Re: Getting rid of correlation issues between CL and volume parameters
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
Quoted reply history
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
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