Repeatability of induced sputum measurements in moderate to severe asthma

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.

Remote FEV1 Monitoring in Asthma Patients: A Pilot Study

Forced expiratory volume in one second (FEV₁ ) is a critical parameter for the assessment of lung function for both clinical care and research in patients with asthma. While asthma is defined by variable airflow obstruction, FEV₁ is typically assessed during clinic visits. Mobile spirometry (mSpirometry) allows more frequent measurements of FEV₁ , resulting in a more continuous assessment of lung function over time and its variability. Twelve patients with moderate asthma were recruited in a single-center study and were instructed to perform pulmonary function tests at home twice daily for 28 days and weekly in the clinic. Daily and mean subject compliances were summarized. The agreement between clinic and mobile FEV₁ was assessed using correlation and Bland-Altman analyses. The test-retest reliability for clinic and mSpirometry was assessed by interclass correlation coefficient (ICC). Simulation was conducted to explore if mSpirometry could improve statistical power over clinic counterparts. The mean subject compliance with mSpirometry was 70% for twice-daily and 85% for at least once-daily. The mSpirometry FEV₁ were highly correlated and agreed with clinic ones from the same morning (r = 0.993) and the same afternoon (r = 0.988) with smaller mean difference for the afternoon (0.0019 L) than morning (0.0126 L) measurements. The test-retest reliability of mobile (ICC = 0.932) and clinic (ICC = 0.942) spirometry were comparable. Our simulation analysis indicated greater power using dense mSpirometry than sparse clinic measurements. Overall, we have demonstrated good compliance for repeated at-home mSpirometry, high agreement and comparable test-retest reliability with clinic counterparts, greater statistical power, suggesting a potential for use in asthma clinical research.

Severe bronchial asthma in children: a review of novel biomarkers used as predictors of the disease

Severe asthma or therapy-resistant asthma in children is a heterogeneous disease that affects all age-groups. Given its heterogeneity, precision in diagnosis and treatment has become imperative, in order to achieve better outcomes. If one is thus able to identify specific patient phenotypes and endotypes using the appropriate biomarkers, it will assist in providing the patient with more personalized and appropriate treatment. However, there appears to be a huge diagnostic gap in severe asthma, as there is no single test yet that accurately determines disease phenotype. In this paper, we review the published literature on some of these biomarkers and their possible role in bridging this diagnostic gap. We also highlight the cellular and molecular mechanisms involved in severe asthma, in order to show the basis for the novel biomarkers. Some markers useful for monitoring therapy and assessing airway remodeling in the disease are also discussed. A review of the literature was conducted with PubMed to gather baseline data on the subject. The literature search extended to articles published within the last 40 years. Although biomarkers specific to different severe asthma phenotypes have been identified, progress in their utility remains slow, because of several disease mechanisms, the variation of biomarkers at different levels of inflammation, changes in relying on one test over time (eg, from sputum eosinophilia to blood eosinophilia), and the degree of invasive tests required to collect biomarkers, which limits their applicability in clinical settings. In conclusion, several biomarkers remain useful in recognizing various asthma phenotypes. However, due to disease heterogeneity, identification and utilization of ideal and defined biomarkers in severe asthma are still inconclusive. The development of novel serum/sputum-based biomarker panels with enhanced sensitivity and specificity may lead to prompt diagnosis of the disease in the future.

Evaluation of New Drugs for Asthma and COPD: Endpoints, Biomarkers and Clinical Trial Design

There remains a considerable need to develop novel therapies for patients with asthma and chronic obstructive pulmonary disease (COPD). The greatest challenge at the moment is measuring the effects of novel anti-inflammatory drugs, as these drugs often cause only small effects on lung function. Measurements that demonstrate the pharmacological and clinical effects of these drugs are needed. Furthermore, we now recognise that only subgroups of patients are likely to respond to these novel drugs, so using biomarkers to determine the clinical phenotype most suitable for such therapies is important. An endotype is a subtype of a (clinical) condition defined by a distinct pathophysiological mechanism. An endotype-driven approach may be more helpful in drug development, enabling drugs to be targeted specifically towards specific biological mechanisms rather than clinical characteristics. This requires the development of biomarkers to define endotypes and/or to measure drug effects. This newer approach should continue alongside efforts to optimise the measurement of clinical endpoints, including patient-reported outcome measurements, required by drug regulatory authorities.

Longitudinal stability of asthma characteristics and biomarkers from the Airways Disease Endotyping for Personalized Therapeutics (ADEPT) study

Background

Asthma is a biologically heterogeneous disease and development of novel therapeutics requires understanding of pathophysiologic phenotypes. There is uncertainty regarding the stability of clinical characteristics and biomarkers in asthma over time. This report presents the longitudinal stability over 12 months of clinical characteristics and clinically accessible biomarkers from ADEPT.

Methods

Mild, moderate, and severe asthma subjects were assessed at 5 visits over 12 months. Assessments included patient questionnaires, spirometry, bronchodilator reversibility, fractional exhaled nitric oxide (FENO), and biomarkers measured in induced sputum.

Results

Mild (n = 52), moderate (n = 55), and severe (n = 51) asthma cohorts were enrolled from North America and Western Europe. For all clinical characteristics and biomarkers, group mean data showed no significant change from visit to visit. However, individual data showed considerable variability. FEV1/FVC ratio showed excellent reproducibility while pre-bronchodilator FEV1 and FVC were only moderately reproducible. Of note bronchodilator FEV1 reversibility showed low reproducibility, with the nonreversible phenotype much more reproducible than the reversible phenotype. The 7-item asthma control questionnaire (ACQ7) demonstrated moderate reproducibility for the combined asthma cohorts, but the uncontrolled asthma phenotype (ACQ7 > 1.5) was inconstant in mild and moderate asthma but stable in severe asthma. FENO demonstrated good reproducibility, with the FENO-low phenotype (FENO < 35 ppb) more stable than the FENO-high phenotype (FENO ≥ 35 ppb). Induced sputum inflammatory phenotypes showed marked variability across the 3 sputum samples taken over 6 months.

Conclusions

The ADEPT cohort showed group stability, individual stability in some parameters e.g. low FEV1/FVC ratio, and low FENO, but marked individual variability in other clinical characteristics and biomarkers e.g. type-2 biomarkers over 12 months. This variability is possibly related to seasonal variations in climate and allergen exposure, medication changes and acute exacerbations. The implications for patient selection strategies based on clinical biomarkers may be considerable.

Molecular phenotyping and biomarker development: are we on our way towards targeted therapy for severe asthma?

Although different phenotypes of severe asthma can be identified, all are characterized by common symptoms. Due to their heterogeneity, they exhibit differences in pathogenesis, etiology and clinical responses to therapeutic approaches. The identification of distinct molecular phenotypes to define severe asthmatic patients will allow us to better understand the pathophysiology of the disease and thus to more precisely target the treatment for each patient. To achieve this goal, a systematic search for new, reliable and stable biomarkers specific for each phenotype is essential. This review focuses on the current known molecular phenotypes of severe asthma and highlights the need for biomarkers that could (either alone or in combination) be predictive of the treatment outcome.

Biomarkers and severe asthma: a critical appraisal

Severe asthma (SA) is a clinically and etiologically heterogeneous respiratory disease which affects among 5–10 % of asthmatic patients. Despite high-dose therapy, a large patients percentage is not fully controlled and has a poor quality of life. In this review, we describe the biomarkers actually known in scientific literature and used in clinical practice for SA assessment and management: neutrophils, eosinophils, periostin, fractional exhaled nitric oxide, exhaled breath condensate and galectins. Moreover, we give an overview on clinical and biological features characterizing severe asthma, paying special attention to the potential use of these ones as reliable markers. We finally underline the need to define different biomarkers panels to select patients affected by severe asthma for specific and personalized therapeutic approach.