Respiratory adverse event profiles in cystic fibrosis placebo subjects in short- and long-term inhaled therapy trials

Potential implicit bias in attribution of adverse events in randomized controlled trials in cystic fibrosis

INTRODUCTION

Although work to date in cystic fibrosis (CF) has elucidated frequencies and characteristics of adverse events, the accuracy of attribution of relatedness to study drug by investigators has not been assessed. We aimed to determine whether there was an association of attribution by group allocation in CF clinical trials.

METHODS

We conducted a secondary analysis from 4 CF trials of all persons who experienced an AE. Our primary outcome was the odds of an AE related to active study drug and predictor of interest was the treatment allocation. We constructed a multivariable generalized estimating equation model allowing for repeated measures.

RESULTS

A total of 785 subjects (47.5% female, mean age 12 years) had 11,974 AEs, of which 430 were serious. AE attribution was greater with receipt of active study drug as compared to placebo but did not reach statistical significance (OR 1.38, 95% CI 0.98-1.82). Significantly associated factors included female sex (OR 0.58, 95% 0.39-0.87), age (OR 1.24, 95% CI 1.06-1.46) and baseline lung function (per 10%, OR 1.16, 95% CI 1.05-1.28).

CONCLUSION

In our large study, there was a non-significant but greater odds of AE attribution (a key element of clinical trial reporting) to active study drug based on assigned treatment to study drug or control which suggests that there is a trend in physicians to attribute blinded safety data to the active drug. Interestingly, females were less likely to have AE attribution to study drug and warrants further work in development and validation of monitoring guidelines and processes.

Rates of adverse and serious adverse events in children with cystic fibrosis

BACKGROUND

Cystic fibrosis (CF) is an autosomal recessive disease characterized by chronic sinopulmonary symptoms and chronic gastrointestinal symptoms that begins in infancy. Children with CF are increasingly being included in clinical trials. In order to fully evaluate the impact of new therapies in future clinical trials, an understanding of baseline adverse event (AE) rates in children with CF is needed. To address this, we determined the rates of common AEs in pediatric patients with CF who participated in two clinical trials.

METHODS

We reviewed AEs for placebo recipients in the AZ0004 study and inhaled tobramycin recipients in the Early Pseudomonas Infection Control (EPIC) clinical trial. AEs were categorized based on Medical Dictionary for Regulatory Activities (MedDRA) coding classifications and pooled into common, batched AE descriptors. AE rates were estimated from negative binomial models according to age groups, severity of lung disease, and season.

RESULTS

A total of 433 children had 8,266 total AEs reported, or 18.1 (95% CI 17.0, 19.2) AEs per person per year. Respiratory AEs were the most commonly reported AEs, with a rate of 7.6 events per person-year. The total SAE rate was 0.33 per person per-year. Cough was the most commonly reported respiratory AE, with 61% of subjects reporting at least one episode of cough within 4 months. The rate ratio of any AE was higher in Spring, Fall, and Winter, compared with Summer.

CONCLUSIONS

AEs occur commonly in pediatric CF clinical trial participants. Season of enrollment could affect AE rates.

Cystic Fibrosis Human Organs-on-a-Chip

Cystic fibrosis (CF) is an autosomal recessive disease caused by mutations in the cystic fibrosis transmembrane regulator (CFTR) gene: the gene product responsible for transporting chloride and bicarbonate ions through the apical membrane of most epithelial cells. Major clinical features of CF include respiratory failure, pancreatic exocrine insufficiency, and intestinal disease. Many CF animal models have been generated, but some models fail to fully capture the phenotypic manifestations of human CF disease. Other models that better capture the key characteristics of the human CF phenotype are cost prohibitive or require special care to maintain. Important differences have been reported between the pathophysiology seen in human CF patients and in animal models. These limitations present significant limitations to translational research. This review outlines the study of CF using patient-derived organs-on-a-chip to overcome some of these limitations. Recently developed microfluidic-based organs-on-a-chip provide a human experimental model that allows researchers to manipulate environmental factors and mimic in vivo conditions. These chips may be scaled to support pharmaceutical studies and may also be used to study organ systems and human disease. The use of these chips in CF discovery science enables researchers to avoid the barriers inherent in animal models and promote the advancement of personalized medicine.

Risk of hemoptysis in cystic fibrosis clinical trials: A retrospective cohort study

BACKGROUND

Cystic fibrosis (CF) is characterized by airway infection and inflammation resulting in respiratory complications including hemoptysis. The objectives of this study were to characterize the risk of hemoptysis attributable to underlying disease and in the presence of standard of care therapy.

METHODS

This retrospective cohort study estimated hemoptysis rates overall and by relevant risk factors utilizing adverse event data from longitudinal prospective CF clinical trials.

RESULTS

Of the 1008 participants, 73% were ≤18 years old; of 929 with available spirometry, 27% had an FEV1<70% predicted. During the average 8.2 months of follow-up, 8% experienced ≥1 hemoptysis events (95% CI: 6%, 10%). Of the 125 events, 76% were mild in severity and only 9% were serious. Hemoptysis rates were greater among adults than children, those with FEV1<70% predicted, and participants infected with P. aeruginosa but not with S. aureus.

CONCLUSIONS

Hemoptysis is a common adverse event among CF clinical trial participants, and particularly in adults with more severe lung disease. These results can be used to predict event occurrence in future clinical trials.