Sanofi (China)

<bold>Background:</bold> Bronchiectasis (BE) involves chronic systemic and local airway inflammation, and blood- and sputum- biomarkers can reflect systemic and localized airway inflammation. <bold>Objective:</bold> To identify optimal methods of sputum samples for downstream multi-omics biomarker analysis and compare inflammation profiles between sputum and serum in patients (pts) with BE. <bold>Methods:</bold> Frozen Raw sputum (FRS), frozen sputum plugs (FSP), and freshly processed sputum (FS) was obtained from BE pts and healthy participants. The supernatant was used for proteomic analysis, while cells were used for RNA extraction and RNA quality assessment. Serum samples were collected from BE pts. OLINK panel was used for proteomics-based profiling of immune biomarkers. RNA quality was assessed using RNA integrity number (RIN) and DV200 (% of RNA fragments >200 bp) values. <bold>Results:</bold> Saliva contamination hinders biomarker analysis suggesting that FRS may not be a suitable sample source. Only FS yielded high-quality RNA (RIN>7, DV200>30%). Similar inflammatory protein profile in FS and FSP suggests that both can be used in clinical settings. Proteomic profiles of BE pts reveal distinct patterns between sputum and serum. For instance, the neutrophil chemoattractant CXCL8, and the pro-inflammatory receptor OLR1 were significantly elevated in sputum vs serum (P<0.0001). Nevertheless, chemokines including CCL11, CCL13, and CCL8, were predominantly elevated in serum (P<0.0001 for all). <bold>Conclusion:</bold> Freshly processed sputum is optimal for multi-omics biomarker analysis. Analysis of blood and sputum aids in assessment of inflammation and identification of biomarkers that reflect the multifaceted nature of BE.

Abstract RATIONALE: Achieving meaningful improvements in asthma control remains an unmet need. Rilzabrutinib is an investigational oral Bruton's tyrosine kinase inhibitor. In a phase 2 proof-of-concept study, there was a reduction in loss of asthma control (LOAC) events with rilzabrutinib vs placebo at Week 12 in participants with poorly controlled moderate-to-severe asthma. Here, rescue medication use during the treatment period is reported. METHODS: This was a 12-week, double-blind, placebo-controlled, 2-staggered-cohort study in adults with moderate-to-severe asthma who were poorly controlled on inhaled corticosteroid (ICS)/long-acting β2 adrenergic agonist (LABA) therapy (NCT05104892). 196 eligible participants were randomized 1:1 first to rilzabrutinib 800mg (400mg twice daily; N=32) or placebo (N=32), and then to rilzabrutinib 1200mg (400mg three times daily; N=64) or placebo (N=68) added to medium-to-high-dose ICS/LABA, which was withdrawn Weeks 4-9. The primary endpoint was the proportion of participants with a LOAC event over 12 weeks. The use of ≥6 additional reliever puffs of a short-acting β2 adrenergic agonist (SABA) in a 24-hour period vs baseline on 2 consecutive days was one criterion for a LOAC event. Exploratory outcomes were the use of ≥2-5 additional reliever puffs of SABA for symptom relief vs baseline on 2 consecutive days over 12 weeks. RESULTS: At Week 12, the reduction in LOAC events observed with rilzabrutinib was mostly driven by a decrease in the proportion of participants requiring rescue medication defined as ≥6 additional reliever puffs, with 6.3% vs 12.5% for rilzabrutinib 800mg vs placebo (relative risk reduction [RRR]: 50%) and 0% vs 10.3% for rilzabrutinib 1200mg vs placebo (RRR: 100%), respectively. An overall reduction in participants requiring SABA with rilzabrutinib was observed across different thresholds over 12 weeks: 9.4% vs 21.9% for rilzabrutinib 800mg vs placebo (RRR: 57%) and 6.3% vs 23.5% for rilzabrutinib 1200mg vs placebo (RRR: 73%), respectively, required ≥3 additional reliever puffs; 6.3% vs 18.8% for rilzabrutinib 800mg vs placebo (RRR: 66%) and 4.7% vs 17.6% for rilzabrutinib 1200mg vs placebo (RRR: 73%), respectively, required ≥4 additional reliever puffs; and 6.3% vs 12.5% for rilzabrutinib 800mg vs placebo (RRR: 50%) and 1.6% vs 14.7% for rilzabrutinib 1200mg vs placebo (RRR: 89%), respectively, required ≥5 additional reliever puffs. CONCLUSIONS: Rilzabrutinib treatment in participants with poorly controlled moderate-to-severe asthma was associated with a reduction in LOAC events, which was mainly driven by a reduction in SABA use. Overall, the use of SABA was reduced with rilzabrutinib vs placebo in both cohorts over 12 weeks.

To evaluate early glycaemic control (glycated haemoglobin [HbA1c] < 7.0% [<53.0 mmol/mol], fasting plasma glucose [FPG] ≤ 7.0 mmol/L or postprandial glucose [PPG] ≤ 10.0 mmol/L) with iGlarLixi versus insulin glargine 100 U/mL (Gla-100) in Asian people with suboptimally controlled type 2 diabetes (T2D) on oral antidiabetic drugs (OADs) in LixiLan-O-AP or basal insulin (BI) ± OADs in LixiLan-L-CN. This post hoc analysis evaluated changes from baseline to Week 12 in HbA1c, FPG and PPG, hypoglycaemia incidence and the rates of target HbA1c achievement at Weeks 8 and 12. Median time to glycaemic control (i.e., time to 50% achieving target HbA1c, FPG or PPG) was also assessed. At Week 12, mean HbA1c reductions were greater with iGlarLixi versus Gla-100 in LixiLan-O-AP (-1.6% vs. -1.1% [-17.0 vs. -12.0 mmol/mol]) and LixiLan-L-CN (-1.3% vs. -0.5% [-13.9 vs. -5.4 mmol/mol]). PPG reductions were greater with iGlarLixi, while FPG reductions and hypoglycaemia incidence were similar. At Weeks 8 and 12, more participants had achieved target HbA1c or PPG with iGlarLixi versus Gla-100 in both studies. Median time to achieve HbA1c and PPG targets was shorter with iGlarLixi versus Gla-100 in LixiLan-O-AP (85 vs. 126 days and 84 vs. 167 days) and LixiLan-L-CN (85 vs. 239 days and 85 days vs. not estimable); median time to achieve FPG target was similar in LixiLan-O-AP (57 vs. 57 days) and LixiLan-L-CN (29 vs. 30 days). In Asian people with T2D suboptimally controlled on OADs or BI, iGlarLixi provided comprehensive earlier glycaemic control than Gla-100.

BackgroundGallbladder cancer (GBC) is a highly aggressive malignancy, with limited survival profiles after curative surgeries. This study aimed to develop a practical model for predicting the postoperative overall survival (OS) in GBC patients.MethodsPatients from three hospitals were included. Two centers (N = 102 and 100) were adopted for model development and internal validation, and the third center (N = 85) was used for external testing. Univariate and stepwise multivariate Cox regression were used for feature selection. A nomogram for 1-, 3-, and 5-year postoperative survival rates was constructed accordingly. Performance assessment included Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves. Kaplan-Meier curves were utilized to evaluate the risk stratification results of the nomogram. Decision curves were used to reflect the net benefit.ResultsEight factors, TNM stage, age-adjusted Charlson Comorbidity Index (aCCI), body mass index (BMI), R0 resection, blood platelet count, and serum levels of albumin, CA125, CA199 were incorporated in the nomogram. The time-dependent C-index consistently exceeded 0.70 from 6 months to 5 years, and time-dependent ROC revealed an area under the curve (AUC) of over 75% for 1-, 3-, and 5-year survival. The calibration curves, Kaplan-Meier curves and decision curves also indicated good prognostic performance and clinical benefit, surpassing traditional indicators TNM staging and CA199 levels. The reliability of results was further proved in the independent external testing set.ConclusionsThe novel nomogram exhibited good prognostic efficacy and robust generalizability in GBC patients, which might be a promising tool for aiding clinical decision-making.

Abstract Background To develop a machine learning model tool for identifying postoperative patients with major bleeding based on electronic medical record system. Methods This study used the available information in the National Health and Medical Big Data (Eastern) Center in Jiangsu Province of China. We randomly selected the medical records of 2,000 patients who underwent in-hospital tumor resection surgery between January 2018 and December 2021 from the database. Physicians classified each note as present or absent for a major bleeding event during the postoperative hospital stay. Feature engineering was created by bleeding expressions, high frequency related expressions and quantitative logical judgment. Logistic regression (LR), K-nearest neighbor (KNN), and convolutional neural network(CNN) were developed and trained using the 1600-note training set. The main outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for each model. Results Major bleeding was present in 4.31% of training set and 4.75% of test set. For the training set, LR method has the sensitivity of 1.0000 and specificity of 0.8152 while CNN method has the sensitivity of 0.9710 and specificity of 0.9027. LR and CNN methods both perform well in the sensitivity and specificity in the test set. Although the KNN method has high specificity in the training set and test set, its sensitivity is very low in both sets. Conclusions Both LR method and CNN method perform well in identifying major bleeding occurring in postoperative patients with malignant tumors, with high sensitivity and specificity.