Hi Chiaying, I didn’t realize that you were using D1 and setting the RATE to -2. I personally haven’t coded it this way for an IV route (may others who have could chime in). I’ve seen D1 being estimated as a parameter in an extravascular model. My first inclination, without viewing your results in detail, is to provide the actual rate in the data file and remove D1. You may just get the same results but at least you can rule out that the problem is due to your existing parameterization.
Ken From: Chiaying Lin [mailto:[email protected]] Sent: Tuesday, September 08, 2015 6:17 PM To: Ken Luu; [email protected] Subject: Re: [NMusers] two compartment with nonlinear elimination Hi Ken, It's given via iv infusion. The infusion rate (RATE) and ruraiton (DUR) are included in the data file. I also have defined D1=DUR in the $PK block. There is something wrong? #ID TIME AMT NDV LNDV EVID MDV CMT BW AGE DOSE RATE DUR 1 0 81.3 . . 1 1 1 81.3 50 81.3 -2 1 1 0.25 4.8 1.568616 1 81.3 50 81.3 . 1 1 1 18 2.890372 1 81.3 50 81.3 . 1 2015-09-08 23:34 GMT+08:00 Ken Luu <[email protected]<mailto:[email protected]>>: Chiaying, Your concentration-time profiles seem to suggest that there’s an absorption phase which was not accounted for in your model/control stream. What was the route of administration? Ken From: [email protected]<mailto:[email protected]> [mailto:[email protected]<mailto:[email protected]>] On Behalf Of Chiaying Lin Sent: Tuesday, September 08, 2015 7:57 AM To: [email protected]<mailto:[email protected]> Subject: [NMusers] two compartment with nonlinear elimination Dear NMusers, I'm a NM beginner modeling for a monoclonal antibody. Below is 12 subjects's individual predicted plot , it seems that nonlinear elimination (Michaelis-Menten) model can fit the data well. However, when running the control stream (two compartment models with linear and non-linear elimination), the resulting Q THETA SE% and OMEGA SE% are quite large even thought successful minimization.From the Correlation Matrix , I find that THETA (1) (VMAX) and THETA(2) (KM)are highly correlated (9.67E-01). Another problem is the .fit file can't be produced. I have tried various error models and initial estimations, however, none had better improvement and often got error message (error 134 or parameter estimate is near its boundary). Does re-parameterization helpful ? or need to change to other models? Any suggestions for solving the problems are highly appreciated. (control stream will be send in another e-mail)
