Asthma

Other Diseases

Asthma

Asthma Challenges

Complex Disease Process – Asthma is a complex disease disease process with many phenotypes that can complicate clinical trials. For example, mucus plugging has recently been discovered as an important factor in 58% of asthma patients. Trial sponsors can leverage precision imaging biomarkers to better distinguish phenotypes and assess treatment response across airways, tissue, function, and vasculature.

Weak Conventional Endpoints – Traditional trial endpoints like pulmonary function tests, the 6-minute walk test, and exacerbations are all useful; however, they are often lagging indicators with limited precision when compared to quantitative imaging measures of lung anatomy and function.

Challenging Imaging Operations – Inclusion of quantitative imaging can pose challenges for trials sites, resulting in drop-out sites or poor data.

Mario Castro, MD – Pulmonologist

More Highlights from Dr. Castro

VIDA Lung Intelligence for Asthma Clinical Trials

VIDA produces up to 15,000 precision lung metrics per imaging study. These measures, called quantitative CT imaging biomarkers (QCT), span airways, tissue, vasculature, and lung function. Many of these biomarkers are important in the evaluation of patients with asthma, both for clinical care and in evaluation of treatment response. For more information on these biomarkers, please complete this brief form 👇.

VIDA Intelligence Portal is a respiratory trial imaging orchestration platform designed to ease the imaging operations of a lung trial. Portal applies AI-powered intelligence to automate tasks that are mundane and/or prone to human error. The portal also provides eLearning, quality control, data security, drag and drop data exchange, team communications and much more. The result is clinical trial sites capable of acquiring high quality clinical trial imaging data with ease.

Intelligence services leverage imaging and operational data gold mines to maximize their value for trial sponsors. For example, retrospective data analysis services examine existing datasets to surface valuable new insights. Site performance monitoring gives sponsors dynamic operational dashboards to proactively view the health of trial sites, the data they are submitting, and more. Subject screening services assist sponsors in filtering out candidates who meet exclusion criteria.

Asthma Biomarkers

VIDA is proud to be the QCT imaging provider for SARP, the world’s most comprehensive study of adults and children with severe asthma.

Asthma Studies Utilizing VIDA’s Precision Imaging

Svenningsen, S. et al. Effects of Dupilumab on Mucus Plugging and Ventilation Defects in Patients with Moderate-to-Severe Asthma: A Randomized, Double-Blind, Placebo-Controlled Trial. Am J Respir Crit Care Med 208, 995–997 (2023).

Leung, C. et al. A Novel Air Trapping Segment Score Identifies Opposing Effects of Obesity and Eosinophilia on Air Trapping in Asthma. Am J Respir Crit Care Med (2023) http://doi.org/10.1164/rccm.202305-0802OC.

Dunican, E. M. et al. Mucus plugs in patients with asthma linked to eosinophilia and airflow obstruction. J Clin Invest 128, 997–1009 (2018).

Shim, S. S. et al. Lumen area change (Delta Lumen) between inspiratory and expiratory CT as a measure of severe outcomes in asthma. J Allergy Clin Immunol 142, 1773-1780.e9 (2018).

Choi, S. et al. Differentiation of quantitative CT imaging phenotypes in asthma versus COPD. BMJ Open Respiratory Research 4, e000252 (2017).

Trivedi, A. et al. "Using Imaging as a Biomarker for Asthma". J Allergy Clin Immunol 139, 1–10 (2017).

Aysola, R. S. et al. Airway remodeling measured by multidetector computed tomography is increased in severe asthma and correlates with pathology. Chest 134, 1183–1191 (2008).

Herth, F. J. F. et al. The Modern Art of Reading Computed Tomography Images of the Lungs: Quantitative CT. Respiration 95, 8–17 (2018).

Witt, C. A. et al. Longitudinal changes in airway remodeling and air trapping in severe asthma. Acad Radiol 21, 986–993 (2014).

Choi, S. et al. Registration-based assessment of regional lung function via volumetric CT images of normal subjects vs. severe asthmatics. J Appl Physiol (1985) 115, 730–742 (2013).

Donohue, K. M. et al. Asthma and lung structure on computed tomography: the Multi-Ethnic Study of Atherosclerosis Lung Study. J Allergy Clin Immunol 131, 361-368.e1–11 (2013).

Busacker, A. et al. A multivariate analysis of risk factors for the air-trapping asthmatic phenotype as measured by quantitative CT analysis. Chest 135, 48–56 (2009).

Choi, S. et al. Quantitative computed tomography imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes. J Allergy Clin Immunol 140, 690-700.e8 (2017).

Choi, S. et al. Quantitative assessment of multiscale structural and functional alterations in asthmatic populations. J Appl Physiol (1985) 118, 1286–1298 (2015).

Jarjour, N. N. et al. Severe Asthma. Am J Respir Crit Care Med 185, 356–362 (2012).

Trivedi, A. P. et al. Quantitative CT Characteristics of Cluster Phenotypes in the Severe Asthma Research Program Cohorts. Radiology 210363 (2022) http://doi.org/10.1148/radiol.210363.

Cosentino, J. et al. Analysis of Asthma-Chronic Obstructive Pulmonary Disease Overlap Syndrome Defined on the Basis of Bronchodilator Response and Degree of Emphysema. Ann Am Thorac Soc 13, 1483–1489 (2016).

Reinhardt, J., D’Souza, N. & Hoffman, E. Intra-Thoracic Airway Measurement: EmphEx Vivo Validation. (1997).

Reinhardt, J., Park, W., Hoffman, E. & Sonka, M. Intrathoracic airway wall detection using graph search and scanner PSF information. in Medical Imaging (1997). http://doi.org/10.1117/12.274033.

McIntosh, M. J. et al. Asthma Control, Airway Mucus, and 129Xe MRI Ventilation After a Single Benralizumab Dose. Chest S001236922200424X (2022) http://doi.org/10.1016/j.chest.2022.03.003.

Tran, C. et al. Luminal mucus plugs are spatially associated with airway wall thickening in severe COPD and asthma: A single-centered, retrospective, observational study. Respiratory Medicine 202, 106982 (2022).

Tattersall, M. C. et al. Skeletal Muscle Adiposity and Lung Function Trajectory in the Severe Asthma Research Program (SARP). Am J Respir Crit Care Med rccm.202203-0597OC (2022) http://doi.org/10.1164/rccm.202203-0597OC.

Nordenmark, L. H., Griffiths, J. M. & Parnes, J. R. Tezepelumab and Mucus Plugs in Patients with Moderate-to-Severe Asthma. (2023).

Mukherjee, M. et al. Benralizumab Normalizes Sputum Eosinophilia in Severe Asthma Uncontrolled by Anti-IL5 Antibodies: A Single-Blind, Placebo-controlled Clinical Trial. Am J Respir Crit Care Med rccm.202308-1413LE (2023) http://doi.org/10.1164/rccm.202308-1413LE.

Moslemi, A. et al. Differentiating COPD and asthma using quantitative CT imaging and machine learning. Eur Respir J 60, 2103078 (2022).

Boomer, J. et al. Increased Muc5AC and Decreased Ciliated Cells in Severe Asthma Partially Restored by Inhibition of IL-4Rα Receptor. Am J Respir Crit Care Med rccm.202307-1266OC (2024) http://doi.org/10.1164/rccm.202307-1266OC.