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.

New coronavirus (SARS-CoV-2) treatments and vaccines are under development to combat the COVID-19 disease. 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, new 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.

COVID-19 caused by SARS-CoV-2 was first identified in Japan on January 15 ^th , 2020, soon after the pandemic originated in Wuhan, China. Subsequently, Japan experienced three distinct waves of the outbreak in the span of a year and has been attributed to new exogenous strains and evolving existing strains. Japan engaged very early on in tracking different COVID-19 strains and have sequenced approximately 5% of all confirmed cases. While Japan has enforced stringent airport surveillance on cross-border travelers and returnees, some carriers appear to have advanced through the quarantine stations undetected. In this study 30493 genomes sampled in Japan were analyzed to understand the strains, heterogeneity and temporal evolution of different SARS-CoV-2 strains. We identified 12 discrete strains with a substantial number of cases with most strains possessing the spike (S) D614G and nucleocapsid (N) 203_204delinsKR mutations. 155 distinct strains have been introduced into Japan and 39 of them were introduced after strict quarantine policy was implemented. In particular, the B.1.1.7 strain, that emerged in the United Kingdom (UK) in September 2020, has been circulating in Japan since late 2020 after eluding cross-border quarantine stations. Similarly, the B.1.351 strain dubbed the South African variant, P.1 Brazilian strain and R.1 strain with the spike E484K mutation have been detected in Japan. At least 14 exogenous B.1.1.7 sub-strains have been independently introduced in Japan as of late March 2021, and these strains carry mutations that give selective advantage including N501Y, H69_V70del, and E484K that confer increased transmissibility, reduced efficacy to vaccines and possible increased virulence. Furthermore, various strains, which harbor multiple variants in the PCR primers and the probe developed by National Institute of Infectious Disease (NIID), are emerging. It is imperative that the quarantine policy be revised, cross-border surveillance reinforced, and new public health measures implemented to mitigate further transmission of this deadly disease and to identify strains that may engender resistance to vaccines.

Background The causes, degree and disruptive nature of mid-study database updates and other pain points were evaluated to understand if and how the clinical data management function is managing rapid growth in data volume and diversity. Methods Tufts Center for the Study of Drug Development (Tufts CSDD)—in collaboration with IBM Watson Health—conducted an online global survey between September and October 2020. Results One hundred ninety four verified responses were analyzed. Planned and unplanned mid-study updates were the top challenges mentioned and their management was time intensive. Respondents reported an average of 4.1 planned and 3.7 unplanned mid-study updates per clinical trial. Conclusion Mid-study database updates are disruptive and present a major opportunity to accelerate cycle times and improve efficiency, particularly as protocol designs become more flexible and the diversity of data, most notably unstructured data, increases.

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.