Predictive factors of response to inhaled corticosteroids in newly diagnosed asthma: A real-world observational study

Promising treatment biomarkers in asthma

Asthma is a highly heterogenous disease which researchers over time have attempted to classify into different phenotypes and endotypes to improve diagnosis, prognosis and treatment. Earlier classifications based on reaction to environmental allergens, age, sex and lung function have evolved, and today, the use of precision medicine guided by biomarkers offers new perspectives on asthma management. Identifying biomarkers that may reveal the underlying pathophysiology of the disease will help to select the patients who will benefit most from specific treatments. This review explores the classification of asthma phenotypes and focuses on the most recent advances in using biomarkers to guide treatment.

Upper airway disease diagnosis as a predictive biomarker of therapeutic response to biologics in severe asthma

Asthma is a heterogeneous disease sharing airway instability but with different biology, risk factors, and response-to-therapy patterns. Biologics have revolutionized the one-size-fits-to-all approach to personalized medicine in severe asthma (SA), which relies on the identification of biomarkers that define distinct endotypes. Thus, blood eosinophils and, to some extent, exhaled nitric oxide (FeNO) can predict the response to approved anti-type 2 (T2) biologics (anti-IgE, anti-IL-5, and anti-IL-4R alpha), whereas age at onset and comorbidities such as anxiety/depression, obesity, reflux, and upper airway disease (UAD) also influence therapeutic responses in SA. In this article, focusing on the predictive value of biomarkers for the therapeutic response to biologics in SA, we first summarize the level of prediction achieved by T2 biomarkers (blood eosinophils, FeNO) and then review whether data support the predictive value of upper airway diagnosis on such outcomes. Post hoc analysis of most studies with T2 biologics suggests that chronic rhinosinusitis with nasal polyps (CRSwNP) and, to a lower extent, allergic rhinitis may help in predicting clinical response. Considering that T2 biologics are now also approved for the treatment of severe CRSwNP, diagnosis of upper airway disease is a key step in determining eligibility for such therapy.

Measurement of Blood Eosinophils in Asthma and Chronic Obstructive Pulmonary Disease

Eosinophils are important effector cells in airway inflammation, as pleiotropy and heterogeneity can be linked to various pathophysiologies in asthma and chronic obstructive pulmonary disease (COPD). Sputum eosinophils can reflect the heterogeneity of airway inflammation, and owing to their traits, blood eosinophils can be a surrogate and potential biomarker for managing both conditions. Blood eosinophils are activated via the stimulation of type 2 cytokines, such as interleukin (IL)-5, IL-4/13, granulocyte-macrophage colony-stimulating factor, IL-33, and thymic stromal lymphopoietin. There is sufficient evidence to support the relationship between the blood eosinophil count and clinical outcomes, including pulmonary function decline, exacerbations, all-cause mortality, and treatment response to inhaled corticosteroids and biologics. Thus, there is growing interest in the use of blood eosinophils for the management of these diseases. Compiling recent evidence, we herein review the significance of measuring blood eosinophils in asthma and COPD.

Recent insights in the role of biomarkers in severe asthma management

Contemporary asthma management requires a proactive and individualized approach, combining precision diagnosis and personalized treatment. The introduction of biologic therapies for severe asthma to everyday clinical practice, increases the need for specific patient selection, prediction of outcomes and monitoring of these costly and long-lasting therapies. Several biomarkers have been used in asthma in disease identification, prediction of asthma severity and prognosis, and response to treatment. Novel advances in the area of personalized medicine regarding disease phenotyping and endotyping, encompass the development and application of reliable biomarkers, accurately quantified using robust and reproducible methods. The availability of powerful omics technologies, together with integrated and network-based genome data analysis, and microbiota changes quantified in serum, body fluids and exhaled air, will lead to a better classification of distinct phenotypes or endotypes. Herein, in this review we discuss on currently used and novel biomarkers for the diagnosis and treatment of asthma.

Treatment Response Biomarkers in Asthma and COPD

Chronic obstructive pulmonary disease (COPD) and asthma are two of the most common chronic diseases worldwide. Both diseases are heterogenous and complex, and despite their similarities, they differ in terms of pathophysiological and immunological mechanisms. Mounting evidence supports the presence of several phenotypes with various responses to treatment. A systematic and thorough assessment concerning the diagnosis of both asthma and COPD is crucial to the clinical management of the disease. The identification of different biomarkers can facilitate targeted treatment and monitoring. Thanks to the presence of numerous immunological studies, our understanding of asthma phenotypes and mechanisms of disease has increased markedly in the last decade, and several treatments with monoclonal antibodies are available. There are compelling data that link eosinophilia with an increased risk of COPD exacerbations but a greater treatment response and lower all-cause mortality. Eosinophilia can be considered as a treatable trait, and the initiation of inhaled corticosteroid in COPD patients with eosinophilia is supported in many studies. In spite of advances in our understanding of both asthma and COPD in terms pathophysiology, disease mechanisms, biomarkers, and response to treatment, many uncertainties in the management of obstructive airways exist.

Asthma Phenotyping in Primary Care: Applying the International Severe Asthma Registry Eosinophil Phenotype Algorithm Across All Asthma Severities

BACKGROUND

We developed an eosinophil phenotype gradient algorithm and applied it to a large severe asthma cohort (International Severe Asthma Registry).

OBJECTIVE

We sought to reapply this algorithm in a UK primary care asthma cohort, quantify the eosinophilic phenotype, and assess the relationship between the likelihood of an eosinophilic phenotype and asthma severity/health care resource use (HCRU).

METHODS

Patients age 13 years and older with active asthma and blood eosinophil count or 1 or greater, who were included from the Optimum Patient Care Research Database and the Clinical Practice Research Datalink, were categorized according to the likelihood of eosinophilic phenotype using the International Severe Asthma Registry gradient eosinophilic algorithm. Patient demographic, clinical and HCRU characteristics were described for each phenotype.

RESULTS

Of 241,006 patients, 50.3%, 22.2%, and 21.9% most likely (grade 3), likely (grade 2), and least likely (grade 1), respectively, had an eosinophilic phenotype, and 5.6% had a noneosinophilic phenotype (grade 0). Compared with patients with noneosinophilic asthma, those most likely to have an eosinophilic phenotype tended to have more comorbidities (percentage with Charlson comorbidity index of ≥2: 28.2% vs 6.9%) and experienced more asthma attacks (percentage with one or more attack: 24.8% vs 15.3%). These patients were also more likely to have asthma that was difficult to treat (31.1% vs 18.3%), to receive more intensive treatment (percentage on Global Initiative for Asthma 2020 step 4 or 5: 44.2% vs 27.5%), and greater HCRU (eg, 10.8 vs 7.9 general practitioner all-cause consultations per year).

CONCLUSIONS

The eosinophilic asthma phenotype predominates in primary care and is associated with greater asthma severity and HCRU. These patients may benefit from earlier and targeted asthma therapy.

The pharmacoeconomics of the state-of-the-art drug treatments for asthma: a systematic review

Asthma is a chronic disease characterized by significant morbidities and mortality, with a large impact on socio-economic resources and a considerable burden on health-care systems. In the standard care of asthma, inhaled corticosteroids (ICS) associated with long-acting β-adrenoceptor agonists (LABA) are a reliable and often cost-effective choice, especially if based on the single inhaler therapy (SIT) strategy; however, in a subset of patients it is not possible to reach an adequate asthma control. In these cases, it is possible to resort to other pharmacologic options, including corticosteroids (OCS) or biologics. Unfortunately, OCS are associated with important side effects, whilst monoclonal antibodies (mAbs) allow excellent results, even if far more expensive. Up to now, the economic impact of asthma has not been compared with equivalent indicators in several studies. In fact, a significant heterogeneity of the cost analysis is evident in literature, for which the assessment of the real cost-effectiveness of asthma therapies is remarkably complex. To maximize the cost-effectiveness of asthma strategies, especially of biologics, attention must be paid on phenotyping and identification of predictors of response. Several studies were included, involving comparative analysis of drug treatments for asthma, comparative analysis of the costs and consequences of therapies, measurement and evaluation of direct drug costs, and the reduction of health service use. The initial research identified 389 articles, classified by titles and abstracts. A total of 311 articles were excluded as irrelevant and 78 articles were selected. Pharmacoeconomic studies on asthma therapies often report conflicting data also due to heterogeneous indicators and different populations examined. A careful evaluation of the existing literature is extremely important, because the scenario is remarkably complex, with an attempt to homogenize and interpret available data. Based on these studies, the improvement of prescriptive appropriateness and the reduction of the use of healthcare resources thanks to controller medications and to innovative therapies such as biologics partially reduce the economic burden of these treatments. A multidisciplinary stakeholder approach can also be extremely helpful in deciding between the available options and thus optimizing healthcare resources.