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Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions
Simon Arsène1, Claire Couty1, Igor Faddeenkov1
1Novadiscovery SA, Lyon, France.
Abstract:
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.
Insights
Non-pharmaceutical interventions (NPIs) like mask-wearing significantly impact respiratory tract infection (RTI) clinical trial results. Researchers recommend diverse metrics and adaptive designs to assess treatment efficacy amidst changing disease patterns.
Area of Science:
- Epidemiology
- Clinical Trial Design
- Infectious Disease Modeling
Background:
- Non-pharmaceutical interventions (NPIs) for COVID-19 disrupted seasonal viral epidemic patterns, profoundly affecting respiratory disease clinical trials.
- Uncertainty in viral transmission dynamics poses challenges for designing and interpreting clinical trial results for respiratory tract infections (RTIs).
Purpose of the Study:
- To develop and utilize an epidemiological model to simulate the impact of NPIs on RTI clinical trial outcomes.
- To evaluate the efficacy of OM-85 as an RTI prophylaxis under various simulated NPI scenarios.
Main Methods:
- Developed a coupled epidemiological and mechanistic model of viral RTI episodes.
- Employed a virtual population and in silico trials to explore reduced viral transmission mimicking NPIs.
- Assessed the impact of different NPI levels (e.g., mask-wearing, lockdowns) on efficacy metrics.
Main Results:
- Ratio-based efficacy metrics were primarily affected by strict lockdowns, while absolute benefit was impacted by intermediate NPIs.
- Clinical trials may achieve relative efficacy endpoints despite NPIs, but assessing clinical relevance becomes challenging.
- Recruitment hurdles and misaligned eligibility criteria can further complicate trial interpretation.
Conclusions:
- Reporting a variety of benefit assessment metrics is crucial for evaluating treatments during periods of NPIs.
- Adaptive trial designs and statistical analyses are recommended to navigate NPI-induced uncertainties.
- Eligibility criteria for respiratory infection trials should be re-evaluated to align with current disease burden and transmission dynamics.
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