Around the world, researchers are working extremely hard to develop new treatments and interventions for COVID-19 with new clinical trials opening nearly every day. This directory provides you with information, including enrollment detail, about these trials. In some cases, researchers are able to offer expanded access (sometimes called compassionate use) to an investigational drug when a patient cannot participate in a clinical trial.
The information provided here is drawn from ClinicalTrials.gov. If you do not find a satisfactory expanded access program here, please search in our COVID Company Directory. Some companies consider expanded access requests for single patients, even if they do not show an active expanded access listing in this database. Please contact the company directly to explore the possibility of expanded access.
Emergency INDs
To learn how to apply for expanded access, please visit our Guides designed to walk healthcare providers, patients and/or caregivers through the process of applying for expanded access. Please note that given the situation with COVID-19 and the need to move as fast as possible, many physicians are requesting expanded access for emergency use. In these cases, FDA will authorize treatment by telephone and treatment can start immediately. For more details, consult FDA guidance. Emergency IND is the common route that patients are receiving convalescent plasma.
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Displaying 250 of 313University Hospital, Basel, Switzerland
The study is to investigate the antibody response in the blood and saliva of people with a known COVID-19 infection in the canton of Baselland.
Ministerio de Salud de Ciudad Autónoma de Buenos Aires
The pandemic of a new coronavirus SARS-COV-2, which causes COVID-19 disease, has spread rapidly and is a major public health challenge. While the focus is primarily on containing the number of cases and finding alternative therapies, information is still lacking to elucidate the dynamics of viral circulation and to understand the distribution of the infection in the population. The cases reported in Argentina and worldwide could plausibly represent only a small proportion of the number of asymptomatic or poorly symptomatic cases that exist in society. However, the magnitude of this dissociation between symptomatic cases and asymptomatic persons is unknown. Knowing this information is of strategic importance as it will allow the estimation of a community prevalence and the evaluation of the best containment strategy. In fact, although all social distancing measures are now indispensable, the feasibility of prolonging the measure over time is a complex issue and in any case will require population-based information. The best way to approach the estimation of a true population prevalence is to take representative samples from the population and test them periodically. These experiences were carried out in other contexts showing heterogeneous results within the community studied. In Spain, for example, the range of antibodies present in the population varied from 1.1% to 14.2%, also showing that an important part of the population had had contact with the virus without symptoms. Studies in Switzerland and the United States also show similar findings. However, these estimates are not automatically transferable to other settings. The city of Buenos Aires has a particular demographic composition with an important group of the population living in shantytowns (it is estimated that between 7% and 10% of the population lives in shantytowns) and with much heterogeneity among the different communes of the city. In the villas, the incidence rates of COVID-19 infection differ significantly from those present in the group "outside the village". However, there is also an important difference in the incidence rates by commune, even without considering the villas. Thus, it is important to know the sero-epidemiology of antibodies against SARS-COV2 in a representative sample of the city of Buenos Aires. For this purpose, a nationally produced test (COVIDAR IgG) developed by professionals from CONICET and Instituto Leloir will be used. The aim of this initiative is to estimate the true dimension of the COVID-19 epidemic in the City of Buenos Aires, by studying the immunological status of the Buenos Aires population in relation to SARS-Cov2, as well as to observe the evolution of the infection among the population, since this information is essential to guide future public health measures related to the control of COVID-19. To achieve this objective, a comprehensive sero-epidemiological study will be carried out to provide estimates of past SARS-Cov2 infection with sufficient precision to be representative of the sero-epidemiological status of the Buenos Aires city population.
Presidency of Health Institute Turkey (TUSEB)
COVID-19 is an infectious disease caused by a newly discovered Coronavirus which was first identified in Wuhan, China in December 2019. Then the novel coronavirus outbreak was described and announced as a pandemic by World Health Organization (WHO) on March 11, 2020. Reverse transcription-polymerase chain reaction (RT-PCR) is currently the gold standard test for diagnosis of COVID-19. Nevertheless, due to its high false-negative rates (%10-50), diagnosis and treatment decisions do not depend on RT-PCR alone. Clinical presentation of patient and radiological findings are also important. However, neither clinical presentation nor computed tomography (CT) findings are specific for COVID-19. As a consequence of these challenges, the diagnosis of the disease and the protection of the community health become more difficult. The investigators of this study hypothesized that deep learning-based decision support system may help for definitive diagnosis of COVID-19. The aim is to develop a deep learning-based decision support system algorithm based on clinical presentation of patient, laboratory and CT findings and RT-PCR data. Previously, deep learning algorithms with the use of widely known deep neural network architectures such as Inception, UNet, ResNet were developed. However all of these studies were based on CT findings. There are not any deep learning study in literature combining the clinical, radiological, and laboratory findings of patients. The project is based on the available data of COVID-19 patients that will be obtained from the Ministry of Health. Then the data will be evaluated for relevance and reliability and labeled for the training of machine. Following the anonymization of data, data will be processed according to the predetermined inclusion-exclusion criteria. Thorax CT data will be labeled as typical / indeterminate / atypical / negative for COVID-19 pneumonia. Also, CT images of patients with known non-COVID-19 diseases will be labeled for the training of machine. Then, fever, lymphocyte count, neutrophil to lymphocyte ratio, contact information, RT-PCR findings will be labeled. Subsequently, the patients will be labeled and the machine will be trained with deep learning method with the help of this grouped and labeled data. Following the training phase, the algorithm will be tested and if the machine reaches the target specificity and sensitivity, the prototype will be tested. And then, the prototype will be embedded into the hospital software system. This software and algorithm will serve as an early warning system for clinicians and provide a better diagnostic rate especially with decreasing false-negative results. The effects of a pandemic cannot be measured by only the number of people diagnosed and isolated, or treatment provided. A pandemic affects not only community health but also individuals' psychological status, education, teaching methods, working models, daily lifestyles, producer/consumer behaviors, supply/demand balance; in other words every single area of life. On top of that, a pandemic causes long-term damages hard to reverse. The software will increase the diagnostic success rates, help to control the pandemic and minimize the collateral damages mentioned above. The investigators believe that, the product that will be produced at the end of this project will be of great benefit in controlling the secondary wave of COVID-19 expected to occur.
Hopital Foch
The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is complicated by pneumonia (15 to 20% of cases) requiring hospitalization with oxygen therapy. Almost 20 to 25% of hospitalized patients require intensive care and resuscitation; half die. The main cause of death is acute respiratory distress syndrome (ARDS). However, some deaths have been linked to pulmonary embolism (PE). Recognition of PE is important because there is specific treatment to limit its own mortality. The identification of biological parameters of hemostasis predictive of thromboembolic disease is crucial in these patients. To evaluate the frequency of PE in the patients having to be hospitalized is to practice of a systematic thoracic angiography scanner in the patients having no contra-indication for its realization, as well as during hospitalization in patients deteriorating without any other obvious cause. The thromboembolic events and disturbances of the coagulation system described in patients with SARS-CoV-2 pneumonitis suggest that this viral infection is associated with an increase in the activation of coagulation contributing to the occurrence of thrombosis and especially from PE.
Assiut University
Novel coronavirus disease 19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this pneumonia was first emerged in December 2019 in Wuhan, China and rapidly spread around the world . Coagulopathy is one of the most significant prognostic factors in patients with COVID-19 and is associated with increased mortality and admission to critical care. Most observed coagulopathy in patients hospitalized with COVID-19 (COVID-19-associated coagulopathy) is characterized by increased D-dimer and fibrinogen levels. 71% of patients who did not survive hospitalization reported to have developed disseminated intravascular coagulation
physIQ, Inc.
In this study we will be monitoring for patient events (emergency department admission, hospital admission, admission to an observation unit, or death) and evaluating the feasibility and utility of using pinpointIQ in the management of patients with COVID-19. Vital sign (physiology data) is collected to build a Covid Decompensation Index and contribute data to a Covid Digital Hub supported by the National Institutes of Health.
Beaufort
This multicentre prospective study will enroll a sufficient number of patients to afford approximately 60 positives and > 40 negatives (as determined by the SOC - Comparator method) in the United States and/or Canada. One to three sites in the United States and/or Canada will participate over an approximate 12-week enrolment period. The actual enrolment period will be dependent upon prevalence of Covid-19. Once positives sample size is achieved, expected SARC-CoV-2 negative subjects will be permitted. This study is observational and will not impact the medical management of the patient. The results of the Spartan Test will be blinded to the clinical staff during the study and will not impact the medical management of the subject. Once informed consent is obtained and eligibility is confirmed, subject demographics, and patient reported COVID-19 symptoms will be recorded. For the purposes of this study, enrolment will be defined as the collection of the two study-specific nasopharyngeal (NP) samples for Spartan's Test. Each patient's active involvement in the study will last for approximately 30 minutes. To support the EUA, a minimum of 30 individual natural positive clinical specimens will be collected from patients suspected of SARS-CoV-2 infection by a healthcare provider in COVID-19 disease endemic regions in the United States. Additionally, a minimum of 30 individual negative samples will also be used to support the EUA from patients in the United States. Once subjects are consented and recruited for the study, three nasopharyngeal samples for each patient will be collected by trained operators at the clinical site. The first sample will be tested at the clinical site according to standard of care protocols currently in place for the sites' nasopharyngeal swab-based SARS-CoV-2 RT-PCR testing. The second nasopharyngeal sample will be tested at the site using the Spartan COVID-19 v2 System. The third nasopharyngeal sample will be tested using the Spartan COVID-19 v2 System only when the test conducted with the second nasopharyngeal swab does not produce a positive or negative result. The sample for the SOC test will be collected prior to the samples for the Spartan COVID-19 v2 System as per clinical regulations.
Aytu BioPharma, Inc.
This pilot study will assess the safety and effectiveness of UV light treatment in hospitalized patients with COVID-19.
NIHR Lancashire Clinical Research Facility
The purpose of this study is to document the feasibility and tolerability of low dose thoracic radiotherapy in patients with WHO level 5 COVID 19 infections.
Brigham and Women's Hospital
The overall objective of this investigation is to understand the patient response to a robotic platform used to facilitate telehealth triage in the emergency department during the COVID-19 pandemic. The COVID-19 pandemic has altered the manner in which emergency department triage is completed. Attempts at cohorting individuals with potential COVID-19 disease in order to prevent disease transmission to healthcare workers and minimize the use of personal protective equipment (PPE) have renewed interest in telemedical solutions as a method to triage and manage individuals with COVID-19. This investigation deploys a legged robotic platform to facilitate agile, highly mobile telemedicine to manage COVID-19 patients in the emergency department. The primary objective is to measure the patient response to interacting with these systems.