Efficacy and Safety of Inhaled Glycopyrronium Bromide in COPD: A Randomized, Parallel Group, Dose-Ranging Study (GLIMMER).
Edward KerwinGregory FeldmanJames PearleLuis De La CruzMelinda EdwardsCarolyn BeaudotGeorge GeorgesPublished in: COPD (2021)
This Phase II, randomized, parallel group study was conducted as part of US regulatory requirements to identify the most appropriate dose of the long-acting muscarinic antagonist glycopyrronium bromide (GB) for use in a single-inhaler triple-therapy combination with the inhaled corticosteroid beclomethasone dipropionate plus the long-acting β2-agonist formoterol fumarate. Eligible subjects were adults with COPD and post-bronchodilator forced expiratory volume in 1 s (FEV1) 40-80% predicted. Subjects were randomized to receive inhaled double-blind GB 6.25, 12.5, 25 or 50 µg or placebo, all twice daily (BID), or open-label tiotropium 18 µg once daily for six weeks. The primary objective was to evaluate the efficacy of GB versus placebo in terms of FEV1 area under the curve between 0 and 12 h at Week 6. Of 733 subjects randomized, 682 (93.0%) completed the study. For the primary endpoint, all GB doses were superior to placebo (p < 0.05), with a dose-response up to 25 µg BID, and 25 and 50 µg BID both superior to 6.25 µg BID (p < 0.05). Results for the secondary spirometry endpoints were consistent with the primary endpoint. Overall, the efficacy of GB 25 and 50 µg BID was broadly consistent with that of tiotropium. The incidence of adverse events, both overall and for the most common preferred terms, was low and similar in all treatment groups, including placebo (overall, 22.3-29.3%). Based on the totality of the efficacy and safety data, the optimal GB dose is 25 µg BID.
Keyphrases
- double blind
- placebo controlled
- phase ii
- phase iii
- open label
- clinical trial
- study protocol
- phase ii study
- cystic fibrosis
- chronic obstructive pulmonary disease
- lung function
- squamous cell carcinoma
- stem cells
- big data
- transcription factor
- physical activity
- mesenchymal stem cells
- deep learning
- bone marrow
- machine learning
- electronic health record