Nowadays, “personalized medicine” is starting to replace the current “one size fits all” approach. The goal is to have the right drug with the right dose for the right patient at the right time and location. An example of personalized pulmonary healthcare planning is the targeted pulmonary drug delivery methodology. However, traditional in vitro and in vivo studies are limited and not sufficient for the personalized treatment plan development purpose. Specifically, due to the invasive nature and imaging limitations, animal studies and clinical tests are lack of operational flexibility and will not be able to provide insightful high-resolution patient-specific data. Therefore, alternative methods should be developed to conquer these bottlenecks. Models based on the computational fluid-particle dynamics (CFPD) method play a critical role in exploring alternate study designs and provide high-resolution data in the noninvasive, cost-effective, and time-saving manner. The in silico methodologies can fill the knowledge gap due to the deficiency of traditional in vitro and in vivo methods, as well as make breakthroughs to pave the way to establish a reliable and efficient numerical investigation framework for pulmonary healthcare on a patient-specific level. In this presentation, the speaker will discuss the research progress and challenges on create the individualized digital twin for in silico pulmonary healthcare planning, with details on how to use computational fluid-particle dynamics to simulate inhaled aerosol transport, deposition, and translocation in human respiratory systems. Topics include: (1) Reconstruction of a whole-lung configuration to enable to simulation of inspiration-expiration full breathing cycle on particle transport and deposition; (2) Inter-subject variability studies for a more statistically robust numerical analysis, i.e., CFPD simulation with “error bars”; (3) Establishment of a multiscale model to bring the simulation from lung deposition to health endpoints, i.e., translocation in the whole body; and (4) how machine learning will pave the way to the future of in silico healthcare planning.

Presenter(s)

Yu Feng