The convergence of pharmacometrics and quantitative systems pharmacology in pharmaceutical research and development

Quantitative systems pharmacology (QSP) models are an important facet of pharmaceutical and clinical research as they combine mechanistic models of physiology in health and disease with pharmacokinetics/pharmacody-namics to predict systems-level effects. The quantitative clinical pharmacology toolbox has traditionally included both mechanistic modeling and population approaches, collectively called pharmacometrics, but the current landscape requires the optimization and use of multiple models together. Here, we explore several case studies in drug development that exemplify three approaches for using QSP and pharmacometrics models together - par-allel synchronization, cross-informative use, and sequential integration. While these approaches are increasingly applied in drug development, achieving a true convergence of QSP and pharmacometrics that fully exploits their synergy will require new tools and methods that enable greater technical integration, in addition to nurturing scientists with diverse modeling expertise that enable cross-discipline strategy. Extensions of existing methods used in each approach as well as additional resources including machine learning models, data-at-scale, end-to-end computation platforms, and real-time analytics will enable this convergence.

Automated summary

Quantitative systems pharmacology (QSP) models are crucial in pharmaceutical and clinical research, combining mechanistic models of physiology with pharmacokinetics/pharmacodynamics to predict systems-level effects. Integrating QSP and pharmacometrics models can be achieved through parallel synchronization, cross-informative use, and sequential integration, but it requires new tools, methods, and diverse modeling expertise for true convergence.