A continued learning approach for model-informed precision dosing: updating models in clinical practice

TL;DR

This paper introduces a sequential hierarchical Bayesian method for updating models in model-informed precision dosing, allowing real-world adaptation and continued learning across clinical settings without sharing individual patient data.

Contribution

It presents a novel approach for model updating in MIPD that separates individual and population parameter updates, facilitating multi-center learning with privacy preservation.

Findings

Effective model updates using patient data during treatment

Enables sharing of summary data across hospitals

Improves model accuracy for personalized dosing

Abstract

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.