From: "Sreenivasa Rao Vanapalli" <svanapal@blue.weeg.uiowa.edu>

Subject: Parameterization!!!

Date: Tue, 8 May 2001 16:51:26 -0500

Hello NMUsers

I have data obtained after oral administration and trying to fit to a two compartment model with NONMEM. I know that the data fit to two comaprtment. Intitially I fitted the data with WinNonlin and now trying population compartmental model. When I tried with microconstants (KA, K12, K21, K, Vd) I could compare these estimates with WinNonlin values. But with clearance parameters, the estimates are quite different. The estimate for central compartment with clearance parameter (ADVAN4 TRANS4) was only 0.5 liters. Where as VD estimate with microconstant parameters (ADVAN4)was 13 liters. Same case with KA also. Can some one explain why this is happening?

Regards

Sreenivasa Rao Vanapalli, Ph.D,

Janssen Postdoctoral Research Scholar,

S411 PHAR, College of Pharmacy,

University of Iowa, Iowa City, IA-52242

319-353-5157 (Office)

319-337-2687 (Home)

From: bvatul@ufl.edu

Subject: Re: Parameterization!!!

Date: Tue, 8 May 2001 22:58:41 -0400

Hello

Did you try to analyse the data by fixing any parameter which it is unable to reliably estimate. Also comparison of the DV PRED plots will give you much needed information.

Atul

From: "Bachman, William" <bachmanw@globomax.com>

Subject: RE: Parameterization!!!

Date: Wed, 9 May 2001 16:25:30 -0400

Sometimes one parameterization can be more stable for a given set of data than another parameterization. It may be related to how the random errors enter into the model rather than the fixed effect parameters. You could try reparameterizing within TRANS1 to get the more important parameters like CL. (You already have KA AND V2). It's less likely the peripheral parameters will be important in your model. They are typically poorly defined. Also, once you begin to explain more of the variability in your data through addition of covariates, it may be possible to go back and try the TRANS4 parameterization (this time incorporating the covariates you've discovered) and obtain a successful minimization comparable to the TRANS1 fit.

$SUB ADVAN4 TRANS1

$PK ;reparam for CL

CL =THETA(1)*EXP(ETA(1))

KA =THETA(2)*EXP(ETA(2))

K23=THETA(3)*EXP(ETA(3))

K32=THETA(4)*EXP(ETA(4))

V2 =THETA(5)*EXP(ETA(5))

K=CL/V2

S2=V2

William J. Bachman, Ph.D.

GloboMax LLC

7250 Parkway Dr., Suite 430

Hanover, MD 21076

Voice (410) 782-2212

FAX (410) 712-0737

bachmanw@globomax.com

-----Original Message-----

From: Sreenivasa Rao Vanapalli [mailto:svanapal@blue.weeg.uiowa.edu]

Sent: Wednesday, May 09, 2001 3:51 PM

To: Bachman, William

Subject: RE: Parameterization!!!

Yes I did try as you said. But the result is same. I'm really wondering what is going on behind the screen. TRANS1 fit gives better estimates. With TRANS4 I tried fixing the VD value. But V3 esimate became astronomical so was KA. And corresponding predicted values (more than 100 times the observed values!!!). I'm really not sure what to do. I need to do some covariate effect studies once the this model issue is settled.

Sreenivasa Vanapalli

-----Original Message-----

From: Bachman, William [mailto:bachmanw@globomax.com]

Sent: Wednesday, May 09, 2001 2:47 PM

To: 'Sreenivasa Rao Vanapalli'

Subject: RE: Parameterization!!!

How did the objective function values and the goodness of fit plots compare between TRANS1 and TRANS4? Are you sure you have a global minimum in both fits? (try different initial estimates to verify). If TRANS1 fit is better, try calculating new initial estimates for TRANS4 based on the TRANS1 final estimates and the relationships between the two parameterizations. Also be aware of potential for flip-flop with your model.

William J. Bachman, Ph.D.

GloboMax LLC

7250 Parkway Dr., Suite 430

Hanover, MD 21076

Voice (410) 782-2212

FAX (410) 712-0737

bachmanw@globomax.com

From: "Gibiansky, Leonid" <gibianskyl@globomax.com>

Subject: RE: Parameterization!!!

Date: Wed, 9 May 2001 16:47:50 -0400

I would guess that the difference is due to the correlation of ETAs (you may check it by plotting individual estimates of ETA1 vs ETA2 or CL vs V2). Try block structure of the OMEGA matrix. Example:

CL=THETA(1)*EXP(ETA(1))

V2=THETA(2)*EXP(ETA(2))

$OMEGA BLOCK(2)

1

0.1

1

If you use correlated structure with TRANS1 and TRANS4 (correlating pairs of alternative parameters) you may get closer results.

Leonid

From: Nick Holford <n.holford@auckland.ac.nz>

Subject: Re: Parameterization!!!

Date: Thu, 10 May 2001 09:07:48 +1200

I agree with Leonid's suggestion. I would also point out that if you do not use a BLOCK to allow correlation between your parameters your model is almost certainly more wrong than usual. I cannot imagine a realistic circumstance where CL and V would not be correlated (e.g. both will increase with increasing body size, or both will increase if F increases if the dose is oral) so always start with a BLOCK and only take it off if your data or other priors inform you that the assumption of no correlation is reasonable.

--

Nick Holford, Divn Pharmacology & Clinical Pharmacology

University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand

email:n.holford@auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556

http://www.phm.auckland.ac.nz/Staff/NHolford/nholford.htm