Dupilumab improves upper and lower airway disease control in chronic rhinosinusitis with nasal polyps and asthma.

BACKGROUND: Chronic rhinosinusitis with nasal polyps (CRSwNP) and type 2 asthma share the same inflammatory pathophysiology and are frequent comorbidities. Dupilumab, a fully human monoclonal antibody, blocks the shared receptor component for interleukin 4 and interleukin 13, which are key and central drivers of type 2 inflammation. OBJECTIVE: We report the effect of dupilumab vs placebo on outcome measures of the upper and lower airways and health-related quality of life (HRQoL) in the pooled population of patients with CRSwNP and comorbid asthma from the phase 3 SINUS-24 (NCT02912468) and SINUS-52 (NCT02898454) studies. METHODS: In these randomized, double-blind, placebo-controlled trials, patients received subcutaneous dupilumab 300 mg (n = 438) or placebo (n = 286) every 2 weeks on a background of mometasone furoate nasal spray. Changes from baseline at week 24 in the upper and lower airway outcome measures are reported. RESULTS: Of the 724 patients randomized, 428 (59.1%) had comorbid asthma. In patients with asthma at week 24, dupilumab vs placebo improved the nasal polyp score (-2.04), patient-reported nasal congestion score (-1.04), Lund-Mackay computed tomography scan score (-6.43), peak nasal inspiratory flow (46.15 L/min), and 22-item sinonasal outcome test score (-21.42; all P < .001). The forced expiratory volume in 1 second and 6-item asthma control questionnaire scores were also markedly improved with dupilumab vs placebo. The most common adverse events (nasopharyngitis, headache, injection-site erythema, worsening of nasal polyposis, and asthma) were more frequent with placebo than dupilumab. CONCLUSION: Dupilumab improved upper and lower airway outcome measures and HRQoL in patients with severe CRSwNP and comorbid asthma and was well tolerated. TRIAL REGISTRATION: ClinicalTrials.gov Identifiers: NCT02912468 (SINUS-24) and NCT02898454 (SINUS-52).

Sequential designs for individualized dosing in phase I cancer clinical trials.

This paper addresses dose finding in clinical trials where individuals exhibit biologic characteristics that alter the toxicity risks of the individuals. In these situations, instead of determining a dose that works for every patient, the trial aims to identify a dosing algorithm that prescribes dose according to the patient's biomarker or pharmacokinetic expression. Specifically, we aim to dose patients around a pre-specified level of area under the curve of irinotecan concentration using the patients' baseline phenotypes that predict drug clearance. We propose least squares recursion procedures to estimate the dosing algorithm sequentially with an aim to treat patients in the trial around the true unknown dosing algorithm, and introduce a novel application of an eigenvalue theory that guarantees convergence to the true dosing algorithms. Our simulation study demonstrates that using an eigenvalue constraint improves the efficiency of the recursion by as large as 40%, while concentrating in-trial patient allocation around the true dosing algorithm. We also provide practical guidance on design calibration, and design future irinotecan studies based on data from our first trial.