CATALYST a randomised early phase adaptive trial for new drugs for SARS-CoV-2+ patients
- Funded by Department of Health and Social Care / National Institute for Health and Care Research (DHSC-NIHR), UK Research and Innovation (UKRI)
- Total publications:2 publications
Grant number: MC_PC_20007
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$552,150Funder
Department of Health and Social Care / National Institute for Health and Care Research (DHSC-NIHR), UK Research and Innovation (UKRI)Principal Investigator
Prof. Pamela KearnsResearch Location
United KingdomLead Research Institution
University of BirminghamResearch Priority Alignment
N/A
Research Category
Therapeutics research, development and implementation
Research Subcategory
Phase 2 clinical trial
Special Interest Tags
N/A
Study Type
Clinical
Clinical Trial Details
Clinical Trial, Phase II
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
CATALYST is a randomised, open-label, multi-arm, adaptive, phase II trial to assess the potential efficacy of drugs in the early phase setting, rapidly determining which new therapies should be considered for larger-scale testing within ongoing national phase III platform trials. SARS-CoV-2 virus can cause severe pneumonia and multi-organ failure by sequestration of virally-infected pulmonary and circulating macrophages, dendritic cells and abnormal antibody response to viral antigens, which promote an injurious, proinflammatory microenvironment. Licensed and novel drugs are now available with potential to target this harmful proinflammatory response and/or directly act as antivirals, allowing a healthy adaptive immunity to emerge to suppress the viral infection and reduce viral replication respectively. The primary outcome measure, the ratio of oxygen saturation to fractional inspired oxygen concentration (SpO2/FiO2), will be modelled using Bayesian multi-level models that allow for nesting of the repeated measures data. Specifically, posterior probabilities for the treatment and treatment/time interaction covariates will be used to conduct decision making. Data will be analysed in subsets; each treatment against the control group, with modelling conducted on each dataset. Any important covariates will be incorporated accordingly into the model structure. All comparisons will be performed with regard to control arm data.
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