PopED and SSE comparison

Hi, I have been working on a fairly complex differential equation based model with an objective to optimize study design particularly for number of subjects in an experiment. The PK model is a lme model between dose and AUC and the PKPD model consists of a placebo component and a drug effect component with fixed PK model parameters from the PK model (both developed in NONMEM). Design optimization is run using PopED. My interest lies particularly in the drug effect parameters of the model (Emax and EAUC50). I have log transformed parameters as part of the MU model (I am using SAEM, in NM7.2) and I calculated NONMEM %RSEs for the untransformed parameters as SE(log_transformed)*100, which were around 11 and 25 %RSE. When the same design was set up in PopED and evaluated using FO and reduced FIM option, the precision of the parameters from the expected FIM (calculated as sqrt(expected parameter variances) * 100) were over predicted for the drug effect parameters particularly EAUC50 (~18% and 75%). When I ran an SSE (N=200, given the complexity of the model and the long run times associated with it, inspite of using parallelization) with the original design, the %RSE (calculated as the sd *100/sqrt(200) from the sse_results.csv), showed much smaller imprecision smaller than what NONMEM provided (< 2%RSE). I evaluated the precision for other designs using PopED and for a few of those designs ran the SSE as well. I have a similar observation that the PopED precisions were much larger than the SSE runs. I have the following questions: 1. Am I missing something in the calculation of %RSE involving log transformed parameters that I am seeing such odd results from the three approaches? Is there a better way to compare these results across these approaches in such a case of log transformed parameters (eg. using CI of the log transformed parameters)? 2. Does estimation method (SAEM in NONMEM vs FO/FOCE in PopED) play a role in such differences? 3. Should SSE be considered gold standard? How should I interpret the results if I see a bias in the model parameters from SSE? 4. As you are aware, we can fix some of the parameters in PopED and do an evaluation. To compare such results with SSE, should I fix the same parameters that were fixed in PopED and run an SSE? I would like to hear your thoughts on what is the best way to identify a future design in such a situation? I appreciate your timely help! Thanks, Pavan.