Prediction of olanzapine exposure in individual patients using physiologically based pharmacokinetic modelling and simulation

Abstract

Olanzapine exposure in individual patients was predicted using PBPK M&S. Repurposing of available PBPK M&S platforms is an option for model-informed precision dosing and requires further study to examine clinical potential.

Virtual Twins: Understanding the Data Required for Model‐Informed Precision Dosing

“…VTs are a promising new approach for MIPD. To date, VTs have been generated by individualizing a limited number of systems parameters in established PBPK platforms . Which data are required routinely, how these data are generated and stored, how models are best individualized and updated, and whether VTs can be deployed clinically for accurate dosing decisions, are all areas of uncertainties that require addressing (e.g., prediction of complex drug‐drug‐gene‐disease interactions).…”

Physiologically Based Pharmacokinetic Modeling to Identify Physiological and Molecular Characteristics Driving Variability in Drug Exposure

“…However, as these models are built by fitting observed parameters to reported exposure in trial populations, these models are implicitly limited to describing the influence of observed parameters and cannot readily predict or define the influence of unknown factors that underpin the observed effect. Understanding these core covariates that drive BSV in drug exposure will facilitate the optimal implementation of clinical precision dosing platforms such as Virtual Twin . In practice, PBPK model‐informed biomarker discovery is the first step in a process of model‐informed precision dosing.…”

Real‐World Data and Physiologically‐Based Mechanistic Models for Precision Medicine

“…In addition, RWD and large‐scale patient datasets can be the starting point to generate disease‐relevant virtual populations and allow more meaningful prediction of the factors that affect drug disposition and potentially drug response. Approaches such as the “virtual twin,” whereby individual‐relevant covariates are integrated to predict drug disposition at the individual patient level, could be leveraged to enable precision dosing and individualized clinical trials. However, harnessing the power of such data sources will require significant efforts related to data curation, harmonization, and integration.…”