Twenty years ago there were already many publications about population PK/PD analyses, but no research had been done on the best way to combine PK and PD data. A fundamental assumption of PK/PD analyses was that the PK response drives the PD response. Analysing PK and PD simultaneously was considered to be the gold standard, avoiding that potential PK model miss-specification would inflate PD parameter standard error estimates (SEs), but could be computationally difficult. Sequential PK/PD analysis was computationally simpler but estimation error in the PK is ignored and so PD parameter SEs may be underestimated. A body of simulation work was presented looking at 4 different methods to combine PK and PD data.
- Simultaneous PK/PD analysis.
- Fit PK data. Fix individual PK parameters. Fit PD data.
- Fit PK data. Fix population PK parameters. Fit PD data.
- Fit PK data. Fix population PK parameters (but retain the PK data in the analysis data file). Fit PD data.
The simulation results of 20 years ago found that methods 1 and 4 performed equally well, and method 2 performed least well.
Moving forward 20 years the fundamental assumption that PK response drives the PD response hasn’t changed; or has it….? Two examples will be presented where there is a three-way interaction between the disease/host, PK and PD, and which renders the question of how to best combine PK and PD data still a relevant discussion even today.