Study Design and Simulation Approach

Abstract

Modeling and simulation techniques are a mainstay of clinical drug development and are particularly useful to support clinical trials in children. If a pediatrician wants to use these tools most efficiently, a basic understanding of the principles and methods of classical and novel techniques of modeling and simulation is essential. Key elements comprise the definition and description of terms like deterministic simulation, Monte Carlo simulation, classical "top down" or novel "bottom up" approach, as well as the term "virtual world simulation." The illustrated examples in this chapter from pediatric clinical trials will help to understand and demonstrate these key elements. The importance of the understanding of developmental physiology and pharmacokinetics will become visible when explaining novel "bottom up" approaches like physiologically based pharmacokinetic simulations which also bridge to current research tools from other areas such as systems biology using mathematical models to describe biological systems.

Pediatric Cardiovascular Drug Development and Research: Integration of Modeling and Simulation as One Future Direction

“…Thus, Monte-Carlo simulations do not result in a discrete outcome, such as a concentration-time or response-time profile but rather in a distribution of outcomes for which a central tendency and a distribution (eg, 90% confidence interval) can be defined. 30 As such, Monte-Carlo simulations have the advantage of inherently providing a measure of credibility and likelihood for simulation outcomes that is usually lacking without this technique. 30 The use of this method for modeling and simulation allows the description of population pharmacokinetics and pharmacodynamics.…”

“…30 As such, Monte-Carlo simulations have the advantage of inherently providing a measure of credibility and likelihood for simulation outcomes that is usually lacking without this technique. 30 The use of this method for modeling and simulation allows the description of population pharmacokinetics and pharmacodynamics. In pediatric clinical pharmacology, population PK and population PK/PD analyses are frequently employed to identify significant determinants of drug disposition and response and to derive and optimize dosing regimens for drug development and applied pharmacotherapy.…”

“…Deductive approaches are referred to as ''top-down'' or ''analytic'' and are based on generalized concepts in medical science, such as clinical pharmacology with PK and PD models. 30 Deductive approaches are verification-driven, data-mining processes that allow the expression of preconceived facts or theories in model terms and are subject to a test of their validity within the context of the model system and the available data, ultimately providing reasons for validation or invalidation. 30 A simple example of a ''top-down'' modeling and simulation exercise is shown in Figure 1A.…”

“…30 Deductive approaches are verification-driven, data-mining processes that allow the expression of preconceived facts or theories in model terms and are subject to a test of their validity within the context of the model system and the available data, ultimately providing reasons for validation or invalidation. 30 A simple example of a ''top-down'' modeling and simulation exercise is shown in Figure 1A. Here, a 1-compartment PK model preconceiving a certain drug distribution according to the low lipophilicity of sotalol was used to describe discrete data sets of sotalol plasma concentrations in an adult, an infant, and a newborn.…”

“…In pediatric clinical pharmacology, population PK and population PK/PD analyses are frequently employed to identify significant determinants of drug disposition and response and to derive and optimize dosing regimens for drug development and applied pharmacotherapy. 30 The development of deductive models is characterized by constant rebuilding and refinement of the model given the results of continuous hypothesis testing. As such, ''top-down'' modeling is clearly a data-driven process in which the model is iteratively refined to optimally describe the observed data.…”