Clinical trials report no increased malignancy risk with benralizumab in severe asthma; however, trial populations are highly selective. To better define real-world safety, malignancy risk was compared among patients treated with benralizumab, other (non-benralizumab) biologics, or non-biologic therapies. Data from adults with severe asthma accrued to the International Severe Asthma Registry (ISAR) and a United States severe asthma registry (CHRONICLE) between 1 November 2017, and 31 December 2023, were analyzed. Cohorts were defined by exposure to benralizumab, other asthma biologics, and non-exposure to biologic therapies. First new-onset malignancies were assessed. Incidence per 1000 person-years (PY) and adjusted incidence rate ratios (aIRR) were estimated with an inverse probability-weighted Poisson model, with propensity scores incorporating demographics, comorbidities, baseline asthma medication, systemic steroid use, and relevant medical history, with residual adjustment for age, sex, body mass index, region, and smoking. Among 12,493 patients, 43,434.8 PY were accrued (benralizumab, 7749.1 PY; other biologics, 21,054.1 PY; non-biologics, 14,631.6 PY), with a median (range) follow-up of 3.3 (0–6.2) years across cohorts. Seventy-five patients reported new malignancies: benralizumab, 17 (0.7

BACKGROUND:The way in which risk predictors combine and contribute to severe asthma exacerbations may differ between clinical trials and real-world settings. RESEARCH QUESTION:How do the interactive pathways of risk predictors leading to severe asthma exacerbations compare under clinical trials vs real-world settings? STUDY DESIGN AND METHODS:The analysis involved 345 patients with severe asthma from the placebo arms of 2 international randomized controlled trials (RCTs), compared with 6,814 biologic-naïve patients from the International Severe Asthma Registry (ISAR). Sixteen key risk predictors, including demographics, biomarkers, lung function, health care use, exacerbation history, long-term oral corticosteroid use, asthma control, and nasal polyps, were covered. The outcome was the occurrence of severe asthma exacerbations over the 365 days after study enrollment. Bayesian networks (BNs), obtained from machine learning combined with expert knowledge, elucidated significant interplay processes of risk predictors that led to severe asthma exacerbations. External validation was performed in each cohort. RESULTS:The RCTs revealed 44 significant arcs (ie, probabilistic interdependency) between 16 risk factors, whereas the ISAR showed 170. Despite this difference, the main downstream prediction pathways were consistent across both settings, with 2 key pathways: total serum IgE level influenced blood eosinophils to predict future severe exacerbations, and severe exacerbation history directly predicted future severe exacerbations. In external validation, RCT-BN generalized well to ISAR patients (area under the receiver operating characteristic curve, 0.68), whereas ISAR-BN underperformed in RCT patients (area under the receiver operating characteristic curve, 0.50), and ISAR-BN demonstrated better calibration. INTERPRETATION:Our results show that the core pathways predicting severe asthma exacerbations were similar in both RCTs and real-world settings, with comparable predictive performance.

Purpose:Time to biologic initiation for the treatment of severe asthma (SA) is affected by many factors, including ease of access to biologics, which is often subject to approval by regulatory authorities and reimbursement criteria by relevant agencies. We investigated the association between ease of biologic access (using the biologic accessibility score [BACS] as a proxy) and post-biologic asthma outcomes, including remission. Methods:This ecological study, using data from CHRONICLE (a US severe asthma registry), the International Severe Asthma Registry (ISAR), and the Optimum Patient Care Research Database (OPCRD), included patients with SA from 21 countries. Associations at the country level, between BACS, a composite score of prescription criteria for biologics in SA, and the proportion of patients with a favorable asthma outcome 1-year post-biologic in each setting (ie ISAR country or in the CHRONICLE or OPCRD datasets) were tested. Several definitions of favorable outcome were used, including proportion of patients who achieved clinical remission (defined using 2, 3 and 4 domains), experienced no exacerbations, had well- or partly controlled asthma, a percent predicted forced expiratory volume in 1 second (ppFEV1) or percent predicted peak expiratory flow rate (ppPEFR) ≥80%, and no long-term oral corticosteroid (LTOCS) use. Results:A total of 9,183 patients were included. A higher BACS (as a proxy of easier access to biologics) was associated with a higher likelihood of achieving clinical remission (p≤0.001), no exacerbations (p<0.001), well- or partly controlled asthma (p=0.047), a ppFEV1 or ppPEFR ≥80% (p=0.004), and no need for LTOCS (p=0.045) 1 year post-biologic initiation. Conclusion:Easier access to biologics for patients with SA, a prerequisite for shorter time-to-initiation, was associated with a greater probability of achieving clinical remission and other favorable asthma outcomes. Initiating biologics earlier in the asthma disease course may help unlock greater therapeutic potential in SA. These findings warrant confirmation in additional studies to further establish the causal relationship between biologic accessibility, timing of initiation, and clinical outcomes in SA.

BACKGROUND:Severe asthma (SA) is associated with frequent exacerbations and high treatment costs. OBJECTIVES:To develop and validate an individualized risk calculator for severe exacerbations in SA, and evaluate its clinical utility for guiding personalized clinical decisions. METHODS:Patients with SA were identified from combined data from the International Severe Asthma Registry (2015-2022) and NOVEL observational longiTudinal studY (2016-2023) across 30 countries and regions. The prediction end point was the 12-month risk of 1 or more or 2 or more severe exacerbations. Using expert input and Bayesian network analysis, 11 routinely measured predictors were identified, measured within the past 12 months. A mixed-effects, zero-inflated negative binomial model was developed, adjusting for between-country variability and biologic drop-in effects. Internal-external cross-validation was performed using the natural clustering by country settings. RESULTS:Data from 9911 patients with SA were used. Essential predictors included age, sex, past 12-month severe exacerbations, asthma control, chronic rhinosinusitis, FEV1 to forced vital capacity ratio, percent predicted FEV1, blood eosinophils, fractional exhaled nitric oxide, and long-term oral corticosteroid and macrolide use. The model also adapted setting-specific baseline risks. In the internal-external cross-validation, across broad geographical and health care variability, the model showed excellent calibration and informative, generalizable discrimination (pooled area under the time-dependent receiver-operating characteristics curve of 0.63 [95% CI, 0.60-0.66] for ≥1 and 0.68 [95% CI, 0.64-0.72] for ≥2 exacerbations). Decision curve analysis showed clear net benefit across risk thresholds. CONCLUSIONS:The Risk of Exacerbation in Severe Asthma model quantifies SA exacerbation risk using routinely available predictors and demonstrates potential clinical utility.

Introduction Although important learnings come from traditionally designed large prospective asthma cohorts, highly restrictive inclusion and exclusion criteria limit generalisability to clinical practice. Moreover, small sample sizes for important disease subtypes, narrow scope of clinical data collection and limited biomarker assessments reduce the power of some studies to detect important and diverse longitudinal disease courses. The Real-world and Genomic data-based Asthma Insights through Network Analysis (REGAIN) study takes a novel approach to asthma cohort development by employing a pragmatic definition of asthma and simplified study procedures for biospecimen and data collection. REGAIN will produce a large scale, real-world, longitudinal clinical and molecular description of asthma powered to characterise and compare clinically relevant asthma subtypes. This design will provide insights on distinct longitudinal trajectories of disease, predictors of response to therapies and likelihood of clinical remission, all of which should help guide asthma management.Methods and analysis REGAIN is a clinical observational retrospective and prospective cohort study designed to determine large scale, real-world longitudinal clinical and molecular descriptions of asthma according to types of treatment, level of asthma control and inflammatory biology based on clinical biomarkers. Key questions include predictors of change in asthma control as well as timing and durability of clinical remission on biological therapy. To complement these clinical insights, REGAIN will produce one of the largest multiscale data sets in asthma that will include demographic and clinical features, inflammatory biomarkers, responses to therapy with inhaled steroids and other inhaled controllers with or without asthma biologics, and serial airway epithelium and peripheral blood transcriptomics and proteomics. REGAIN targets enrolment of 780 participants with asthma fitting one of five prespecified asthma subtypes with the aim of better characterising under-studied groups and allowing comparative analyses to elucidate important differential therapeutic responses and clinical trajectories. We target enrolment of 400 healthy controls to provide a healthy state molecular description of the tissues sampled in REGAIN participants with asthma. Participants with asthma are followed prospectively for 18 months with assessment of longitudinal clinical status including prospective clinical data collection, integration of electronic medical record data and serial biospecimen collection at 6 and 18 months. Participants with asthma starting treatment with asthma biologics undergo additional clinical assessment and biospecimen sampling at 3 months to track early clinical and molecular response to therapy. Healthy participants without asthma are evaluated cross-sectionally on enrolment without longitudinal follow-up in order to compare molecular profiles for airway epithelium and blood. An optional study component for participants with asthma employs a mobile phone application, digital inhaler monitors and home digital peak flow measurements and contributes data on real-time medication use, serial lung function and geolocated environmental data relevant to asthma.Ethics and dissemination The REGAIN protocol and all amendments were approved by The Icahn School of Medicine at Mount Sinai Program for Protection of Human Subjects (PPHS19-0358), and all participants provided written informed consent. Enrolment began in November 2019 and was completed in February 2024. Results will be presented at local, national and international meetings, and results will be submitted to peer-reviewed journals for consideration for publication.Trial registration number NCT06623435.

RATIONALE: Clinical remission is increasingly the goal of asthma treatment, however therapeutic response and mechanisms underpinning remission are poorly understood. To advance mechanistic understanding of treatment response in asthma, we conducted the RE al-world and G enomic data-based A sthma I nsights through N etwork analysis (REGAIN) study, a prospective, observational real world asthma study combining clinical, molecular, and digital health data. METHODS: REGAIN enrolled adult patients with asthma in 5 subgroups based on type 2 (T2) inflammation and treatment. We generated genetic and transcriptomic data from nasal brushing samples to complement clinical data. We constructed a multi-modal Bayesian causal network of asthma which was combined with weighted gene co-expression network analysis in a hybrid graph to capture dynamic cell-cell interactions related to clinical outcomes. Clinical remission at 6 months was defined as Asthma Control Test (ACT) ≥ 20, no exacerbations, and no use of chronic oral corticosteroids for asthma. RESULTS: We recruited 791 individuals in the initial analysis (528 asthma and 263 healthy). Participants with asthma included (1) T2-high on inhaled therapy (n=178), (2) T2-high on biologics (n=134), (3) T2-low on inhaled therapy (n=105), (4) T2-high newly initiated on biologics (T2denovo, n=58), and (5) T2-high who failed treatment with at least 2 biologics (n=35). From an integrated network analysis, we identified a central role of goblet, ciliated, and mucous-ciliated cells in asthma severity and remission in response to biologics. Higher baseline ciliated cell frequency was observed in patients with asthma achieving remission vs. non-remission in T2denovo subgroup. Further, mucous-ciliated cell frequency increased in those achieving remission vs. non-remission after 6 months of biologics treatment. To better understand the role of mucous-ciliated cells in therapeutic response, we employed air-liquid interface cultured airway epithelial cell organoids chronically exposed to IL-13 to model airway epithelium changes associated with chronic severe asthma. Removal of IL-13, to model therapeutic intervention, led to resolution of goblet cell metaplasia via differentiation of goblet cells into mucous-ciliated cells and was associated with restoration of ciliation, confirming our network-derived hypothesis. Conclusions: Mucous-ciliated cell frequency increased in those achieving remission vs. those in non-remission with biologic therapy, implying that mucous-ciliated epithelial cells may play a role in the restoration of airway homeostasis in asthma. Collectively, these data suggest that following biologic-induced suppression of airway inflammation, ciliated epithelial cell restoration is required to achieve clinical remission, establishing a framework for understanding mechanisms underlying asthma remission

Background: Asthma characterization using blood eosinophil count (BEC) (among other biomarkers and clinical indices) is recommended in severe asthma (SA), but the masking effect of oral corticosteroids (OCS), makes this challenging. Aim: Our aim was to explore the effect of OCS use (both intermittent [iOCS] and long-term [LTOCS]) prior to biologic initiation on SA phenotype and biomarker profile in real-life and to characterize the burden of SA among patients prescribed LTOCS by biomarker profile. Methods: This was a registry-based cohort study, including data from 23 countries collected between 2003 and 2023 and shared with the Internatonal Severe Asthma Registry (ISAR). Patients with SA were categorized into 3 cohorts, those with: (i) no prescription for OCS, (ii) prescription(s) for iOCS (ie, ≤90 days in previous 12-months, usually short courses for exacerbations), and (iii) prescriptions for LTOCS (ie, >90 days in previous 12-months). Biomarker distribution (ie, BEC, fractional exhaled nitric oxide [FeNO], and total Immunoglobulin E [IgE]) were quantified in the year prior to biologic initiation in patients with SA according to OCS prescription pattern. Phenotypes were characterized for those prescribed LTOCS according to BEC cut-off (<150 and ≥ 150 cells/μL). Results: Of 4305 patients included, 5.0% (n = 215), 54.1% (n = 2330) and 40.9% (n = 1760) were prescribed no OCS, iOCS, and LTOCS, respectively. The BEC distribution varied by prescription pattern and LTOCS dose (<5 mg to ≥20 mg/day); BEC was <150 cells/μL in 28.6% (n = 369/1288) of LTOCS patients, compared to 19.5% (n = 284/1460) of iOCS patients and 14.0% (n = 21/150) of those in the no OCS group. Median BEC was also significantly lower in the LTOCS versus the iOCS group (310 vs 400 cells/μL; p < 0.001). A similar pattern was noted for IgE, but not FeNO. Among LTOCS patients with BEC <150 cells/μL, 39.9% experienced ≥4 exacerbations, 75.1% had uncontrolled asthma symptoms and 55.9% had evidence of persistent airflow obstruction (compared with 40.9%, 76.2% and 59.5% of those with BEC ≥150 cells/μL, respectively). Conclusions: OCS, whether prescribed intermittently or long term, affect BEC distribution potentially leading to heightened risk of phenotype misclassification and influencing subsequent treatment decisions. FeNO appears to be less susceptible to OCS-induced suppression. Disease burden was high for those in the LTOCS group and was high independent of dose and BEC. Our findings highlight the importance of considering OCS use, even intermittent use, when characterizing SA, and suggests the need for earlier phenotyping and alternative treatment strategies for LTOCS patients with low BEC.

BACKGROUND:Asthma with low levels of type 2 (T2) biomarkers is poorly understood. OBJECTIVE:To characterize severe asthma phenotypes and compare changes in asthma outcomes from pre- to postbiologic treatment along a gradient of T2 involvement. METHODS:This was a registry-based cohort study including data from 24 countries. Biomarker distribution (blood eosinophil count, fractional exhaled nitric oxide, and IgE) was quantified before biologic initiation. Clusters were identified using a 5-component Gaussian finite mixture model and phenotypically characterized. Changes in asthma and health care utilization outcomes between 1-year pre- and postbiologic initiation were compared between clusters and by biologic class. RESULTS:Among 3675 patients, 5 biomarker clusters were identified along a gradient of T2 involvement: cluster A with the lowest T2 involvement (16.4%), cluster B (20.4%), cluster C (22.9%), cluster D (30.3%), and cluster E with the highest T2 involvement (10.0%). In multivariable analysis, biologic use was associated with improved outcomes in all clusters but tended to be better at the higher end of the T2 spectrum. For example, patients in cluster C had a significantly greater increase in forced expiratory volume in 1 second compared with cluster A (difference 0.16 L [95% confidence interval: 0.08, 0.25]; P < .001). The odds of uncontrolled asthma were approximately 0.6 for all clusters compared with cluster A. Overall, exacerbation rates were lower, and greater improvements in lung function and asthma control were noted for anti-IL-5/5 receptor (R) (but not anti-IgE or anti-IL-4Rα) for all clusters compared with cluster A. CONCLUSION:T2-targeting biologics have utility in the management of asthma with low T2 involvement, but more effective therapies are needed. Further research is warranted to identify specific pathogenic pathways at the lower end of the T2 spectrum that can be effectively targeted by biologics.

The International Severe Asthma Registry (ISAR) was established in 2017 to advance the understanding of severe asthma and its management, thereby improving patient care worldwide. As the first global registry for adults with severe asthma, ISAR enabled individual registries to standardize and pool their data, creating a comprehensive, harmonized dataset with sufficient statistical power to address key research questions and knowledge gaps. Today, ISAR is the largest repository of real-world data on severe asthma, curating data on nearly 35,000 patients from 28 countries worldwide, and has become a leading contributor to severe asthma research. Research using ISAR data has provided valuable insights on the characteristics of severe asthma, its burdens and risk factors, real-world treatment effectiveness, and barriers to specialist care, which are collectively informing improved asthma management. Besides changing clinical thinking via research, ISAR aims to advance real-world practice through initiatives that improve registry data quality and severe asthma care. In 2024, ISAR refined essential research variables to enhance data quality and launched a web-based data acquisition and reporting system (QISAR), which integrates data collection with clinical consultations and enables longitudinal data tracking at patient, center, and population levels. Quality improvement priorities include collecting standardized data during consultations and tracking and optimizing patient journeys via QISAR and integrating primary/secondary care pathways to expedite specialist severe asthma management and facilitate clinical trial recruitment. ISAR envisions a future in which timely specialist referral and initiation of biologic therapy can obviate long-term systemic corticosteroid use and enable more patients to achieve remission.

RATIONALE: Despite greater understanding of asthma heterogeneity and pathogenesis, real-world longitudinal disease trajectories in the era of biologics and predictors of treatment response remain unclear. The RE al-world and G enomic data-based A sthma I nsights through N etwork analysis (REGAIN) study is an 18-month prospective observational study that combines clinical, genomic, transcriptomic, and mobile health data to address these key knowledge gaps. METHODS: REGAIN enrolled adult patients with mild to severe asthma recruited from the Mount Sinai Health System and National Jewish Health. Subgroups were defined by type 2 (T2) inflammation, asthma control, and treatment at baseline. Study visits occurred at 0-, 6-, and 18-months for all asthma subgroups and an additional 3-month visit for those initiated on biologics. Clinical remission was defined as Asthma Control Test (ACT) ≥ 20, absence of exacerbations and systemic corticosteroids for asthma. RESULTS: Our analyses included 528 adults with asthma. Subgroups were defined as those with T2-high asthma who had: 1) well controlled symptoms on inhaled therapy (T2-high STEP, n=178), 2) partly to well controlled symptoms on biologics (T2-high bio, n=134), 3) uncontrolled symptoms and newly initiated on biologics (T2-high denovo, n=58), and 4) any level of symptom control after previously failing at least 2 biologics (T2-high failed, n=35). A fifth subgroup included those with T2-low asthma with any level of symptom control on inhaled therapy (T2-low STEP, n=105). At baseline, T2-high failed had significantly higher proportions of obesity, gastroesophageal reflux disease (GERD), lower educational status, and longer duration of disease (all P <.01). At 6-months, most participants in T2-high STEP (73.2%) and T2-high bio (64.6%) retained control. In contrast, T2-high failed represented a refractory subgroup with 92.3% uncontrolled at 6 months. For T2-high denovo, after initiation of biologic therapy, 45.5% remained uncontrolled, 25% achieved moderate control, and 29.5% achieved clinical remission. Of those achieving clinical remission, 30.7% were able to significantly reduce inhaled therapy. Clinical remission at 6 months predicted persistence of remission at 18 months. CONCLUSIONS: Early clinical remission at 6 months predicted sustained remission. T2-high failed represented a persistently refractory group despite treatment with different biologics and highlights potentially important risk factors including longer duration of disease, comorbidities of GERD and obesity, and impacts from social determinants of health represented by lower educational status. Understanding these real-world longitudinal disease trajectories enhance our precision medicine insights, allowing improved identification of those at highest risk in addition to those likely to achieve and retain clinical remission.

BACKGROUND:Accurate risk prediction of exacerbations is pivotal in severe asthma management. Multiple risk factors are at play, but the pathway of risk prediction remains unclear. RESEARCH QUESTION:How do the interplays of clinically relevant predictors lead to severe exacerbations in patients with severe asthma? STUDY DESIGN AND METHODS:Patients with severe asthma (n = 6,814, aged ≥ 18 years), biologic naive, were identified from the Severe Asthma Registry (2017-2021). Relevant predictors covered demographics, lung function, inflammation biomarkers, health care use, medications, exacerbation history, and comorbidities. A Bayesian network, representing the prediction process of severe exacerbations, was obtained by combining expert knowledge and machine learning algorithms. Internal validation was performed. The proposed influence diagram integrated decision and utility nodes into the prediction pathway. RESULTS:The Bayesian network analysis revealed that blood eosinophil count, fractional exhaled nitric oxide level, and FEV1 directly influenced the transition between prior and future severe exacerbations. The presence of chronic rhinosinusitis indirectly affected such transition by directly influencing blood eosinophil count, fractional exhaled nitric oxide, and % predicted FEV1. Macrolide use independently affected history of exacerbations to influence future severe asthma exacerbations. Model discrimination was moderate in 10-fold cross-validation and leave-1-country-out cross-validation, and model calibration was high in train-test data. INTERPRETATION:This study identified an essential prediction pathway of severe exacerbation, which involves the influence of chronic rhinosinusitis on the immediate predictors of risk transition from current to future severe asthma exacerbations. Macrolide use was another essential prediction pathway identified. The findings support shared clinical decision-making in severe asthma treatment.

Rationale: Although clinical trials have documented the oral corticosteroid (OCS)-sparing effect of biologics in patients with severe asthma, little is known about whether this translates to a reduction of new-onset OCS-related adverse outcomes. Objective: To compare the risk of developing new-onset OCS-related adverse outcomes between biologic initiators and noninitiators. Methods: This was a longitudinal cohort study using pooled data from the International Severe Asthma Registry (ISAR; 16 countries) and the Optimum Patient Care Research database (OPCRD; United Kingdom). For biologic initiators, the index date was the date of biologic initiation. For noninitiators, it was the date of enrollment (for ISAR) or a random medical appointment date (for OPCRD). Inverse probability of treatment weighting was used to improve comparability between groups, and weighted Cox proportional hazard models were used to estimate the hazard ratios (HRs) of developing OCS-related adverse outcomes for up to 5 years from the index date. Measurements and Main Results: A total of 42,908 patients were included. Overall, 27.3% and 4.7% of biologic initiators and noninitiators were long-term OCS users (daily intake ⩾90 consecutive days in year before the index date), with a mean prednisolone-equivalent daily dose of 10.2 mg and 6.2 mg, respectively. Compared with noninitiators, biologic initiators had decreased rate of developing any OCS-related adverse outcome (HR [95% confidence interval (CI)]: 0.82 [0.72-0.93]; P = 0.002), primarily driven by reduced rate of developing diabetes (0.62 [0.45-0.87]; P = 0.006), major cardiovascular events (0.65 [0.44-0.97]; P = 0.034), and anxiety and/or depression (0.68 [0.55-0.85]; P = 0.001). There were no significant differences in the rates of new-onset cataract (HR, 0.77 [95% CI, 0.47-1.25]), sleep apnea (HR, 0.82 [95% CI, 0.78-1.41]), or other OCS-related adverse outcomes assessed (e.g., osteoporosis). The results were consistent across both datasets. Conclusions: Our findings highlight the role for biologics in preventing new-onset OCS-related adverse outcomes in patients with severe asthma.

BACKGROUND:For severe asthma (SA) management, real-world evidence on the effects of biologic therapies in reducing the burden of oral corticosteroid (OCS) use is limited. OBJECTIVE:To estimate the efficacy of biologic initiation on total OCS (TOCS) exposure in patients with SA from real-world specialist and primary care settings. METHODS:From the International Severe Asthma Registry (ISAR, specialist care) and the Optimum Patient Care Research Database (OPCRD, primary care, United Kingdom), adult biologic initiators were identified and propensity score-matched with non-initiators (ISAR, 1:1; OPCRD, 1:2). The impact of biologic initiation on TOCS (including bursts for exacerbations) daily dose in the first- and second-year follow-up period was estimated using multivariable generalized linear models. RESULTS:Among 5,663 patients (ISAR 48%, OPCRD 52%), the odds ratios (ORs) of biologic initiators achieving TOCS cessation in the first and second years of follow-up were 2.38 (95% CI, 1.87-3.04) and 2.11 (95% CI, 1.65-2.70), whereas the ORs of low (0- to 5-mg) TOCS intake were 1.62 (95% CI, 1.40-1.86) and 1.40 (95% CI, 1.21-1.61), respectively. Compared with non-initiators, biologic initiators had a substantially higher chance of achieving greater than 75% reduction from baseline (OR [95% CI] = 2.35 [2.06-2.68] and 1.53 [1.35-1.73] in first and second years, respectively). These findings remained persistent and robust when analyses were repeated with one country setting removed at a time. CONCLUSIONS:Biologic initiation in patients with SA led to substantial reduction in TOCS exposure, particularly in the first year. Future analyses will explore the impact on OCS-related adverse health events.

RATIONALE: Asthma is a heterogeneous airway disease treated using various approaches based on disease severity. To uncover regulatory mechanisms and advance precision treatment of asthma, we developed a framework that leverages the rich gene correlation structures of the comprehensive, multimodal, longitudinal genomic, transcriptomic, and clinical data from over 750 participants in the RE al-world and G enomic data-based A sthma I nsights through N etwork analysis (REGAIN) study. METHODS: To understand mechanisms underlying therapeutic response, we employed a data-driven, unbiased approach that made no a priori assumptions about disease processes to integrate airway gene expression and genotype data while simultaneously considering clinical measurements over time. To this end, we developed a novel hybrid network approach that leveraged the rich information of coexpression networks and the statistically inferred causality inherent in Bayesian networks. We constructed networks from baseline visit data on all study participants and additional networks from longitudinal molecular data specific to remission and non-remission in response to biologic treatment, and then characterized the core regulatory processes represented in these data-driven network components. We next examined the network components associated with asthma and drug response to identify components comprised of co-regulated sets of genes differentially expressed between patients achieving and not-achieving remission on biologics as well as changes in the network structure itself that are driven by differentially coregulated groups of genes. RESULTS: Remission correlated with network modules that changed in both their level of expression and connectivity within and between modules. Systematic winnowing of the network modules most significantly related to remission based on objective statistical thresholds allowed us to focus on a core set of five modules most related to remission in nasal brushing epithelium. The five core airway modules represent five cell types involved in squamous metaplasia, goblet metaplasia, and mucociliary clearance in airway epithelium. Furthermore, our novel hybrid network approach allowed us to characterize inter-module interactions and their association with remission. We detected a significant gain of connectivity between mucous ciliated and goblet cells associated with remission, suggesting transitions between goblet and mucous-ciliated cells during remission. Conclusions: Our novel hybrid network approach demonstrated the dynamics of mucous ciliated cells during remission. This network-centric method offers novel insights regarding common pathways for asthma remission and new targets for asthma therapeutic development can be generalized to investigate other respiratory diseases through similar comprehensive profiling of accessible nasal epithelium.

Management of allergic and immunologic diseases over the past several years has transformed with the advent of targeted and biologic therapeutics. These medications help tackle common clinical issues such as poorly controlled asthma, atopic dermatitis, chronic spontaneous urticaria, and nasal polyposis, but also provide options for patients with rare conditions including hereditary angioedema, periodic fever syndromes and monogenic immunodeficiencies. In this chapter, we review disease pathogenesis, mechanisms of action of targeted therapeutics, emerging options for treatment, as well as safety and efficacy data. Our goal is to help clinicians make informed decisions with their patients.