Learning from real world data about combinatorial treatment selection for COVID-19.

COVID-19 is an unprecedented global pandemic with a serious negative impact on virtually every part of the world. Although much progress has been made in preventing and treating the disease, much remains to be learned about how best to treat the disease while considering patient and disease characteristics. This paper reports a case study of combinatorial treatment selection for COVID-19 based on real-world data from a large hospital in Southern China. In this observational study, 417 confirmed COVID-19 patients were treated with various combinations of drugs and followed for four weeks after discharge (or until death). Treatment failure is defined as death during hospitalization or recurrence of COVID-19 within four weeks of discharge. Using a virtual multiple matching method to adjust for confounding, we estimate and compare the failure rates of different combinatorial treatments, both in the whole study population and in subpopulations defined by baseline characteristics. Our analysis reveals that treatment effects are substantial and heterogeneous, and that the optimal combinatorial treatment may depend on baseline age, systolic blood pressure, and c-reactive protein level. Using these three variables to stratify the study population leads to a stratified treatment strategy that involves several different combinations of drugs (for patients in different strata). Our findings are exploratory and require further validation.

Dual-target one-step nested PCR for sensitive detection of SARS-CoV-2 nucleic acids.

The Corona Virus Disease reported in 2019 (COVID-19) poses a significant threat to human and public health. Its early and accurate detection can reduce the spread and recurrence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Real-time reverse transcription fluorescent quantitative polymerase chain reaction (RT-qPCR) is the "gold standard" for detecting the nucleic acid of SARS-CoV-2. This study developed and tested a dual-target (ORF1ab and N gene) one-step nested RT-qPCR (DTO-N-PCR) to detect SARS-CoV-2. Ten-fold serial dilutions of mixed synthetic DNA from SARS-CoV-2 ORF1ab and N gene were used as templates to test the sensitivity of DTO-N-PCR. Its specificity was subsequently tested using throat swab specimens from 10 COVID-19 patients and 35 healthy participants. DTO-N-PCR was more sensitive and specific than conventional RT-qPCR. It has unique features, including a dual-target (ORF1ab and N gene), rapid one-step operation of reverse transcription and PCR, four pairs of inner and outer primers, and specific probes. These features aid in its rapid, accurate, and efficient detection of SARS-CoV-2 RNA.

Effect of a genetically engineered interferon-alpha versus traditional interferon-alpha in the treatment of moderate-to-severe COVID-19: a randomised clinical trial.

BACKGROUND: There are few effective therapies for coronavirus disease 2019 (COVID-19) upon the outbreak of the pandemic. To compare the effectiveness of a novel genetically engineered recombinant super-compound interferon (rSIFN-co) with traditional interferon-alpha added to baseline antiviral agents (lopinavir-ritonavir or umifenovir) for the treatment of moderate-to-severe COVID-19. METHOD: In this multicenter randomized (1:1) trial, patients hospitalized with moderate-to-severe COVID-19 received either rSIFN-co nebulization or interferon-alpha nebulization added to baseline antiviral agents for no more than 28 days. The primary endpoint was the time to clinical improvement. Secondary endpoints included the overall rate of clinical improvement assessed on day 28, the time to radiological improvement and virus nucleic acid negative conversion. RESULTS: A total of 94 patients were included in the safety set (46 patients assigned to rSIFN-co group, 48 to interferon-alpha group). The time to clinical improvement was 11.5 days versus 14.0 days (95% CI 1.10 to 2.81, p = .019); the overall rate of clinical improvement on day 28 was 93.5% versus 77.1% (difference, 16.4%; 95% CI 3% to 30%); the time to radiological improvement was 8.0 days versus 10.0 days (p = .002), the time to virus nucleic acid negative conversion was 7.0 days versus 10.0 days (p = .018) in the rSIFN-co and interferon alpha arms, respectively. Adverse events were balanced with no deaths among groups. CONCLUSIONS AND RELEVANCE: rSIFN-co was associated with a shorter time of clinical improvement than traditional interferon-alpha in the treatment of moderate-to-severe COVID-19 when combined with baseline antiviral agents. rSIFN-co therapy alone or combined with other antiviral therapy is worth to be further studied.Key messagesThere are few effective therapies for coronavirus disease 2019 (COVID-19) upon the outbreak of the pandemic. Interferon alphas, by inducing both innate and adaptive immune responses, have shown clinical efficacy in treating severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus.In this multicenter, head-to-head, randomized, clinical trial which included 94 participants with moderate-to-severe COVID-19, the rSIFN-co plus antiviral agents (lopinavir-ritonavir or umifenovir) was associated with a shorter time of clinical improvement than interferon-alpha plus antiviral agents.

Target Virus or Target Ourselves for COVID-19 Drugs Discovery?-Lessons learned from anti-influenza virus therapies.

The COVID-19 pandemic, after it was reported in December 2019, is a highly contagious and now spreading to over 190 countries, causing a severe public health burden. Currently, there is no vaccine or specific drug to treat COVID-19, which is caused by a novel coronavirus, SARS-2-CoV. For this emergency, the FDA has approved Remdesivir and Hydroxychloroquine for treatment of COVID-19 as Emergency Use Authorization. However, even after this pandemic, COVID-19 may still have a chance to come back. Therefore, we need to come out with new strategies for drug discovery for combating COVID-19 in the future.

Kinetics of SARS-CoV-2 positivity of infected and recovered patients from a single center.

Recurrence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive detection in infected but recovered individuals has been reported. Patients who have recovered from coronavirus disease 2019 (COVID-19) could profoundly impact the health care system. We sought to define the kinetics and relevance of PCR-positive recurrence during recovery from acute COVID-19 to better understand risks for prolonged infectivity and reinfection. A series of 414 patients with confirmed SARS-Cov-2 infection, at The Second Affiliated Hospital of Southern University of Science and Technology in Shenzhen, China from January 11 to April 23, 2020. Statistical analyses were performed of the clinical, laboratory, radiologic image, medical treatment, and clinical course of admission/quarantine/readmission data, and a recurrence predictive algorithm was developed. 16.7% recovered patients with PCR positive recurring one to three times, despite being in strict quarantine. Younger patients with mild pulmonary respiratory syndrome had higher risk of PCR positivity recurrence. The recurrence prediction model had an area under the ROC curve of 0.786. This case series provides characteristics of patients with recurrent SARS-CoV-2 positivity. Use of a prediction algorithm may identify patients at high risk of recurrent SARS-CoV-2 positivity and help to establish protocols for health policy.

Can We Reduce Mortality of COVID-19 if We do Better in Glucose Control?

The SARS-CoV-2 has infected more than 3 million people and caused more than 240,000 death globally. Among the COVID-19 patients, the prevalence of people with other chronic diseases, such as diabetes, high blood pressure, and coronary heart disease is much higher than others. More strikingly, the survival rate of diabetic patients is also much lower than in non-diabetic patients. In addition to the general damage of high glucose to cells and tissues, a recent discovery that high glucose activates interferon regulatory factor 15 promotes influenza virus -induced cytokine storm. This discovery may shed light on the high incidence of diabetes in COVID-19. Several diabetes prevention strategies together with recent significant data-driven diabetes prediction approaches, which may help COVID-19 treatments, have been proposed.