolimus concentration from 50 patients (retrospective data) then I
*

*>> assumed all patient took a drug at same time (every 12 hours: AM, PM on
*

*>> time) and Trough concentrations were monitored at 11.50 hours (Before the
*

*>> next morning dose 30 minutes).
*

*>> Briefly, tacrolimus was reported high inter- & intra-variability and
*

*>> primarily metabolized by liver via Cytochrome enzyme and eliminated via
*

*>> bile.
*

*>>
*

*>> ;Model Desc: Final model
*

*>> ;Project Name: step3cov
*

*>> ;Project ID: NO PROJECT DESCRIPTION
*

*>> ;Project ID: NO PROJECT DESCRIPTION
*

*>>
*

*>> $PROB RUN# ALTHGBTB
*

*>> $INPUT C ID TIME AMT ADDL II TAD DV MDV EVID BW POD AST ALT ALP GGT TB DB
*

*>> ALB HGB HCT BUN SCR
*

*>> $DATA MASTER.CSV IGNORE=C
*

*>> $SUBROUTINES ADVAN2 TRANS2
*

*>> $PK
*

*>>
*

*>> TVCL=THETA(1)*EXP(THETA(4)*(ALT/388))*((HGB/10.50)**THETA(5))*((TB/4.7)**THETA(6))
*

*>> CL=TVCL*EXP(ETA(1))
*

*>> TVV=THETA(2)
*

*>> V=TVV*EXP(ETA(2))
*

*>> TVKA=THETA(3)
*

*>> KA=TVKA*EXP(ETA(3))
*

*>> S2=V/1000
*

*>>
*

*>> $ERROR
*

*>> IPRE=F
*

*>> W= 1
*

*>> IRES= DV-IPRE
*

*>> IWRE=(DV-IPRE)/W
*

*>> Y = F + ERR(1)
*

*>>
*

*>> $EST METHOD=1 INTERACTION PRINT=5 MAX=9999 SIG=3 MSFO=ALTHGBTB.msf
*

*>> $THETA
*

*>> (0,20) ;[CL/F]
*

*>> (0,500) ;[V/F]
*

*>> (fixed,4.48) ;[KA]
*

*>> (0.001);[ALT]
*

*>> (0.001);[HGB]
*

*>> (0.001);[TB]
*

*>>
*

*>> $OMEGA
*

*>> 0.04 ;[P] omega(1,1)
*

*>> 0.04 ;[P] omega(2,2)
*

*>> (fixed,0) ;[A] omega(3,3)
*

*>> $SIGMA
*

*>> 0.04 ;[A] sigma(1,1)
*

*>>
*

*>> $COV
*

*>> $TABLE ID CL V KA ETA1 ETA2 ETA3 PRED RES WRES IPRE IWRE CPRED CWRES TIME
*

*>> AMT ADDL II TAD DV BW POD AST ALT ALP GGT TB DB ALB HGB HCT BUN SCR TIME
*

*>> ONEHEADER NOPRINT FILE=ALTHGBTB.tab
*

*>> $TABLE ID TIME CL V KA ETA1 ETA2 ETA3 ONEHEADER NOPRINT FILE=PATABALTHGBTB
*

*>> $TABLE ID BW POD AST ALT ALP GGT TB DB ALB HGB HCT BUN SCR ONEHEADER
*

*>> NOPRINT FILE=COTABALTHGBTB
*

*>> $TABLE ID ONEHEADER NOPRINT FILE=CATABALTHGBTB
*

*>> $TABLE ID TIME PRED RES WRES IPRE IWRE CPRED CWRES ONEHEADER NOPRINT
*

*>> FILE=SDTABALTHGBTB
*

*>> $TABLE ID CL V KA NOAPPEND NOPRINT FILE=ALTHGBTB.par
*

*>> $TABLE ID ETA1 ETA2 ETA3 NOAPPEND NOPRINT FILE=ALTHGBTB.eta
*

*>>
*

*>>
*

*>>
*

*>> On Tue, Jul 9, 2019 at 1:27 AM Luann Phillips <luann *

*>> wrote:
*

*>>
*

*>>> Vichapat,
*

*>>>
*

*>>> I just had another thought. You may want to check CL/F as a function of
*

*>>> time post transplant. As an initial, look you could try
*

*>>> CL=BLCL + BLCL*MAX*TIME/(TIME50+TIME)
*

*>>> where BLCL would be CL/F when TIME=0 or baseline CL
*

*>>> MAX = maximum proportional increase in CL relative to Baseline
*

*>>> TIME50=the time since transplot required to achieve 50% of the maximum
*

*>>> value of CL/F post transplant.
*

*>>>
*

*>>> If this shows significant improvement in model fit, then you should try
*

*>>> a model with continuous time in the $DES block.
*

*>>>
*

*>>> Best regards,
*

*>>> Luann
*

*>>>
*

*>>> ------------------------------
*

*>>> *From: *"Vichapat Tharanon" <vichapat.t *

*>>> *To: *"nmusers" <nmusers *

*>>> *Sent: *Monday, July 8, 2019 10:21:12 AM
*

*>>> *Subject: *[NMusers] Is it possible that IIV (%CV) of final model was
*

*>>> higher than IIV of base model?
*

*>>>
*

*>>> Dear All,
*

*>>>
*

*>>>
*

*>>> I am a hospital pharmacist and I am working on NONMEM as a
*

*>>> new user. I have modeled the oral immediate-released tacrolimus (Prograf)
*

*>>> in adult liver transplant patients.
*

*>>>
*

*>>> Most of the data were trough concentration (about 1170 levels) from routine
*

*>>> monitoring tacrolimus data in the period of first day post-transplantation
*

*>>> to 6 months. The model was constructed by NONMEM 7.2 using FOCE
*

*>>> INTERACTION methods with the subroutines ADVAN2 TRANS2 (one compartment
*

*>>> model with linear absorption and elimination). The ka could not be
*

*>>> estimated and then was fixed at 4.48 h-1. The IIIV and RUV were
*

*>>> described by exponential and additive error model, respectively. Forward
*

*>>> addition of a liver enzyme (ALT), Hemoglobin and total bilirubin (TB) on
*

*>>> CL/F reduced OFV significantly (delta OFV ~98, 42, 28, respectively) but
*

*>>> IIV of CL/F was increased from 37.2% to 38.1%. It was found that no
*

*>>> significant covariates influenced to V/F but IIV of V/F was also
*

*>>> increased from 55% to 63%. Residual variability was reduced from a SD
*

*>>> of 2.80 to 2.65, when compared final model and base model.
*

*>>>
*

*>>> I feel uncomfortable with these findings. Is it possible
*

*>>> that IIV of CL/F and V/F were rising after adding the significant
*

*>>> covariates whereas %RSE of the CL/F and V/F estimate as well as IIV of CL/F
*

*>>> and IIV of V/F in final model were slightly decreasing. May I have your
*

*>>> comment or suggestion; I would really appreciate it. Thank you in
*

*>>> advance.
*

*>>>
*

*>>> Best regards,
*

*>>>
*

*>>> Pete
*

*>>>
*

*>>
*

*>
*

Received on Tue Jul 09 2019 - 14:52:13 EDT

Date: Wed, 10 Jul 2019 01:52:13 +0700

Dear Luann,

"How did you determine that ALT, HGB, and TB should be in the model

for TVCL?" I have determined these covariates as significant covariates by

stepwise forward selection and backward elimination. Sincerely, I really

concerned that I answered you in the right way?

kind regards,

Pete

On Wed, Jul 10, 2019 at 1:23 AM Luann Phillips <luann

wrote:

Received on Tue Jul 09 2019 - 14:52:13 EDT