In Vitro and In Silico Investigations on Drug Delivery in the Mouth-Throat Models with Handihaler®

Huang, Fen; Zhou, Xudong; Dai, Wu-Sheng; Yu, Jiaqi; Zhou, Zongyan; Tong, Zhenbo; Yu, Aibing

doi:10.1007/s11095-022-03386-9

Cited by 14 publications

(6 citation statements)

References 56 publications

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“…The simulated data on the deposition fraction of drug particles in the mouth and throat areas under different peak inspiratory rates are summarized in Table 3. Table 3 indicates that the rate of oropharyngeal deposition of drug particles at 30 L/min is approximately 56.47%, which is similar to the rates of oropharyngeal deposition obtained from previous in vitro experiments [31,32]. Thus, the results of the numerical simulation obtained in this study are reliable.…”

Section: Resultssupporting

confidence: 86%

Development of a Smart Metered-Dose Inhaler for Asthma Based on Computational Fluid Dynamics

Zhang,

Wei

2023

Symmetry

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Asthma is a common respiratory disease with symptoms such as repeated wheezing, shortness of breath, and coughing. However, currently, asthma cannot be cured but only controlled or relieved using medication. The metered-dose inhaler (MDI) is known to lead to high deposition fractions of drug particles in the mouth and throat, resulting in inadequate drug efficacy. Therefore, herein, the factors influencing the deposition fraction of asthma drugs in the mouth and throat regions were explored by computational fluid dynamics and a smart MDI for asthma was designed. The smart MDI was designed based on the obtained simulation results, which demonstrated that the deposition fraction gradually increased from 55.78% to 65.75% with an increase in the peak inspiratory flow rate at an angle of incidence of 0°. The deposition fraction first decreased and then increased as the angle of incidence increased at a constant peak inspiratory flow rate. The deposition fraction increased as the inspiration–press interval time increased at a constant angle of incidence and peak inspiratory flow rate. Meanwhile, performance analysis of the designed smart MDI indicated that the inhaler could effectively reduce the deposition fraction of drugs in the mouth and throat regions by 17% on average.

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Section: Resultssupporting

confidence: 86%

Development of a Smart Metered-Dose Inhaler for Asthma Based on Computational Fluid Dynamics

Zhang,

Wei

2023

Symmetry

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“…Because an MMAD below 5 µm is typically assumed to be necessary for deposition in the lower airways [ 83 ], it is concluded that all three rod formulations will reach the pertinent targets. Moreover, the efficacy of these particles is comparable to commercial DPI formulations using the HandiHaler ® [ 84 , 85 ] while being able to deliver higher amounts of API due to the larger volume of the delivery system.…”

Section: Resultsmentioning

confidence: 97%

Aspherical, Nano-Structured Drug Delivery System with Tunable Release and Clearance for Pulmonary Applications

Pioch,

Fischer,

Schneider

2024

Pharmaceutics

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Addressing the challenge of efficient drug delivery to the lungs, a nano-structured, microparticulate carrier system with defined and customizable dimensions has been developed. Utilizing a template-assisted approach and capillary forces, particles were rapidly loaded and stabilized. The system employs a biocompatible alginate gel as a stabilizing matrix, facilitating the breakdown of the carrier in body fluids with the subsequent release of its nano-load, while also mitigating long-term accumulation in the lung. Different gel strengths and stabilizing steps were applied, allowing us to tune the release kinetics, as evaluated by a quantitative method based on a flow-imaging system. The micro-cylinders demonstrated superior aerodynamic properties in Next Generation Impactor (NGI) experiments, such as a smaller median aerodynamic diameter (MMAD), while yielding a higher fine particle fraction (FPF) than spherical particles similar in critical dimensions. They exhibited negligible toxicity to a differentiated macrophage cell line (dTHP-1) for up to 24 h of incubation. The kinetics of the cellular uptake by dTHP-1 cells was assessed via fluorescence microscopy, revealing an uptake-rate dependence on the aspect ratio (AR = l/d); cylinders with high AR were phagocytosed more slowly than shorter rods and comparable spherical particles. This indicates that this novel drug delivery system can modulate macrophage uptake and clearance by adjusting its geometric parameters while maintaining optimal aerodynamic properties and featuring a biodegradable stabilizing matrix.

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“…Large language models (LLMs) pretrained on large text corpora have shown impressive performances on various tasks (Brown et al, 2020;Scao et al, 2022). Some recent works (Wei et al, 2022;Sanh et al, 2022) have explored multitask training of language models on labeled datasets paired with natural language instructions to enhance zero-shot generalization, a procedure called instruction finetuning. In contrast, MaNtLE generates explanations when prompted with feature-prediction pairs.…”

Section: Multi-task Training Of Language Modelsmentioning

confidence: 99%

“…The goal of MaNtLE is to explain the rationale of classifiers on realworld tasks. To develop MaNtLE, we fine-tune a T5-Large model on thousands of synthetic classification tasks, each paired with natural language explanations, in a multi-task learning setup following recent research (Wei et al, 2022;Sanh et al, 2022;. In §3.5, we discuss inference procedures to improve explanation quality and adapt the model trained on synthetic data for real-world tasks.…”

Section: Introductionmentioning

confidence: 99%

MaNtLE: Model-agnostic Natural Language Explainer

Menon,

Zaman,

Srivastava

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Understanding the internal reasoning behind the predictions of machine learning systems is increasingly vital, given their rising adoption and acceptance. While previous approaches, such as LIME generate algorithmic explanations by attributing importance to input features for individual examples, recent research indicates that practitioners prefer examining language explanations that explain sub-groups of examples (Lakkaraju et al., 2022). In this paper, we introduce MaNtLE, a model-agnostic natural language explainer that analyzes a set of classifier predictions and generates faithful natural language explanations of classifier rationale for structured classification tasks. MaNtLE uses multi-task training on thousands of synthetic classification tasks to generate faithful explanations. Our experiments indicate that, on average, MaNtLE-generated explanations are at least 11% more faithful compared to LIME and Anchors explanations across three tasks. Human evaluations demonstrate that users predict model behavior better using explanations from MaNtLE compared to other techniques. 1

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In Vitro and In Silico Investigations on Drug Delivery in the Mouth-Throat Models with Handihaler®

Cited by 14 publications

References 56 publications

Development of a Smart Metered-Dose Inhaler for Asthma Based on Computational Fluid Dynamics

Development of a Smart Metered-Dose Inhaler for Asthma Based on Computational Fluid Dynamics

Aspherical, Nano-Structured Drug Delivery System with Tunable Release and Clearance for Pulmonary Applications

MaNtLE: Model-agnostic Natural Language Explainer

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