Severe asthma and personalized approach in the choice of biologic.


Journal

Current opinion in allergy and clinical immunology
ISSN: 1473-6322
Titre abrégé: Curr Opin Allergy Clin Immunol
Pays: United States
ID NLM: 100936359

Informations de publication

Date de publication:
01 08 2022
Historique:
pubmed: 3 7 2022
medline: 22 7 2022
entrez: 2 7 2022
Statut: ppublish

Résumé

Severe asthma requires intensive pharmacological treatment to achieve disease control. Oral corticosteroids are effective, but their use is burdened with important side effects. Biologics targeting the specific inflammatory pathways underpinning the disease have been shown to be effective but not all patients respond equally well. As we treat more patients than those who can respond, our inability to predict responders has important healthcare costs considering that biologics are expensive drugs. Thus, a more precise choice of the 'right patients' to be prescribed with the 'right biologics' would be desirable. Machine learning techniques showed that it is possible to increase our ability to predict outcomes in patients treated with biologics. Recently, we identified by cluster analysis four different clusters within the T2 high phenotype with differential benralizumab response. Two of these clusters, characterized by higher levels of inflammatory markers, showed the highest response rate (80-90%). Machine learning holds promise for asthma research enabling us to predict which patients will respond to which drug. These techniques can facilitate the diagnostic workflow and increase the chance of selecting the more appropriate treatment option for the individual patient, enhancing patient care and satisfaction.

Identifiants

pubmed: 35779061
doi: 10.1097/ACI.0000000000000829
pii: 00130832-202208000-00011
doi:

Substances chimiques

Adrenal Cortex Hormones 0
Anti-Asthmatic Agents 0
Biological Products 0

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

268-275

Informations de copyright

Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.

Références

Di Bona D, Fiorino I, Taurino M, et al. Long-term ‘real-life’ safety of omalizumab in patients with severe uncontrolled asthma: a nine-year study. Respir Med 2017; 130:55–60.
Canonica GW, Colombo GL, Rogliani P, et al. Omalizumab for severe allergic asthma treatment in Italy: a cost-effectiveness analysis from PROXIMA study. Risk Manag Healthc Policy 2020; 13:43–53.
Pelaia C, Paoletti G, Puggioni F, et al. Interleukin-5 in the pathophysiology of severe asthma. Front Physiol 2019; 10:1514.
Marone G, Granata F, Pucino V, et al. The intriguing role of interleukin 13 in the pathophysiology of asthma. Front Pharmacol 2019; 10:1387.
Agache I, Beltran J, Akdis C, et al. Efficacy and safety of treatment with biologicals (benralizumab, dupilumab, mepolizumab, omalizumab and reslizumab) for severe eosinophilic asthma. A systematic review for the EAACI Guidelines – recommendations on the use of biologicals in severe asthma. Allergy 2020; 75:1023–1042.
Brusselle GG, Koppelman GH. Biologic therapies for severe asthma. N Engl J Med 2022; 386:157–171.
Khalaf K, Paoletti G, Puggioni F, et al. Asthma from immune pathogenesis to precision medicine. Semin Immunol 2019; 46:101294.
Chung KF. Precision medicine in asthma: linking phenotypes to targeted treatments. Curr Opin Pulm Med 2018; 24:4–10.
König IR, Fuchs O, Hansen G, et al. What is precision medicine? Eur Respir J 2017; 50:1700391.
Pavord ID, Beasley R, Agusti A, et al. After asthma: redefining airways diseases. Lancet 2018; 391:350–400.
Global Initiative for Asthma. Global strategy for asthma management and prevention, 2021. Available at: www.ginasthma.org . Accessed April 16, 2022.
Wenzel SE. Asthma phenotypes: the evolution from clinical to molecular approaches. Nat Med 2012; 18:716–725.
Lambrecht BN, Hammad H, Fahy JV. The cytokines of asthma. Immunity 2019; 50:975–991.
Kuruvilla ME, Lee FE, Lee GB. Understanding asthma phenotypes, endotypes, and mechanisms of disease. Clin Rev Allergy Immunol 2019; 56:219–233.
Salter B, Lacy P, Mukherjee M. Biologics in asthma: a molecular perspective to precision medicine. Front Pharmacol 2022; 12:793409.
Marone G, Spadaro G, Braile M, et al. Tezepelumab: a novel biological therapy for the treatment of severe uncontrolled asthma. Expert Opin Invest Drugs 2019; 28:931–940.
Porsbjerg CM, Sverrild A, Lloyd CM, et al. Antialarmins in asthma: targeting the airway epithelium with next-generation biologics. Eur Respir J 2020; 56:2000260.
Koski RR, Grzegorczyk KM. Comparison of monoclonal antibodies for treatment of uncontrolled eosinophilic asthma. J Pharm Pract 2020; 33:513–522.
Kroes JA, Zielhuis SW, van Roon EN, Ten Brinke A. Prediction of response to biological treatment with monoclonal antibodies in severe asthma. Biochem Pharmacol 2020; 179:113978.
Agusti A, Bel E, Thomas M, et al. Treatable traits: toward precision medicine of chronic airway diseases. Eur Respir J 2016; 47:410–419.
Papaioannou AI, Diamant Z, Bakakos P, Loukides S. Towards precision medicine in severe asthma: treatment algorithms based on treatable traits. Respir Med 2018; 142:15–22.
Samitas K, Zervas E, Gaga M. T2-low asthma: current approach to diagnosis and therapy. Curr Opin Pulm Med 2017; 23:48–55.
Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol 2019; 20:e262–e273.
Alonso-Betanzos A, Bolón-Canedo V. Big-data analysis, cluster analysis, and machine-learning approaches. Adv Exp Med Biol 2018; 1065:607–626.
Haldar P, Pavord ID, Shaw DE, et al. Cluster analysis and clinical asthma phenotypes. Am J Respir Crit Care Med 2008; 178:218–224.
Moore WC, Meyers DA, Wenzel SE, et al. National heart, lung, and blood institute's severe asthma research program. Identification of asthma phenotypes using cluster analysis in the severe asthma research program. Am J Respir Crit Care Med 2010; 181:315–323.
Wu W, Bleecker E, Moore W, et al. Unsupervised phenotyping of severe asthma research program participants using expanded lung data. J Allergy Clin Immunol 2014; 133:1280–1288.
Denton E, Price DB, Tran TN, et al. Cluster analysis of inflammatory biomarker expression in the international severe asthma registry. J Allergy Clin Immunol Pract 2021; 9:2680–2688.e7.
Wu W, Bang S, Bleecker ER, et al. Multiview cluster analysis identifies variable corticosteroid response phenotypes in severe asthma. Am J Respir Crit Care Med 2019; 199:1358–1367.
Phipatanakul W, Mauger DT, Sorkness RL, et al. Severe Asthma Research Program. Effects of age and disease severity on systemic corticosteroid responses in asthma. Am J Respir Crit Care Med 2017; 195:1439–1448.
Di Bona D, Crimi C, D’Uggento AM, et al. Effectiveness of benralizumab in severe eosinophilic asthma: distinct sub-phenotypes of response identified by cluster analysis. Clin Exp Allergy 2022; 52:312–323.
Bleecker ER, FitzGerald JM, Chanez P, et al. SIROCCO Study Investigators. Efficacy and safety of benralizumab for patients with severe asthma uncontrolled with high-dosage inhaled corticosteroids and long-acting β2-agonists (SIROCCO): a randomised, multicentre, placebo-controlled phase 3 trial. Lancet 2016; 388:2115–2127.
FitzGerald JM, Bleecker ER, Nair P, et al. CALIMA Study Investigators. Benralizumab, an antiinterleukin-5 receptor α monoclonal antibody, as add-on treatment for patients with severe, uncontrolled, eosinophilic asthma (CALIMA): a randomised, double-blind, placebo-controlled phase 3 trial. Lancet 2016; 388:2128–2141.
Yamada H, Nakajima M, Matsuyama M, et al. Identification of distinct phenotypes related to benralizumab responsiveness in patients with severe eosinophilic asthma. PLoS One 2021; 16:e0248305.
Dávila I, Campo P, Cimbollek S, et al. Cluster sub-analysis of patients with severe asthma who responded to omalizumab. J Investig Allergol Clin Immunol 2022; [Online ahead of print].
Senna G, Guerriero M, Paggiaro PL, et al. SANI-Severe Asthma Network in Italy: a way forward to monitor severe asthma. Clin Mol Allergy 2017; 15:
FitzGerald JM, Tran TN, Alacqua M, et al. International severe asthma registry (ISAR): protocol for a global registry. BMC Med Res Methodol 2020; 20:
Fontanella S, Cucco A, Custovic A. Machine learning in asthma research: moving toward a more integrated approach. Expert Rev Respir Med 2021; 15:609–621.
Choy G, Khalilzadeh O, Michalski M, et al. Current applications and future impact of machine learning in radiology. Radiology 2018; 288:318–328.

Auteurs

Danilo Di Bona (D)

Department of Emergency and Organ Transplantation, School and Chair of Allergology and Clinical Immunology, University of Bari Aldo Moro, Bari.

Federico Spataro (F)

Department of Emergency and Organ Transplantation, School and Chair of Allergology and Clinical Immunology, University of Bari Aldo Moro, Bari.

Palma Carlucci (P)

Department of Emergency and Organ Transplantation, School and Chair of Allergology and Clinical Immunology, University of Bari Aldo Moro, Bari.

Giovanni Paoletti (G)

IRCCS Humanitas Research Hospital.
Department of Biomedical Sciences, Humanitas University, Milan, Italy.

Giorgio W Canonica (GW)

IRCCS Humanitas Research Hospital.
Department of Biomedical Sciences, Humanitas University, Milan, Italy.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH