Dataset Information

Flow Patterns and Particle Residence Times in the Oral Cavity during Inhaled Drug Delivery.

ABSTRACT: Pulmonary drug delivery aims to deliver particles deep into the lungs, bypassing the mouth-throat airway geometry. However, micron particles under high flow rates are susceptible to inertial impaction on anatomical sites that serve as a defense system to filter and prevent foreign particles from entering the lungs. The aim of this study was to understand particle aerodynamics and its possible deposition in the mouth-throat airway that inhibits pulmonary drug delivery. In this study, we present an analysis of the aerodynamics of inhaled particles inside a patient-specific mouth-throat model generated from MRI scans. Computational Fluid Dynamics with a Discrete Phase Model for tracking particles was used to characterize the airflow patterns for a constant inhalation flow rate of 30 L/min. Monodisperse particles with diameters of 7 μm to 26 μm were introduced to the domain within a 3 cm-diameter sphere in front of the oral cavity. The main outcomes of this study showed that the time taken for particle deposition to occur was 0.5 s; a narrow stream of particles (medially and superiorly) were transported by the flow field; larger particles > 20 μm deposited onto the oropharnyx, while smaller particles < 12 μm were more disperse throughout the oral cavity and navigated the curved geometry and laryngeal jet to escape through the tracheal outlet. It was concluded that at a flow rate of 30 L/min the particle diameters depositing on the larynx and trachea in this specific patient model are likely to be in the range of 7 μm to 16 μm. Particles larger than 16 μm primarily deposited on the oropharynx.

SUBMITTER: Vara Almirall B

PROVIDER: S-EPMC9612176 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Flow Patterns and Particle Residence Times in the Oral Cavity during Inhaled Drug Delivery.

Vara Almirall Brenda B Inthavong Kiao K Bradshaw Kimberley K Singh Narinder N Johnson Aaron A Storey Pippa P Salati Hana H

Pharmaceuticals (Basel, Switzerland) 20221013 10

Pulmonary drug delivery aims to deliver particles deep into the lungs, bypassing the mouth−throat airway geometry. However, micron particles under high flow rates are susceptible to inertial impaction on anatomical sites that serve as a defense system to filter and prevent foreign particles from entering the lungs. The aim of this study was to understand particle aerodynamics and its possible deposition in the mouth−throat airway that inhibits pulmonary drug delivery. In this study, we present a ...[more]

PMID: 36297371

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Project description:Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.

Dataset Information

Flow Patterns and Particle Residence Times in the Oral Cavity during Inhaled Drug Delivery.

Publications

Flow Patterns and Particle Residence Times in the Oral Cavity during Inhaled Drug Delivery.

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