The COVID-19 pandemic has had an unprecedented and disruptive impact on people’s health and lives worldwide. In addition to burdening people’s health in the short-term in the form of infection, illness, and mortality, there has been an enormous negative impact on clinical research. Clinical trials experienced challenges in ensuring patient safety and enrolling new patients throughout the pandemic. Here, we investigate and quantify the negative impact that the COVID-19 pandemic has industry-sponsored clinical trials, both in the USA and worldwide. We find a negative correlation between the severity of the COVID-19 pandemic and clinical trial screening rate, with the relationship being strongest during the first three months of the pandemic compared to the entire duration of the pandemic. This negative statistical relationship holds across therapeutic areas, across states in the USA despite the heterogeneity of responses at the state-level, and across countries. This work has significant implications for the management of clinical trials worldwide in response to the fluctuating severity of COVID-19 moving forward and for future pandemics.

The COVID-19 pandemic has had an unprecedented and disruptive impact on people’s health and lives worldwide. In addition to burdening people’s health in the short-term in the form of infection, illness, and mortality, there has been an enormous negative impact on clinical research. Clinical trials experienced challenges in ensuring patient safety and enrolling new patients throughout the pandemic. Here, we investigate and quantify the negative impact that the COVID-19 pandemic has industry-sponsored clinical trials, both in the United States and worldwide. We find a negative correlation between the severity of the COVID-19 pandemic and clinical trial enrollment rate, with the relationship being strongest during the first three months of the pandemic compared to the entire duration of the pandemic. This negative statistical relationship holds across therapeutic areas, across states in the US despite the heterogeneity of responses at the state-level, and across countries. This work has significant implications for the management of clinical trials worldwide in response to the fluctuating severity of COVID-19 moving forward, and for future pandemics.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, there are a limited number of effective treatments. A variety of drugs that have been approved for other diseases are being tested for the treatment of COVID-19, and thus far only remdesevir, dexamethasone, baricitinib, tofacitinib, tocilizumab, and sarilumab have been recommended by the National Institutes of Health (NIH) COVID-19 Treatment Guidelines Panel for the therapeutic management of hospitalized adults with COVID-19. Using a disease biology modeling approach, we constructed a protein-protein interactome network based on COVID-19- associated genes/proteins described in research literature together with known protein-protein interactions in epithelial cells. Phenotype and disease enrichment analysis of the COVID-19 disease biology model demonstrated strong statistical enrichments consistent with patients’ clinical presentation. The model was used to interrogate host biological response induced by SARS-CoV-2 and identify COVID-19 drug treatment candidates that may inform on drugs currently being evaluated or provide insight into possible targets for potential new therapeutic agents. We focused on cancer drugs as they are often used to control inflammation, inhibit cell division, and modulate the host microenvironment to control the disease. From the top 30 COVID-19 drug candidates, twelve have a role as an antineoplastic agent, seven of which are approved for human use. Altogether, nearly 40% of the drugs identified by our model have been identified by others for COVID-19 clinical trials. Disease biology modeling incorporating disease-associated genes/proteins discussed in the research literature together with known molecular interactions in relevant cell types is a useful method to better understand disease biology and identify potentially effective therapeutic interventions.