Re: [NMusers] Automatic Conversion between NONMEM and nlmixr2 models
Thank you so much for sharing this wonderful news. It will be very useful. El viernes, 2 de junio de 2023, Matthew Fidler escribió: > The nlmixr2/rxode2 team is excited to announce a tool to help conversion > of NONMEM to rxode2 or nlmixr2 fits with nonmem2rx and babelmixr2. > > With nonmem2rx/babelmixr2 you can do many useful tasks directly converting > between nlmixr2 and NONMEM models; you > > - Convert a NONMEM model to a rxode2 model > > - Do development in nlmixr2 and then run NONMEM from a nlmixr2 model for > reviewers who want to know about NONMEM results. > > - In both conversions, automatically make sure the model is translated > correctly > > Then with nlmixr2 fit models coming from both conversions, you can: > > - Perform simulations of new dosing from the NONMEM model or even simulate > using the uncertainty in your model to simulate new scenarios > > - Modify the model to calculate derived parameters (like AUC). These > parameters slow down NONMEM’s optimization, but can help in your simulation > scenario. > > With nonmem2rx and babelmixr2, convert the imported rxode2 model to a > nlmixr2 object, allowing: > > - Generation of Word and PowerPoint plots with nlmixr2rpt > > - Easy VPC creation (with vpcPlot()) > > - Easy Individual plots with extra solved points. This will show the > curvature of individual and population fits for sparse data-sets (with > augPred()) > > You can even use this conversion to help debug your NONMEM model (or even > try it in nlmixr2 instead) > > - Understand how to simplify the NONMEM model to avoid rounding errors > > - Run nlmixr2’s covariance step when NONMEMs covariance step has failed > > > For more details talk to us at our PAGE poster, read our blog or our > websites: > > https://blog.nlmixr2.org/blog/2023-06-02-nonmem2rx-and-babelmixr2/ > > https://nlmixr2.github.io/nonmem2rx/ > > https://nlmixr2.github.io/babelmixr2/ > > -- Niurys de Castro Suárez, PhD Assistant Professor of Pharmacometrics Associate Researcher Pharmacy Department Institute of Pharmacy and Food, University of Havana Cuba "Una estrella brilla en la hora de nuestro encuentro"
Re: [NMusers] flip-flop without absorption information?
Dear all, thank you so. I think it is necessary to take into account all these explanations and suggestions as well before building the Pop PK model for this peptide. I don't have any dataset from previous IV administration, and, as I mentioned in my email, the current dataset is lacking in information of absorption phase. Best, Niurys El mar, 13 sept 2022 a las 17:46, Leonid Gibiansky (< lgibian...@quantpharm.com>) escribió: > With flip-flop, we always can get 2 solutions (assuming 1-cmpt model > with absorption; 2-cpt case is similar but expressions may differ): > > ka1-CL-V1 and ka2-CL-V2 such that > > ka1=CL/V2 and ka2=CL/V1 > > Note that CL is the same, so info on CL will not help to distinguish > these cases. > > One cannot just fix the volume, as it should have one of the values V1 > or V2, but one can select the "more mechanistic" value if other > information (e.g., about similar compounds) is available, and push the > solution to the right place by providing the bounds on the range of > possible parameter estimates. > > Note that with SC dose, we cannot estimate CL and V, we have apparent CL > and apparent V (related to the underlying CLtrue and Vtrue as > CL=CLtrue/F and V= Vtrue/F, where F is the absolute bioavailability of > SC administration). When apparent CL and apparent V are compared with > parameters for other compounds, F should be taken into account. > > Thank you > Leonid > > > > > On 9/13/2022 10:54 AM, Bonate, Peter wrote: > > In comment to Shan’s statement: > > > > It's my understanding that this flip-flop phenomenon is fundamentally a > > mathematical problem -- that is, if we write down a PK model in its > > analytical form, it becomes rather easy to understand that swapping the > > values between ka and ke (CL/V) would lead to the same output. > > > > This is not true. The values do not swap out. V will be different. > > > > Consider a 1-compartment model with KA=0.1 per h, KEL=0.7 per h, and > > V1=125L. Suppose a 250 mg dose is given. This model has flip-flop > > kinetics. > > > > Now assume we build a model with KA=0.7 per h, KEL=0.1 per h, and V2 is > > unknown, same dose. This model does not have flip-flop. Using the > > simulated data from model 1 as the observed data for model 2, we can fit > > model 2 and find the optimum value of V2. In this case it is 875L. If > > you look at the profiles you will see that they are /exactly/ the same. > > > > So it’s not a matter of just changing the order of the exponents. > > > > If you want to estimate the parameters of a flip-flop model you need a > > data without absorption – IV. Or some other independent assessment of > > CL that does not depend on absorption. > > > > *Peter Bonate, PhD* > > > > Executive Director > > > > Pharmacokinetics, Modeling, and Simulation (PKMS) > > > > Clinical Pharmacology and Exploratory Development (CPED) > > > > Astellas > > > > 1 Astellas Way > > > > Northbrook, IL 60062 > > > > peter.bon...@astellas.com <mailto:peter.bon...@astellas.com> > > > > (224) 619-4901 > > > > Quote of the week – > > > > /“Dancing with the Stars” is not owned by Astellas.**/ > > > > *From:* owner-nmus...@globomaxnm.com *On > > Behalf Of *Shan Pan > > *Sent:* Tuesday, September 13, 2022 3:42 AM > > *To:* Jakob Ribbing ; Niurys.CS > > > > *Cc:* nmusers > > *Subject:* Re: [NMusers] flip-flop without absorption information? > > > > This is an interesting discussion. At the same time I can't get my head > > around the assumption of any covariate on a flip-flop phenomenon. In > > other words, even if there is no information on covariates > > this phenomenon could still exist. > > > > It's my understanding that this flip-flop phenomenon is fundamentally a > > mathematical problem -- that is, if we write down a PK model in its > > analytical form, it becomes rather easy to understand that swapping the > > values between ka and ke (CL/V) would lead to the same output. > > > > In the absence of data on drug absorption as in your case, I think the > > solution could lie in fixing volume of distribution based on any prior > > information, e.g. a reported value in the literature. Otherwise, try to > > fix it to a reasonable estimate and see what happens. > > > > Hope it helps. > > > > Kind regards, > > > > Shan > > > > On Tue, Sep 13, 2022 at 5:36 AM Jakob Ribbing > > mailto:jakob.ribb...@pharmetheus.com>> > > wrote: > > > >
[NMusers] flip-flop without absorption information?
Dear nmusers, I'm working on the population PK of a therapeutic peptide candidate for rheumatoid arthritis. Three dose levels of the peptide were administered as a single subcutaneous injection. We have some limitations, for instance, the dataset is very sparse and is lacking in the information of the absorption phase. Some therapeutic peptides have demonstrated flip-flop PK, specially those administered by subcutaneous or intramuscular. Is it possible to evaluate the flip-flop phenomenon without absorption information? I'll appreciate all your suggestions and advice. Best, Niurys -- Niurys de Castro Suárez, PhD Assistant Professor of Pharmacometrics Associate Research Pharmacy Department Institute of Pharmacy and Food, University of Havana Cuba "Una estrella brilla en la hora de nuestro encuentro"
Re: [NMusers] Help with ODEs for a time varying clearance
Dear all, Thank you very much for clarifying my doubts and giving me some lights. I'm going to keep it in mind. Regards, Niurys MSc Niurys de Castro Suárez Assistant Professor of Pharmacometrics Assistant Research Pharmacy Department Institute of Pharmacy and Food, University of Havana Cuba El 22/07/2021 16:45, "Simbarashe Peter Zvada" escribió: > Thanks Niurys for revisiting this. > It was discussed quite a lot before. > Using T in $DES, as Paolo suggested, is what I always do. > > As far as your code / model file is concerned, I somehow like it as long > as you will be able to explain in simpler terms to non-pharmacometricians > and Physicians what the values mean and implications on situations. > > Thanks, > Simba > > On Thu, Jul 22, 2021, 4:37 PM Leonid Gibiansky > wrote: > >> PK block is executed only once per record so CL(TIME) is constant >> between records, while in the DES block T (time) changes continuously, >> thus implementing time dependence CL(T) exactly rather than >> approximately. The rest is fine. >> Thanks >> Leonid >> >> On 7/22/2021 4:19 PM, Niurys.CS wrote: >> > Dear Leonid, >> > Thanks for yor suggestion. I've never thought in that possibility. >> > However, I have two questions: >> > 1-Why must I write those equations inside $DES? >> > 2-Do you think the ODE that I proposed are Ok? >> > >> > Thank you very much >> > >> > MSc Niurys de Castro Suárez >> > Assistant Professor of Pharmacometrics >> > Assistant Research >> > Pharmacy Department >> > Institute of Pharmacy and Food, >> > University of Havana >> > Cuba >> > >> > El 22/07/2021 16:04, "Leonid Gibiansky" > > <mailto:lgibian...@quantpharm.com>> escribió: >> > >> > Definition of CL should be moved inside the DES block; other than >> > that, looks fine: >> > ; DES -- >> > $DES >> > CL2_TIME = CL2*EXP(-KDES*T) >> > CL_TOTAL = CL2_TIME + CL1 ; total clearance >> > ... >> > >> > Thanks >> > Leonid >> > >> > >> > On 7/22/2021 3:30 PM, Niurys.CS wrote: >> > >> > Dear nmusers, >> > I'm working on the pharmacokinetics of an antiCD20 mAb; I >> > suspect the clearance of this mAb should be time dependent as >> > rituximab’s clearance do. I tried to model this behavior but I’m >> > not sure if the ODEs are correct. Please can you help? I share >> > part of the code. >> > >> > $SUBROUTINE ADVAN13 TOL=9 >> > $MODEL COMP=(CENTRAL) COMP=(PERIPH1) >> > $PK >> > ; STRUCTURAL PARAMETERS -- >> > TVCL1 = THETA(2) ; system-nonspecific clearance >> > TVV1 = THETA(3) >> > TVQ= THETA(4) >> > TVV2 = THETA(5) >> > TVKDES = THETA(6) ; rate constant of the specific clearance >> decay >> > TVCL2 = THETA(7) ; time varying clearance at time zero >> > ; MU_TRANSFORMATION -- >> > MU_1 = LOG(TVCL1) >> > MU_2 = LOG(TVV1) >> > MU_3 = LOG(TVQ) >> > MU_4 = LOG(TVV2) >> > MU_5 = LOG(TVKDES) >> > MU_6 = LOG(TVCL2) >> > ; INDIVIDUAL PARAMETERS -- >> > CL1 = EXP(MU_1+ETA(1)) >> > V1 = EXP(MU_2+ETA(2)) >> > Q= EXP(MU_3+ETA(3)) >> > V2 = EXP(MU_4+ETA(4)) >> > KDES = EXP(MU_5+ETA(5)) >> > CL2 = EXP(MU_6+ETA(6)) >> > S1 = V1 >> > ; INITIAL CONDITIONS -- >> > A_0(1) = 0 >> > A_0(2) = 0 >> > CL2_TIME = CL2*EXP((-KDES)*(TIME)) >> > CL_TOTAL = CL2_TIME + CL1 ; total clearance >> > ; DES -- >> > $DES >> > CONC = A(1)/V1 >> > DADT(1) =-(CL_TOTAL/V1)*A(1)-(Q/V1)*A(1)+(Q/V2)*A(2) >> > DADT(2) = (Q/V1)*A(1)-(Q/V2)*A(2) >> > ;$ERROR -- >> > CONC1 = A(1)/V1 >> > IPRED=-3 >> > IF(CONC1.GT.0) IPRED=LOG(CONC1) >> > W = THETA(1) >> > Y = IPRED+W*EPS(1) >> > IRES=DV-IPRED >> > IWRES=IRES/W >> > >> > Thank you, >> > Niurys >> > >> > MSc Niurys de Castro Suárez >> > Assistant Professor of Pharmacometrics >> > Assistant Research >> > Pharmacy Department >> > Institute of Pharmacy and Food, >> > University of Havana >> > Cuba >> > >> >>
Re: [NMusers] Help with ODEs for a time varying clearance
Dear Leonid, Thanks for yor suggestion. I've never thought in that possibility. However, I have two questions: 1-Why must I write those equations inside $DES? 2-Do you think the ODE that I proposed are Ok? Thank you very much MSc Niurys de Castro Suárez Assistant Professor of Pharmacometrics Assistant Research Pharmacy Department Institute of Pharmacy and Food, University of Havana Cuba El 22/07/2021 16:04, "Leonid Gibiansky" escribió: > Definition of CL should be moved inside the DES block; other than that, > looks fine: > ; DES -- > $DES > CL2_TIME = CL2*EXP(-KDES*T) > CL_TOTAL = CL2_TIME + CL1 ; total clearance > ... > > Thanks > Leonid > > > On 7/22/2021 3:30 PM, Niurys.CS wrote: > >> Dear nmusers, >> I'm working on the pharmacokinetics of an antiCD20 mAb; I suspect the >> clearance of this mAb should be time dependent as rituximab’s clearance do. >> I tried to model this behavior but I’m not sure if the ODEs are correct. >> Please can you help? I share part of the code. >> >> $SUBROUTINE ADVAN13 TOL=9 >> $MODEL COMP=(CENTRAL) COMP=(PERIPH1) >> $PK >> ; STRUCTURAL PARAMETERS -- >> TVCL1 = THETA(2) ; system-nonspecific clearance >> TVV1 = THETA(3) >> TVQ= THETA(4) >> TVV2 = THETA(5) >> TVKDES = THETA(6) ; rate constant of the specific clearance decay >> TVCL2 = THETA(7) ; time varying clearance at time zero >> ; MU_TRANSFORMATION -- >> MU_1 = LOG(TVCL1) >> MU_2 = LOG(TVV1) >> MU_3 = LOG(TVQ) >> MU_4 = LOG(TVV2) >> MU_5 = LOG(TVKDES) >> MU_6 = LOG(TVCL2) >> ; INDIVIDUAL PARAMETERS -- >> CL1 = EXP(MU_1+ETA(1)) >> V1 = EXP(MU_2+ETA(2)) >> Q= EXP(MU_3+ETA(3)) >> V2 = EXP(MU_4+ETA(4)) >> KDES = EXP(MU_5+ETA(5)) >> CL2 = EXP(MU_6+ETA(6)) >> S1 = V1 >> ; INITIAL CONDITIONS -- >> A_0(1) = 0 >> A_0(2) = 0 >> CL2_TIME = CL2*EXP((-KDES)*(TIME)) >> CL_TOTAL = CL2_TIME + CL1 ; total clearance >> ; DES -- >> $DES >>CONC = A(1)/V1 >>DADT(1) =-(CL_TOTAL/V1)*A(1)-(Q/V1)*A(1)+(Q/V2)*A(2) >>DADT(2) = (Q/V1)*A(1)-(Q/V2)*A(2) >> ;$ERROR -- >> CONC1 = A(1)/V1 >> IPRED=-3 >> IF(CONC1.GT.0) IPRED=LOG(CONC1) >> W = THETA(1) >> Y = IPRED+W*EPS(1) >> IRES=DV-IPRED >> IWRES=IRES/W >> >> Thank you, >> Niurys >> >> MSc Niurys de Castro Suárez >> Assistant Professor of Pharmacometrics >> Assistant Research >> Pharmacy Department >> Institute of Pharmacy and Food, >> University of Havana >> Cuba >> >>
[NMusers] Help with ODEs for a time varying clearance
Dear nmusers, I'm working on the pharmacokinetics of an antiCD20 mAb; I suspect the clearance of this mAb should be time dependent as rituximab’s clearance do. I tried to model this behavior but I’m not sure if the ODEs are correct. Please can you help? I share part of the code. $SUBROUTINE ADVAN13 TOL=9 $MODEL COMP=(CENTRAL) COMP=(PERIPH1) $PK ; STRUCTURAL PARAMETERS -- TVCL1 = THETA(2) ; system-nonspecific clearance TVV1 = THETA(3) TVQ= THETA(4) TVV2 = THETA(5) TVKDES = THETA(6) ; rate constant of the specific clearance decay TVCL2 = THETA(7) ; time varying clearance at time zero ; MU_TRANSFORMATION -- MU_1 = LOG(TVCL1) MU_2 = LOG(TVV1) MU_3 = LOG(TVQ) MU_4 = LOG(TVV2) MU_5 = LOG(TVKDES) MU_6 = LOG(TVCL2) ; INDIVIDUAL PARAMETERS -- CL1 = EXP(MU_1+ETA(1)) V1 = EXP(MU_2+ETA(2)) Q= EXP(MU_3+ETA(3)) V2 = EXP(MU_4+ETA(4)) KDES = EXP(MU_5+ETA(5)) CL2 = EXP(MU_6+ETA(6)) S1 = V1 ; INITIAL CONDITIONS -- A_0(1) = 0 A_0(2) = 0 CL2_TIME = CL2*EXP((-KDES)*(TIME)) CL_TOTAL = CL2_TIME + CL1 ; total clearance ; DES -- $DES CONC = A(1)/V1 DADT(1) =-(CL_TOTAL/V1)*A(1)-(Q/V1)*A(1)+(Q/V2)*A(2) DADT(2) = (Q/V1)*A(1)-(Q/V2)*A(2) ;$ERROR -- CONC1 = A(1)/V1 IPRED=-3 IF(CONC1.GT.0) IPRED=LOG(CONC1) W = THETA(1) Y = IPRED+W*EPS(1) IRES=DV-IPRED IWRES=IRES/W Thank you, Niurys MSc Niurys de Castro Suárez Assistant Professor of Pharmacometrics Assistant Research Pharmacy Department Institute of Pharmacy and Food, University of Havana Cuba
RE: [NMusers] Assessment of elimination half life of mAb
Dear all, I'm very grateful for these ideas and explanations. Actually, I was worry about this topic. Previously, I reported the values of half life by NCA; however the clinicians are asking for a half life value estimated by population PK. Many thanks to you in the name of Cuban team. Niurys El 29/04/2021 12:49, "Bonate, Peter" escribió: > All I can say is, Great minds. > > Maybe some of these ideas can help you, Niurys. > > pete > > > > Peter Bonate, PhD > Executive Director > Pharmacokinetics, Modeling, and Simulation (PKMS) > Clinical Pharmacology and Exploratory Development (CPED) > Astellas > 1 Astellas Way, N3.158 > Northbrook, IL 60062 > peter.bon...@astellas.com > (224) 619-4901 > > > It’s been a while since I’ve had something here, but here is a Dad joke. > > Question: Do you know why the math book was sad? > Answer: Because it had so many problems > > > -Original Message- > From: Leonid Gibiansky > Sent: Thursday, April 29, 2021 11:42 AM > To: Justin Wilkins ; Bill Denney < > wden...@humanpredictions.com>; Bonate, Peter ; > Niurys.CS > Cc: nmusers@globomaxnm.com > Subject: Re: [NMusers] Assessment of elimination half life of mAb > > still, half-life of the linear part could be helpful in cases when > non-linearity plays no significant role in elimination, so we tend to > present it together with the washout time simulations. > > Leonid > > > > On 4/29/2021 12:35 PM, Justin Wilkins wrote: > > Hi Bill, all, > > > > I do much the same thing - when there's nonlinearity happening, I've > found it to be effective to plot concentration-time curves by doses and > regimens of interest and mark the times at which the (median?) > clinically-defined threshold for "washout" has been reached in each case. > Of course this starts getting unwieldy when there are lots of doses or > regimens. A less attractive way would be to produce a lookup table. > > > > Sounds like everyone's thinking along the same lines... > > > > Justin > > > > > > -Original Message- > > From: owner-nmus...@globomaxnm.com On > > Behalf Of Bill Denney > > Sent: Thursday, April 29, 2021 6:17 PM > > To: Bonate, Peter ; Leonid Gibiansky > > ; Niurys.CS > > Cc: nmusers@globomaxnm.com > > Subject: RE: [NMusers] Assessment of elimination half life of mAb > > > > Hi Pete, > > > > I agree that it is hard to communicate. I like the general idea of C90 > you propose. I tend to choose something in between your and Leonid's > answer, when possible. I target an answer of "when is the pharmacodynamic > effect <5% of the maximum or therapeutic effect". It does require more > than just the PK, though. And for the just PK answer, I agree with Leonid > and you, targeting some smallish fraction of Cmax is often reasonable for > similar communication. > > > > What I find clinicians typically try to understand when the drug has > washed out. The answer that many have reasonably latched onto is when 5 > half-lives have passed, the drug is washed out. That suggests that about > 3% (2^-5) effect is generally agreed as being washed out. > > > > To Niurys's question about a citation for this, I don't have one either. > > It's just a rule-of-thumb that I have tended to use. > > > > Thanks, > > > > Bill > > > > -Original Message- > > From: owner-nmus...@globomaxnm.com On > > Behalf Of Bonate, Peter > > Sent: Thursday, April 29, 2021 12:01 PM > > To: Leonid Gibiansky ; Niurys.CS > > > > Cc: nmusers@globomaxnm.com > > Subject: RE: [NMusers] Assessment of elimination half life of mAb > > > > I've never really been happy with this. It's an unsatisfactory solution. > > You have a nonlinear drug. Let's assume you have an approved drug. > It's given at some fixed dose. The clinician wants to know what is the > drug's half-life so they can washout their patient and start them on some > other therapy. We go back to them and say, we can't give you a half-life > because it's a nonlinear drug, but once the kinetics become linear the > half-life is X hours. That is a terrible answer. Maybe we need to come up > with a new term, call it C90, the time it takes for Cmax to decline by > 90%. That we can do. We don't even need an analytical solution, we can > eyeball it. We could even get fancy and do it in a population model. C90 > - the time it takes for Cmax to decline 90% in 90% of patients. Of course, > for nonlinear drugs, C90 only holds for that dose. Change in dose results > in a new C90. > > Just a thought. > > > &
Re: [NMusers] Assessment of elimination half life of mAb
Dear Leonid, Many thanks for clearing up my doubt. Can you suggest me any paper to go into this topic in any depth. Best, Niurys El 28/04/2021 19:34, "Leonid Gibiansky" escribió: > There is no such thing as half-life of elimination for the nonlinear drug. > But one can compute something like half-life: > > 1. Half-life of the linear part (defined by CL, V1, V2, Q): this defines > the half-life at high doses/high concentrations when nonlinear elimination > is saturated. > > 2. Washout time: for the linear drug, 5 half-lives can be used to define > washout time. During this time, concentrations drop approximately 2^5=32 > times. So one can simulate the desired dosing (single dose or steady > state), find the time from Cmax to Cmax/32 and call it washout time (or > time to Cmax/64 to be conservative) > > Thanks > Leonid > > > On 4/28/2021 5:17 PM, Niurys.CS wrote: > >> Dear all >> I need some help to assess the elimination half life of a monoclonal >> antibody. >> The model that describes the data is a QSS aproximation of TMDD with Rmax >> constant. The model includes two binding process of mAb to its target: in >> central and peripheral compartments. >> Is there any specific equation to calcule lambda z and the elimination >> half life for each of the TMDD aproximations? >> Thanks >> Niurys >> >>
[NMusers] Assessment of elimination half life of mAb
Dear all I need some help to assess the elimination half life of a monoclonal antibody. The model that describes the data is a QSS aproximation of TMDD with Rmax constant. The model includes two binding process of mAb to its target: in central and peripheral compartments. Is there any specific equation to calcule lambda z and the elimination half life for each of the TMDD aproximations? Thanks Niurys
Re: [NMusers] nlmixr 2.0 in on CRAN
Dear Wenping and nlmixr team, Thanks for sharing with us your work!!! Hugs, Niurys
Re: [NMusers] bootstrap
Dear Nick and Bill, Thank you for your answers, and please apologize me for din´t give you information about my model. I share with you part of my code as well as some results. My model corresponds to QSS with constant Rtot approximation of TMDD The parameter estimates obtained with this model are pausible, and are similar to prevuios study (Rodriguez L et al, 2015). The GOF and VPC look good. The FLAGS correspond to some outliers based on DV of some IDs (concentration of product). So, I don´t know if excluding these values, the boostrap will work correctly. ;-- $DATA data_19.csv IGNORE=@ IGNORE=(FLAG.EQ.2) IGNORE=(FLAG.EQ.3) ;-- $SUBROUTINE ADVAN13 TOL=9 ;-- $MODEL COMP=(CENTRAL) COMP=(PERIPH1) COMP(PERIPH2) ;-- $PK TVCL= THETA(1) TVV1= THETA(2) TVQ = THETA(3) TVV2 = THETA (4) TVKSS = THETA (5) TVKINT = THETA (6) IF(DOS.EQ.50) TVRMAX= 0.175 IF(DOS.GT.50)TVRMAX=THETA(7) CL = TVCL;*EXP(ETA(1)) V1 = TVV1*EXP(ETA(1)) Q = TVQ*EXP(ETA(2)) V2 = TVV2*EXP(ETA(3)) KSS = TVKSS;*EXP(ETA(4)) KINT = TVKINT RMAX = TVRMAX K = CL/V1 K12 = Q/V1 K21 = Q/V2 S1 = V1 Thank you, Niurys 2019-05-10 12:45 GMT-05:00, Bill Denney : > Hi Niurys, > > The simplest method to ensure a good bootstrap is often to simplify the > data > file by removing rows that should not be used for the modeling before > running the bootstrap. Notably, if you exclude an entire subject either > based on the ID column or another column, usually the boostrap will not > work > correctly. > > I believe that most if not all tools generate the bootstrap with new ID > column values (the ID is given a new sequential value based on sampling > order). If you exclude an entire subject based on another column, the you > will not have the expected number of subjects in the analysis because all > the bootstrap tools that I know of don't account for exclusions with making > the new data file. > > If this doesn't help, giving more info will help. (What tool are you using > for bootstrap? What command line are you running? Can you share the model > and a snippet of the data?) > > Thanks, > > Bill > > -----Original Message- > From: owner-nmus...@globomaxnm.com On Behalf > Of Niurys.CS > Sent: Friday, May 10, 2019 12:17 PM > To: nmusers > Subject: [NMusers] bootstrap > > Dear nmusers, > > > I have a big doubt. When I used the bootstrap to evaluate my model, I had > some bugs. In my code I use IGNORE statements based on FLAGS for some > outliers. I don't know if I remove these IGNORE statements, the bootstrap > will run well. Can you give me some suggestions??? > > > Regards > > Niurys de Castro Suárez > > -- > > MSc Niurys de Castro Suárez > Profesor Asistente Farmacometría > Investigador Agregado > Departamento Farmacia > Instituto de Farmacia y Alimentos, Universidad de La Habana Cuba "Una > estrella brilla en la hora de nuestro encuentro" > -- MSc Niurys de Castro Suárez Profesor Asistente Farmacometría Investigador Agregado Departamento Farmacia Instituto de Farmacia y Alimentos, Universidad de La Habana Cuba "Una estrella brilla en la hora de nuestro encuentro"
[NMusers] bootstrap
Dear nmusers, I have a big doubt. When I used the bootstrap to evaluate my model, I had some bugs. In my code I use IGNORE statements based on FLAGS for some outliers. I don't know if I remove these IGNORE statements, the bootstrap will run well. Can you give me some suggestions??? Regards Niurys de Castro Suárez -- MSc Niurys de Castro Suárez Profesor Asistente Farmacometría Investigador Agregado Departamento Farmacia Instituto de Farmacia y Alimentos, Universidad de La Habana Cuba "Una estrella brilla en la hora de nuestro encuentro"
Re: [NMusers] Help with a PK code
Dears Leonid and Saeheum, First of all, thank you for your suggestions, they are very useful and cleared up some queries I had. Leonid, regarding your question about measurement of free or total ligand concentration, in this study we only measured the free ligand concentration. Thank so much, Regards, Niurys 2019-04-23 12:00 GMT-05:00, Leonid Gibiansky : > The code will work as written (if you add ENDIF after TVVM = THETA (6), > and define all parameters CL, V1, Q, V2, KINT, ...) but mechanistically, > this is not a good idea to have two models for two dose levels. You may > want to try QSS model with non-constant Rtot (MM model is usually good > when Rtot is low, while QSS is good when Rtot has accumulation, so may > be this is why you see MM model at low doses and QSS at high doses). > > Also, what is measured, is it free or total concentration? This part of > the code was not shown, and it depends on the assay (for QSS part of the > model). > > Thanks > Leonid > > > On 4/23/2019 8:34 AM, Niurys.CS wrote: >> Dear All, >> >> I'm working on the population pharmacokinetics of a mAb, in this study >> 4 dose levels (50, 100, 200 and 400 mg) were evaluated. I tested >> different models, but none of them fit well; that's why I decided to >> find for each dose level the best model. I found the two lower dose >> levels fitted to Michaelis Menten + CL linear model and the two higher >> dose levels fitted to QSS Rtot model. >> I think if I use this code, I'll find the best model for my data, so I >> appreaciate your suggestions: >> >> $PK >> >> TVCL= THETA(1) >> TVV1= THETA(2) >> TVQ = THETA(3) >> TVV2 = THETA (4) >> >> TVKM = 0 >> TVVM = 0 >> >> IF(DOS.LT.200) THEN >> TVKM = THETA (5) >> TVVM = THETA (6) >> >> TVKSS = 0 >> TVKINT = 0 >> TVKDEG = 0 >> TVRMAX = 0 >> >> IF(DOS.GT.100) THEN >> >> TVKSS = THETA (7) >> TVKKINT = THETA(8) >> TVRMAX = THETA(9) >> >> ENDIF >> >> >> K = CL/V1 >> K12 = Q/V1 >> K21 = Q/V2 >> S1 = V1 >> >> ;-- >> $DES >> >> CONC=0.5*(A(1)/V1-RMAX-KSS)+0.5*SQRT((A(1)/V1-RMAX-KSS)**2+4*KSS*A(1)/V1) >> >> DADT(1) = >> -(K+K12)*CONC*V1+K21*A(2)-KINT*RMAX*CONC*V1/(KSS+CONC)-VM*CONC*V1/(KM+CONC) >> DADT(2) = K12*CONC*V1-K21*A(2) >> >> >> Thank you, >> >> Regards, >> Niurys de Castro >> > -- MSc Niurys de Castro Suárez Profesor Asistente Farmacometría Investigador Agregado Departamento Farmacia Instituto de Farmacia y Alimentos, Universidad de La Habana Cuba "Una estrella brilla en la hora de nuestro encuentro"
[NMusers] Help with a PK code
Dear All, I'm working on the population pharmacokinetics of a mAb, in this study 4 dose levels (50, 100, 200 and 400 mg) were evaluated. I tested different models, but none of them fit well; that's why I decided to find for each dose level the best model. I found the two lower dose levels fitted to Michaelis Menten + CL linear model and the two higher dose levels fitted to QSS Rtot model. I think if I use this code, I'll find the best model for my data, so I appreaciate your suggestions: $PK TVCL= THETA(1) TVV1= THETA(2) TVQ = THETA(3) TVV2 = THETA (4) TVKM = 0 TVVM = 0 IF(DOS.LT.200) THEN TVKM = THETA (5) TVVM = THETA (6) TVKSS = 0 TVKINT = 0 TVKDEG = 0 TVRMAX = 0 IF(DOS.GT.100) THEN TVKSS = THETA (7) TVKKINT = THETA(8) TVRMAX = THETA(9) ENDIF K = CL/V1 K12 = Q/V1 K21 = Q/V2 S1 = V1 ;-- $DES CONC=0.5*(A(1)/V1-RMAX-KSS)+0.5*SQRT((A(1)/V1-RMAX-KSS)**2+4*KSS*A(1)/V1) DADT(1) = -(K+K12)*CONC*V1+K21*A(2)-KINT*RMAX*CONC*V1/(KSS+CONC)-VM*CONC*V1/(KM+CONC) DADT(2) = K12*CONC*V1-K21*A(2) Thank you, Regards, Niurys de Castro -- MSc Niurys de Castro Suárez Profesor Asistente Farmacometría Investigador Agregado Departamento Farmacia Instituto de Farmacia y Alimentos, Universidad de La Habana Cuba
[NMusers] To: nmusers
Dear All, I'm working on the population pharmacokinetics of a mAb, in this study 4 dose levels (50, 100, 200 and 400 mg) were evaluated. I tested different models, but none of them fit well; that's why I decided to find for each dose level the best model. I found the two lower dose levels fitted to Michaelis Menten + CL linear model and the two higher dose levels fitted to QSS Rtot model. I think if I use this code, I'll find the best model for my data, so I appreaciate your suggestions: $PK TVCL= THETA(1) TVV1= THETA(2) TVQ = THETA(3) TVV2 = THETA (4) TVKM = 0 TVVM = 0 IF(DOS.LT.200) THEN TVKM = THETA (5) TVVM = THETA (6) TVKSS = 0 TVKINT = 0 TVKDEG = 0 TVRMAX = 0 IF(DOS.GT.100) THEN TVKSS = THETA (7) TVKKINT = THETA(8) TVRMAX = THETA(9) ENDIF K = CL/V1 K12 = Q/V1 K21 = Q/V2 S1 = V1 ;-- $DES CONC=0.5*(A(1)/V1-RMAX-KSS)+0.5*SQRT((A(1)/V1-RMAX-KSS)**2+4*KSS*A(1)/V1) DADT(1) = -(K+K12)*CONC*V1+K21*A(2)-KINT*RMAX*CONC*V1/(KSS+CONC)-VM*CONC*V1/(KM+CONC) DADT(2) = K12*CONC*V1-K21*A(2) Thank you, Regards, Niurys de Castro -- MSc Niurys de Castro Suárez Profesor Asistente Farmacometría Investigador Agregado Departamento Farmacia Instituto de Farmacia y Alimentos, Universidad de La Habana Cuba "Una estrella brilla en la hora de nuestro encuentro"
