modeling steady state-constant infusion data

From: KOLLIAG@bms.com Subject: modeling steady state-constant infusion data Date: 14 Nov 1997 19:39:27 -0500 Dear NONMEM users, I would like to use NONMEM to do some population modeling and I have a couple of questions, since this is a problem that does not seem to be typical of the examples in the books that I have available. First, is the data item specified correctly for the following multiple dose steady state, infusion type data? Second what kind of structural model would be appropriate for these data, and is it reasonable to assume that the ADVAN1-2 /Trans1-2 could give an adequate model for such data? The final objective is to do population modeling, after combining 5 similar data sets, to estimate clearance and volume of distribution, and investigate the possible effects of covariates (age, gender, concomitant medication). For about 15 subjects, we have transdermal patches applied daily for six weeks (one patch per subject per day). The patch is removed at the end of the day and a new patch is applied. At the end of each week, when steady state has been attained, the last patch of that week is removed and PK measurements are obtained. Then a set of new patches (possibly different size) are applied for another week, and pk measurements are obtained at the end of the second week, and so on until the last week. So, the size of the patches (and doses to be delivered) remain the same within each week, but can differ in different weeks. I treat these data as a constant rate infusion problem with multiple doses, and I run it as steady state data I used the folowing data item for one subject: DV is concentration in ng/mL, and time is in Days. ------------------------------------------------------------- Subject Dosemg Days DV AMT RAteperday ADDL II EVID ------------------------------------------------------------- 1 1.25 1 . 1.25 1.25 6 1 1 1 1.25 8 0.46 . . . . 0 1 1.25 8 . 1.25 1.25 6 1 1 1 1.25 15 0.48 . . . . 0 1 1.25 15 . 1.25 1.25 6 1 1 1 1.25 22 0.17 . . . . 0 1 2.50 22 . 2.50 2.50 6 1 1 1 2.50 29 0.90 . . . . 0 1 3.75 29 . 3.75 3.75 6 1 1 1 3.75 36 0.68 . . . . 0 ------------------------------------------------------------- and tried some of the basic models but I am not getting good fits, so I question whether the way the data step was specified was correct. If someone has any advice, comments, on the above specification, or suggestions for how to model such data, I would really appreciate it. Thanks in advance. Georgia Kollia, Ph.D. Biostatistics and Data Management Bristol-Myers Squibb, Princeton, NJ