Accelerating Clinical Trial Design and Operations

Fully-integrated, component-based CDMS offers flexibility, customization, and efficiency Effective and efficient clinical data collection and management is one of the key factors affecting clinical trial success and is of heightened importance during the COVID-19 pandemic.1 Not only have the scope and complexity of clinical trials continued to increase over the past decade, but the volume and diversity of clinical study data grows ever larger. Researchers have accelerated the development of vaccines and therapeutics for COVID-19 as evidenced by the 4,846 trials found on clinicaltrials. gov.2 The COVID-19 pandemic presents a unique opportunity for understanding technologies that can enable trial data management and their effects on streamlining;and expediting clinical trial design and implementation. Specific measurements evaluated in this study were: database build efficiency, velocity of data collection, frequency of protocol amendments on the database, and the time impact of mid-study updates to the database. [...]the ability to execute mid-study updates or post-go live changes with minimal to no downtime (< one hour) allowed the users and sponsors to work in parallel rather than in serial fashion, speeding up trial start dates and implementation of protocol amendments, and accommodating adaptive COVID-19 trial design.

A Narrative Review of Emerging Therapeutics for COVID-19.

The novel severe acute respiratory syndrome coronavirus 2, the causal agent of coronavirus disease 2019 (COVID-19), quickly spread around the world, resulting in the most aggressive pandemic experienced in more than 100 years. Research on targeted therapies and vaccines has been initiated on an unprecedented scale and speed but will take months and even years to come to fruition. Meanwhile, the efficacy of emerging therapeutics for use in treating COVID-19 is feverishly being investigated to identify the best available treatment options for dealing with the current wave of disease. This review of publications with a "treatment" tag through June 29, 2020 in the National Library of Medicine's LitCovid literature hub, provides frontline clinicians with a pragmatic summary of the current state of the rapidly evolving evidence supporting emerging candidate therapeutics for COVID-19. Two main categories of pharmaceutical therapeutics are showing promise: those with antiviral activity directly addressing infection and those that counteract the inflammatory cytokine storm induced by severe disease. Preliminary results suggest that other approaches such as convalescent plasma therapy and lung radiation therapy may have some efficacy. The current clinical evidence for potential treatments is preliminary-often small retrospective series or early results of randomized trials-and the science is evolving rapidly. The long-term results from large, well-designed randomized controlled trials will provide definitive evidence for therapeutic effectiveness and are likely months away. The trial landscape for promising therapies is described.

Characterizing Thrombotic Complication Risk Factors Associated With COVID-19 via Heterogeneous Patient Data: Retrospective Observational Study.

BACKGROUND: COVID-19 has been observed to be associated with venous and arterial thrombosis. The inflammatory disease prolongs hospitalization, and preexisting comorbidities can intensity the thrombotic burden in patients with COVID-19. However, venous thromboembolism, arterial thrombosis, and other vascular complications may go unnoticed in critical care settings. Early risk stratification is paramount in the COVID-19 patient population for proactive monitoring of thrombotic complications. OBJECTIVE: The aim of this exploratory research was to characterize thrombotic complication risk factors associated with COVID-19 using information from electronic health record (EHR) and insurance claims databases. The goal is to develop an approach for analysis using real-world data evidence that can be generalized to characterize thrombotic complications and additional conditions in other clinical settings as well, such as pneumonia or acute respiratory distress syndrome in COVID-19 patients or in the intensive care unit. METHODS: We extracted deidentified patient data from the insurance claims database IBM MarketScan, and formulated hypotheses on thrombotic complications in patients with COVID-19 with respect to patient demographic and clinical factors using logistic regression. The hypotheses were then verified with analysis of deidentified patient data from the Research Patient Data Registry (RPDR) Mass General Brigham (MGB) patient EHR database. Data were analyzed according to odds ratios, 95% CIs, and P values. RESULTS: The analysis identified significant predictors (P<.001) for thrombotic complications in 184,831 COVID-19 patients out of the millions of records from IBM MarketScan and the MGB RPDR. With respect to age groups, patients 60 years and older had higher odds (4.866 in MarketScan and 6.357 in RPDR) to have thrombotic complications than those under 60 years old. In terms of gender, men were more likely (odds ratio of 1.245 in MarketScan and 1.693 in RPDR) to have thrombotic complications than women. Among the preexisting comorbidities, patients with heart disease, cerebrovascular diseases, hypertension, and personal history of thrombosis all had significantly higher odds of developing a thrombotic complication. Cancer and obesity were also associated with odds>1. The results from RPDR validated the IBM MarketScan findings, as they were largely consistent and afford mutual enrichment. CONCLUSIONS: The analysis approach adopted in this study can work across heterogeneous databases from diverse organizations and thus facilitates collaboration. Searching through millions of patient records, the analysis helped to identify factors influencing a phenotype. Use of thrombotic complications in COVID-19 patients represents only a case study; however, the same design can be used across other disease areas by extracting corresponding disease-specific patient data from available databases.

Emergence of Drift Variants That May Affect COVID-19 Vaccine Development and Antibody Treatment.

New coronavirus (SARS-CoV-2) treatments and vaccines are under development to combat COVID-19. Several approaches are being used by scientists for investigation, including (1) various small molecule approaches targeting RNA polymerase, 3C-like protease, and RNA endonuclease; and (2) exploration of antibodies obtained from convalescent plasma from patients who have recovered from COVID-19. The coronavirus genome is highly prone to mutations that lead to genetic drift and escape from immune recognition; thus, it is imperative that sub-strains with different mutations are also accounted for during vaccine development. As the disease has grown to become a pandemic, B-cell and T-cell epitopes predicted from SARS coronavirus have been reported. Using the epitope information along with variants of the virus, we have found several variants which might cause drifts. Among such variants, 23403A>G variant (p.D614G) in spike protein B-cell epitope is observed frequently in European countries, such as the Netherlands, Switzerland, and France, but seldom observed in China.