Hi Jonathan, Michelle and Bill,
Thanks for bringing up such an interesting question!
There are several hindrances that often discourage users to immerse themselves in OD:
- The mathematical aspect of the theory itself (Believe me or not but matrices scare people off !!!) and often communication used to describe OD in conferences/meetings highly emphasizes on equations instead of a focus on potential applications. This approach only already discourages people to even start trying.
- Softwares that are not always the most user-friendly and often under-advertised (and developed mainly by academics only), although the PODE (Population Optimum Design of Experiments) group is trying to bring together a bundle of recommended ones through several publications where they perform comparison tests.
- For practical reason, re-coding the final model obtained in NM or Monolix for the OD tool can be a hurdle for many. Hopefully platforms like DDMORE will help.
- The aspect of who should own the decision on design variables: is it the clinical pharmacologist or the statistician? This line is not always crystal clear in teams
- The myth that OD is all about PK samples only, therefore, when it is simple and one- or two-compartment models, why do we even bother to optimize since we can guess where samples should be taken?
- The assumption of a model (although OD has several optimality criteria to take into account the model uncertainty and any OD design should be as a good practice, tested using simulation and re-estimation approaches) and this can be indeed problematic for early phase (when prior information is poor).
- The idea that OD will only give you implausible and non-realistic sampling points (there are actually ways to constraints the numeric values to only integers or so).
The limitations described above are just a few, but enough to explain why OD is slow-taken.
Saying so, OD is definitely useful in multiple areas, even in PK samples!
For instance, pediatric (or elderly or special subgroup) studies require minimum intervention, which sampling points from the adult profile do we take away? How do we take into account age group 's profiles (maturation/allometric scaling etc.) and have in average sampling points that will describe well the clearance ? what about sampling windows?
Another situation could be DDI studies as Bill mentioned.
In my opinion though, I think that most of the benefits go to the optimization of PK/PD or disease progression models, and has not been yet explored enough, either in academics or industries.
For instance, expensive biomarkers sampling or invasive interventions, composite score endpoints that can be trimmed down to the subscores the most informative to drug effet/disease progression etc., enrollment criteria decision-making (in slow disease progression, what age group is the most informative), dose-ranging or adaptive designs (what dose level, how many per group), etc.
There are definitely a lots of opportunities out there to explore for OD.