Deep Lung Research Articles

External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT

ObjectivesThere is a need for CT pulmonary angiography (CTPA) lung segmentation models. Clinical translation requires radiological evaluation of model outputs, understanding of limitations, and identification of failure points. This multicentre study aims to develop an accurate CTPA lung segmentation model, with evaluation of outputs in two diverse patient cohorts with pulmonary hypertension (PH) and interstitial lung disease (ILD).MethodsThis retrospective study develops an nnU-Net-based segmentation model using data from two specialist centres (UK and USA). Model was trained (n = 37), tested (n = 12), and clinically evaluated (n = 176) on a diverse ‘real-world’ cohort of 225 PH patients with volumetric CTPAs. Dice score coefficient (DSC) and normalised surface distance (NSD) were used for testing. Clinical evaluation of outputs was performed by two radiologists who assessed clinical significance of errors. External validation was performed on heterogenous contrast and non-contrast scans from 28 ILD patients.ResultsA total of 225 PH and 28 ILD patients with diverse demographic and clinical characteristics were evaluated. Mean accuracy, DSC, and NSD scores were 0.998 (95% CI 0.9976, 0.9989), 0.990 (0.9840, 0.9962), and 0.983 (0.9686, 0.9972) respectively. There were no segmentation failures. On radiological review, 82% and 71% of internal and external cases respectively had no errors. Eighteen percent and 25% respectively had clinically insignificant errors. Peripheral atelectasis and consolidation were common causes for suboptimal segmentation. One external case (0.5%) with patulous oesophagus had a clinically significant error.ConclusionState-of-the-art CTPA lung segmentation model provides accurate outputs with minimal clinical errors on evaluation across two diverse cohorts with PH and ILD.Clinical relevanceClinical translation of artificial intelligence models requires radiological review and understanding of model limitations. This study develops an externally validated state-of-the-art model with robust radiological review. Intended clinical use is in techniques such as lung volume or parenchymal disease quantification.Key Points• Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical cohorts (pulmonary hypertension and interstitial lung disease).• No segmentation failures and robust review of model outputs by radiologists found 1 (0.5%) clinically significant segmentation error.• Intended clinical use of this model is a necessary step in techniques such as lung volume, parenchymal disease quantification, or pulmonary vessel analysis.Graphical

Inhalable Combination Powder Formulations for Treating Latent and Multidrug-Resistant Tuberculosis: Formulation and In Vitro Characterization.

Tuberculosis (TB) is an infectious disease resulting in millions of deaths annually worldwide. TB treatment is challenging due to a huge number of global latent infections and due to multidrug-resistant forms of TB. Inhaled administration of anti-TB drugs using dry powder inhalers has various advantages over oral administration due to its direct drug delivery and minimization of systemic side effects. Pretomanid (PA-824, PA) is a relatively new drug with potent activity against both active and latent forms of Mycobacterium tuberculosis (Mtb). It is also known for its synergistic effects in combination with pyrazinamide (PYR) and moxifloxacin (MOX). Fixed-dose combination powder formulations of either PYR and PA or PYR and MOX were prepared for inhaled delivery to the deep lung regions where the Mtb habitats were located. Powder formulations were prepared by spray drying using L-leucine as the aerosolization enhancer and were characterized by their particle size, morphology and solid-state properties. In vitro aerosolization behaviour was studied using a Next Generation Impactor, and stability was assessed after storage at room temperature and 30% relative humidity for three months. Spray drying with L-leucine resulted in spherical dimpled particles, 1.9 and 2.4 µm in size for PYR-PA and PYR-MOX combinations, respectively. The powder formulations had an emitted dose of >83% and a fine particle fraction of >65%. PA and MOX showed better stability in the combination powders compared to PYR. Combination powder formulations with high aerosolization efficiency for direct delivery to the lungs were developed in this study for use in the treatment of latent and multidrug-resistant TB infections.

In Situ Noninvasive Observation of Nitric Oxide Fluctuation in SARS-CoV-2 Infection In Vivo by Organic Near-Infrared-II Fluorescent Molecular Nanoprobes.

The pathogenesis understanding of SARS-CoV-2 infection is crucial to prevent the rampant spread of COVID-19 and its contribution to deterioration in health, even death. Nitric oxide (NO), a crucial molecule involved in signal transduction and cytotoxicity, is a possible key regulator in the occurrence and development of COVID-19. However, understanding the pathogenesis of NO in SARS-CoV-2 infection is still in its infancy due to the lack of suitable in situ monitoring probes of NO fluctuation in the complex SARS-CoV-2 infection environment in deep lung tissues. Herein, we developed an activatable near-infrared-II fluorescent molecular nanoprobe (OSNP) that uncages high-resolution and deep-tissue-penetrating near-infrared-II fluorescence signal in specific response to NO for in situ and noninvasive visualization of NO fluctuation in a SARS-CoV-2 infection mouse model in lung tissues. In vivo visualization revealed that the NO level is a positive relationship with SARS-CoV-2 infection progress. With the assistance of immuno-histochemical analyses, we uncovered the NO-involved pathological mechanism, that being the improved NO level is associated with an increase in inducible NO synthase rather than endothelial NO synthase. Our study not only provides the example of a near-infrared-II fluorescent imaging of NO in SARS-CoV-2 infection but also provides opportunities to uncover tunderlying pathomechanism of NO for SARS-Cov-2 infections.

Incense smoke (IS) inhalation exposure system: Physicochemical characterization, IS particle deposition and clearance in human airway using MPPD model

Incense smoke (IS) is source of indoor air pollution and key risk for diverse human diseases. Less information is available regarding controlled IS rodent inhalation exposure system and IS particulate matter (PM) deposition in human airways. Study aimed to demonstrate stable ISPM physicochemical parameters of 10 incense products inside the customized whole body inhalation exposure chamber (without animal) connected to smoke generation unit via aerosol mixing device. IS analyzed for size segregated PM emission, ISPM in vitro aerodynamics (MMAD and GSD determination), fine and ultrafine particle's SEM, SEM-EDX and PAH analysis. Using real life exposure scenario by utilizing MMAD, GSD and PM concentration after Tier 1 exposure assessment as key input parameters, ISPM dosimetry in infant (3 months) and adult (21 years male and female) human airways was calculated using multiple-path particle dosimetry (MPPD 3.04) modeling. Mass median aerodynamic diameter (MMAD) and geometric standard deviation (GSD) ranged between 0.55 and 2.10 μm and 1.22 to 1.77 (polydisperse) respectively. PM1.0 and PM0.1 showed multiple morphology and presence of heavy and trace elements. PAH like acenaphthylene, anthracene, fluorene, naphthalene and phenanthrene were detected (0.84–143.17 μg/g). MPPD results showed higher ISPM deposition in pulmonary region and lowest in trachea bronchial region. ISPM deposition in tissue was higher in lower, peripheral lung as compared to upper and central lung tissue. Whole body inhalation exposure system showed stable IS atmosphere (physicochemical parameters) indicating the device suitability in future inhalation studies. MPPD ISPM deposition fraction and clearance data showed deep lung penetrating and retention behavior with higher risk in infant followed by female and then male. These modeled particle deposition and clearance data may be useful in risk assessment analysis of IS.

Simultaneous object detection and segmentation for patient-specific markerless lung tumor tracking in simulated radiographs with deep learning.

Real-time tumor tracking is one motion management method to address motion-induced uncertainty. To date, fiducial markers are often required to reliably track lung tumors with X-ray imaging, which carries risks of complications and leads to prolonged treatment time. A markerless tracking approach is thus desirable. Deep learning-based approaches have shown promise for markerless tracking, but systematic evaluation and procedures to investigate applicability in individual cases are missing. Moreover, few efforts have been made to provide bounding box prediction and mask segmentation simultaneously, which could allow either rigid or deformable multi-leaf collimatortracking. The purpose of this study was to implement a deep learning-based markerless lung tumor tracking model exploiting patient-specific training which outputs both a bounding box and a mask segmentation simultaneously. We also aimed to compare the two kinds of predictions and to implement a specific procedure to understand the feasibility of markerless tracking on individualcases. We first trained a Retina U-Net baseline model on digitally reconstructed radiographs (DRRs) generated from a public dataset containing 875 CT scans and corresponding lung nodule annotations. Afterwards, we used an independent cohort of 97 lung patients to develop a patient-specific refinement procedure. In order to determine the optimal hyperparameters for automatic patient-specific training, we selected 13 patients for validation where the baseline model predicted a bounding box on planning CT (PCT)-DRR with intersection over union (IoU) with the ground-truth higher than 0.7. The final test set contained the remaining 84 patients with varying PCT-DRR IoU. For each testing patient, the baseline model was refined on the PCT-DRR to generate a patient-specific model, which was then tested on a separate 10-phase 4DCT-DRR to mimic the intrafraction motion during treatment. A template matching algorithm served as benchmark model. The testing results were evaluated by four metrics: the center of mass (COM) error and the Dice similarity coefficient (DSC) for segmentation masks, and the center of box (COB) error and the DSC for bounding box detections. Performance was compared to the benchmark model including statistical testing forsignificance. A PCT-DRR IoU value of 0.2 was shown to be the threshold dividing inconsistent (68%) and consistent (100%) success (defined as mean bounding box DSC > 0.6) of PS models on 4DCT-DRRs. Thirty-seven out of the eighty-four testing cases had a PCT-DRR IoU above 0.2. For these 37 cases, the mean COM error was 2.6 mm, the mean segmentation DSC was 0.78, the mean COB error was 2.7 mm, and the mean box DSC was 0.83. Including the validation cases, the model was applicable to 50 out of 97 patients when using the PCT-DRR IoU threshold of 0.2. The inference time per frame was 170 ms. The model outperformed the benchmark model on all metrics, and the comparison was significant (p < 0.001) over the 37 PCT-DRR IoU > 0.2 cases, but not over the undifferentiated 84 testingcases. The implemented patient-specific refinement approach based on a pre-trained baseline model was shown to be applicable to markerless tumor tracking in simulated radiographs for lungcases.

Design of Pectin-Based Hydrogel Microspheres for Targeted Pulmonary Delivery.

Pulmonary drug delivery via microspheres has gained growing interest as a noninvasive method for therapy. However, drug delivery through the lungs via inhalation faces great challenges due to the natural defense mechanisms of the respiratory tract, such as the removal or deactivation of drugs. This study aims to develop a natural polymer-based microsphere system with a diameter of around 3 μm for encapsulating pulmonary drugs and facilitating their delivery to the deep lungs. Pectin was chosen as the foundational material due to its biocompatibility and degradability in physiological environments. Electrospray was used to produce the pectin-based hydrogel microspheres, and Design-Expert software was used to optimize the production process for microsphere size and uniformity. The optimized conditions were determined to be as follows: pectin/PEO ratio of 3:1, voltage of 14.4 kV, distance of 18.2 cm, and flow rate of 0.95 mL/h. The stability and responsiveness of the pectin-based hydrogel microspheres can be altered through coatings such as gelatin. Furthermore, the potential of the microspheres for pulmonary drug delivery (i.e., their responsiveness to the deep lung environment) was investigated. Successfully coated microspheres with 0.75% gelatin in 0.3 M mannitol exhibited improved stability while retaining high responsiveness in the simulated lung fluid (Gamble's solution). A gelatin-coated pectin-based microsphere system was developed, which could potentially be used for targeted drug delivery to reach the deep lungs and rapid release of the drug.

Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans.

The proposed work aims to develop an algorithm to precisely segment the lung parenchyma in thoracic CT scans. To achieve this goal, the proposed technique utilized a combination of deep learning and traditional image processing algorithms. The initial step utilized a trained convolutional neural network (CNN) to generate preliminary lung masks, followed by the proposed post-processing algorithm for lung boundary correction. First, the proposed method trained an improved 2D U-Net CNN model with Inception-ResNet-v2 as its backbone. The model was trained on 32 CT scans from two different sources: one from the VESSEL12 grand challenge and the other from AIIMS Delhi. Further, the model's performance was evaluated on a test dataset of 16 CT scans with juxta-pleural nodules obtained from AIIMS Delhi and the LUNA16 challenge. The model's performance was assessed using evaluation metrics such as average volumetric dice coefficient (DSCavg), average IoU score (IoUavg), and average F1 score (F1avg). Finally, the proposed post-processing algorithm was implemented to eliminate false positives from the model's prediction and to include juxta-pleural nodules in the final lung masks. The trained model reported a DSCavg of 0.9791 ± 0.008, IoUavg of 0.9624 ± 0.007, and F1avg of 0.9792 ± 0.004 on the test dataset. Applying the post-processing algorithm to the predicted lung masks obtained a DSCavg of 0.9713 ± 0.007, IoUavg of 0.9486 ± 0.007, and F1avg of 0.9701 ± 0.008. The post-processing algorithm successfully included juxta-pleural nodules in the final lung mask. Using a CNN model, the proposed method for lung parenchyma segmentation produced precise segmentation results. Furthermore, the post-processing algorithm addressed false positives and negatives in the model's predictions. Overall, the proposed approach demonstrated promising results for lung parenchyma segmentation. The method has the potential to be valuable in the advancement of computer-aided diagnosis (CAD) systems for automatic nodule detection.

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation Open-Source Deep Learning and Quantitative Measurement Initiative

IntroductionWith global adoption of computed tomography (CT) lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open-source, cloud-based, globally distributed, screening CT imaging data set and computational environment that are compliant with the most stringent international privacy regulations that also protect the intellectual properties of researchers, the International Association for the Study of Lung Cancer sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be used for clinically relevant AI research. MethodsIn this second phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans. ResultsA total of 1394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness more than or equal to 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high-quality CT scans. ConclusionsThese initial experiments revealed that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based data sets.

Inhalation powder development without carrier: How to engineer ultra-flying microparticles?

Particle engineering technologies have led to the commercialization of new inhaled powders like PulmoSolTM or PulmoSphereTM. Such platforms are produced by spray drying, a well-known process popular for its versatility, thanks to wide-ranging working parameters. Whereas these powders contain a high drug-loading, we have studied a low-dose case, in optimizing the production of powders with two anti-asthmatic drugs, budesonide and formoterol. Using a Design of Experiments approach, 27 powders were produced, with varying excipient mixes (cyclodextrins, raffinose and maltodextrins), solution concentrations, and spray drying parameters in order to maximize deep lung deposition, measured through fine particle fraction (next generation impactor). Based on statistical analysis, two powders made of hydropropyl-β-cyclodextrin alone or mixed with raffinose and L-leucine were selected. Indeed, the two powders demonstrated very high fine particle fraction (>55%), considerably better than commercially available products. Deep lung deposition has been correlated to very fine particle size and lower microparticles interactions shown by laser diffraction assays at different working pressures, and particle morphometry. Moreover, the two drugs would be predicted to deposit homogeneously into the lung according to impaction studies. Uniform delivery is fundamental to control symptoms of asthma. In this study, we develop carrier-free inhalation powders promoting very efficient lung deposition and demonstrate the high impact of inter-particular interactions intensity on their aerosolization behaviour.

Abstract P2162: Controlled Porous Structures Of Nitric Oxide-releasing Inhaler For Pulmonary Arterial Hypertension Vascular Reverse Remodeling

Pulmonary arterial hypertension (PAH) is an incurable disease characterized by elevated blood pressure in the pulmonary artery. Nitric oxide (NO) is a potent regulator of hemodynamics and macrophage reprogramming. However, the therapeutic effects of NO depend on its concentration and its half-life is very short. Pulmonary delivery has advantages such as the ability to escape first-pass metabolism and the large absorptive surface area. In this study, we prepared NO-releasing inhalers with controlled porous structures to modulate the deposition in the respiratory system. NO-releasing inhalers with micro-sized pores showed improved nebulization efficiency than nano-sized ones. Furthermore, the cGMP expression and macrophage polarization were evaluated to confirm the vasodilatory and anti-inflammatory effects of NO. The customized nebulizer system was prepared for efficiency test of NO-MPs. The fluorescence intensity of Nile red was analyzed using photoluminescence spectroscopy to confirm the amounts of NO-MPs on the slide glass. As with confocal images, 0.8k BPEI/NO-MPs could arrive the lower region than 25k BPEI/NO-MPs. NO produced by endothelial nitric oxide synthase (eNOS) induce vasodilation by increasing cyclic guanosine monophosphate (cGMP) in vascular smooth muscle cells. Hence, the delivery of NO mimicking the physical production of the endothelium in range of 0.05-0.4 nmol cm -2 min -1 is required to restore vascular homeostasis as NO has a concentration-dependent effect on cells. Therefore, BPEI0.8k/NO-MPs release 0.277 nmol cm -2 min -1 of NO that mimics the environment in healthy endothelium, while BPEI25k/NO-MPs showed a higher concentration of NO release, 0.548 nmol cm -2 min -1 . BPEI0.8k/NO-MPs increased the synthesis of cGMP in cells, whereas BPEI25k/NO-MPs presented adverse effects due to an initial burst release. When cell populations were quantified, BPEI0.8k/NO-MPs increased the number of M2-like cells while decreasing the number of M1-like cells. Furthermore, when we analyzed proteins and gene levels of cells, anti-inflammatory factors increased in BPEI0.8k/NO-MPs treatment groups. In conclusion, NO-releasing inhalers could deliver NO to deep lungs and has the potential for pulmonary vascular recovery.

Mapping the Spatial Extent of Hypoperfusion in Chronic Thromboembolic Pulmonary Hypertension Using Multienergy CT.

To assess if a novel automated method to spatially delineate and quantify the extent of hypoperfusion on multienergy CT angiograms can aid the evaluation of chronic thromboembolic pulmonary hypertension (CTEPH) disease severity. Multienergy CT angiograms obtained between January 2018 and December 2020 in 51 patients with CTEPH (mean age, 47 years ± 17 [SD]; 27 women) were retrospectively compared with those in 110 controls with no imaging findings suggestive of pulmonary vascular abnormalities (mean age, 51 years ± 16; 81 women). Parenchymal iodine values were automatically isolated using deep learning lobar lung segmentations. Low iodine concentration was used to delineate areas of hypoperfusion and calculate hypoperfused lung volume (HLV). Receiver operating characteristic curves, correlations with preoperative and postoperative changes in invasive hemodynamics, and comparison with visual assessment of lobar hypoperfusion by two expert readers were evaluated. Global HLV correctly separated patients with CTEPH from controls (area under the receiver operating characteristic curve = 0.84; 10% HLV cutoff: 90% sensitivity, 72% accuracy, and 64% specificity) and correlated moderately with hemodynamic severity at time of imaging (pulmonary vascular resistance [PVR], ρ = 0.67; P < .001) and change after surgical treatment (∆PVR, ρ = -0.61; P < .001). In patients surgically classified as having segmental disease, global HLV correlated with preoperative PVR (ρ = 0.81) and postoperative ∆PVR (ρ = -0.70). Lobar HLV correlated moderately with expert reader lobar assessment (ρHLV = 0.71 for reader 1; ρHLV = 0.67 for reader 2). Automated quantification of hypoperfused areas in patients with CTEPH can be performed from clinical multienergy CT examinations and may aid clinical evaluation, particularly in patients with segmental-level disease.Keywords: CT-Spectral Imaging (Multienergy), Pulmonary, Pulmonary Arteries, Embolism/Thrombosis, Chronic Thromboembolic Pulmonary Hypertension, Multienergy CT, Hypoperfusion© RSNA, 2023.

The impact ofvaping ethanol-containing electronic cigarette liquids onroadside impairment investigations.

Legal professionals and others have suggested that vaping electronic cigarettes (e-cigs) prior to or during ethanol breath testing may produce false positives. Preliminary breath tests (PBTs) and evidentiary breath tests (EBTs) measure ethanol in exhaled breath and standardized field sobriety tests (SFSTs) are used to assess impairment. Ethanol has been identified in e-cig liquids (e-liquids). Presented are a series of experiments designed to determine the mechanics of vaping ethanol using an e-cig and the effects of vaping ethanol on the SFSTs and breath tests used by law enforcement officers (LEO). Twelve participants (five females, age: 21-32 and seven males, age: 21-55), vaped either one or ten puffs of an e-liquid (0% or 20% ethanol). LEOs assessed impairment using SFSTs (12 and 42 min), PBTs (<1, 27, 32, 37 and 57 min) and EBTs (2, 29, 34, 39 and 59 min) post-vaping. A self-assessment test was administered post-vaping (22 and 52 min). Baseline responses for all measures were collected prior to vaping. Results demonstrated that ethanol in the e-liquids was aerosolized by e-cigs and produced particles that could reach the deep lung tissue based on mean-mass diameter. Ethanol was detected by PBT <3 min after participants vaped one (0.007-0.030 g/210 L) or ten puffs (013-0.074 g/210 L) of a 20% ethanol e-liquid. Ethanol was not detected by PBT at any subsequent time point. Ethanol was not detected by the EBT under any condition. Impairment was not indicated by the SFST. Some subjective effects were reported, but few statistically significant differences between conditions were indicated. A wait period prior to ethanol breath testing is not always mandated, depending on jurisdiction, or observed in all applications, such as workplace testing. The results demonstrate that a wait period must be employed to prevent vaping-related false-positive breath ethanol results.

Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models.

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.

Flexible Biopsy Robot with Force Sensing for Deep Lung Examination

Lung disease has become a leading cause of disease‐related death, making it one of the most serious health problems in the world. Due to the advantages of fast postoperative recovery and small trauma, transbronchoscopic biopsy has become the main method for the diagnosis of lung diseases. However, limited by the large outer diameter of the bronchoscope, insufficient flexibility of the biopsy needle, and lack of force sensing, traditional biopsy operation is simple and rough, and it is difficult to enter the deep narrow areas of the lung for examination. In this article, a flexible biopsy robot with force‐sensing ability is proposed. The robot is composed of a tendon‐actuated catheter and an inner sliding flexible needle as well as their driving systems. The experimental results demonstrate that the robot has good positioning accuracy of 0.72 mm and high flexibility and repeatability. With the cooperation of the commercial bronchoscope, the robot can reach various narrow areas of the lung to perform biopsy operations. The proposed robot has the potential to improve the diagnosis rate of lung diseases and reduce related deaths.

Inhalable dry powder containing lipid polymer hybrid nanoparticles of Nintedanib esylate: In vitro and in vivo evaluations

The key objective of this research work was to develop and evaluate dry powder for inhalation containing lipid polymer hybrid nanoparticles (LPHNPs) loaded with Nintedanib esylate (NE) to target deep lung for the treatment of idiopathic pulmonary fibrosis (IPF). NE loaded LPHNPs (NE-LPHNPs) were prepared by nanoprecipitation method and optimized via Box Behnken Design (BBD). The desirability was found to be 0.905. The optimized NE-LPHNPs exhibited particle size of 99.72 ± 2.60 nm, zeta potential of −26.8 ± 3.2 mV and entrapment efficiency of 88.43 ± 3.90%. Differential scanning calorimetry and X-ray diffraction results showed conversion of NE into an amorphous form. Transmission electron microscopic image depicted discrete, polymeric core and lipid shell structure and spherical shape LPHNPs. In vitro drug release displayed 96.7 ± 1.9% of release at 28 h and followed higuchi model. The dry powder containing lyophilized NE-LPHNPs with inhalac 500 (1:1.5) showed good flow property with mass median aerodynamic diameter of 4 ± 0.28 μm. High deposition into alveolar region was observed by tissue uptake study of fluorescein isothiocyanate loaded LPHNPs. In vivo pulmonary pharmacokinetic study displayed improved area under curve (35.71 fold) than NE suspension. Hence, developed inhalable LPHNPs offers a promising approach for the local delivery of NE.

Lipid-based particle engineering via spray-drying for targeted delivery of antibiotics to the lung

Pulmonary delivery of antibiotics for the treatment of tuberculosis provides several benefits compared to conventional oral and parenteral administration. API-loaded particles delivered directly to alveolar macrophages, where Mycobacterium tuberculosis resides, can reduce the required dose and decrease the severe side effects of conventional treatment. In this work, lipid-microparticles loaded with rifampicin were engineered via spray-drying to be administered as a carrier-free dry powder for inhalation. Although, it is well-known that spray-drying of lipid-based excipients is strongly limited, a completely lipid-based formulation using diglycerol full ester of behenic acid was produced. The solid state of the lipid, providing high melting temperature, absence of polymorphism and monophasic crystallization, led to high yield of spray-dried particles (83%). Inhalable particles of mass median aerodynamic diameter of 2.36 µm, median geometric size of 2.05 µm, and negative surface (-50.03 mV) were engineered. Such attributes were defined for deep lung deposition and targeted delivery of antibiotics to alveolar macrophages. Superior aerodynamic performance as carrier-free DPI was associated to a high fine particle fraction of 79.5 %. No in vitro cytotoxic effects were found after exposing epithelial cell lines and alveolar macrophages. In vitro uptake of particles into alveolar macrophages indicated the efficiency of their targeted delivery. The use of highly processable and safe lipid-based excipients for particle engineering via spray-drying can extend the availability of materials for functionalized applications for pulmonary delivery.

Low-dose intrapulmonary drug delivery device for studies on next-generation therapeutics in mice.

Although nebulizers have been developed for delivery of small molecules in human patients, no tunable device has been purpose-built for targeted delivery of modern large molecule and temperature-sensitive therapeutics to mice. Mice are used most of all species in biomedical research and have the highest number of induced models for human-relevant diseases and transgene models. Regulatory approval of large molecule therapeutics, including antibody therapies and modified RNA highlight the need for quantifiable dose delivery in mice to model human delivery, proof-of-concept studies, efficacy, and dose-response. To this end, we developed and characterized a tunable nebulization system composed of an ultrasonic transducer equipped with a mesh nebulizer fitted with a silicone restrictor plate modification to control the nebulization rate. We have identified the elements of design that influence the most critical factors to targeted delivery to the deep lungs of BALB/c mice. By comparing an in silico model of the mouse lung with experimental data, we were able to optimize and confirm the targeted delivery of over 99% of the initial volume to the deep portions of the mouse lung. The resulting nebulizer system provides targeted lung delivery efficiency far exceeding conventional nebulizers preventing waste of expensive biologics and large molecules during proof-of-concept and pre-clinical experiments involving mice. (Word Count =207).

Quantification and persistence of COVID-19 virus in recently deceased individuals before and after embalming.

The COVID-19 pandemic severely affected the medical education worldwide. The infection risk for medical students and healthcare personnel who work with COVID-19 positive cadavers or tissues remains unclear. Moreover, COVID-19 positive cadavers have been rejected by medical schools, adversely impacting the continuum of medical education. Herein, the viral genome abundance in tissues from four COVID-19 positive donors before and after embalming were compared. Tissue samples were collected from the lungs, liver, spleen, and brain both pre- and postembalming. The possible presence of infectious COVID-19 was determined by inoculating human tissue homogenates onto a monolayer of human A549-hACE2 cells and observing for cytopathic effects up to 72 h postinoculation. A real- time quantitative reverse transcription polymerase chain reaction was performed to quantify COVID-19 present in culture supernatants. Fully intact viral genome sequence was possible to obtain in samples with higher levels of virus, even several days postmortem. The embalming procedure described above substantially reduces the abundance of viable COVID-19 genomes in all tissues, sometimes even to undetectable levels. However, in some cases, COVID-19 RNA can still be detected, and a cytopathic effect can be seen both pre- and postembalmed tissues. This study suggests that embalmed COVID-19 positive cadavers might be used safely with appropriate precautions followed in gross anatomy laboratories and in clinical and scientific research. Deep lung tissue is the best specimen to test for the virus. If the tests on the lung tissues are negative, there is a very low likelihood that other tissues will show positive results.

Customizable resveratrol spray-dried micro-composites for inhalation as a promising contender for treatment of idiopathic pulmonary fibrosis

The past decades have witnessed tremendous expansion in utilization of plant-derived medicines as resveratrol (RES) in treating several diseases like idiopathic pulmonary fibrosis (IPF). RES can exhibit its role in treating IPF via its outstanding antioxidant and anti-inflammatory activities. The goal of this work was to formulate RES-loaded spray-dried composite microparticles (SDCMs) suitable for pulmonary delivery via dry powder inhaler (DPI). They were prepared by spray drying of a previously prepared RES-loaded bovine serum albumin nanoparticles (BSA NPs) dispersion using different carriers. RES-loaded BSA NPs, prepared by the desolvation technique, acquired suitable particle size of 177.67 ± 0.95 nm and entrapment efficiency of 98.7 ± 0.35% with perfectly uniform size distribution and high stability. Considering the attributes of the pulmonary route, NPs were co-spray dried with compatible carriers viz. mannitol, dextran, trehalose, leucine, glycine, aspartic acid, and glutamic acid to fabricate SDCMs. All formulations showed suitable mass median aerodynamic diameter<5 µm; that is suitable for deep lung deposition. However, the best aerosolization behavior was attained from using leucine with fine particle fraction (FPF) of 75.74%, followed by glycine with FPF of 54.7%. Finally, a pharmacodynamic study was conducted on bleomycin-induced mice, and it strongly revealed the role of the optimized formulations in alleviating PF through suppressing the levels of hydroxyproline, tumor necrosis factor-α and matrix metalloproteinase-9 with obvious improvements in the treated lung histopathology. These findings indicate that in addition to leucine, the glycine amino acid, which is not commonly used yet, is very promising in the formulation of DPIs.

Inhalable Solubilized Zileuton for Improved Lung Targeting in vitro and in vivo Analysis

Abstract: Background: Pulmonary diseases that affect the normal functioning of the lungs show airway symptoms ranging from change in airflow to bronchiectasis. Zileuton is an inhibitor of 5-lipoxygenase enzyme that catalyzes the synthesis of leukotrienes. Zileuton is used in the management of inflammatory conditions of the upper airways like obstructive pulmonary conditions and acute lung inflammation. Although a promising therapeutic, zileuton is poorly water-soluble and requires frequent administration to overcome bioavailability issues and maintain therapeutic levels that often lead to adverse reactions, especially to the non-targeted organs. Materials and Methods: Therefore, we designed a rapidly nanoemulsifying formulation of zileuton using Acrysol K150 as an oil, Cremophor EL as a surfactant, and Transcutol HP as a cosolvent. Results: This self-emulsifying composition exhibited showed a mean globule size of 133 ± 3.6 nm with a polydispersity index of 0.38. Scanning electron microscopy (SEM) images revealed the spherical shape of emulsion globules. In vitro lung deposition showed >80% delivery to the deep lung tissue. Mass median aerodynamic diameter of 2.05±0.98 μm for the aerosolized formulation. In vivo, pharmacokinetic studies in Wistar rats by inhalation route showed that the zileuton-loaded nanoemulsifying formulation had a significantly higher concentration in the lung compared to other non-target organs. The in vivo efficacy in the lipopolysaccharide-induced acute lung inflammation model in rats significantly impeded the protein accumulation and neutrophil infiltration in the lungs. Conclusion: The zileuton-loaded nanoemulsifying inhalable formulation successfully improved the therapeutic efficacy of zileuton specifically to the lung thereby minimizing the off-target organ side effects. Keywords: Self-emulsifying, Zileuton, Aerosols, Pharmacodynamics, in vivo, Lung targeting.