Common Best Practice in Modeling and Simulation Across Quantitative Disciplines: A Comparison of Independently Emerging Proposals

Abstract

ABSTRACTIn the last decade, a number of initiatives to define good practices for projects involving modeling and simulation have been put forward. No proposal for best practice in modeling and simulation, however, has been generally and consciously adopted in the pharmaceutical world. This perspective aims to highlight two recent proposals with respect to the use of modeling and simulation in the drug development and regulatory approval process. Both groups define Model-Informed Drug Discovery and Development (MID3) inclusively so as to potentially include the whole community of users of modeling and simulation. Applications in both communities (pharmacometrics and statistics) have many similarities in tools and methods to be used for model building, assumption testing, qualification versus available data, and sensitivity analysis, albeit with a different focus. Both communities are emphatic that the same good practices could apply across the wide range of modeling and simulation applications. A wider adoption of these common good practices in modeling and simulation across and between communities and practitioners is expected to enhance decision-making across drug research and development and regulatory environments.