Modelling of pain intensity and informative dropout in a dental pain model after naproxcinod, naproxen and placebo administration.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

• Modelling has been used to describe the pain relief and dropout for a few non-steroidal anti-inflammatory drugs.

WHAT THIS STUDY ADDS

• This study shows the relationship between dose, plasma concentration, pain intensity and dropout for naproxen and naproxcinod. It also extends previous models by using a visual analogue scale for pain intensity instead of modelling pain relief on a categorical scale, and shows the value of including informative dropout in the simulations for visual predictive checks.

AIMS

To describe pain intensity (PI) measured on a visual analogue scale (VAS) and dropout due to request for rescue medication after administration of naproxcinod, naproxen or placebo in 242 patients after wisdom tooth removal. METHODS Non-linear mixed effects modelling was used to describe the plasma concentrations of naproxen, either formed from naproxcinod or from naproxen itself, and their relationship to PI and dropout. Goodness of fit was assessed by simultaneous simulations of PI and dropout.

RESULTS

Baseline PI for the typical patient was 52.7 mm. The PI was influenced by placebo effects, using an exponential model, and by naproxen concentrations using a sigmoid E(max) model. Typical maximal placebo effect was a decrease in PI by 20.2%, with an onset rate constant of 0.237 h(-1). EC(50) was 0.135 µmol l(-1). A Weibull time-to-event model was used for the dropout, where the hazard was dependent on the predicted PI and by the PI at baseline. Since the dropout was not at random, it was necessary to include the simulated dropout in visual predictive checks (VPC) of PI.

CONCLUSIONS

This model describes the relationship between drug effects, PI and the likelihood of dropout after naproxcinod, naproxen and placebo administration. The model provides an opportunity to describe the effects of other doses or formulations, after dental extraction. VPC created by simultaneous simulations of PI and dropout provides a good way of assessing the goodness of fit when there is informative dropout.