MME will make PK/PD modeling and NONMEM much easier but since modeling itself is not easy, MME has all the tools to make coaching less experienced persons much easier and makes it easier to keep the quality at a high level with a minimum of expert input. If you have no experience you can still take profit of NONMEM by using the standard libraries for model exploration and covariate building.
You also do not have to worry with learning/teaching things around NONMEM like programming in Excel, Splus or Linux or maintaining/validating those own written routines. All you basically need is covered by MME itself.
The program is up to today’s demands for regulatory acceptance and thus offers integrated software and installation validation, complete audit trail and is GCP and 21CFR11 compliant.
The program takes optimal use of the current hyperthreading possibilities of the new Intel processors.
You can use MME instead of compartmental curve fitting programs using standard (user changeable) PK and PK/PD libraries.
Have you ever seen evaluation results stating that k43=0.134*(BSA-1.7)+…. and wonder what this clinically means? MME will give you more clearly readable results. It will calculate AUC, Cmax and t1/2 (whatever they clinically mean is up to each user to decide!) which are easily compared for each covariate in the model. Comparison can now also be made against results from other PK studies.
The program allows running a large number of NONMEM runs at the same time. Each run has its own window and can be controlled independently.
After each run descriptive statistics and graphics are automatically created.
Since all graphics can be performed by the program, the 20 items input restriction for NONMEM only applies for those items truly needed in your model. No other programs like Excel, Splus or SAS are needed. For extended capabilities interfacing to Splus user written routines is possible. MME graphics are interactive. You can magnify graphics or looking at the data behind each individual point. All graphics are automatically saved to disk at a higher resolution so they can be included in a document or presentation.
All runs (whether manually or automatically generated) are saved in our tree-like summary. This shows you the logic and thinking behind the model building process. When scrolling over a run all relevant information about that run is shown.