In-silico modeling of device injections is a promising tool to derisk and optimize the development of injections systems and improve preliminary understanding device/drug compatibility. Such modeling can be leveraged early in development, prior to pre-clinical studies, to reduce testing burden and effectively predict aspects such as the time and forces required for full dose delivery.
BD has developed a model able to predict the injection force through PFS or injection time through PFS/AI system. In this talk, we will describe the two main pillars (scientific knowledge for fluid dynamics physical modeling, as well as realistic primary container and injection device dimensional and mechanical characteristics) that are necessary to simulate a device injection. We will showcase how these two aspects need to be combined to build a robust and predictive numerical model and to enable appropriate systems and parameters selection to achieve desired performance. Case studies of viscous drugs in PFS/AI will be presented to highlight the theoretical aspect of a fluid ejection from a device, the critical inputs required to achieve accurate predictions, the insights gained, and the good agreement between model predictions and experimental results. The ideal application of such modeling along the drug/device development journey to enable decision-making and stage-appropriate investments will also be discussed, along with the limitations of modeling tools and their application.
Learning Objectives:
Gain a fundamental understanding of the theoretical basis and the key product parameters that are required to predict device injection performance and inform delivery solution design and development.
Understand what knowledge and inputs are required to create good models, what insights can be gained, and what the limitations in application and outputs are.
Know when application of models is most helpful in development.
