  Model-informed precision dosing (MIPD) is a quantitative dosing framework
that combines prior knowledge on the drug-disease-patient system with patient
data from therapeutic drug/ biomarker monitoring (TDM) to support
individualized dosing in ongoing treatment.Structural models and prior
parameter distributions used in MIPD approaches typically build on prior
clinical trials that involve only a limited number of patients selected
according to some exclusion/inclusion criteria. Compared to the prior clinical
trial population, the patient population in clinical practice can be expected
to include also altered behavior and/or increased interindividual variability,
the extent of which, however, is typically unknown. Here, we address the
question of how to adapt and refine models on the level of the model parameters
to better reflect this real-world diversity. We propose an approach for
continued learning across patients during MIPD using a sequential hierarchical
Bayesian framework. The approach builds on two stages to separate the update of
the individual patient parameters from updating the population parameters.
Consequently, it enables continued learning across hospitals or study centers,
since only summary patient data (on the level of model parameters) need to be
shared, but no individual TDM data. We illustrate this continued learning
approach with neutrophil-guided dosing of paclitaxel. The present study
constitutes an important step towards building confidence in MIPD and
eventually establishing MIPD increasingly in everyday therapeutic use.
