Association of Ang/Tie2 pathway mediators with endothelial barrier integrity and disease severity in COVID-19.

Endothelial barrier (EB) disruption contributes to acute lung injury in COVID-19, and levels of both VEGF-A and Ang-2, which are mediators of EB integrity, have been associated with COVID-19 severity. Here we explored the participation of additional mediators of barrier integrity in this process, as well as the potential of serum from COVID-19 patients to induce EB disruption in cell monolayers. In a cohort from a clinical trial consisting of thirty patients with COVID-19 that required hospital admission due to hypoxia we demonstrate that i) levels of soluble Tie2 were increase, and of soluble VE-cadherin were decreased when compared to healthy individuals; ii) sera from these patients induce barrier disruption in monolayers of endothelial cells; and iii) that the magnitude of this effect is proportional to disease severity and to circulating levels of VEGF-A and Ang-2. Our study confirms and extends previous findings on the pathogenesis of acute lung injury in COVID-19, reinforcing the concept that EB is a relevant component of this disease. Our results pave the way for future studies that can refine our understanding of the pathogenesis of acute lung injury in viral respiratory disorders, and contribute to the identification of new biomarkers and therapeutic targets for these conditions.

Computational and Experimental Approaches Identify Beta-Blockers as Potential SARS-CoV-2 Spike Inhibitors.

Finding antivirals for SARS-CoV-2 is still a major challenge, and many computational and experimental approaches have been employed to find a solution to this problem. While the global vaccination campaigns are the primary driver of controlling the current pandemic, orally bioavailable small-molecule drugs and biologics are critical to overcome this global issue. Improved therapeutics and prophylactics are required to treat people with circulating and emerging new variants, addressing severe infection, and people with underlying or immunocompromised conditions. The SARS-CoV-2 envelope spike is a challenging target for viral entry inhibitors. Pindolol presented a good docking score in a previous virtual screening using computational docking calculations after screening a Food and Drug Administration (FDA)-approved drug library of 2400 molecules as potential candidates to block the SARS-CoV-2 spike protein interaction with the angiotensin-converting enzyme 2 (ACE-2). Here, we expanded the computational evaluation to identify five beta-blockers against SARS-CoV-2 using several techniques, such as microscale thermophoresis, NanoDSF, and in vitro assays in different cell lines. These data identified carvedilol with a K d of 364 ± 22 nM for the SARS-CoV-2 spike and in vitro activity (EC50 of 7.57 µM, CC50 of 18.07 µM) against SARS-CoV-2 in Calu-3 cells. We have shown how we can apply multiple computational and experimental approaches to find molecules that can be further optimized to improve anti-SARS-CoV-2 activity.

Short-Course of Methylprednisolone Improves Respiratory Functional Parameters After 120 Days in Hospitalized COVID-19 Patients (Metcovid Trial): A Randomized Clinical Trial.

Background: The use of corticosteroids may help control the cytokine storm occurring in acute respiratory failure due to the severe form of COVID-19. We evaluated the postacute effect of corticosteroids used during the acute phase, such as impairment in pulmonary function parameters, on day 120 (D120)-follow-up, in participants who survived over 28 days. Methods: This is a parallel, double-blind, randomized, placebo-controlled phase IIb clinical trial carried out between April 18 and October 9, 2020, conducted in hospitalized patients with clinical-radiological suspicion of COVID-19, aged 18 years or older, with SpO2 ≤ 94% on room air or requiring supplementary oxygen, or under invasive mechanical ventilation (IMV) in a referral center in Manaus, Western Brazilian Amazon. Intravenous methylprednisolone (MP) (0.5 mg/kg) was given two times daily for 5 days to these patients. The primary outcome used for this study was pulmonary function testing at day 120 follow-up visit. Results: Out of the total of surviving patients at day 28 (n = 246) from the Metcovid study, a total of 118 underwent satisfactory pulmonary function testing (62 in the placebo arm and 56 in the MP arm). The supportive treatment was similar between the placebo and MP groups (seven [11%] vs. four [7%]; P = 0.45). At hospital admission, IL-6 levels were higher in the MP group (P < 0.01). Also, the need for ICU (P = 0.06), need for IMV (P = 0.07), and creatine kinase (P = 0.05) on admission also tended to be higher in this group. In the univariate analysis, forced expiratory volume on 1st second of exhalation (FEV1) and forced vital capacity (FVC) at D120 follow-up were significantly higher in patients in the MP arm, being this last parameter also significantly higher in the multivariate analysis independently of IMV and IL-6 levels on admission. Conclusion: The use of steroids for at least 5 days in severe COVID-19 was associated with a higher FVC, which suggests that hospitalized COVID-19 patients might benefit from the use of MP in its use in the long-term, with less pulmonary restrictive functions, attributed to fibrosis. Trial Registration: ClinicalTrials.gov, Identifier: NCT04343729.

Yeast-based high-throughput screens for discovery of kinase inhibitors for neglected diseases.

The discovery and development of a new drug is a complex, time consuming and costly process that typically takes over 10 years and costs around 1 billion dollars from bench to market. This scenario makes the discovery of novel drugs targeting neglected tropical diseases (NTDs), which afflict in particular people in low-income countries, prohibitive. Despite the intensive use of High-Throughput Screening (HTS) in the past decades, the speed with which new drugs come to the market has remained constant, generating doubts about the efficacy of this approach. Here we review a few of the yeast-based high-throughput approaches that can work synergistically with parasite-based, in vitro, or in silico methods to identify and optimize novel antiparasitic compounds. These yeast-based methods range from HTP screens to identify novel hits against promising parasite kinase targets to the identification of potential antiparasitic kinase inhibitors extracted from databases of yeast chemical genetic screens.

Machine Learning Models Identify Inhibitors of SARS-CoV-2.

With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for in vitro testing by several groups. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein, we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA-approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding (Kd 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active in vitro. This combined machine learning and in vitro testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.

Methylprednisolone as Adjunctive Therapy for Patients Hospitalized With Coronavirus Disease 2019 (COVID-19; Metcovid): A Randomized, Double-blind, Phase IIb, Placebo-controlled Trial.

BACKGROUND: Steroid use for coronavirus disease 2019 (COVID-19) is based on the possible role of these drugs in mitigating the inflammatory response, mainly in the lungs, triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aimed to evaluate the efficacy of methylprednisolone (MP) among hospitalized patients with suspected COVID-19. METHODS: A parallel, double-blind, placebo-controlled, randomized, Phase IIb clinical trial was performed with hospitalized patients aged ≥18 years with clinical, epidemiological, and/or radiological suspected COVID-19 at a tertiary care facility in Manaus, Brazil. Patients were randomly allocated (1:1 ratio) to receive either intravenous MP (0.5 mg/kg) or placebo (saline solution) twice daily for 5 days. A modified intention-to-treat (mITT) analysis was conducted. The primary outcome was 28-day mortality. RESULTS: From 18 April to 16 June 2020, 647 patients were screened, 416 were randomized, and 393 were analyzed as mITT, with 194 individuals assigned to MP and 199 to placebo. SARS-CoV-2 infection was confirmed by reverse transcriptase polymerase chain reaction in 81.3%. The mortality rates at Day 28 were not different between groups. A subgroup analysis showed that patients over 60 years old in the MP group had a lower mortality rate at Day 28. Patients in the MP arm tended to need more insulin therapy, and no difference was seen in virus clearance in respiratory secretion until Day 7. CONCLUSIONS: The findings of this study suggest that a short course of MP in hospitalized patients with COVID-19 did not reduce mortality in the overall population. CLINICAL TRIALS REGISTRATION: NCT04343729.

Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning.

COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease's pathophysiology and several discriminating features to patient's health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease's pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application.