RE: Getting rid of correlation issues between CL and volume parameters

-----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Leonid Gibiansky Sent: Dienstag, 26. November 2013 00:51 To: 'nmusers' Subject: Re: [NMusers] Getting rid of correlation issues between CL and volume parameters Another argument in favor of using F1 ~ EXP(ETA(1)) instead of block OMEGA matrix is the covariate modeling. In cases where variability in apparent CL and V is due to the F1 variability, this formulation allows for more mechanistic interpretation of the covariate effects and ETA dependencies on covariates. For example, one can easily explain why ETA_F1 may depend on food while it is less straightforward to interpret ETA_V dependence on food. So while these models (with F1=1 and OMEGA block versus F1=EXP(ETA(1)) and diagnonal OMEGA), may be numerically similar if not equivalent, it could be better to use more mechanistically relevant model and put the variability where it would be expected from the mechanistic point of view. Regards, Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566 On 11/25/2013 1:43 PM, Nick Holford wrote: > Bob, > > You use an estimation method justification for choosing between > estimating the covariance of CL and V and estimating the variance of F. > > An alternative view is to apply a fixed effect assumption based on > pharmacokinetic theory. The fixed effect assumption is that some of > the variation in CL and V is due to differences in bioavailability and > other factors such as linear plasma protein binding and differences in > the actual amount of drug in the oral formulation. This fixed effect > assumption is described in the model by the variance of F. > > It is quite plausible to imagine that there is still some covariance > between CL and V that is not related to the differences in F. For > example, if you did not know the subject's weights and therefore could > not account for the correlated effects of weight on CL and V. The > estimation of the variance of F would only partly account for this > because of the non-linear correlation of weight with CL and V. Another > non-linear correlation would occur if plasma protein binding was > non-linear in the range of measured total concentrations. > > In such case one might propose trying to estimate the covariance of CL > and V as well as including F as a fixed effect and estimating the > variance of F. Do you think that SAEM or IMP would be able to come up > with a reasonable estimate of the covariance of CL and V? > > Best wishes, > > Nick > > > On 26/11/2013 4:04 a.m., Bob Leary wrote: >> Nele, >> Basically what you have done is traded an off diagonal parameter in a two >> dimensional Omega matrix for an extra on-diagonal parameter in a three >> dimensional diagonal Omega matrix. >> Y0u still have 3 Omega parameters either way. >> For methods like SAEM and IMP, the two-dimensional formulation is >> much preferable since you end up in a lower 2-d dimensional eta space >> which a) is easier to sample, >> b) is easily mu-modeled (not the case for the 3-d formulation) , and c) >> SAEM and IMP methods handle full block Omegas very naturally, in fact more >> naturally than >> diagonal Omegas. With FOCEI it is not so clear if there would be any >> difference at all. >> >> >> >> -----Original Message----- >> From:[email protected] >> [mailto:[email protected]] On Behalf Of Mueller-Plock, >> Nele >> Sent: Monday, November 25, 2013 2:05 AM >> To: Leonid Gibiansky; 'nmusers' >> Subject: RE: [NMusers] Getting rid of correlation issues between CL >> and volume parameters >> >> Dear Leonid, >> >> Thanks for your answer. Maybe I was not completely clear about the reasons >> why I tried to account for F1. The reason is that after oral dosing, a >> correlation between CL and should be present, as these parameters in reality >> represent CL/F and V/F. One way to account for this would be to estimate the >> correlation via the $OMEGA BLOCK syntax. As this is sometimes hard to >> estimate, I looked if any alternative is available, and then found the >> discussion of this topic in the provided link >> ( http://www.wright-dose.com/tip2.php). >> >From your answer, I would conclude that the proposed code should only >> >account for random between-subject variability, i.e. it should only >> >consider the ETA on F1, but not the THETA (which in my example had values >> >of 1, 0.8 and 0.5). Is this correct? >> >> So whereas an increase in ETA on F1 without accounting for the >> correlation would automatically result in positive ETA values for CL >> and V, even without any inherent variability in true CL and V, with >> the code >> >> F1=1 >> FF1=EXP(ETA(1)) >> CL=THETA()*EXP(ETA())/FF1 >> V=THETA()*EXP(ETA())/FF1 >> >> this would already be taken care of, and the $OMEGA BLOCK could be omitted. >> Right? >> >> Thanks and best >> Nele >> ______________________________________________________________ >> >> Dr. Nele Mueller-Plock, CAPM >> Principal Scientist Modeling and Simulation Global Pharmacometrics >> Therapeutic Area Group >> >> Takeda Pharmaceuticals International GmbH Thurgauerstrasse 130 >> 8152 Glattpark-Opfikon (Zürich) >> Switzerland >> >> Visitor address: >> Alpenstrasse 3 >> 8152 Glattpark-Opfikon (Zürich) >> Switzerland >> >> Phone: (+41) 44 / 55 51 404 >> Mobile: (+41) 79 / 654 33 99 >> >> mailto:[email protected] >> http://www.takeda.com >> >> -----Original Message----- >> From: Leonid Gibiansky [mailto:[email protected]] >> Sent: Freitag, 22. November 2013 19:44 >> To: Mueller-Plock, Nele; 'nmusers' >> Subject: Re: [NMusers] Getting rid of correlation issues between CL >> and volume parameters >> >> Nele, >> I am not sure why would you like to divide by F1. >> Can we just do it explicitly? >> >> F1=EXP(ETA(1)) >> (or F1=function(dose)*EXP(ETA(1)) >> CL=.. >> V=.. >> >> F1 can be > 1 as it is not absolute but relative (to the other subjects); I >> assume that this is oral dose, not IV, correct? >> >> In your code, be careful not to call it F1 as the nonmem will interpret it >> as bioavailability parameter, and you should not account for it twice. >> >> So it should be either >> F1=EXP(ETA(1)) >> CL=THETA()*EXP(ETA()) >> V=THETA()*EXP(ETA()) >> >> or >> >> F1=1 (can me implicit and omitted) >> FF1=EXP(ETA(1)) >> CL=THETA()*EXP(ETA())/FF1 >> V=THETA()*EXP(ETA())/FF1 >> >> but not >> >> F1=EXP(ETA(1)) >> CL=THETA()*EXP(ETA())/F1 >> V=THETA()*EXP(ETA())/F1 >> >> Leonid >> >> >> >> >> -------------------------------------- >> Leonid Gibiansky, Ph.D. >> President, QuantPharm LLC >> web:www.quantpharm.com >> e-mail: LGibiansky at quantpharm.com >> tel: (301) 767 5566 >> >> >> >> On 11/22/2013 12:14 PM, Mueller-Plock, Nele wrote: >>> Dear all, >>> >>> I have come across an interesting proposal to account for correlation >>> between CL and volume parameters by dividing by bioavailability within the >>> NONMEM control stream: >>> >>> http://www.wright-dose.com/tip2.php >>> >>> I liked the approach, however I have been wondering how exactly to >>> interpret the resulting parameter values for CL and V. >>> >>> As an illustration, a potential problem might be that we have doses of 10, >>> 25 and 50 mg with a fixed bioavailability of 100% for the 10 mg dose, and >>> bioavailabilities of 80% and 50% for the doses of 25 and 50 mg, >>> respectively. In addition, a between-subject variability on F1 of ~30% >>> would be present. >>> >>> If I now code my CL and V as follows: >>> CL=THETA(1)/F1 >>> V=THETA(2)/F1, >>> to account for the correlation between CL and V, what exactly would be the >>> meaning/interpretation of THETA(1) and THETA(2)? >>> As the THETAs would be the same for all doses, the CL of 50 mg would be >>> twice as high as the one for the 10 mg dose – does that make sense, as we >>> already estimated the reduced relative bioavailability using parameter F1? >>> >>> Any comments would be very much appreciated. >>> Thanks and best >>> Nele >>> >>> >>> >>> Dr. Nele Müller-Plock, CAPM >>> Principal Scientist Modeling and Simulation Pharmacometrics >>> Experimental Medicine >>> >>> Takeda Pharmaceuticals International GmbH >>> 8152 Glattpark-Opfikon (Zürich) >>> Switzerland >>> >>> Visitor address: >>> Alpenstrasse 3 >>> 8152 Glattpark-Opfikon (Zürich) >>> Switzerland >>> >>> Phone: (+41) 44 / 55 51 404 >>> Mobile: (+41) 79 / 654 33 99 >>> mailto:[email protected] >>> http://www.takeda.com >>> -------------------------------------------------------------------- >>> >>> The content of this email and of any files transmitted may contain >>> confidential, proprietary or legally privileged information and is intended >>> solely for the use of the person/s or entity/ies to whom it is addressed. >>> If you have received this email in error you have no permission whatsoever >>> to use, copy, disclose or forward all or any of its contents. 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