Influence of allergic status and nasal polyposis on long-term Benralizumab response in eosinophilic severe asthma.

Objective: It is unclear whether Benralizumab effectiveness in severe eosinophilic asthma can be influenced by nasal polyposis (NP) or allergic status associations. We evaluated whether Benralizumab long-term efficacy in asthma outcomes could be different in subjects with atopy (SAEA) compared to the effectiveness in those without allergies (SNAEA) and in individuals with NP compared to those without NP. Methods: This observational retrospective study considered 95 consecutive patients divided into allergic (SAEA; n:65[68.4%]; skin prick tests positive [SPT] and/or IgE values ≥100 UI/mL), and non-allergic (SNAEA; n:30[31.6%], SPT negative and normal IgE levels<100 UI/mL). Overall population was also divided into two groups according to NP presence (NP+:39[41%] and NP-:56[59%]). Benralizumab treatment mean was19.7±7.2 months (range 12-35). Results: No differences in Benralizumab effectiveness were found in asthma outcomes in patients with/without NP. SNOT-22 improvement was higher in NP+ (-22±24) compared to NP- groups (6.33±15.5;p=0.055). FEV1 (16.33±19.22%), ACT(7.45±3.95) increases and frequency of SABA use (3.37±4.99) reduction were higher in SAEA compared to what obtained in non-allergic subjects (FEV1:8.15±15.6%,p=0.043; ACT:4.89±3.57,p=0.005; SABA use:-1.16±1.84;p=0.015). 93.8% of SAEA patients whereas only 72.2% of SNAEA individuals reduced OC doses at least half after Benralizumab (p=0.035). These results were partially confirmed by linear regression models showing associations between allergic status and FEV1, ACT and SABA use changes (ß=8.37;p=0.048, ß=2.056;p=0.033 and ß=-2.184;p=0.042 respectively). Conclusion: Benralizumab effectiveness in asthma appears to be independent of NP presence. The allergic eosinophilic disease, compared to just eosinophilic asthma, may be a more severe phenotype. Benralizumab may have greater efficacy in SAEA on some outcomes.

Severe asthma and long-term Benralizumab effectiveness in real-life.

OBJECTIVE: Long-term efficacy of Benralizumab in real life is not clearly known. We assessed the long-term effectiveness persistence to anti-IL-5R treatment in a group of severe eosinophilic asthmatics. PATIENTS AND METHODS: We retrospectively analyzed 95 individuals affected by severe asthma (36 males  ̶ 37.9%; mean age 58.1 ± 12.2) treated with Benralizumab (mean time 19.7 ± 7.2 months, range 12-35). Outcomes were evaluated at the beginning and at the end of patients' treatment periods. RESULTS: Mean baseline blood eosinophils were 897.5 ± 720.1 cells/µL (11 ± 5.6%) decreasing to 7.4 ± 20.6 cells/µL (0.97 ± 0.26%; p < 0.0001) after Benralizumab. FENO likewise decreased from 63.9 ± 68.4 to 28.4 ± 23.6 ppb, while FEV1% significantly improved (p < 0.0001). Mean FEF25-75 also increased from 45.8 ± 24.6% to 60.7 ± 24.6%, whereas RAW dropped from 202.15 ± 109.6% to 135.2 ± 54.75% (p < 0.0001). Also, lung volumes greatly decreased. ACT/ACQ significantly improved, while exacerbations number fell from 4.1 ± 2.4, before anti-IL-5R, to 0.33 ± 0.77, after treatment (p < 0.0001). Rhinitis severity levels and SNOT-22 also changed favorably. Patients that took long-term OCs were 71.6% before treatment, decreasing to 23.2% after Benralizumab (p < 0.0001), with an OCs dose reduction from 14.8 ± 8.9 to 1.45 ± 2.8 mg/day (p < 0.0001). 51.6% of subjects used SABA as needed before Benralizumab, falling to 4.2% after treatment. Several patients showed a reduction of ICS doses, SABA use and maintenance therapy step-down. Clinical/biological response with anti-IL-5R remained constant or even improved in terms of exacerbations or maintenance therapy reductions over time. On the contrary, FEF25-75% improvement slowed down in the long-term. No relationship was found between baseline blood eosinophil number and therapeutic response. CONCLUSIONS: Long-term Benralizumab effectiveness persistence in all outcomes in real life was confirmed.

Efficacy and safety of first-line checkpoint inhibitors-based treatments for non-oncogene-addicted non-small-cell lung cancer: a systematic review and meta-analysis.

BACKGROUND: Frontline immune checkpoint inhibitors (ICI)-based regimens in non-oncogene-addicted non-small-cell lung cancer (NSCLC) have been deeply investigated. To rank the available therapeutic options, we carried out a systematic review and Bayesian meta-analysis. METHODS: A comprehensive search for randomized controlled trials (RCTs) of ICI regimens, and a pairwise and a network meta-analysis (NMA) with an all-comers and a stratified strategy were conducted. Endpoints were overall survival (OS), progression-free survival (PFS), objective response rate (ORR) and treatment-related adverse events (TRAEs). RESULTS: Nineteen RCTs involving 17 treatment regimens were included. For the all-comers population, pembrolizumab/chemotherapy (CT) and cemiplimab were most likely the best treatments. For programmed death-ligand 1 (PD-L1) <1% nivolumab/ipilimumab with/without CT, for PD-L1 >1% and 1%-49% pembrolizumab/CT and for PD-L1 >50% cemiplimab ranked first for OS. In non-squamous (NSQ), pembrolizumab with/without CT ranked first for OS; cemiplimab ranked worse than the unselected population. In squamous (SQ), pooled hazard ratio (HR) showed a better chance in improving efficacy for combination strategy, while monotherapy did not, except for cemiplimab that ranked second. Atezolizumab/CT/bevacizumab ranked first in most subgroups for PFS. Direct comparison showed a non-statistically significant benefit of ICI regimens for the liver metastases cohort in OS, with a good ranking for pembrolizumab/CT and atezolizumab/bevacizumab/CT. Regarding brain metastases, all ICI regimens demonstrated an improvement in OS and PFS compared to CT. Nivolumab/ipilimumab/CT ranked better in this subset. CONCLUSIONS: Our meta-analysis updated on the most recent findings demonstrates that different ICI treatments rank differently in specific NSCLC settings (histology, biomarker and clinical presentation) offering a novel challenging scenario for clinical decision making and research planning.