A continued learning approach for model‐informed precision dosing: Updating models in clinical practice

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 also include 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, because 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 toward building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.

Multi‐model averaging improves the performance of model‐guided infliximab dosing in patients with inflammatory bowel diseases

“…Recently, the PRECISION 21 trial using a single-model approach implemented in a Bayesian dashboard for infliximab dosing showed significant clinical benefit over label dosing during maintenance therapy. Due to the acknowledged benefits of MIPD in personalized medicine, 11,26 great efforts are being made to improve components of MIPD, such as methods for the selection of models 13,27 and methods for the estimation of parameters. 28,29 In this study, we investigated alternative approaches allowing the incorporation of multiple PopPK models simultaneously for MIPD.…”

Evaluating and Improving Neonatal Gentamicin Pharmacokinetic Models Using Aggregated Routine Clinical Care Data

“…One method of improving model predictiveness for MIPD is continuous learning, in which an initial model is used in MIPD and then refit as additional data become available [ 17 , 18 , 19 ]. Previous work has shown a 3–13% reduction in error for vancomycin models using data from only 200 patients [ 17 ], and significant improvements in target attainment for busulfan in a prospective dosing study [ 18 ].…”

Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing

“…59,[74][75][76][77] An approach that could further these CDS systems is to provide a continuous or self-learning process to automate or semiautomate model refinement as new data becomes available. 59,78,79 This type of system could extract data directly from the EHR or a database/network and could even be set up across institutions. 79 In addition, with advances in mobile and wearable biosensor technologies and devices, data could be collected in real time and provide feedback to patients and clinicians, as well as be connected to CDS systems.…”

“…59,78,79 This type of system could extract data directly from the EHR or a database/network and could even be set up across institutions. 79 In addition, with advances in mobile and wearable biosensor technologies and devices, data could be collected in real time and provide feedback to patients and clinicians, as well as be connected to CDS systems. 17,75 Figure 2 illustrates a potential EHR-integrated CDS system for MIPD.…”