Predicting Unacceptable Pain in Cardiac Surgery Patients Receiving Morphine Maintenance and Rescue Doses: A Model-Based Pharmacokinetic-Pharmacodynamic Analysis

Advances in pharmacokinetic-pharmacodynamic modelling for pediatric drug development: extrapolations and exposure-response analyses

INTRODUCTION

Pharmacokinetic (PK)-Pharmacodynamic (PD) and exposure-response (E-R) modeling are critical parts of pediatric drug development. By integrating available knowledge and supportive data to support the design of future studies and pediatric dose selection, these techniques increase the efficiency of pediatric drug development and lowers the risk of exposing pediatric study participants to suboptimal or unsafe dose regimens.

AREAS COVERED

The role of PK, PK-PD and E-R modeling within pediatric drug development and pediatric dose selection is discussed. These models allow investigation of the impact of age and bodyweight on PK and PD in children, despite the often sparse data on the pediatric population. Also discussed is how E-R analyses strengthen the evidence basis to support (full or partial) extrapolation of drug efficacy from adults to children, and between different pediatric age groups.

EXPERT OPINION

Accelerated pediatric drug development and optimized pediatric dosing guidelines are expected from three future developments: (1) Increased focus on E-R modeling of currently approved drugs in children resulting in (novel) E-R modeling techniques and best practices, (2) increased use of real-world data for E-R (3) increased implementation of available population PK and E-R information in pediatric drug dosing guidelines.

Finding the right hazard function for time‐to‐event modeling: A tutorial and Shiny application

Parametric time‐to‐event analysis is an important pharmacometric method to predict the probability of an event up until a certain time as a function of covariates and/or drug exposure. Modeling is performed at the level of the hazard function describing the instantaneous rate of an event occurring at that timepoint. We give an overview of the parametric time‐to‐event analysis starting with graphical exploration by Kaplan–Meier plotting for the event data including censoring and nonparametric hazard estimators such as the kernel‐based visual hazard comparison for the underlying hazard. The most common hazard functions including the exponential, Gompertz, Weibull, log‐normal, log‐logistic, and circadian functions are described in detail. A Shiny application was developed to graphically guide the modeler which of the most common hazard functions presents a similar shape compared to the data in order to guide which hazard functions to test in the parametric time‐to‐event analysis. For the chosen hazard function(s), the Shiny application can additionally be used to explore corresponding parameter values to inform on suitable initial estimates for parametric modeling as well as on possible covariate or treatment relationships to certain parameters. Moreover, it can be used for the dissemination of results as well as communication, training, and workshops on time‐to‐event analysis. By guiding the modeler on which functions and what parameter values to test and compare as well as to assist in dissemination, the Shiny application developed here greatly supports the modeler in complicated parametric time‐to‐event modeling.