New glimmers of hope for controlling COVID-19
July 1, 2020
New identification of genetic basis of COVID-19 susceptibility will aid treatment
Italian researchers are reporting that they have been able to identify the genetic basis of susceptibility to Covid-19 infection. This has important implications for treatment and drug development. The clinical presentation of COVID-19 varies from patient to patient and understanding individual genetic susceptibility to the disease is therefore vital to prognosis, prevention, and the development of new treatments.
Scientists have been able to identify the genetic and molecular basis of this susceptibility to infection as well as to the possibility of contracting a more severe form of the disease. Professor Alessandra Renieri, who is the Director of the Medical Genetics Unit at the University Hospital of Siena in Italy, is part of a team that collected genomic samples from COVID patients across the whole of Italy in order to try to identify the genetic bases of the high level of clinical variability they showed.
Using whole exome sequencing (WES) to study the first data from 130 COVID patients from Siena and other Tuscan institutions, they were able to uncover a number of common susceptibility genes that were linked to a favorable or unfavorable outcome.
“We believe that variations in these genes may determine disease progression,” said Renieri.
These results will have significant implications for health and healthcare policy. Understanding the genetic profile of patients may allow the repurposing of existing medicines for specific therapeutic approaches against COVID-19 as well as speeding the development of new antiviral drugs. Being able to identify patients susceptible to severe pneumonia and their responsiveness to specific drugs will allow rapid public health treatment interventions.
Cancer drug repurposed to treat severe COVID-19
Early data from a clinical study is suggesting that blocking the Bruton tyrosine kinase (BTK) protein with the cancer drug acalabrutinib may provide clinical benefits to some patients with severe COVID-19. Researchers observed that the off-label use of acalabrutinib, a BTK inhibitor that is approved to treat several blood cancers, was associated with reduced respiratory distress and a reduction in the overactive immune response in most of the treated patients.
Researchers conducted a prospective off-label clinical study that included 19 patients with a confirmed COVID-19 diagnosis that required hospitalization. Of these patients, 11 had been receiving supplemental oxygen for a median of two days, and eight others had been on ventilators for a median of 1.5 days (range 1 to 22 days). Within one to three days after they began receiving acalabrutinib, the majority of patients in the supplemental oxygen group experienced a substantial drop in inflammation and their breathing improved.
In this study, 8 of these 11 patients were able to come off supplemental oxygen and were discharged from the hospital. Although the
benefit of acalabrutinib was less dramatic in patients on ventilators, 4 of the 8 patients were able to come off the ventilator, two of whom were eventually discharged. The authors note that the ventilator patient group was extremely clinically diverse and included patients who had been on a ventilator for prolonged periods of time and had major organ dysfunction. Two of the patients in this group died.
Blood samples from patients in the study showed that levels of interleukin-6 (IL-6), a major cytokine associated with hyper inflammation in severe COVID-19, decreased after treatment with acalabrutinib. Counts of lymphocytes (a type of white blood cell) also rapidly improved in most patients. A low lymphocyte count has been associated with worse outcome for patients with severe COVID-19.
The researchers also tested blood cells from patients with severe COVID-19 who were not in the study. In comparison with samples from healthy volunteers, they found that the patients with severe COVID-19 had higher activity of the BTK protein and greater production of IL-6. These findings suggest that acalabrutinib may have been effective because its target (BTK) is hyperactive in severe COVID-19 immune cells. The authors of the study caution that this strategy now must be tested in a randomized, controlled clinical trial in order to understand the best and safest treatment options for patients with severe COVID-19.
App can help to determine COVID-19 disease severity
A new mobile app can help clinicians determine which individuals with COVID-19 are likely to have severe cases. Created by researchers at NYU College of Dentistry, the app uses artificial intelligence (AI) to assess risk factors and key biomarkers from blood tests, producing a COVID-19 “severity score.”
Current diagnostic tests for COVID-19 detect viral RNA to determine whether someone does or does not have the virus, but they do not provide clues as to how sick a COVID-positive patient may become.
“Identifying and monitoring those at risk for severe cases could help hospitals prioritize care and allocate resources like ICU beds and ventilators. Likewise, knowing who is at low risk for complications could help reduce hospital admissions while these patients are safely managed at home,” said lead investigator John T. McDevitt, PhD, who is a professor of biomaterials at NYU College of Dentistry, New York, New York. “We want doctors to have both the information they need and the infrastructure required to save lives. COVID-19 has challenged both of these key areas.”
Using data from 160 hospitalized COVID-19 patients in Wuhan, China, the researchers identified four biomarkers measured in blood tests that were significantly elevated in patients who died versus those who recovered (C-reactive protein (CRP), myoglobin (MYO), procalcitonin (PCT), and cardiac troponin I (cTnI)). These biomarkers can signal complications that are relevant to COVID-19, including acute inflammation, lower respiratory tract infection, and poor cardiovascular health.
The team then built a model using the biomarkers as well as age and sex, two established risk factors. They trained the model using a machine learning algorithm, a type of AI, to define the patterns of COVID-19 disease and predict its severity. When a patient’s biomarkers and risk factors are entered into the model, it produces a numerical COVID-19 severity score ranging from 0 (mild or moderate) to 100 (critical).
The model was validated using data from 12 hospitalized COVID-19 patients from Shenzhen, China, which confirmed that the model’s severity scores were significantly higher for the patients that died versus those who were discharged. As New York City emerged as the epicenter of the pandemic, the researchers further validated the model using data from more than 1,000 New York City COVID-19 patients. To make the tool available and convenient for doctors, they developed a mobile app that can be used at point-of-care to quickly calculate a patient’s severity score.
John Schieszer is an award-winning national journalist and radio and podcast broadcaster of The Medical Minute. He can be reached at firstname.lastname@example.org.