A multiscale absorption and transit model for oral drug delivery: Formulation and applications during fasting conditions

Predictive Design and Analysis of Drug Transport by Multiscale Computational Models Under Uncertainty

Computational modeling of drug delivery is becoming an indispensable tool for advancing drug development pipeline, particularly in nanomedicine where a rational design strategy is ultimately sought. While numerous in silico models have been developed that can accurately describe nanoparticle interactions with the bioenvironment within prescribed length and time scales, predictive design of these drug carriers, dosages and treatment schemes will require advanced models that can simulate transport processes across multiple length and time scales from genomic to population levels. In order to address this problem, multiscale modeling efforts that integrate existing discrete and continuum modeling strategies have recently emerged. These multiscale approaches provide a promising direction for bottom-up in silico pipelines of drug design for delivery. However, there are remaining challenges in terms of model parametrization and validation in the presence of variability, introduced by multiple levels of heterogeneities in disease state. Parametrization based on physiologically relevant in vitro data from microphysiological systems as well as widespread adoption of uncertainty quantification and sensitivity analysis will help address these challenges.

A quasi-3D model of the whole lung: airway extension to the tracheobronchial limit using the constrained constructive optimization and alveolar modeling, using a sac-trumpet model

Existing computational models used for simulating the flow and species transport in the human airways are zero-dimensional (0D) compartmental, three-dimensional (3D) computational fluid dynamics (CFD), or the recently developed quasi-3D (Q3D) models. Unlike compartmental models, the full CFD and Q3D models are physiologically and anatomically consistent in the mouth and the upper airways, since the starting point of these models is the mouth-lung surface geometry, typically created from computed tomography (CT) scans. However, the current resolution of CT scans limits the airway detection between the 3rd-4th and 7th-9th generations. Consequently, CFD and the Q3D models developed using these scans are generally limited to these generations. In this study, we developed a method to extend the conducting airways from the end of the truncated Q3D lung to the tracheobronchial (TB) limit. We grew the lung generations within the closed lung lobes using the modified constrained constructive optimization, creating an aerodynamically optimized network aiming to produce equal pressure at the distal ends of the terminal segments. This resulted in a TB volume and lateral area of ∼165 cc and ∼2000 cm², respectively. We created a "sac-trumpet" model at each of the TB outlets to represent the alveoli. The volumes of the airways and the individual alveolar generations match the anatomical values by design: with the functional residual capacity at 2611 cc. Lateral surface areas were scaled to match the physiological values. These generated Q3D whole lung models can be efficiently used for conducting multiple breathing cycles of drug transport and deposition simulations.