Questions on FOCE and log transformed data

Dear NMusers: I have two questions regarding model fitting. 1. FOCE vs. FOCE with INTERACTION. I have a rich data from phase I study. Drug was administered by iv infusion. I used a one-compartment model with nonlinear clearance (Michaelis-Menten kinetics) to fit this data. And I tried both FOCE and FOCE with INTERACTION. The FOCE method generated a reasonable fit, while FOCE with INTERACTION generated a biased prediction (underpredict) of concentration. I thought FOCE with INTERACTION usually generate better result than FOCE. Does this mean my model is just not good enough? I used a proportional plus additional residual error model. 2. I also tried to fit log transformed data, but in the PRED vs. DV plot, the points at lower concentrations are much more scattered than those at higher concentrations. And this forms a trend that points are getting closer and closer to the line as the concentration goes up. Does that mean log transformation of my data is not appropriate or something is wrong with my residual error model? The concentration ranges from 2 ng/ml to 1600 ng/ml. The residual error model I used is listed as below: $ERROR CALLFL=0 IPRED=-3 IF(F.GT.0)IPRED=LOG(F); to avoid LOG(0)run-time error Y=IPRED+EPS(1) Any suggestion will be highly appreciated! Huali