A Partial Differential Equation Approach to Inhalation Physiologically Based Pharmacokinetic Modeling.

CPT Pharmacometrics Syst Pharmacol

Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.

Published: October 2018

The heterogeneous nature of the lungs and the range of processes affecting pulmonary drug disposition make prediction of inhaled drugs challenging. These predictions are critical, as the local exposure cannot be measured and current inhalation physiologically based pharmacokinetic (PBPK) models do not capture all necessary features. Utilizing partial differential equations, we present an inhalation PBPK model to describe the heterogeneity in both lung physiology and particle size. The model mechanistically describes important processes, such as deposition, mucociliary clearance, and dissolution. In addition, simplifications are introduced to reduce computational cost without loss of accuracy. Three case studies exemplify how the model can enhance our understanding of pulmonary drug disposition. Specific findings include that most small airways can be targeted by inhalation, and overdosing may eradicate the advantage of inhalation. The presented model can guide the design of inhaled molecules, formulations, as well as clinical trials, providing opportunities to explore regional targeting.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202470PMC
http://dx.doi.org/10.1002/psp4.12344DOI Listing

Publication Analysis

Top Keywords

partial differential
8
inhalation physiologically
8
physiologically based
8
based pharmacokinetic
8
pulmonary drug
8
drug disposition
8
inhalation
5
differential equation
4
equation approach
4
approach inhalation
4

Similar Publications

The aroma of yak milk powder is a crucial sensory indicator for evaluating its quality and flavor. Yak milk powders collected from different lactation periods exhibit distinct flavors, but no studies have thoroughly investigated the aroma characteristics and variation patterns of yak milk powders across these periods. This study identified and analyzed the volatile compounds in freeze-dried colostrum powder (YCSP), freeze-dried mature milk powder (YMMP), and freeze-dried ending milk powder (YEMP) using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS) and multivariate statistical analysis.

View Article and Find Full Text PDF

The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas texture information was analyzed via the gray-level co-occurrence matrix (GLCM) and Law's texture feature analysis.

View Article and Find Full Text PDF

In the development of inflammatory bowel disease (IBD), peritoneal macrophages contribute to the resident intestinal macrophage pool. Previous studies have demonstrated that oral administration of L-fucose exerts an immunomodulatory effect and repolarizes the peritoneal macrophages in vivo in mice. In this study, we analyzed the phenotype and metabolic profile of the peritoneal macrophages from mice, as well as the effect of L-fucose on the metabolic and morphological characteristics of these macrophages in vitro.

View Article and Find Full Text PDF

Understanding transcriptional heterogeneity in cancer cells and its implication for treatment response is critical to identify how resistance occurs and may be targeted. Such heterogeneity can be captured by in vitro studies through clonal barcoding methods. We present TraCSED (Transformer-based modeling of Clonal Selection and Expression Dynamics), a dynamic deep learning approach for modeling clonal selection.

View Article and Find Full Text PDF

Synergistic learning with multi-task DeepONet for efficient PDE problem solving.

Neural Netw

January 2025

School of Engineering, Brown University, United States of America; Division of Applied Mathematics, Brown University, United States of America. Electronic address:

Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!