Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions

• Published in: Nature communications Vol. 13; no. 1; p. 1980
• Main Authors: Arsène, Simon, Couty, Claire, Faddeenkov, Igor, Go, Natacha, Granjeon-Noriot, Solène, Šmít, Daniel, Kahoul, Riad, Illigens, Ben, Boissel, Jean-Pierre, Chevalier, Aude, Lehr, Lorenz, Pasquali, Christian, Kulesza, Alexander
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.04.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2397; 692/308/153; 692/699/255/2514; Clinical trials; Clinical Trials as Topic; Communicable Disease Control - methods; Computer applications; Coronaviruses; COVID-19; COVID-19 - prevention & control; Disease transmission; Epidemic models; Epidemics; Epidemiology; Humanities and Social Sciences; Humans; Infections; Mimicry; multidisciplinary; Pandemics; Pharmaceuticals; Prophylaxis; Respiration Disorders; Respiratory diseases; Respiratory tract; Respiratory tract diseases; Respiratory Tract Infections - epidemiology; SARS-CoV-2; Science; Science (multidisciplinary); Statistical analysis; Viral diseases; Viral infections; Virus Diseases - epidemiology
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-29534-8

Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI. Ratio-based efficacy metrics are only impacted under strict lockdown whereas absolute benefit already is with intermediate NPIs (eg. mask-wearing). Consequently, despite NPI, trials may meet their relative efficacy endpoints (provided recruitment hurdles can be overcome) but are difficult to assess with respect to clinical relevance. These results advocate to report a variety of metrics for benefit assessment, to use adaptive trial design and adapted statistical analyses. They also question eligibility criteria misaligned with the actual disease burden. A computational mechanistic viral infection model and trial simulation advocates for adaptation of respiratory disease clinical trials whose chances of success and interpretability are being degraded under COVID-19 pandemic mitigation measures.