Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients.

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

Different patterns of excess all-cause mortality by age and sex in Hungary during the 2nd and 3rd waves of the COVID-19 pandemic.

It is well accepted that COVID-19-related mortality shows a strong age dependency. However, temporal changes in the age distribution of excess relative mortality between waves of the pandemic are less frequently investigated. We aimed to assess excess absolute mortality and the age-distribution of all-cause mortality during the second and third waves of the COVID-19 pandemic in Hungary compared to the same periods of non-pandemic years. Rate ratios for excess all-cause mortality with 95% confidence intervals and the number of excess deaths for the second (week 41 of 2020 through week 4 of 2021) and third waves (weeks 7-21 of 2021) of the COVID pandemic for the whole of Hungary compared to the same periods of the pre-pandemic years were estimated for 10-year age strata using Poisson regression. Altogether, 9771 (95% CI: 9554-9988) excess deaths were recorded during the second wave of the pandemic, while it was lower, 8143 (95% CI: 7953-8333) during the third wave. During the second wave, relative mortality peaked for ages 65-74 and 75-84 (RR 1.37, 95%CI 1.33-1.41, RR 1.38, 95%CI 1.34-1.42). Conversely, during the third wave, relative mortality peaked for ages 35-44 (RR 1.43, 95%CI 1.33-1.55), while those ≥65 had substantially lower relative risks compared to the second wave. The reduced relative mortality among the elderly during the third wave is likely a consequence of the rapidly increasing vaccination coverage of the elderly coinciding with the third wave. The hugely increased relative mortality of those 35-44 could point to non-biological causes, such as less stringent adherence to non-pharmaceutical measures in this population.

PIKfyve-specific inhibitors restrict replication of multiple coronaviruses in vitro but not in a murine model of COVID-19.

The ongoing COVID-19 pandemic has claimed more than 6 million lives and continues to test the world economy and healthcare systems. To combat this pandemic, the biological research community has shifted efforts to the development of medical countermeasures, including vaccines and therapeutics. However, to date, the only small molecules approved for the treatment of COVID-19 in the United States are the nucleoside analogue Remdesivir and the protease inhibitor Paxlovid, though multiple compounds have received Emergency Use Authorization and many more are currently being tested in human efficacy trials. One such compound, Apilimod, is being considered as a COVID-19 therapeutic in a Phase II efficacy trial. However, at the time of writing, there are no published efficacy data in human trials or animal COVID-19 models. Here we show that, while Apilimod and other PIKfyve inhibitors have potent antiviral activity in various cell lines against multiple human coronaviruses, these compounds worsen disease in a COVID-19 murine model when given prophylactically or therapeutically.

Comparison of clinical characteristics of patients with pandemic SARS-CoV-2-related and community-acquired pneumonias in Hungary - a pilot historical case-control study.

The distinction between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related and community-acquired pneumonias poses significant difficulties, as both frequently involve the elderly. This study aimed to predict the risk of SARS-CoV-2-related pneumonia based on clinical characteristics at hospital presentation. Case-control study of all patients admitted for pneumonia at Semmelweis University Emergency Department. Cases (n = 30) were patients diagnosed with SARS-CoV-2-related pneumonia (based on polymerase chain reaction test) between 26 March 2020 and 30 April 2020; controls (n = 82) were historical pneumonia cases between 1 January 2019 and 30 April 2019. Logistic models were built with SARS-CoV-2 infection as outcome using clinical characteristics at presentation. Patients with SARS-CoV-2-related pneumonia were younger (mean difference, 95% CI: 9.3, 3.2-15.5 years) and had a higher lymphocyte count, lower C-reactive protein, presented more frequently with bilateral infiltrate, less frequently with abdominal pain, diarrhoea, and nausea in age- and sex-adjusted models. A logistic model using age, sex, abdominal pain, C-reactive protein, and the presence of bilateral infiltrate as predictors had an excellent discrimination (AUC 0.88, 95% CI: 0.81-0.96) and calibration (p = 0.27-Hosmer-Lemeshow test). The clinical use of our screening prediction model could improve the discrimination of SARS-CoV-2 related from other community-acquired pneumonias and thus help patient triage based on commonly used diagnostic approaches. However, external validation in independent datasets is required before its clinical use.