# A joint model of longitudinal pharmacokinetic and time-to-event data to study exposure–response relationships: a proof-of-concept study with alectinib

## TL;DR

This study shows that joint models can better capture the relationship between drug levels and cancer outcomes compared to traditional methods, using alectinib as an example.

## Contribution

The paper introduces a joint modeling approach that combines longitudinal pharmacokinetic and survival data for exposure–response analysis.

## Key findings

- Traditional Cox models did not show a significant exposure–response relationship for alectinib.
- The joint model revealed an 11% reduction in cancer progression per unit increase in average transformed trough concentration.
- Joint models provide deeper insights into drug exposure and survival outcomes than standard methods.

## Abstract

In exposure–response analyses of oral targeted anticancer agents, longitudinal plasma trough concentrations are often aggregated into a single value even though plasma trough concentrations can vary over time due to dose adaptations, for example. The aim of this study was to compare joint models to conventional exposure–response analyses methods with the application of alectinib as proof-of-concept.

Joint models combine longitudinal pharmacokinetic data and progression-free survival data to infer the dependency and association between the two datatypes. The results from the best joint model and the standard and time-dependent cox proportional hazards models were compared. To normalize the data, alectinib trough concentrations were normalized using a sigmoidal transformation to transformed trough concentrations (TTC) before entering the models.

No statistically significant exposure–response relationship was observed in the different Cox models. In contrast, the joint model with the current value of TTC in combination with the average TTC over time did show an exposure–response relationship for alectinib. A one unit increase in the average TTC corresponded to an 11% reduction in progression (HR, 0.891; 95% confidence interval, 0.805–0.988).

Joint models are able to give insights in the association structure between plasma trough concentrations and survival outcomes that would otherwise not be possible using Cox models. Therefore, joint models should be used more often in exposure–response analyses of oral targeted anticancer agents.

The online version contains supplementary material available at 10.1007/s00280-024-04698-w.